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9,626,649 | 2 | 3 |
2. The computer-implemented method of claim 1 wherein the plurality of experience descriptions comprises a plurality of experience description types and at least one of the plurality of experience description types comprises a degree of experience description type or a years of experience description type.
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2. The computer-implemented method of claim 1 wherein the plurality of experience descriptions comprises a plurality of experience description types and at least one of the plurality of experience description types comprises a degree of experience description type or a years of experience description type. 3. The computer-implemented method of claim 2 wherein the user interactive table and user interactive comparison table display displays the experience description type associated with each job level when the user selects the experience description type, and inhibits displaying the other experience description types and the plurality of expectation descriptions not associated with the user selection.
| 0.5 |
8,402,046 | 2 | 3 |
2. The method of claim 1 , wherein executing the query using the one or more sense descriptions in the second language comprises querying one or more documents in the second language.
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2. The method of claim 1 , wherein executing the query using the one or more sense descriptions in the second language comprises querying one or more documents in the second language. 3. The method of claim 2 , wherein the method further comprises translating one or more query results into the original language.
| 0.5 |
5,537,526 | 31 | 32 |
31. The method of claim 30, including the step of embedding data in a model.
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31. The method of claim 30, including the step of embedding data in a model. 32. The method of claim 31, including the step of overriding protocols associated with the embedded data.
| 0.5 |
8,610,606 | 1 | 6 |
1. A method of dynamically selecting a Huffman dictionary from a bank of predefined Huffman dictionaries, said method comprising: receiving the output, including statistics, of a repetition removal algorithm applied to an input data stream and storing said output in a buffer; calculating a compatibility grade for each predefined Huffman dictionary in said bank of dictionaries and storing them in a data structure; using said data structure, comparing the calculated compatibility grades and selecting the predefined dictionary corresponding to the highest compatibility grade; and performing static Huffman size estimation on the output of the buffer and ending the compressed block in the event said static Huffman size estimation outperforms the speculatively selected predefined Huffman dictionary.
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1. A method of dynamically selecting a Huffman dictionary from a bank of predefined Huffman dictionaries, said method comprising: receiving the output, including statistics, of a repetition removal algorithm applied to an input data stream and storing said output in a buffer; calculating a compatibility grade for each predefined Huffman dictionary in said bank of dictionaries and storing them in a data structure; using said data structure, comparing the calculated compatibility grades and selecting the predefined dictionary corresponding to the highest compatibility grade; and performing static Huffman size estimation on the output of the buffer and ending the compressed block in the event said static Huffman size estimation outperforms the speculatively selected predefined Huffman dictionary. 6. The method according to claim 1 , wherein said predefined dictionary is selected using a hash-based mechanism.
| 0.771255 |
7,900,143 | 5 | 7 |
5. The method of claim 1 including determining whether the characters in the file are defined according to the first code format.
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5. The method of claim 1 including determining whether the characters in the file are defined according to the first code format. 7. The method of claim 5 wherein if said first code format is not utilized, using a surrogate area of Unicode.
| 0.642857 |
7,966,625 | 1 | 6 |
1. A method in a data processing system for extending Web services to include call flow interactions, the method comprising: receiving a description language document that describes one or more Web services interface components; associating a Web services interface component within the one or more Web services interface components with one or more call flow segments; inserting a call flow binding into the description language document to form an extended description language document, wherein the call flow binding associates an interaction operation with a binding point in a given call flow segment within the one or more call flow segments; and executing a converged application based on the extended description language document.
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1. A method in a data processing system for extending Web services to include call flow interactions, the method comprising: receiving a description language document that describes one or more Web services interface components; associating a Web services interface component within the one or more Web services interface components with one or more call flow segments; inserting a call flow binding into the description language document to form an extended description language document, wherein the call flow binding associates an interaction operation with a binding point in a given call flow segment within the one or more call flow segments; and executing a converged application based on the extended description language document. 6. The method of claim 1 , wherein the interaction operation is a prescribe operation, the method further comprising: responsive to receiving an invocation from a service oriented architecture integration platform, receiving a set of descriptive rules for decision making within the call flow segment.
| 0.539755 |
9,128,915 | 1 | 8 |
1. A method for improving accuracy of data matching in a middleware machine environment by identifying a similarity between language characters of a character set of a language, wherein each language character has a unique structure, the method comprising: providing a language character match engine, wherein the language character match engine executes on one or more microprocessor, wherein the language character match engine comprises a plurality of encoding components, including at least a first encoding component and a second encoding component and a third encoding component; using the language character match engine to generate a composite similarity score set for the character set of the language wherein said similarity index comprises a composite similarity score for each of a plurality of pairs of language characters of the character set of the language; wherein the composite similarity score for each of the plurality of pairs of language characters is prepared by, receiving the pair of language characters with the language character match engine, using the first encoding component to encode each language character of the pair of language characters based on the unique structure of each language character and generate, for each language character, a first-encoded string of identification characters representing the unique structure of the language character, comparing the first-encoded strings of identification characters for each of the pair of language characters to one another to generate a first-encoding similarity score for the pair of language characters, using the second encoding component to encode each language character of the pair of language characters based on the unique structure of each language character and generate, for each language character, a second-encoded string of identification characters representing the unique structure of the language character, comparing the second-encoded strings of identification characters for each of the pair of language characters to one another to generate a second-encoding similarity score for the pair of language characters, using the third encoding component to encode each language character of the pair of language characters based on the unique structure of each language character and generate, for each language character, a third-encoded string of identification characters representing the unique structure of the language character, comparing the third-encoded strings of identification characters for each of the pair of language characters to one another to generate a third-encoding similarity score for the pair of language characters, and combining the first-encoding similarity score, the second-encoding similarity score, and the third-encoding similarity score for the pair of language characters to generate a composite similarity score for the pair of language characters.
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1. A method for improving accuracy of data matching in a middleware machine environment by identifying a similarity between language characters of a character set of a language, wherein each language character has a unique structure, the method comprising: providing a language character match engine, wherein the language character match engine executes on one or more microprocessor, wherein the language character match engine comprises a plurality of encoding components, including at least a first encoding component and a second encoding component and a third encoding component; using the language character match engine to generate a composite similarity score set for the character set of the language wherein said similarity index comprises a composite similarity score for each of a plurality of pairs of language characters of the character set of the language; wherein the composite similarity score for each of the plurality of pairs of language characters is prepared by, receiving the pair of language characters with the language character match engine, using the first encoding component to encode each language character of the pair of language characters based on the unique structure of each language character and generate, for each language character, a first-encoded string of identification characters representing the unique structure of the language character, comparing the first-encoded strings of identification characters for each of the pair of language characters to one another to generate a first-encoding similarity score for the pair of language characters, using the second encoding component to encode each language character of the pair of language characters based on the unique structure of each language character and generate, for each language character, a second-encoded string of identification characters representing the unique structure of the language character, comparing the second-encoded strings of identification characters for each of the pair of language characters to one another to generate a second-encoding similarity score for the pair of language characters, using the third encoding component to encode each language character of the pair of language characters based on the unique structure of each language character and generate, for each language character, a third-encoded string of identification characters representing the unique structure of the language character, comparing the third-encoded strings of identification characters for each of the pair of language characters to one another to generate a third-encoding similarity score for the pair of language characters, and combining the first-encoding similarity score, the second-encoding similarity score, and the third-encoding similarity score for the pair of language characters to generate a composite similarity score for the pair of language characters. 8. The method of claim 1 , further comprising determining an edit distance between each of the plurality of pairs of language characters based on the composite similarity score of each of the pairs of language characters of the language.
| 0.866704 |
9,588,999 | 8 | 13 |
8. A computer program product for a database storage reclaiming program, the computer program product comprising: one or more computer-readable storage media and program instructions stored on the one or more computer-readable storage media, the program instructions comprising: program instructions to retrieve a list of data elements for deletion from a database catalog, wherein the list of data elements for deletion details one or more data elements contained in a database; program instructions to determine whether first data elements of the one or more data elements on the list of data elements for deletion have been active in one or more static Structured Query Language (SQL) statements, wherein the one or more SQL statements are persistent and created before runtime; program instructions to remove the first data elements of the one or more data elements from the list of data elements for deletion that have been determined to be active in the one or more static SQL statements; program instructions to determine whether second data elements of the one or more data elements on the list of data elements for deletion have been active in one or more dynamic SQL statements, wherein the one or more dynamic SQL statements are non-persistent and created at runtime; program instructions to remove the second data elements of the one or more data elements from the list of data elements for deletion that have been determined to be active in the one or more dynamic SQL statements; program instructions to determine whether third data elements of the one or more data elements on the list of data elements for deletion are associated with one or more data elements not on the list of data elements for deletion; program instructions to remove the third data elements of the one or more data elements from the list of data elements for deletion that are determined to be associated with the one or more data elements not on the list of data elements for deletion; program instructions to determine whether fourth data elements of the one or more data elements on the list of data elements for deletion are used in a source code of one or more applications; and program instructions to remove the fourth data elements of the one or more data elements from the list of data elements for deletion that are determined to be used in the source code of the one or more applications.
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8. A computer program product for a database storage reclaiming program, the computer program product comprising: one or more computer-readable storage media and program instructions stored on the one or more computer-readable storage media, the program instructions comprising: program instructions to retrieve a list of data elements for deletion from a database catalog, wherein the list of data elements for deletion details one or more data elements contained in a database; program instructions to determine whether first data elements of the one or more data elements on the list of data elements for deletion have been active in one or more static Structured Query Language (SQL) statements, wherein the one or more SQL statements are persistent and created before runtime; program instructions to remove the first data elements of the one or more data elements from the list of data elements for deletion that have been determined to be active in the one or more static SQL statements; program instructions to determine whether second data elements of the one or more data elements on the list of data elements for deletion have been active in one or more dynamic SQL statements, wherein the one or more dynamic SQL statements are non-persistent and created at runtime; program instructions to remove the second data elements of the one or more data elements from the list of data elements for deletion that have been determined to be active in the one or more dynamic SQL statements; program instructions to determine whether third data elements of the one or more data elements on the list of data elements for deletion are associated with one or more data elements not on the list of data elements for deletion; program instructions to remove the third data elements of the one or more data elements from the list of data elements for deletion that are determined to be associated with the one or more data elements not on the list of data elements for deletion; program instructions to determine whether fourth data elements of the one or more data elements on the list of data elements for deletion are used in a source code of one or more applications; and program instructions to remove the fourth data elements of the one or more data elements from the list of data elements for deletion that are determined to be used in the source code of the one or more applications. 13. The computer program product of claim 8 , wherein the determining whether the fourth data elements of the one or more data elements on the list of data elements for deletion are used in the source code of the one or more applications includes analyzing one or more application source codes and workload logic in order to extract the one or more SQL statements and cross reference the extracted one or more SQL statements with the one or more data elements on the list of data elements for deletion.
| 0.546112 |
7,930,709 | 1 | 5 |
1. A method of increasing efficiency in a dynamic linker on a computing system having a processor, the method comprising: determining a length of a first string; determining a first check segment of the first string; selecting a second string from a plurality of strings; determining a second check segment of the second string based on the length of the first string; determining that the first check segment matches the second check segment; determining that the first string matches the second string in response to the first check segment matching the second check segment; and linking, using the processor, a table reference by the second string to an application in response to the first string matching the second string.
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1. A method of increasing efficiency in a dynamic linker on a computing system having a processor, the method comprising: determining a length of a first string; determining a first check segment of the first string; selecting a second string from a plurality of strings; determining a second check segment of the second string based on the length of the first string; determining that the first check segment matches the second check segment; determining that the first string matches the second string in response to the first check segment matching the second check segment; and linking, using the processor, a table reference by the second string to an application in response to the first string matching the second string. 5. A non-transitory computer-readable medium comprising computer-executable instructions for performing the method of claim 1 .
| 0.725108 |
7,555,490 | 5 | 25 |
5. A method comprising: selecting a numeric attribute that is associated with items in a database of items, at least some of the items in the database have a numeric value for the selected numeric attribute, wherein the numeric attribute describes a physical characteristic or property of the items; linking the selected numeric attribute to a descriptive word; linking the selected numeric attribute to a function; executing the function on each numeric value of the selected numeric attribute; assigning a descriptive word to at least one item based on the results of executing the function such that the descriptive word can be subsequently used in a search to identify the at least one item and functions as an alias for the numeric value of the selected numeric attribute associated with the at least one item; and generating, following the steps of selecting, linking the selected numeric attribute to a descriptive word, linking the selected numeric attribute to a function, executing, and assigning and in response to an initial, user search query containing the descriptive word, a list and displaying the list to a user, the list having only those items to which the descriptive word used in the search query has been assigned in the step of assigning, the search query entered by the user before an item of the database having a value for the selected attribute is displayed.
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5. A method comprising: selecting a numeric attribute that is associated with items in a database of items, at least some of the items in the database have a numeric value for the selected numeric attribute, wherein the numeric attribute describes a physical characteristic or property of the items; linking the selected numeric attribute to a descriptive word; linking the selected numeric attribute to a function; executing the function on each numeric value of the selected numeric attribute; assigning a descriptive word to at least one item based on the results of executing the function such that the descriptive word can be subsequently used in a search to identify the at least one item and functions as an alias for the numeric value of the selected numeric attribute associated with the at least one item; and generating, following the steps of selecting, linking the selected numeric attribute to a descriptive word, linking the selected numeric attribute to a function, executing, and assigning and in response to an initial, user search query containing the descriptive word, a list and displaying the list to a user, the list having only those items to which the descriptive word used in the search query has been assigned in the step of assigning, the search query entered by the user before an item of the database having a value for the selected attribute is displayed. 25. The method of claim 5 , further comprising repeating executing the function upon the modification of values of items in the database and repeating assigning a descriptive word after repeating executing the function.
| 0.553061 |
9,436,918 | 1 | 6 |
1. A computer-implemented process for predicting a text span forming either a single word or a series of two or more words that a user intended to select, comprising: using a computer to perform the following process actions: receiving a document comprising a string of characters; receiving a location pointer indicating a particular location in the document; inputting the document and the location pointer to a plurality of different candidate text span generation methods; receiving a ranked list of one or more scored candidate text spans from each of the different candidate text span generation methods; using a machine-learned ensemble model to re-score each of the scored candidate text spans received from each of the different candidate text span generation methods, the ensemble model being trained using a machine learning method and features from a dataset of true intended user text span selections; and receiving a ranked list of re-scored candidate text spans from the ensemble model.
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1. A computer-implemented process for predicting a text span forming either a single word or a series of two or more words that a user intended to select, comprising: using a computer to perform the following process actions: receiving a document comprising a string of characters; receiving a location pointer indicating a particular location in the document; inputting the document and the location pointer to a plurality of different candidate text span generation methods; receiving a ranked list of one or more scored candidate text spans from each of the different candidate text span generation methods; using a machine-learned ensemble model to re-score each of the scored candidate text spans received from each of the different candidate text span generation methods, the ensemble model being trained using a machine learning method and features from a dataset of true intended user text span selections; and receiving a ranked list of re-scored candidate text spans from the ensemble model. 6. The process of claim 1 , wherein the different candidate text span generation methods comprise either: a plurality of different linguistic unit detector methods; or a plurality of different heuristic methods; or a combination of one or more different linguistic unit detector methods and one or more different heuristic methods.
| 0.5 |
7,893,866 | 1 | 3 |
1. A system for generating a look-ahead flight path comprising: a first database component operatively coupled with an electronic order of battle operative to maintain data extracted from said electronic order of battle; a second database component operative to maintain a plurality of platform position model data; a third database component operative to maintain final scoring array data; and a computer processor operatively coupled with said first database component, said second database component, said third database component, and a power supply; said computer processor programmed with a dynamic mission re-planning algorithm and instructions to execute said dynamic mission re-planning algorithm; said dynamic mission re-planning algorithm operable to calculate look-ahead flight path data.
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1. A system for generating a look-ahead flight path comprising: a first database component operatively coupled with an electronic order of battle operative to maintain data extracted from said electronic order of battle; a second database component operative to maintain a plurality of platform position model data; a third database component operative to maintain final scoring array data; and a computer processor operatively coupled with said first database component, said second database component, said third database component, and a power supply; said computer processor programmed with a dynamic mission re-planning algorithm and instructions to execute said dynamic mission re-planning algorithm; said dynamic mission re-planning algorithm operable to calculate look-ahead flight path data. 3. The system of claim 1 wherein said second database component includes a second heading, a second speed and a second altitude corresponding to a position of a protected entity platform.
| 0.808793 |
7,627,631 | 1 | 7 |
1. A system, comprising: one or more processors; a plug-in framework, wherein the plug-in framework includes a plug-in manager and a set of interfaces for plug-ins to implement; a process engine to execute and manage workflows, and wherein the process engine executes one or more business logic plug-ins which process business messages, wherein the one or more business logic plug-ins include a home interface that allows the plug-in manager to detect deployed business logic plug-ins by traversing a naming and directory tree, and wherein the plug-manager manages all of the plug-ins in the framework and the plug-in manager is the central hub through which all plug-in related client requests are routed; an integration studio, wherein an integration studio can be used at design time to define workflows and at run time to monitor running workflows; an integration framework, wherein the integration framework provides router logic plug-ins and filter logic plug-ins for at least one protocol used for the business messages; and an integration repository, wherein the integration repository is a database that can store workflow templates and workflow instances, wherein at least one of the workflow instances is a collaborative workflow that implements a role of a conversation definition configured in a collaborate repository, and the integration framework also provides a collaborative plug-in to allow a user to create the collaborative workflow.
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1. A system, comprising: one or more processors; a plug-in framework, wherein the plug-in framework includes a plug-in manager and a set of interfaces for plug-ins to implement; a process engine to execute and manage workflows, and wherein the process engine executes one or more business logic plug-ins which process business messages, wherein the one or more business logic plug-ins include a home interface that allows the plug-in manager to detect deployed business logic plug-ins by traversing a naming and directory tree, and wherein the plug-manager manages all of the plug-ins in the framework and the plug-in manager is the central hub through which all plug-in related client requests are routed; an integration studio, wherein an integration studio can be used at design time to define workflows and at run time to monitor running workflows; an integration framework, wherein the integration framework provides router logic plug-ins and filter logic plug-ins for at least one protocol used for the business messages; and an integration repository, wherein the integration repository is a database that can store workflow templates and workflow instances, wherein at least one of the workflow instances is a collaborative workflow that implements a role of a conversation definition configured in a collaborate repository, and the integration framework also provides a collaborative plug-in to allow a user to create the collaborative workflow. 7. The system of claim 1 , wherein plug-in data adheres to an XML DTD.
| 0.875887 |
8,526,577 | 22 | 24 |
22. A computer-readable storage device comprising instructions that, when executed by a processor, cause the processor to perform operations comprising: processing a verbal input from a caller; accessing an information store that is logically external to the computer-readable storage device to retrieve content based on at least one query, wherein the at least one query is associated with the verbal input, and wherein the information store determines whether to suspend processing of the at least one query until the content is available when information store content of the information store is being modified and modifications to the information store content could influence an answer to the at least one query; and receiving the answer to the at least one query, wherein the answer is used as part of a response to the caller.
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22. A computer-readable storage device comprising instructions that, when executed by a processor, cause the processor to perform operations comprising: processing a verbal input from a caller; accessing an information store that is logically external to the computer-readable storage device to retrieve content based on at least one query, wherein the at least one query is associated with the verbal input, and wherein the information store determines whether to suspend processing of the at least one query until the content is available when information store content of the information store is being modified and modifications to the information store content could influence an answer to the at least one query; and receiving the answer to the at least one query, wherein the answer is used as part of a response to the caller. 24. The computer-readable storage device of claim 22 , wherein the operations further comprise: determining a first action-object and a first value associated with the first action-object, wherein the first value is derived from the verbal input; and retrieving content associated with the first value.
| 0.736014 |
8,150,885 | 1 | 8 |
1. A method of cross-referencing data within a searchable database on a computer device including a memory device, wherein a directory tree structure is stored on the memory device and includes nodes and branches comprising links between the nodes, the directory tree structure including a designated category for each node, the method comprising: generating at least two data pointers corresponding to a specific node, wherein a first data pointer of the at least two data pointers points from the specific node to a first item of data within the searchable database via a first navigation path through the directory tree structure, wherein a second data pointer of the at least two data pointers points from the specific node to a second item of data within the searchable database via a second navigation path through the directory tree structure, and wherein the first and second items of data are related to the designated category of the specific node; and generating at least one node pointer, wherein the at least one node pointer points from a first node located in the first navigation path to a second node located in the second navigation path, wherein the first navigation path is different from the second navigation path, wherein the first node and the second node are each different from the specific node; wherein, at each node within the directory tree structure, the searchable database is searchable by selecting any data pointers or node pointers corresponding to the node or by performing one or more of a keyword search, a hierarchical search, a dichotomous key search, and a parametric search.
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1. A method of cross-referencing data within a searchable database on a computer device including a memory device, wherein a directory tree structure is stored on the memory device and includes nodes and branches comprising links between the nodes, the directory tree structure including a designated category for each node, the method comprising: generating at least two data pointers corresponding to a specific node, wherein a first data pointer of the at least two data pointers points from the specific node to a first item of data within the searchable database via a first navigation path through the directory tree structure, wherein a second data pointer of the at least two data pointers points from the specific node to a second item of data within the searchable database via a second navigation path through the directory tree structure, and wherein the first and second items of data are related to the designated category of the specific node; and generating at least one node pointer, wherein the at least one node pointer points from a first node located in the first navigation path to a second node located in the second navigation path, wherein the first navigation path is different from the second navigation path, wherein the first node and the second node are each different from the specific node; wherein, at each node within the directory tree structure, the searchable database is searchable by selecting any data pointers or node pointers corresponding to the node or by performing one or more of a keyword search, a hierarchical search, a dichotomous key search, and a parametric search. 8. The method as claimed in claim 1 wherein the directory tree structure is generated by a server.
| 0.881928 |
9,547,716 | 9 | 16 |
9. A display device, comprising: a media data processing module configured to process media data; a media data output unit configured to output the processed media data; an audio input unit configured to receive a speech search command for additional data about the outputted media data from a user, the speech search command including at least one query word; and a processor configured to determine whether the at least one query word matches a query term that is full and searchable based on pronunciation patterns of the user, when the at least one query word matches a query term that is full and searchable and the pronunciation pattern is a first pattern, perform a search for the additional data using the query term, and when the at least one query word does not match the query term that is full and searchable and the pronunciation pattern is a second pattern, determine the query term from a predetermined amount of the audio data prior to receiving the speech search command and perform the search for the additional data using the query term, wherein the first pattern means at least one of that the user's accent is clear and that there is no mumbling in the user's pronunciation, and wherein the second pattern means at least one of that the user's accent is unclear and that there is mumbling in the user's pronunciation.
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9. A display device, comprising: a media data processing module configured to process media data; a media data output unit configured to output the processed media data; an audio input unit configured to receive a speech search command for additional data about the outputted media data from a user, the speech search command including at least one query word; and a processor configured to determine whether the at least one query word matches a query term that is full and searchable based on pronunciation patterns of the user, when the at least one query word matches a query term that is full and searchable and the pronunciation pattern is a first pattern, perform a search for the additional data using the query term, and when the at least one query word does not match the query term that is full and searchable and the pronunciation pattern is a second pattern, determine the query term from a predetermined amount of the audio data prior to receiving the speech search command and perform the search for the additional data using the query term, wherein the first pattern means at least one of that the user's accent is clear and that there is no mumbling in the user's pronunciation, and wherein the second pattern means at least one of that the user's accent is unclear and that there is mumbling in the user's pronunciation. 16. The display device of claim 9 , wherein the processor is further configured to determine whether at least one query word corresponds to a full and searchable query term or a partial query term.
| 0.735925 |
9,953,092 | 5 | 6 |
5. The Internet Search Engine as claimed in claim 4 , wherein the user or the server computer assigns a weight to particular regions of the ontology, or to the entire ontology.
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5. The Internet Search Engine as claimed in claim 4 , wherein the user or the server computer assigns a weight to particular regions of the ontology, or to the entire ontology. 6. The Internet Search Engine as claimed in claim 5 , wherein email context ontology is assigned a higher weight than browser context ontology.
| 0.69181 |
7,793,216 | 1 | 5 |
1. A method comprising: dividing a first version of a document into one or more sections; removing formatting from one or more of the sections; generating a condensed version of the document that includes one or more links corresponding to the one or more sections; transmitting the condensed version of the document to a mobile device for display; receiving a modified version of the document from the mobile device, the modified version including one or more edits to one or more of the sections; and re-aggregating the modified one or more sections with unmodified sections to form a revised document.
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1. A method comprising: dividing a first version of a document into one or more sections; removing formatting from one or more of the sections; generating a condensed version of the document that includes one or more links corresponding to the one or more sections; transmitting the condensed version of the document to a mobile device for display; receiving a modified version of the document from the mobile device, the modified version including one or more edits to one or more of the sections; and re-aggregating the modified one or more sections with unmodified sections to form a revised document. 5. The method of claim 1 , wherein the one or more sections correspond to one or more paragraphs in the first version of the document.
| 0.768166 |
7,509,406 | 13 | 15 |
13. A system for managing terminal services (TS) accounts for online utilization of a hosted application, the system comprising: a first computer operative to: monitor at least one of a supply of available TS accounts, a demand for TS accounts, and a status of each TS account; create one or more TS accounts in response to detecting that the supply of available TS accounts is below a buffer level wherein the buffer level comprises a minimum number of TS accounts having an available status, the number of TS accounts having an available status comprising at least one TS account; and provision a TS account for use in accessing a TS session that provides online use of the hosted application wherein each provisioned TS account is associated with a unique profile; wherein the supply of available TS accounts is replenished whenever the supply of available TS accounts is below the buffer level, thereby ensuring TS accounts are available for a plurality of users utilizing the hosted application via online TS sessions.
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13. A system for managing terminal services (TS) accounts for online utilization of a hosted application, the system comprising: a first computer operative to: monitor at least one of a supply of available TS accounts, a demand for TS accounts, and a status of each TS account; create one or more TS accounts in response to detecting that the supply of available TS accounts is below a buffer level wherein the buffer level comprises a minimum number of TS accounts having an available status, the number of TS accounts having an available status comprising at least one TS account; and provision a TS account for use in accessing a TS session that provides online use of the hosted application wherein each provisioned TS account is associated with a unique profile; wherein the supply of available TS accounts is replenished whenever the supply of available TS accounts is below the buffer level, thereby ensuring TS accounts are available for a plurality of users utilizing the hosted application via online TS sessions. 15. The system of claim 13 , wherein the first computer is further operative to receive and store at least one of account control parameters, time parameters, and account information parameters for managing the TS accounts wherein the control parameters include at least one of the following: a language associated with each TS account created; the buffer level; and a bulk-create value comprising a number of TS accounts to be created when the supply of available TS accounts is below the buffer level; wherein the time parameters include at least one of the following: a first polling frequency comprising a time value between periodic queries of TS accounts for creating accounts; a second polling frequency comprising a time value between periodic queries of TS accounts for purging accounts; a TS account duration comprising a time span between provisioning a TS account and deletion of the TS account; a warning time comprising a time span between provisioning a TS account and rendering a warning to a user during a TS session; and a TS session duration comprising a time limit at which a TS session is disconnected; and wherein the account information parameters include at least one of the following: a status associated with each TS account created comprising one of available, in use wherein in use status indicates the TS account has been provisioned, and cleanup wherein cleanup status indicates the account is tagged for deletion; a username produced and associated with a provisioned TS account in response to the demand for a TS account; and a password generated, encrypted, and associated with the provisioned TS account in response to the demand for a TS account.
| 0.5 |
9,892,098 | 2 | 4 |
2. The machine-readable medium of claim 1 , further comprising instructions to: detect actuation of a pagination navigation control of the web browser; and transmit a request for a different subset of the plurality of item listings based on the actuation and based on the pagination navigation information.
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2. The machine-readable medium of claim 1 , further comprising instructions to: detect actuation of a pagination navigation control of the web browser; and transmit a request for a different subset of the plurality of item listings based on the actuation and based on the pagination navigation information. 4. The machine-readable medium of claim 2 , wherein the request for a different subset of the plurality of item listings includes a requested change to the number of item listings on a page.
| 0.551887 |
8,824,785 | 20 | 29 |
20. A system for segregating handwritten information from typographic information on a document, the system comprising: a memory operative to store an electronic document image of a document, the electronic document image comprising a plurality of pixels, wherein each of the plurality of pixels comprises a characteristic of a plurality of characteristics; an interface coupled with the memory and operative to receive the electronic document image; and a processor coupled with the interface and operative to receive, via the interface, the electronic document image of the document, identify a first, a second, and a third most frequently occurring characteristic of the plurality of pixels, wherein the pixels of the plurality of pixels comprising the first most frequently occurring characteristic of the plurality of characteristics represent a background of the document, determine typographic information of the document, wherein the typographic information is represented by the pixels of the plurality of pixels of the electronic document image which comprise the second most frequently occurring characteristic of the plurality of characteristics, determine handwritten information of the document, wherein the handwritten information is represented by the pixels of the plurality of pixels of the electronic document image which comprise the third most frequently occurring characteristic of the plurality of characteristics, and derive a first representation of the handwritten information and a second representation of the typographic information.
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20. A system for segregating handwritten information from typographic information on a document, the system comprising: a memory operative to store an electronic document image of a document, the electronic document image comprising a plurality of pixels, wherein each of the plurality of pixels comprises a characteristic of a plurality of characteristics; an interface coupled with the memory and operative to receive the electronic document image; and a processor coupled with the interface and operative to receive, via the interface, the electronic document image of the document, identify a first, a second, and a third most frequently occurring characteristic of the plurality of pixels, wherein the pixels of the plurality of pixels comprising the first most frequently occurring characteristic of the plurality of characteristics represent a background of the document, determine typographic information of the document, wherein the typographic information is represented by the pixels of the plurality of pixels of the electronic document image which comprise the second most frequently occurring characteristic of the plurality of characteristics, determine handwritten information of the document, wherein the handwritten information is represented by the pixels of the plurality of pixels of the electronic document image which comprise the third most frequently occurring characteristic of the plurality of characteristics, and derive a first representation of the handwritten information and a second representation of the typographic information. 29. The system of claim 20 wherein the characteristic of each of the plurality of pixels substantially comprises a color of a plurality of colors.
| 0.78209 |
7,571,383 | 1 | 4 |
1. A retrieval system for retrieving document data which have a content specified by an inputted retrieval statement among a plurality of document data, the system comprising: a storage device comprising: a document database that stores the plurality of document data; a concept database that stores a plurality of pre-specified concepts using a hierarchical structure in which a first concept including a second concept is a higher layer of the second concept; a concept extraction rule database that stores concept extraction rules comprising sets of one or more of the keywords and concepts indicating meanings of the one or more keywords, a processor configured for: extracting document concepts on the basis of keywords contained in the respective document data, the document concepts being the concepts corresponding to the document data; extracting a retrieval statement concept on the basis of a keyword contained in the retrieval statement, the retrieval statement concept being the concept corresponding to said retrieval statement; retrieving document data in which the retrieval statement concept is a higher or lower layer of a document concept among the plurality of document data; outputting document data retrieved by a concept retrieving section, as the document data containing the content specified by the retrieval statement; wherein the processor extracts the concept contained in the concept extraction rule as the retrieval statement concept if said retrieval statement comprises the one or more keywords contained in any of the concept extraction rules, wherein the processor extracts the concept contained in the concept extraction rule and uses said concept as the document concept if said document data include the one or more keywords contained in any of the concept extraction rules, and wherein the retrieval system further comprises: a retrieval index database that stores, for each of the document data, an association between the document data and the document concept of the document data extracted by the document data concept extracting section, wherein the concept retrieving section outputs said document data corresponding to the document concept of said document concept stored in the retrieval index database before the retrieval statement is inputted; information storage space storing a synonym database that stores an association between a predetermined word or phrase and the keyword that is a synonym of the word or phrase; a processor configured to perform data normalizing section that normalizes the document data by replacing the word or phrase contained in each of said document data with the keyword that is the synonym of the word or phrase; and information storage space storing a retrieval statement normalizing section that normalizes the retrieval statement by replacing the word or phrase contained in said retrieval statement with the keyword that is the synonym of the word or phrase, wherein the processor extracts the document concept from the normalized document data, and the retrieval statement concept extracting section extracts the retrieval statement concept from the normalized retrieval statement; wherein the processor is configured to: acquire a retrieval statement higher concept that is a higher-layer concept of said retrieval statement concept if the retrieval statement concept does not match the document concept; and output the document data as a retrieval result if the retrieval statement higher concept matches the document concept; wherein: the concept database stores each of said plurality of concepts as a node of the first or second hierarchical structure, the processor extracts the first document concept belonging to the first hierarchical structure and the second document concept belonging to the second hierarchical structure in association with the document data, the processor extracts the retrieval statement concept belonging to the first hierarchical structure and the second retrieval statement concept belonging to the second hierarchical structure in association with the retrieval statement, the processor acquires the first retrieval statement higher concept that is a higher layer of the first retrieval statement concept and the second retrieval statement higher concept that is a higher layer of the second retrieval statement concept if the first retrieval statement concept does not match the first document concept and if the second retrieval statement concept does not match the second document concept, and the processor outputs the first document data as a retrieval result if the number of the first document data in which the first retrieval statement higher concept is the same as the second retrieval statement concept and in which the first document concept is the same as the second document concept is smaller than that of the second document data in which the first retrieval statement higher concept is the same as the second retrieval statement concept and in which the first document concept is the same as the second document concept, or wherein the processor is configured to replace the retrieval statement concept with the retrieval statement lower concept: if all the document data having the document concept that is the same as the retrieval statement concept have the document concept that is the same as a retrieval statement lower concept that is a lower layer of the retrieval statement concept; and outputting the document data in which the retrieval statement lower concept matches the document concept, as a retrieval unit; and wherein: the concept database stores the plurality of concepts that identify a plurality of defects in a product, the document database stores the document data indicating contents of each of the plurality of defects, the retrieval statement concept extracting section extracts the retrieval statement concept corresponding to the retrieval statement used to retrieve said defects in the product, and the processor outputs the document data retrieved, as said document data indicating the contents of the defects in the product inputted by a user; or wherein: the concept database stores the plurality of concepts in a lower layer of the concept indicating that there are defects in components of the product, using a hierarchical structure in which the concepts indicating states of the defects in the components are provided, the document data concept extracting section extracts the document concept indicating that there is a defect in one of the components, on the basis of the keyword contained in the document data, the retrieval statement concept extracting section extracts the retrieval statement concept indicating the state of the defect in the one of said components, on the basis of the keyword contained in the retrieval statement, and wherein the concept retrieving section comprises: a higher concept acquiring section that acquires a retrieval statement higher concept that is the concept indicating that there is the defect in the one of said components, the concept being a higher layer of the retrieval statement concept; and a generalized concept outputting section that outputs, as a retrieval result, the document data having the document concept indicating that there is the defect in the one of the components, the document concept matching the retrieval statement higher concept; and further comprising a component database that uses a hierarchical structure to store inclusive relationships among the components of the product, wherein: the processor further extracts the document concept indicating the component described in the document data, on the basis of the keyword contained in the document data, the processor further extracts the retrieval statement concept indicating the component described in the retrieval statement concept extracting section, on the basis of the keyword contained in the retrieval statement, the processor acquires the concept that is a higher layer of the first retrieval statement concept indicating that there is the defect in the component or a state of the defect in the component, and the concept that is a higher layer of the second retrieval statement concept indicating the component, and the processor outputs, as a retrieval result, the document data having the document concept that matches the first retrieval statement concept and the document concept that matches the second retrieval statement concept if at least one of the first retrieval statement concept and the second retrieval statement concept is the concept in the higher layer; or a product database that uses a hierarchical structure to store inclusive relationships among the names of a plurality of the products, wherein the document data concept extracting section further extracts the document concept indicating the product name described in the document data, on the basis of the keyword contained in said document data, the retrieval statement concept extracting section further extracts the retrieval statement concept indicating the product name described in the retrieval statement concept extracting section, on the basis of the keyword contained in the retrieval statement, the higher concept acquiring section acquires the concept that is a higher layer of the first retrieval statement concept indicating that there is the defect in the component or a state of the defect in the component, and the concept that is a higher layer of the second retrieval statement concept indicating the product name, and the generalized concept outputting section outputs, as a retrieval result, the document data having the document concept that matches the first retrieval statement concept and the document concept that matches the second retrieval statement concept if at least one of the first retrieval statement concept and the second retrieval statement concept is the concept in the higher layer.
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1. A retrieval system for retrieving document data which have a content specified by an inputted retrieval statement among a plurality of document data, the system comprising: a storage device comprising: a document database that stores the plurality of document data; a concept database that stores a plurality of pre-specified concepts using a hierarchical structure in which a first concept including a second concept is a higher layer of the second concept; a concept extraction rule database that stores concept extraction rules comprising sets of one or more of the keywords and concepts indicating meanings of the one or more keywords, a processor configured for: extracting document concepts on the basis of keywords contained in the respective document data, the document concepts being the concepts corresponding to the document data; extracting a retrieval statement concept on the basis of a keyword contained in the retrieval statement, the retrieval statement concept being the concept corresponding to said retrieval statement; retrieving document data in which the retrieval statement concept is a higher or lower layer of a document concept among the plurality of document data; outputting document data retrieved by a concept retrieving section, as the document data containing the content specified by the retrieval statement; wherein the processor extracts the concept contained in the concept extraction rule as the retrieval statement concept if said retrieval statement comprises the one or more keywords contained in any of the concept extraction rules, wherein the processor extracts the concept contained in the concept extraction rule and uses said concept as the document concept if said document data include the one or more keywords contained in any of the concept extraction rules, and wherein the retrieval system further comprises: a retrieval index database that stores, for each of the document data, an association between the document data and the document concept of the document data extracted by the document data concept extracting section, wherein the concept retrieving section outputs said document data corresponding to the document concept of said document concept stored in the retrieval index database before the retrieval statement is inputted; information storage space storing a synonym database that stores an association between a predetermined word or phrase and the keyword that is a synonym of the word or phrase; a processor configured to perform data normalizing section that normalizes the document data by replacing the word or phrase contained in each of said document data with the keyword that is the synonym of the word or phrase; and information storage space storing a retrieval statement normalizing section that normalizes the retrieval statement by replacing the word or phrase contained in said retrieval statement with the keyword that is the synonym of the word or phrase, wherein the processor extracts the document concept from the normalized document data, and the retrieval statement concept extracting section extracts the retrieval statement concept from the normalized retrieval statement; wherein the processor is configured to: acquire a retrieval statement higher concept that is a higher-layer concept of said retrieval statement concept if the retrieval statement concept does not match the document concept; and output the document data as a retrieval result if the retrieval statement higher concept matches the document concept; wherein: the concept database stores each of said plurality of concepts as a node of the first or second hierarchical structure, the processor extracts the first document concept belonging to the first hierarchical structure and the second document concept belonging to the second hierarchical structure in association with the document data, the processor extracts the retrieval statement concept belonging to the first hierarchical structure and the second retrieval statement concept belonging to the second hierarchical structure in association with the retrieval statement, the processor acquires the first retrieval statement higher concept that is a higher layer of the first retrieval statement concept and the second retrieval statement higher concept that is a higher layer of the second retrieval statement concept if the first retrieval statement concept does not match the first document concept and if the second retrieval statement concept does not match the second document concept, and the processor outputs the first document data as a retrieval result if the number of the first document data in which the first retrieval statement higher concept is the same as the second retrieval statement concept and in which the first document concept is the same as the second document concept is smaller than that of the second document data in which the first retrieval statement higher concept is the same as the second retrieval statement concept and in which the first document concept is the same as the second document concept, or wherein the processor is configured to replace the retrieval statement concept with the retrieval statement lower concept: if all the document data having the document concept that is the same as the retrieval statement concept have the document concept that is the same as a retrieval statement lower concept that is a lower layer of the retrieval statement concept; and outputting the document data in which the retrieval statement lower concept matches the document concept, as a retrieval unit; and wherein: the concept database stores the plurality of concepts that identify a plurality of defects in a product, the document database stores the document data indicating contents of each of the plurality of defects, the retrieval statement concept extracting section extracts the retrieval statement concept corresponding to the retrieval statement used to retrieve said defects in the product, and the processor outputs the document data retrieved, as said document data indicating the contents of the defects in the product inputted by a user; or wherein: the concept database stores the plurality of concepts in a lower layer of the concept indicating that there are defects in components of the product, using a hierarchical structure in which the concepts indicating states of the defects in the components are provided, the document data concept extracting section extracts the document concept indicating that there is a defect in one of the components, on the basis of the keyword contained in the document data, the retrieval statement concept extracting section extracts the retrieval statement concept indicating the state of the defect in the one of said components, on the basis of the keyword contained in the retrieval statement, and wherein the concept retrieving section comprises: a higher concept acquiring section that acquires a retrieval statement higher concept that is the concept indicating that there is the defect in the one of said components, the concept being a higher layer of the retrieval statement concept; and a generalized concept outputting section that outputs, as a retrieval result, the document data having the document concept indicating that there is the defect in the one of the components, the document concept matching the retrieval statement higher concept; and further comprising a component database that uses a hierarchical structure to store inclusive relationships among the components of the product, wherein: the processor further extracts the document concept indicating the component described in the document data, on the basis of the keyword contained in the document data, the processor further extracts the retrieval statement concept indicating the component described in the retrieval statement concept extracting section, on the basis of the keyword contained in the retrieval statement, the processor acquires the concept that is a higher layer of the first retrieval statement concept indicating that there is the defect in the component or a state of the defect in the component, and the concept that is a higher layer of the second retrieval statement concept indicating the component, and the processor outputs, as a retrieval result, the document data having the document concept that matches the first retrieval statement concept and the document concept that matches the second retrieval statement concept if at least one of the first retrieval statement concept and the second retrieval statement concept is the concept in the higher layer; or a product database that uses a hierarchical structure to store inclusive relationships among the names of a plurality of the products, wherein the document data concept extracting section further extracts the document concept indicating the product name described in the document data, on the basis of the keyword contained in said document data, the retrieval statement concept extracting section further extracts the retrieval statement concept indicating the product name described in the retrieval statement concept extracting section, on the basis of the keyword contained in the retrieval statement, the higher concept acquiring section acquires the concept that is a higher layer of the first retrieval statement concept indicating that there is the defect in the component or a state of the defect in the component, and the concept that is a higher layer of the second retrieval statement concept indicating the product name, and the generalized concept outputting section outputs, as a retrieval result, the document data having the document concept that matches the first retrieval statement concept and the document concept that matches the second retrieval statement concept if at least one of the first retrieval statement concept and the second retrieval statement concept is the concept in the higher layer. 4. The retrieval system according to claim 1 , further comprising: a synonym database that stores an association between a predetermined word or phrase and said keyword that is a synonym of the word or phrase; a document data normalizing section that normalizes the document data by replacing said word or phrase contained in each of said document data with said keyword that is the synonym of the word or phrase; and a retrieval statement normalizing section that normalizes said retrieval statement by replacing said word or phrase contained in said retrieval statement with said keyword that is the synonym of the word or phrase, wherein said document data concept extracting section extracts said document concept from said normalized document data, and said retrieval statement concept extracting section extracts said retrieval statement concept from said normalized retrieval statement.
| 0.661742 |
7,756,256 | 1 | 7 |
1. A method of providing unified messaging services in a computing environment that includes a plurality of message types, said method comprising: providing a unified message that can represent a plurality of message types; providing one or more features associated with said unified message; receiving a selection that identifies a first selected feature of said one or more features; determining, based on said first selected feature, if each one of said plurality of message types should be used for said unified message; and transforming said unified message into a first message type of said plurality of message types when said determining determines that said first message type can be used.
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1. A method of providing unified messaging services in a computing environment that includes a plurality of message types, said method comprising: providing a unified message that can represent a plurality of message types; providing one or more features associated with said unified message; receiving a selection that identifies a first selected feature of said one or more features; determining, based on said first selected feature, if each one of said plurality of message types should be used for said unified message; and transforming said unified message into a first message type of said plurality of message types when said determining determines that said first message type can be used. 7. A method as recited in claim 1 , wherein each one of said message types adhere to one or more of the following: a message delivery protocol, and a message format.
| 0.771468 |
9,047,380 | 10 | 17 |
10. A non-transitory computer-readable medium storing instructions that, when executed by a computer, cause the computer to perform a method of providing one or more targeted keywords associated with a document, the method comprising: receiving a set of user-selected keywords and a specification of a market segment associated with the document from a user, the market segment associated with an industry; determining, by computer a set of similar keywords based on the user selected keywords, wherein the determining involves: generating a first set of similar keywords, by selecting keywords correlated with the user selected keywords; calculating a competitiveness score based on the number of other users bidding for the keyword relative to all other keywords for each keyword in the first set of similar keywords; and filtering out a set of keywords with the top competitiveness scores from the set of similar keywords; determining a set of weighted market-specific keywords based on the filtered set of similar keywords, the user-selected keywords, and keywords in documents that are associated with other users in the market segment, wherein the set of market specific keywords includes: one or more of the keywords in the documents, one or more of the user-selected keywords, and one or more of keywords from the filtered set of similar keywords; providing at least a subset of the set of market-specific keywords to the user, the subset comprising at least the top weighted keywords of the set of market-specific keywords; receiving, from the user, a selection of one or more keywords from the provided subset of market specific keywords; and incrementing, in a data structure comprising weights associated with keywords, weights for each of the one or more keywords in the received selection of keywords, wherein the weight for each selected keyword is incremented by a predetermined value each time the keyword is selected such that the weight directly relates to a total number of selections of the keyword.
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10. A non-transitory computer-readable medium storing instructions that, when executed by a computer, cause the computer to perform a method of providing one or more targeted keywords associated with a document, the method comprising: receiving a set of user-selected keywords and a specification of a market segment associated with the document from a user, the market segment associated with an industry; determining, by computer a set of similar keywords based on the user selected keywords, wherein the determining involves: generating a first set of similar keywords, by selecting keywords correlated with the user selected keywords; calculating a competitiveness score based on the number of other users bidding for the keyword relative to all other keywords for each keyword in the first set of similar keywords; and filtering out a set of keywords with the top competitiveness scores from the set of similar keywords; determining a set of weighted market-specific keywords based on the filtered set of similar keywords, the user-selected keywords, and keywords in documents that are associated with other users in the market segment, wherein the set of market specific keywords includes: one or more of the keywords in the documents, one or more of the user-selected keywords, and one or more of keywords from the filtered set of similar keywords; providing at least a subset of the set of market-specific keywords to the user, the subset comprising at least the top weighted keywords of the set of market-specific keywords; receiving, from the user, a selection of one or more keywords from the provided subset of market specific keywords; and incrementing, in a data structure comprising weights associated with keywords, weights for each of the one or more keywords in the received selection of keywords, wherein the weight for each selected keyword is incremented by a predetermined value each time the keyword is selected such that the weight directly relates to a total number of selections of the keyword. 17. The computer-readable storage medium of claim 10 , wherein the received selection of one or more keywords from the provided subset of market-specific keywords facilitate a search-engine-determined ranking of the document in response to a search query from another user.
| 0.5 |
8,156,152 | 8 | 10 |
8. The method of claim 1 , further comprising: determining a file content relevance for each File URI; and ranking the identified File URIs according to the file content relevance.
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8. The method of claim 1 , further comprising: determining a file content relevance for each File URI; and ranking the identified File URIs according to the file content relevance. 10. The method of claim 8 , wherein the file content relevance comprises at least one of the following: a visit count, a reference count, or a type of the non-text binary file identified by the respective File URI.
| 0.5 |
9,836,450 | 10 | 12 |
10. A natural language platform configured to classify a document in natural language processing using a natural language model stored in one or more data files, the natural language platform comprising: a memory configured to store the one or more data files; and a processor coupled to the memory and configured to: access one or more feature types from the one or more data files, the one or more feature types each defining a data structure configured to access a tokenized sequence of the document and generate linguistic features from content within the tokenized sequence; perform a tokenizing operation of the document, the tokenizing operation configured to generate one or more tokenized sequences from the content within the document; generate a plurality of features for the document from the one or more tokenized sequences, based on parameters defined by the one or more feature types and on parameters defined in task configuration data in the one or more data files, the task configuration data associated with a type of task analysis that the natural language model is configured to classify the document into; access a plurality of probabilities stored in the one or more data files, each probability among the plurality of probabilities associated with a feature among the plurality of features and defining a pre-computed likelihood that said feature predicts a presence or absence of a label that the document is to be classified into; wherein: the plurality of probabilities are pre-computed during a model training process configured to train the natural language model to classify documents according to at least said label and said task analysis; the one or more data files is configured to store each probability in a logarithmic scale that is converted to said probability by the processor; the one or more data files is configured to store a table of rows and columns, wherein a first column comprises the plurality of features, a second column comprises a first category of probabilities among the plurality of probabilities that describes a first likelihood that a feature in the first column belonging to the same row satisfies a first attribute of said label, and a third column comprises a second category of probabilities among the plurality of probabilities that describes a second likelihood that said feature in the first column belonging to the same row satisfies a second attribute of said label; and the first attribute of said label represents a likelihood that said feature in the same row appears at a beginning of a span of the document, the second attribute of said label represents a likelihood that said feature in the same row appears inside said span of the document, and a fourth column comprises a third category of probabilities among the plurality of probabilities that represents a third likelihood that said feature in the same row appears outside said span of the document; compute a prediction score indicating how likely the document is to be classified into said label, based on the plurality or probabilities; classify the document into said label based on comparing the prediction score to a threshold; and train the natural language model at least based on the classified document.
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10. A natural language platform configured to classify a document in natural language processing using a natural language model stored in one or more data files, the natural language platform comprising: a memory configured to store the one or more data files; and a processor coupled to the memory and configured to: access one or more feature types from the one or more data files, the one or more feature types each defining a data structure configured to access a tokenized sequence of the document and generate linguistic features from content within the tokenized sequence; perform a tokenizing operation of the document, the tokenizing operation configured to generate one or more tokenized sequences from the content within the document; generate a plurality of features for the document from the one or more tokenized sequences, based on parameters defined by the one or more feature types and on parameters defined in task configuration data in the one or more data files, the task configuration data associated with a type of task analysis that the natural language model is configured to classify the document into; access a plurality of probabilities stored in the one or more data files, each probability among the plurality of probabilities associated with a feature among the plurality of features and defining a pre-computed likelihood that said feature predicts a presence or absence of a label that the document is to be classified into; wherein: the plurality of probabilities are pre-computed during a model training process configured to train the natural language model to classify documents according to at least said label and said task analysis; the one or more data files is configured to store each probability in a logarithmic scale that is converted to said probability by the processor; the one or more data files is configured to store a table of rows and columns, wherein a first column comprises the plurality of features, a second column comprises a first category of probabilities among the plurality of probabilities that describes a first likelihood that a feature in the first column belonging to the same row satisfies a first attribute of said label, and a third column comprises a second category of probabilities among the plurality of probabilities that describes a second likelihood that said feature in the first column belonging to the same row satisfies a second attribute of said label; and the first attribute of said label represents a likelihood that said feature in the same row appears at a beginning of a span of the document, the second attribute of said label represents a likelihood that said feature in the same row appears inside said span of the document, and a fourth column comprises a third category of probabilities among the plurality of probabilities that represents a third likelihood that said feature in the same row appears outside said span of the document; compute a prediction score indicating how likely the document is to be classified into said label, based on the plurality or probabilities; classify the document into said label based on comparing the prediction score to a threshold; and train the natural language model at least based on the classified document. 12. The natural language platform of claim 10 , wherein the plurality of probabilities comprise a first probability that said feature appears at a beginning of a subset or the document, a second probability that said feature appears at an inside of the subset of the document, and a third probability that said feature appears on an outside of the subset or the document.
| 0.5 |
10,019,516 | 1 | 4 |
1. A computer implemented method of matching ontologies, the method comprising: for each pair of entities of a first ontology and a second ontology, wherein each pair of entities includes a first entity from a first plurality of entities of the first ontology and a second entity from a second plurality of entities of the second ontology, and wherein the first entity and the second entity of each pair of entities is of a same data type: determining a vector of similarities for the pair of entities; determining a confidence score for the vector of similarities; determining a relation score for each relation type based on the vector of similarities to measure relatedness between the first entity of the pair of entities and the second entity of the pair of entities; and generating a mapping ontology based on the relation type, the relation score, and the confidence score of each pair of entities, wherein a relation type is a relevance relation type defined as: μ rel ( s → ) = 1 4 s na + 1 2 s co + 1 4 s st , where s na is a name-based similarity, s co is a containment similarity, and s st is a structural similarity.
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1. A computer implemented method of matching ontologies, the method comprising: for each pair of entities of a first ontology and a second ontology, wherein each pair of entities includes a first entity from a first plurality of entities of the first ontology and a second entity from a second plurality of entities of the second ontology, and wherein the first entity and the second entity of each pair of entities is of a same data type: determining a vector of similarities for the pair of entities; determining a confidence score for the vector of similarities; determining a relation score for each relation type based on the vector of similarities to measure relatedness between the first entity of the pair of entities and the second entity of the pair of entities; and generating a mapping ontology based on the relation type, the relation score, and the confidence score of each pair of entities, wherein a relation type is a relevance relation type defined as: μ rel ( s → ) = 1 4 s na + 1 2 s co + 1 4 s st , where s na is a name-based similarity, s co is a containment similarity, and s st is a structural similarity. 4. The method of claim 1 , wherein determining the vector of similarities includes at least one of determining name-based similarity, determining overlapping similarity, determining containment similarity, or determining structural similarity.
| 0.595 |
9,298,365 | 12 | 13 |
12. The character recognition method according to claim 9 , wherein the detecting includes: for each time when one stroke in handwriting is received, collating information indicating a reference stroke included in a dictionary data with a set of strokes that are not decided for forming a character, among input strokes, by referring to the dictionary data in which a plurality of associations between the character code and information indicating a reference stroke are registered each time; and detecting each of the plurality of character codes sequentially based on a collation result.
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12. The character recognition method according to claim 9 , wherein the detecting includes: for each time when one stroke in handwriting is received, collating information indicating a reference stroke included in a dictionary data with a set of strokes that are not decided for forming a character, among input strokes, by referring to the dictionary data in which a plurality of associations between the character code and information indicating a reference stroke are registered each time; and detecting each of the plurality of character codes sequentially based on a collation result. 13. The character recognition method according to claim 12 , wherein the collating includes: calculating a similarity between the set of strokes that are not decided for forming a character and a plurality of reference strokes included in the dictionary data to acquire a character code associated with the reference stroke having the maximum similarity; deciding the character code acquired at the previous time for a set of strokes input up to the previous time among the set of strokes that are not decided for forming a character when the maximum similarity at the present time is less than or equal to the maximum similarity at the previous time and the maximum similarity at the previous time is equal to or larger than a threshold value.
| 0.5 |
10,019,910 | 8 | 11 |
8. A computer-implemented method comprising: accessing a learning schedule indicative of when, for each of a plurality of learning episodes, content corresponding to the learning episode is to be presented via an electronic app; adjusting, using one or more processors, the learning schedule based on a past performance of a user, a past performance of a group of other users, a target performance time, and a target performance metric, the adjusting including changing a number of learning episodes included in the plurality of learning episodes; automatically identifying, using the one or more processors, a presentation time for an episode of the plurality of learning episodes based on the adjusted learning schedule; and displaying, at a device of the user, an interface that includes an electronic notification at the presentation time, the electronic notification including one or more elements configured to receive input corresponding to a request to access an electronic content object associated with the episode, wherein the electronic content object is displayed at the device of the user upon detecting that the one or more elements have received input corresponding to the request.
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8. A computer-implemented method comprising: accessing a learning schedule indicative of when, for each of a plurality of learning episodes, content corresponding to the learning episode is to be presented via an electronic app; adjusting, using one or more processors, the learning schedule based on a past performance of a user, a past performance of a group of other users, a target performance time, and a target performance metric, the adjusting including changing a number of learning episodes included in the plurality of learning episodes; automatically identifying, using the one or more processors, a presentation time for an episode of the plurality of learning episodes based on the adjusted learning schedule; and displaying, at a device of the user, an interface that includes an electronic notification at the presentation time, the electronic notification including one or more elements configured to receive input corresponding to a request to access an electronic content object associated with the episode, wherein the electronic content object is displayed at the device of the user upon detecting that the one or more elements have received input corresponding to the request. 11. The computer-implemented method as recited in claim 8 , further comprising: determining the past performance of the user based on one or more first inputs associated with the user and received via a first interface; and determining the past performance of the group of other users based on one or more second inputs associated with the group of other users and received via one or more second interfaces.
| 0.685185 |
9,052,819 | 1 | 5 |
1. A method for determining an intended gesture-based input command from an incomplete gesture-based input command that is supplied to a gesture-based touch screen display that includes at least a touch sensitive region, the method comprising the steps of: receiving an incomplete gesture-based input command on the touch sensitive region of the gesture-based touch screen device, the incomplete gesture-based input command including a gesture profile and a gesture direction; generating gesture signals in response to the input command, the gesture signals including data representative of the gesture profile and the gesture direction; processing the gesture signals, in a processor, to predict the intended gesture-based input command; retrieving, with the processor, the intended gesture-based command from an electronically stored standard gesture library; implementing, with the processor, the intended gesture-based command; retrieving, with the processor and based on the intended gesture-based command, one or more task requirements from an electronically stored task requirements library, the one or more task requirements comprising one or more tasks that are related to the intended gesture-based command; and continuously rendering, on the touch screen display, one or more graphical user interface elements representative of the one or more tasks that are related to the intended gesture-based command until a user does not interact with the one or more graphical user interface elements within a predetermined time period.
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1. A method for determining an intended gesture-based input command from an incomplete gesture-based input command that is supplied to a gesture-based touch screen display that includes at least a touch sensitive region, the method comprising the steps of: receiving an incomplete gesture-based input command on the touch sensitive region of the gesture-based touch screen device, the incomplete gesture-based input command including a gesture profile and a gesture direction; generating gesture signals in response to the input command, the gesture signals including data representative of the gesture profile and the gesture direction; processing the gesture signals, in a processor, to predict the intended gesture-based input command; retrieving, with the processor, the intended gesture-based command from an electronically stored standard gesture library; implementing, with the processor, the intended gesture-based command; retrieving, with the processor and based on the intended gesture-based command, one or more task requirements from an electronically stored task requirements library, the one or more task requirements comprising one or more tasks that are related to the intended gesture-based command; and continuously rendering, on the touch screen display, one or more graphical user interface elements representative of the one or more tasks that are related to the intended gesture-based command until a user does not interact with the one or more graphical user interface elements within a predetermined time period. 5. The method of claim 1 , wherein the step of processing the gesture signals, in a processor, to predict the intended gesture-based input command comprises: comparing the gesture profile and gesture direction to standard gesture-based commands electronically stored in the standard gesture library.
| 0.621519 |
8,788,991 | 18 | 19 |
18. A computer-readable medium that is not a transitory propagating signal, the computer-readable medium including instructions, which when executed by the computer, cause the computer to perform operations comprising: converting source code of an uncompiled executable software application into an automaton comprising a plurality of interconnected states; converting the automaton into a netlist, the netlist comprising instances corresponding to states of the automaton, wherein the instances correspond to hardware elements of the parallel machine, wherein converting the automaton into a netlist includes grouping states of the automaton together based on a physical design of the parallel machine; and converting the netlist into an image, the image comprising compiled binary data to program the parallel machine to correspond to the instances of the netlist, such that the compiled binary data is arranged to program the parallel machine to provide the functionality specified by the source code of the uncompiled executable software application when the image is loaded onto the parallel machine.
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18. A computer-readable medium that is not a transitory propagating signal, the computer-readable medium including instructions, which when executed by the computer, cause the computer to perform operations comprising: converting source code of an uncompiled executable software application into an automaton comprising a plurality of interconnected states; converting the automaton into a netlist, the netlist comprising instances corresponding to states of the automaton, wherein the instances correspond to hardware elements of the parallel machine, wherein converting the automaton into a netlist includes grouping states of the automaton together based on a physical design of the parallel machine; and converting the netlist into an image, the image comprising compiled binary data to program the parallel machine to correspond to the instances of the netlist, such that the compiled binary data is arranged to program the parallel machine to provide the functionality specified by the source code of the uncompiled executable software application when the image is loaded onto the parallel machine. 19. The computer-readable medium of claim 18 , wherein the automaton is a homogeneous automaton.
| 0.813953 |
9,183,499 | 1 | 9 |
1. A computer-implemented method, comprising: obtaining a first quality model that was trained using a first set of training entities; identifying a set of candidate entities, where each candidate entity is different from each of the training entities; for each candidate entity in the set of candidate entities: obtaining a first quality score for the candidate entity; obtaining one or more neighbor features for neighbor entities of the candidate entity, where each neighbor entity of the candidate entity is an entity that is linked to the candidate entity; obtaining one or more entity specific feature values for the candidate entity, where each entity specific feature value is determined independent of the neighbor entities of the candidate entity; and determining a second quality score for the candidate entity using the first quality model, the second quality score being computed based on the first quality score, the neighbor features, and the entity specific feature values.
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1. A computer-implemented method, comprising: obtaining a first quality model that was trained using a first set of training entities; identifying a set of candidate entities, where each candidate entity is different from each of the training entities; for each candidate entity in the set of candidate entities: obtaining a first quality score for the candidate entity; obtaining one or more neighbor features for neighbor entities of the candidate entity, where each neighbor entity of the candidate entity is an entity that is linked to the candidate entity; obtaining one or more entity specific feature values for the candidate entity, where each entity specific feature value is determined independent of the neighbor entities of the candidate entity; and determining a second quality score for the candidate entity using the first quality model, the second quality score being computed based on the first quality score, the neighbor features, and the entity specific feature values. 9. The method of claim 1 , wherein each entity is represented by a node in an irregular graph, and wherein an entity is linked to a candidate entity if the nodes representing the entity and the candidate entity share an edge in the irregular graph.
| 0.792642 |
8,819,000 | 1 | 6 |
1. A computer-implemented method, comprising: receiving an original query including a first limitation that constrains a search; modifying the original query to obtain a modified query in which the first limitation that constrains the search has been omitted; obtaining, from a search engine system, first search results responsive to the modified query, wherein the first search results have an associated ranking determined by the search engine system, and wherein each of the first search results refers to a respective resource; identifying one or more common characteristics shared by two or more resources, each of the two or more resources corresponding to a different highly-ranked result of the first search results; generating a second modified query comprising the original query and a second limitation representing the one or more common characteristics, the second limitation requiring results responsive to the second modified query to reference a resource having the one or more common characteristics; obtaining second search results responsive to the second modified query, wherein each of the second search results refers to a resource having the one or more common characteristics; and providing the second search results in a response to the original query.
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1. A computer-implemented method, comprising: receiving an original query including a first limitation that constrains a search; modifying the original query to obtain a modified query in which the first limitation that constrains the search has been omitted; obtaining, from a search engine system, first search results responsive to the modified query, wherein the first search results have an associated ranking determined by the search engine system, and wherein each of the first search results refers to a respective resource; identifying one or more common characteristics shared by two or more resources, each of the two or more resources corresponding to a different highly-ranked result of the first search results; generating a second modified query comprising the original query and a second limitation representing the one or more common characteristics, the second limitation requiring results responsive to the second modified query to reference a resource having the one or more common characteristics; obtaining second search results responsive to the second modified query, wherein each of the second search results refers to a resource having the one or more common characteristics; and providing the second search results in a response to the original query. 6. The computer-implemented method of claim 1 , wherein generating a second modified query comprises automatically generating the second modified query, without receiving a request to add the second limitation.
| 0.837209 |
10,146,861 | 9 | 10 |
9. An apparatus, comprising: a memory that stores program instructions; and a processor that reads the program instructions and implements a method of providing search results to a user, the method comprising: accessing an ontology providing a plurality of classifications, where each classification comprises a set of terms; accessing a fact table comprising at least two facts, wherein each fact comprises a document and each fact is associated with one or more classification from the plurality of classifications and there is at least one classification that relates at least one fact to multiple terms of the at least one classification; receiving a user query request specifying one or more condition terms from one or more classifications or text words or text phrases; having a set of predetermined report patterns, where each report pattern comprises one or more report classifications representing one or more dimensions of visualization of the reports corresponding to the report pattern, and wherein the set of predetermined report pattern is used to compute and output reports to users; receiving a user query and associating the user query with a report pattern; and computing the requested report by assigning one or more numeric aggregate measures to each combination of a particular report term in the report pattern classifications, wherein at least one aggregate measure is computed after receiving the user query from a set of qualifying facts that are associated with every combination of the particular report terms and also relate to the condition terms or text words or text phrases in the user query request.
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9. An apparatus, comprising: a memory that stores program instructions; and a processor that reads the program instructions and implements a method of providing search results to a user, the method comprising: accessing an ontology providing a plurality of classifications, where each classification comprises a set of terms; accessing a fact table comprising at least two facts, wherein each fact comprises a document and each fact is associated with one or more classification from the plurality of classifications and there is at least one classification that relates at least one fact to multiple terms of the at least one classification; receiving a user query request specifying one or more condition terms from one or more classifications or text words or text phrases; having a set of predetermined report patterns, where each report pattern comprises one or more report classifications representing one or more dimensions of visualization of the reports corresponding to the report pattern, and wherein the set of predetermined report pattern is used to compute and output reports to users; receiving a user query and associating the user query with a report pattern; and computing the requested report by assigning one or more numeric aggregate measures to each combination of a particular report term in the report pattern classifications, wherein at least one aggregate measure is computed after receiving the user query from a set of qualifying facts that are associated with every combination of the particular report terms and also relate to the condition terms or text words or text phrases in the user query request. 10. The apparatus of claim 9 , wherein at least one term is associated to other terms by means of either hierarchy or relationship the aggregate measure is computed from a set of qualifying facts that are associated with every combination of the particular report terms or of terms associated to the particular report terms and also relate to the condition terms or to terms associated to the condition terms.
| 0.5 |
9,471,723 | 13 | 14 |
13. The data processing system of claim 10 , wherein the modify data structure further identifies an array of reservoir data blocks in the model to be modified and one of the nodes computes a global index of the identified array and further including the processor nodes each identifying the grid blocks in the global index allocated to them.
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13. The data processing system of claim 10 , wherein the modify data structure further identifies an array of reservoir data blocks in the model to be modified and one of the nodes computes a global index of the identified array and further including the processor nodes each identifying the grid blocks in the global index allocated to them. 14. The data processing system of claim 13 , the processor nodes each sorting the local of the identified array of reservoir data blocks allocated to that processor node.
| 0.5 |
9,679,256 | 31 | 38 |
31. One or more non-transitory computer-readable media containing instructions which, as a result of execution by a computing device, configure the computing device so as to cause the computing device to automatically evaluate the linguistic quality of free-response text answers submitted by students in response to examination prompts using discriminative preference ranking of predetermined linguistic text features, said configured computing device generating a trained model weight vector for subsequent use in automatically evaluating said free-response text answers by: accessing a plurality of training linguistic vectors (x 1 , x 2 , x 3 , . . . x n ) each training linguistic vector comprising a plurality of numerical values representing predetermined linguistic features of text comprising sentences within a training text, at least some of said predetermined linguistic features representing at least one of lexical, part-of-speech or parsing of words within said sentences; accessing, for each of a plurality of predetermined pairs of said training linguistic vectors (x i , x j ), predetermined ranking data (r i , r j ) that defines which one of the pair of training linguistic vectors (x i , x j ) is representative of a better training script; accessing an initial weight vector (w i ) comprising a plurality of numerical weights corresponding to the plurality of numerical values in the training vectors; generating a plurality of pairwise difference training vectors (x j −x i ) each difference training vector being calculated as a difference between a pair of said training linguistic vectors ranked by said ranking data; and performing an iterative process to adapt said initial weight vector (w i ) to a trained model weight vector (w m ) by: i) calculating a dot product between a current weight vector and each pairwise difference training vector to generate a respective scalar value for each pairwise difference training vector; ii) determining, for each pairwise difference training vector, if the current weight vector misclassified the pairwise difference training vector in dependence upon a comparison result obtained by comparing the scalar value for the pairwise difference training vector with a predetermined threshold; iii) generating an aggregate vector (ã) by summing the pairwise difference training vectors that said determining step determines are misclassified and normalizing the summed result with a current timing factor; iv) calculating a new weight vector by arithmetically combining numerical values of the current weight vector with respectively, corresponding numerical values of the generated aggregate vector; and v) repeating steps i) through iv) until the current timing factor reaches a predetermined condition, whereupon the then current weight vector becomes said trained model weight vector (w m ); and subsequently using said trained model weight vector to automatically evaluate the linguistic quality of each of plural input free-text answers submitted for evaluation by: generating a linguistic vector for an input free-text answer that is to be evaluated; calculating a dot product between the trained model weight vector and the linguistic vector for the input free-text answer that is to be evaluated to generate a scalar value for the input free-text answer; and outputting an evaluation of the input free-text answer using the scalar value generated for the input free-text answer.
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31. One or more non-transitory computer-readable media containing instructions which, as a result of execution by a computing device, configure the computing device so as to cause the computing device to automatically evaluate the linguistic quality of free-response text answers submitted by students in response to examination prompts using discriminative preference ranking of predetermined linguistic text features, said configured computing device generating a trained model weight vector for subsequent use in automatically evaluating said free-response text answers by: accessing a plurality of training linguistic vectors (x 1 , x 2 , x 3 , . . . x n ) each training linguistic vector comprising a plurality of numerical values representing predetermined linguistic features of text comprising sentences within a training text, at least some of said predetermined linguistic features representing at least one of lexical, part-of-speech or parsing of words within said sentences; accessing, for each of a plurality of predetermined pairs of said training linguistic vectors (x i , x j ), predetermined ranking data (r i , r j ) that defines which one of the pair of training linguistic vectors (x i , x j ) is representative of a better training script; accessing an initial weight vector (w i ) comprising a plurality of numerical weights corresponding to the plurality of numerical values in the training vectors; generating a plurality of pairwise difference training vectors (x j −x i ) each difference training vector being calculated as a difference between a pair of said training linguistic vectors ranked by said ranking data; and performing an iterative process to adapt said initial weight vector (w i ) to a trained model weight vector (w m ) by: i) calculating a dot product between a current weight vector and each pairwise difference training vector to generate a respective scalar value for each pairwise difference training vector; ii) determining, for each pairwise difference training vector, if the current weight vector misclassified the pairwise difference training vector in dependence upon a comparison result obtained by comparing the scalar value for the pairwise difference training vector with a predetermined threshold; iii) generating an aggregate vector (ã) by summing the pairwise difference training vectors that said determining step determines are misclassified and normalizing the summed result with a current timing factor; iv) calculating a new weight vector by arithmetically combining numerical values of the current weight vector with respectively, corresponding numerical values of the generated aggregate vector; and v) repeating steps i) through iv) until the current timing factor reaches a predetermined condition, whereupon the then current weight vector becomes said trained model weight vector (w m ); and subsequently using said trained model weight vector to automatically evaluate the linguistic quality of each of plural input free-text answers submitted for evaluation by: generating a linguistic vector for an input free-text answer that is to be evaluated; calculating a dot product between the trained model weight vector and the linguistic vector for the input free-text answer that is to be evaluated to generate a scalar value for the input free-text answer; and outputting an evaluation of the input free-text answer using the scalar value generated for the input free-text answer. 38. The computer-readable media of claim 31 , wherein the instructions are further to cause the computing device to detect, through usage of an ISA model, whether an input text is responsive to a prompt.
| 0.794949 |
8,712,850 | 32 | 62 |
32. A computer-implemented method comprising: receiving, using one or more processors, a request for content, wherein the content is of a form of an advertisement; identifying an eligible advertisement from an inventory of advertisements; receiving a creative associated with the eligible advertisement wherein the creative includes a title, at least one line of additional text and optionally a reference portion; determining a portion of text from the at least one line of additional text to promote into the title including a portion beginning with text at a beginning of a first of the at least one line of additional text, wherein determining includes evaluating the at least one line of additional text to identify the portion; promoting, using the one or more processors, the portion of text into the title; adjusting a remainder of the creative based on the promoted portion; and providing the advertisement responsive to the request including providing the creative including the title with the promoted portion.
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32. A computer-implemented method comprising: receiving, using one or more processors, a request for content, wherein the content is of a form of an advertisement; identifying an eligible advertisement from an inventory of advertisements; receiving a creative associated with the eligible advertisement wherein the creative includes a title, at least one line of additional text and optionally a reference portion; determining a portion of text from the at least one line of additional text to promote into the title including a portion beginning with text at a beginning of a first of the at least one line of additional text, wherein determining includes evaluating the at least one line of additional text to identify the portion; promoting, using the one or more processors, the portion of text into the title; adjusting a remainder of the creative based on the promoted portion; and providing the advertisement responsive to the request including providing the creative including the title with the promoted portion. 62. The method of claim 32 wherein evaluating the at least one line of additional text includes determining a likelihood that the at least one line of additional text includes a first line of text that constitutes a sentence, and promoting the first line of text when the likelihood is above a first threshold.
| 0.5 |
10,083,154 | 26 | 29 |
26. A mobile hand-held device, comprising: a processor; a wireless communications device, to facilitate wireless communication with a network that supports access to the Internet; a touch-sensitive display; and flash memory, operatively coupled to the processor, in which a plurality of instructions are stored comprising a plurality of software components including an HTML rendering engine, wherein the instructions, when executed by the mobile hand-held device, enable the mobile hand-held device to, receive an HTML document comprising HTML-based content including HTML code and cascading style sheet (CSS) code and content associated with the HTML document; produce scalable content by, processing the HTML-based content with the rendering engine to render an interpreted page layout, functionality, and design of the content associated with the HTML document in accordance with the HTML code and CSS code; logically grouping selected content into HTML objects, each HTML object including associated display content; defining a primary datum corresponding to the interpreted page layout; and, for each HTML object, defining an object datum corresponding to a layout location datum for the HTML object's associated display content; generating a vector from the primary datum to the object datum for the HTML object; and creating a reference that links the HTML object to the vector that is generated; and employ at least one of the scalable content or data derived therefrom to, render a view of the HTML document on the touch-sensitive display at a first zoom level under which the HTML document is displayed to fit across a width of the touch-sensitive display; and render views of the HTML document on the touch-sensitive display in response to associated user inputs to enable the HTML document to be viewed at various zoom levels and panned views while preserving the interpreted page layout, functionality, and design of the content associated with the HTML document at each zoom level and panned view including the first zoom level.
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26. A mobile hand-held device, comprising: a processor; a wireless communications device, to facilitate wireless communication with a network that supports access to the Internet; a touch-sensitive display; and flash memory, operatively coupled to the processor, in which a plurality of instructions are stored comprising a plurality of software components including an HTML rendering engine, wherein the instructions, when executed by the mobile hand-held device, enable the mobile hand-held device to, receive an HTML document comprising HTML-based content including HTML code and cascading style sheet (CSS) code and content associated with the HTML document; produce scalable content by, processing the HTML-based content with the rendering engine to render an interpreted page layout, functionality, and design of the content associated with the HTML document in accordance with the HTML code and CSS code; logically grouping selected content into HTML objects, each HTML object including associated display content; defining a primary datum corresponding to the interpreted page layout; and, for each HTML object, defining an object datum corresponding to a layout location datum for the HTML object's associated display content; generating a vector from the primary datum to the object datum for the HTML object; and creating a reference that links the HTML object to the vector that is generated; and employ at least one of the scalable content or data derived therefrom to, render a view of the HTML document on the touch-sensitive display at a first zoom level under which the HTML document is displayed to fit across a width of the touch-sensitive display; and render views of the HTML document on the touch-sensitive display in response to associated user inputs to enable the HTML document to be viewed at various zoom levels and panned views while preserving the interpreted page layout, functionality, and design of the content associated with the HTML document at each zoom level and panned view including the first zoom level. 29. The mobile hand-held device of claim 26 , wherein execution of the instructions further enables the user to zoom in on a user-selectable portion of a view of the HTML document in response to a user interface input made via the touch-sensitive display.
| 0.640845 |
9,298,680 | 13 | 16 |
13. A data processing system comprising: a processor; and a memory coupled to the processor, wherein the memory comprises instructions which, when executed by the processor, cause the processor to: open a plurality of selected hypertext documents, create an affinity matrix for each of the selected hypertext documents, calculate an affinity indicator for each pair of selected hypertext documents in the affinity matrix according to a comparison between semantic information of a content of each selected hypertext document of the pair, group the selected hypertext documents into a set of groups by assigning each selected hypertext document to at least one group in the set of the groups according to the affinity indicators, wherein a number of selected hypertext documents in each group of the set of groups is limited to a predetermined number of selected hypertext documents and wherein the instructions to group the selected hypertext documents into the set of groups further causes the processor to: group the selected hypertext documents into a particular group up to the predetermined number of selected hypertext documents by: initializing the set of groups by assigning each single hypertext document to its own group and setting an affinity index for each group accordingly, and repeating the steps of: determining a pair of initialized groups with highest affinity indexes, merging the pair of initialized groups into a new group, for each new pair of groups, calculating a sum of the corresponding selected hypertext documents within the group, determining whether the sum of the corresponding selected hypertext documents exceeds the predetermined number of selected hypertext documents, blocking each new pair of groups between the new group and each other group to prevent the merging thereof when the sum of the corresponding selected hypertext documents exceeds the predetermined number of selected hypertext documents, and calculating the affinity index for each new group according to the affinity index between the initialized groups and the corresponding other group, until the highest affinity index is lower than the affinity threshold and the number of the groups is lower than or equal to a predetermined number of groups, and display the selected hypertext documents in an arrangement corresponding to the grouping thereof.
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13. A data processing system comprising: a processor; and a memory coupled to the processor, wherein the memory comprises instructions which, when executed by the processor, cause the processor to: open a plurality of selected hypertext documents, create an affinity matrix for each of the selected hypertext documents, calculate an affinity indicator for each pair of selected hypertext documents in the affinity matrix according to a comparison between semantic information of a content of each selected hypertext document of the pair, group the selected hypertext documents into a set of groups by assigning each selected hypertext document to at least one group in the set of the groups according to the affinity indicators, wherein a number of selected hypertext documents in each group of the set of groups is limited to a predetermined number of selected hypertext documents and wherein the instructions to group the selected hypertext documents into the set of groups further causes the processor to: group the selected hypertext documents into a particular group up to the predetermined number of selected hypertext documents by: initializing the set of groups by assigning each single hypertext document to its own group and setting an affinity index for each group accordingly, and repeating the steps of: determining a pair of initialized groups with highest affinity indexes, merging the pair of initialized groups into a new group, for each new pair of groups, calculating a sum of the corresponding selected hypertext documents within the group, determining whether the sum of the corresponding selected hypertext documents exceeds the predetermined number of selected hypertext documents, blocking each new pair of groups between the new group and each other group to prevent the merging thereof when the sum of the corresponding selected hypertext documents exceeds the predetermined number of selected hypertext documents, and calculating the affinity index for each new group according to the affinity index between the initialized groups and the corresponding other group, until the highest affinity index is lower than the affinity threshold and the number of the groups is lower than or equal to a predetermined number of groups, and display the selected hypertext documents in an arrangement corresponding to the grouping thereof. 16. The data processing system according to claim 13 , wherein the instructions to group the selected hypertext documents into the set of groups causes the processor to: group the selected hypertext documents according to an affinity threshold for the affinity indicators.
| 0.597633 |
7,715,635 | 13 | 14 |
13. The computing device of claim 12 , wherein determining metrics for each identified paragraph further comprises, for each identified paragraph, determining the level of hierarchical nesting of the textual content.
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13. The computing device of claim 12 , wherein determining metrics for each identified paragraph further comprises, for each identified paragraph, determining the level of hierarchical nesting of the textual content. 14. The computing device of claim 13 , wherein determining metrics for each identified paragraph further comprises, for each identified paragraph, determining the width of the paragraph's bounding region.
| 0.5 |
9,940,307 | 1 | 7 |
1. A method comprising: identifying, by a processor, a first entity referenced in a document, wherein the first entity is identified using a first word or phrase located in a first portion of the document and having a first root word, wherein identifying the first entity comprises: identifying an n-gram in the document corresponding to the first entity, wherein identifying the n-gram comprises: converting text in the document to a root form of the text by performing at least one of (i) converting a plural form of the text to a singular form of the text or (ii) converting a possessive version of the text to a non-possessive version of the text, removing, from the text, at least one of (i) an article or (ii) a preposition, splitting the text into token elements, and applying, to the text, at least one of segmentation, n-gram extraction, or part-of-speech tagging, accessing a feature ontology, wherein the feature ontology comprises a mapping between a plurality of entities identified as n-grams and a plurality of classifications for the entities, wherein the feature ontology includes a character class that maps character entities to corresponding character references in the document, a setting class that maps setting entities to corresponding setting references in the document, and an emotion class that maps emotion entities to corresponding emotion reference in the document, and modifying the feature ontology to include the n-gram corresponding to the first entity; associating, by the processor, the first entity with a multimedia asset; determining, by the processor, that a second word or phrase in a second portion of the document refers to the first entity, wherein the second word or phrase includes a second root word that is different from the first root word in the first word or phrase; generating, by the processor, a layout for the second portion of the document based on determining that the second word or phrase refers to the first entity, wherein the layout includes the multimedia asset associated with the first entity; and rendering, by the processor, the layout with the second portion of the document for display.
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1. A method comprising: identifying, by a processor, a first entity referenced in a document, wherein the first entity is identified using a first word or phrase located in a first portion of the document and having a first root word, wherein identifying the first entity comprises: identifying an n-gram in the document corresponding to the first entity, wherein identifying the n-gram comprises: converting text in the document to a root form of the text by performing at least one of (i) converting a plural form of the text to a singular form of the text or (ii) converting a possessive version of the text to a non-possessive version of the text, removing, from the text, at least one of (i) an article or (ii) a preposition, splitting the text into token elements, and applying, to the text, at least one of segmentation, n-gram extraction, or part-of-speech tagging, accessing a feature ontology, wherein the feature ontology comprises a mapping between a plurality of entities identified as n-grams and a plurality of classifications for the entities, wherein the feature ontology includes a character class that maps character entities to corresponding character references in the document, a setting class that maps setting entities to corresponding setting references in the document, and an emotion class that maps emotion entities to corresponding emotion reference in the document, and modifying the feature ontology to include the n-gram corresponding to the first entity; associating, by the processor, the first entity with a multimedia asset; determining, by the processor, that a second word or phrase in a second portion of the document refers to the first entity, wherein the second word or phrase includes a second root word that is different from the first root word in the first word or phrase; generating, by the processor, a layout for the second portion of the document based on determining that the second word or phrase refers to the first entity, wherein the layout includes the multimedia asset associated with the first entity; and rendering, by the processor, the layout with the second portion of the document for display. 7. The method of claim 1 , wherein identifying the first entity comprises: performing natural language processing on a plurality of words in the document; and based on performing the natural language processing, identifying an intangible entity characterized or described by the plurality of words, the intangible entity comprising at least one of a mood, a tone, or an emotion.
| 0.853261 |
8,224,923 | 1 | 9 |
1. A computer-implemented method of characterizing unregistered domain names performed by one or more processors, the method comprising: obtaining a list of a plurality of resolution requests for a plurality of unregistered domain names, wherein each resolution request includes a timestamp field, a requesting-machine identifier field, and an unregistered domain name field; determining a number of occurrences of resolution requests for each of the plurality of unregistered domain names; computing a plurality of groupings based on the number of occurrences; and associating a score with each of the unregistered domain names.
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1. A computer-implemented method of characterizing unregistered domain names performed by one or more processors, the method comprising: obtaining a list of a plurality of resolution requests for a plurality of unregistered domain names, wherein each resolution request includes a timestamp field, a requesting-machine identifier field, and an unregistered domain name field; determining a number of occurrences of resolution requests for each of the plurality of unregistered domain names; computing a plurality of groupings based on the number of occurrences; and associating a score with each of the unregistered domain names. 9. The method of claim 1 wherein determining includes filtering the list of resolution requests based on at least the requesting-machine identifier field.
| 0.695652 |
8,375,052 | 15 | 16 |
15. A apparatus comprising: one or more processors; a receiver configured to receive information about a proposed outgoing message stored in memory, the received information comprising text content of a body portion of the proposed outgoing message and metadata of the proposed outgoing message; a first classifier configured to classify the proposed outgoing message into one of a plurality of specified classes, to obtain an expected class of the proposed outgoing message based on the metadata of the proposed outgoing message and not the text content of the body portion of the proposed outgoing message, the first classifier being a multi-way classifier comprising N*(N-1)/2 sub-classifiers where N is the number of the plurality of specified classes and each sub-classifier provides a one-to-one classification determination between two specified classes, the metadata comprising one or more of: information on whether the proposed outgoing message is part of an email thread; information associated with a file attached to the proposed outgoing message; information in a subject line of the proposed outgoing message; information on voting buttons associated with the proposed outgoing message; and information on one or more required receipts associated with the proposed outgoing message; a second classifier different from the first classifier configured to obtain an actual class of the proposed outgoing message based on the text content of the body portion of the proposed outgoing message and not the metadata of the proposed outgoing message; a comparing unit configured to compare the expected class of the proposed outgoing message based on the metadata and the actual class of the proposed outgoing message based on the text content of the body portion; and a triggering unit configured to trigger an alert in response to the comparison failing to yield a match.
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15. A apparatus comprising: one or more processors; a receiver configured to receive information about a proposed outgoing message stored in memory, the received information comprising text content of a body portion of the proposed outgoing message and metadata of the proposed outgoing message; a first classifier configured to classify the proposed outgoing message into one of a plurality of specified classes, to obtain an expected class of the proposed outgoing message based on the metadata of the proposed outgoing message and not the text content of the body portion of the proposed outgoing message, the first classifier being a multi-way classifier comprising N*(N-1)/2 sub-classifiers where N is the number of the plurality of specified classes and each sub-classifier provides a one-to-one classification determination between two specified classes, the metadata comprising one or more of: information on whether the proposed outgoing message is part of an email thread; information associated with a file attached to the proposed outgoing message; information in a subject line of the proposed outgoing message; information on voting buttons associated with the proposed outgoing message; and information on one or more required receipts associated with the proposed outgoing message; a second classifier different from the first classifier configured to obtain an actual class of the proposed outgoing message based on the text content of the body portion of the proposed outgoing message and not the metadata of the proposed outgoing message; a comparing unit configured to compare the expected class of the proposed outgoing message based on the metadata and the actual class of the proposed outgoing message based on the text content of the body portion; and a triggering unit configured to trigger an alert in response to the comparison failing to yield a match. 16. The apparatus as claimed in claim 15 further comprising a pre-processing unit configured to pre-process the text content of the body portion of the proposed outgoing message and inputting results of the pre-processing into the second classifier.
| 0.503984 |
9,508,338 | 4 | 5 |
4. A computing system, comprising: at least one processor; a memory device including instructions operable to be executed by the at least one processor to perform a set of actions, configuring the at least one processor: to receive input text; to identify a location in the input text for a breath; to identify a duration for the breath in the location; to determine a breath sound for the location, the breath sound determined from a plurality of breath sounds; and to synthesize speech corresponding to the input text using the duration and data corresponding to the breath sound, the synthesized speech comprising the breath sound at substantially the location.
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4. A computing system, comprising: at least one processor; a memory device including instructions operable to be executed by the at least one processor to perform a set of actions, configuring the at least one processor: to receive input text; to identify a location in the input text for a breath; to identify a duration for the breath in the location; to determine a breath sound for the location, the breath sound determined from a plurality of breath sounds; and to synthesize speech corresponding to the input text using the duration and data corresponding to the breath sound, the synthesized speech comprising the breath sound at substantially the location. 5. The computing system of claim 4 , wherein the at least one processor is further configured to identify punctuation in the input text, wherein the instructions further comprise instructions to configure the at least one processor to identify the location based at least in part on the identified punctuation.
| 0.537313 |
7,613,687 | 40 | 41 |
40. A method of optimizing searching as in claim 31 further including: responding to a user search query; and processing search result hits resulting from the query by clustering the search result hits based on categories defined in the classification system.
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40. A method of optimizing searching as in claim 31 further including: responding to a user search query; and processing search result hits resulting from the query by clustering the search result hits based on categories defined in the classification system. 41. A method of optimizing searching as in claim 40 wherein processing search result hits resulting from the query by clustering the search result hits based on categories defined in the classification system further includes assigning a search result hit to a category in the classification system.
| 0.5 |
9,201,866 | 24 | 27 |
24. A system comprising: one or more processors configured to perform operations that include: accessing, using the one or more processors, a mood evaluation index from a first electronic data store, wherein the mood evaluation index maps text to mood categories, wherein text describes a state associated with at least one of the mood categories, and wherein a mood category is associated with a state; mapping text to a mood category using the mood evaluation index, wherein the mood evaluation index associates a state with the text; calculating a mood weight for the text, wherein mood weights are calculated using a mood evaluation index, and wherein mood weights represent an intensity of the state that the text describes; accessing a message from a second electronic data store, wherein the message includes text mapped by the mood evaluation index; using the mood evaluation index to identify text included in the message that is mapped to a mood category; determining whether a modifier is in proximity to identified text; adjusting the mood weight of the identified text that is in proximity to the modifier; summing the mood weights of the identified text, wherein summing includes determining a category score corresponding to the mood category; and outputting the category score corresponding to the mood category to a third electronic data store, wherein the modifier is a negation, and wherein the adjusting the mood weight of the identified text that is in proximity to the modifier includes changing one of a positive mood to a negative mood or changing a negative mood to a positive mood.
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24. A system comprising: one or more processors configured to perform operations that include: accessing, using the one or more processors, a mood evaluation index from a first electronic data store, wherein the mood evaluation index maps text to mood categories, wherein text describes a state associated with at least one of the mood categories, and wherein a mood category is associated with a state; mapping text to a mood category using the mood evaluation index, wherein the mood evaluation index associates a state with the text; calculating a mood weight for the text, wherein mood weights are calculated using a mood evaluation index, and wherein mood weights represent an intensity of the state that the text describes; accessing a message from a second electronic data store, wherein the message includes text mapped by the mood evaluation index; using the mood evaluation index to identify text included in the message that is mapped to a mood category; determining whether a modifier is in proximity to identified text; adjusting the mood weight of the identified text that is in proximity to the modifier; summing the mood weights of the identified text, wherein summing includes determining a category score corresponding to the mood category; and outputting the category score corresponding to the mood category to a third electronic data store, wherein the modifier is a negation, and wherein the adjusting the mood weight of the identified text that is in proximity to the modifier includes changing one of a positive mood to a negative mood or changing a negative mood to a positive mood. 27. The system of claim 24 , further comprising operations for accessing one or more mood scales, wherein the mood scales comprise at least one positive mood and a corresponding negative mood.
| 0.68 |
9,021,380 | 1 | 16 |
1. A method comprising: outputting, by a computing device and for display at a presence-sensitive display, a graphical keyboard comprising a plurality of keys; receiving, by the computing device, an indication of a multi-touch gesture detected at the presence-sensitive display, the multi-touch gesture performed by a user having a first finger and a second finger, the multi-touch gesture comprising a first sub-gesture of the first finger that traverses a first group of keys of the plurality of keys and a second sub-gesture of the second finger that traverses a second group of keys of the plurality of keys, the first sub-gesture being disjoint from the second sub-gesture, wherein at least a portion of the first sub-gesture is performed simultaneously with at least a portion the second sub-gesture; determining, by the computing device and in response to receiving the indication of the first sub-gesture of the first finger and the second sub-gesture of the second finger, a candidate word based at least in part on the first and second groups of keys, wherein the determining comprises: determining, by the computing device and based at least in part on the first sub-gesture of the first finger, a first group of points on the presence-sensitive display traversed by the first sub-gesture; determining, by the computing device and based at least in part on the second sub-gesture of the second finger, a second group of points on the presence-sensitive display traversed by the second sub-gesture; determining, by the computing device and based at least in part on the first and second groups of points on the presence-sensitive display, at least one probability that at least one key included in at least one of the first and second groups of keys is associated with at least one of the first sub-gesture of the first finger and the second sub-gesture of the second finger; and determining, by the computing device, the candidate word based at least in part on the at least one probability that the at least one key is associated with at least one of the first sub-gesture of the first finger and the second sub-gesture of the second finger; and outputting, by the computing device and for display at the presence-sensitive display, the candidate word.
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1. A method comprising: outputting, by a computing device and for display at a presence-sensitive display, a graphical keyboard comprising a plurality of keys; receiving, by the computing device, an indication of a multi-touch gesture detected at the presence-sensitive display, the multi-touch gesture performed by a user having a first finger and a second finger, the multi-touch gesture comprising a first sub-gesture of the first finger that traverses a first group of keys of the plurality of keys and a second sub-gesture of the second finger that traverses a second group of keys of the plurality of keys, the first sub-gesture being disjoint from the second sub-gesture, wherein at least a portion of the first sub-gesture is performed simultaneously with at least a portion the second sub-gesture; determining, by the computing device and in response to receiving the indication of the first sub-gesture of the first finger and the second sub-gesture of the second finger, a candidate word based at least in part on the first and second groups of keys, wherein the determining comprises: determining, by the computing device and based at least in part on the first sub-gesture of the first finger, a first group of points on the presence-sensitive display traversed by the first sub-gesture; determining, by the computing device and based at least in part on the second sub-gesture of the second finger, a second group of points on the presence-sensitive display traversed by the second sub-gesture; determining, by the computing device and based at least in part on the first and second groups of points on the presence-sensitive display, at least one probability that at least one key included in at least one of the first and second groups of keys is associated with at least one of the first sub-gesture of the first finger and the second sub-gesture of the second finger; and determining, by the computing device, the candidate word based at least in part on the at least one probability that the at least one key is associated with at least one of the first sub-gesture of the first finger and the second sub-gesture of the second finger; and outputting, by the computing device and for display at the presence-sensitive display, the candidate word. 16. The method of claim 1 , wherein the first group of keys are associated with a first group of characters, wherein the second group of keys are associated with a second group of characters, and wherein the candidate word comprises characters that are not included in either the first group of characters or the second group of characters, such that the computing device performs auto-correct functionality to output the candidate word.
| 0.807828 |
8,364,694 | 5 | 6 |
5. A method for searching for digital media information available from an online media store, said method comprising: receiving a search hints request from a client application operating on a client device, the search hints request including at least a character string including at least one character; determining a set of search hints based on the character string, wherein said determining of the set of search hints obtains the matching search hints from a hints data structure and wherein the set of search hints correspond to digital media assets available in an online media repository and at least sales popularity data; obtaining a location of the client device; eliminating from the set of search hints those of the search hints in the set of search hints that are associated with a location other than the location of the client device; obtaining a media popularity indication for each of a plurality of the search hints in the set of search hints; and sending a portion of the search hints in the set of search hints to the client application on the client device, the portion of the search hints sent to the client application being less than all the search hints in the set of search hints.
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5. A method for searching for digital media information available from an online media store, said method comprising: receiving a search hints request from a client application operating on a client device, the search hints request including at least a character string including at least one character; determining a set of search hints based on the character string, wherein said determining of the set of search hints obtains the matching search hints from a hints data structure and wherein the set of search hints correspond to digital media assets available in an online media repository and at least sales popularity data; obtaining a location of the client device; eliminating from the set of search hints those of the search hints in the set of search hints that are associated with a location other than the location of the client device; obtaining a media popularity indication for each of a plurality of the search hints in the set of search hints; and sending a portion of the search hints in the set of search hints to the client application on the client device, the portion of the search hints sent to the client application being less than all the search hints in the set of search hints. 6. The method of claim 5 , wherein the location is a country.
| 0.959868 |
8,099,430 | 1 | 4 |
1. A computer method of navigating information comprising: receiving a first source of information and one or more second sources of information, each second source having a parent-child relationship with the first source, the first source being the parent; automatically extracting keywords from the first source and each of the second sources in a manner such that, for each extracted keyword, the keyword correlates the first source and at least one second source, resulting in a respective set of second sources for each keyword and resulting in precise keywords that enhance retrieval of second sources of information; and displaying to a user a listing of the keywords resulting from the automatic extracting, the displayed listing enabling the user to navigate the one or more second sources, different keywords in the displayed listing effectively referencing the different respective sets of second sources and the different respective sets of second sources having subject matter of the first source of information shown to the user, wherein the automatic extracting utilizes a semantic lexicon tool, and includes: extracting initial keywords from the first source; forming an initial taxonomy from the extracted initial keywords; detecting in the second sources words that match the initial taxonomy but that do not duplicate the extracted initial keywords of the first source; and combining the extracted initial keywords from the first source and the detected words from the second sources, said combining forming the listing of keywords.
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1. A computer method of navigating information comprising: receiving a first source of information and one or more second sources of information, each second source having a parent-child relationship with the first source, the first source being the parent; automatically extracting keywords from the first source and each of the second sources in a manner such that, for each extracted keyword, the keyword correlates the first source and at least one second source, resulting in a respective set of second sources for each keyword and resulting in precise keywords that enhance retrieval of second sources of information; and displaying to a user a listing of the keywords resulting from the automatic extracting, the displayed listing enabling the user to navigate the one or more second sources, different keywords in the displayed listing effectively referencing the different respective sets of second sources and the different respective sets of second sources having subject matter of the first source of information shown to the user, wherein the automatic extracting utilizes a semantic lexicon tool, and includes: extracting initial keywords from the first source; forming an initial taxonomy from the extracted initial keywords; detecting in the second sources words that match the initial taxonomy but that do not duplicate the extracted initial keywords of the first source; and combining the extracted initial keywords from the first source and the detected words from the second sources, said combining forming the listing of keywords. 4. A method as claimed in claim 1 wherein the displayed listing is any of a tag cloud, a taxonomy, and an ordered list.
| 0.85125 |
8,954,360 | 15 | 16 |
15. The method of claim 14 , wherein normalizing further comprises generating a singular form of a term from the request for search terms.
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15. The method of claim 14 , wherein normalizing further comprises generating a singular form of a term from the request for search terms. 16. The method of claim 15 , wherein parsing further comprises analyzing words and sentences in the search terms to determine concepts, definitions, and relations.
| 0.5 |
10,007,648 | 1 | 7 |
1. A method for supporting creation of a manual of a program product, the method implemented by a processing unit, and the method comprising: receiving, via an editor for creating the manual, a start tag of a user-interface element of the program product for which the manual is being created; in response to receipt of the start tag, activating an acquisition mode of a user-interface of the program product, wherein, in the acquisition mode, character strings of the user-interface are displayed with respective master-identifiers appended; receiving a selection of a character string corresponding to the user-interface element; recording, in the editor, following the start tag of the user-interface element, the character string and the master-identifier; receiving, via the editor, an end tag of the user-interface element; in response to receipt of the end tag, deactivating the acquisition mode of the user-interface of the program product; receiving, via the editor, a description of the user-interface element; detecting a category of the user-interface element, and a screen position of the user-interface element; in response to receipt of the description, recording an entry in a screen character string control table, the entry comprising, the character string, the master-identifier associated with the character string, a language identifier of the character string, the category of the user-interface element, and the screen position of the user-interface element; and maintaining consistency between the character string displayed on the user-interface element and in the manual.
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1. A method for supporting creation of a manual of a program product, the method implemented by a processing unit, and the method comprising: receiving, via an editor for creating the manual, a start tag of a user-interface element of the program product for which the manual is being created; in response to receipt of the start tag, activating an acquisition mode of a user-interface of the program product, wherein, in the acquisition mode, character strings of the user-interface are displayed with respective master-identifiers appended; receiving a selection of a character string corresponding to the user-interface element; recording, in the editor, following the start tag of the user-interface element, the character string and the master-identifier; receiving, via the editor, an end tag of the user-interface element; in response to receipt of the end tag, deactivating the acquisition mode of the user-interface of the program product; receiving, via the editor, a description of the user-interface element; detecting a category of the user-interface element, and a screen position of the user-interface element; in response to receipt of the description, recording an entry in a screen character string control table, the entry comprising, the character string, the master-identifier associated with the character string, a language identifier of the character string, the category of the user-interface element, and the screen position of the user-interface element; and maintaining consistency between the character string displayed on the user-interface element and in the manual. 7. The method of claim 1 , further comprising recording, in the entry of the screen character string control table, a positional information that is indicative of where the character string is recorded for being displayed on the user-interface element.
| 0.888496 |
8,285,719 | 9 | 11 |
9. A method for relational analysis of data, comprising: (a) receiving an input data set is represented as a set of exponential family distributions {{F (j) } j=1 m , {S (j) } j=1 m , {R (ij) } i,j=1 m }; (b) storing an initial estimate of a set of matrices {tilde over (Ω)}, comprising: membership matrices {Λ (j) } j=1 m , attribute expectation matrices {Θ (j) } j=1 m for attribute matrices F (j) , homogeneous relation expectation matrices {Γ (j) } j=1 m for homogeneous relation matrices S (j) , and heterogeneous relation expectation matrices {γ (ij) } i,j=1 m , for heterogeneous relation matrices R (ij) ; (c) with an automated processor, iteratively computing, for each respective value of i and j, a posterior function , wherein Pr({C (j) }|F (j) } j=1 m ,{S (j) } j=1 m ,{R (ij) } i,j=1 m ,{tilde over (Ω)}), wherein C (j) is a cluster indicator matrix: Λ ( j ) using Λ hp ( 1 ) = Pr ( C hp ( 1 ) = 1 ❘ F , S , R , Ω ~ ) , Θ ( j ) using Θ · g = ∑ p = 1 n 1 F · p Pr ( C gp ( 1 ) = 1 ❘ F , S , R , Ω ~ ) ∑ p = 1 n 1 Pr ( C gp ( 1 ) = 1 ❘ F , S , R , Ω ~ ) , Γ ( j ) using Γ gh = ∑ p , q = 1 n 1 S pq Pr ( C gp ( 1 ) = 1 , C hq ( 1 ) = 1 ❘ F , S , R , Ω ~ ) ∑ p , q = 1 n 1 Pr ( C gp ( 1 ) = 1 , C hq ( 1 ) = 1 ❘ F , S , R , Ω ~ ) , Υ ( ij ) using Υ gh = ∑ p = 1 n 1 ∑ q = 1 n 2 R pq Pr ( C gp ( 1 ) = 1 , C hq ( 2 ) = 1 ❘ F , S , R , Ω ~ ) ∑ p = 1 n 1 ∑ q = 1 n 2 Pr ( C gp ( 1 ) = 1 , C hq ( 2 ) = 1 ❘ F , S , R , Ω ~ ) ; (d) comparing the stored set of matrices {tilde over (Ω)} with a resulting set of matrices Ω of an iteration of said computing to determine a convergence; and (e) outputting a converged result Ω.
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9. A method for relational analysis of data, comprising: (a) receiving an input data set is represented as a set of exponential family distributions {{F (j) } j=1 m , {S (j) } j=1 m , {R (ij) } i,j=1 m }; (b) storing an initial estimate of a set of matrices {tilde over (Ω)}, comprising: membership matrices {Λ (j) } j=1 m , attribute expectation matrices {Θ (j) } j=1 m for attribute matrices F (j) , homogeneous relation expectation matrices {Γ (j) } j=1 m for homogeneous relation matrices S (j) , and heterogeneous relation expectation matrices {γ (ij) } i,j=1 m , for heterogeneous relation matrices R (ij) ; (c) with an automated processor, iteratively computing, for each respective value of i and j, a posterior function , wherein Pr({C (j) }|F (j) } j=1 m ,{S (j) } j=1 m ,{R (ij) } i,j=1 m ,{tilde over (Ω)}), wherein C (j) is a cluster indicator matrix: Λ ( j ) using Λ hp ( 1 ) = Pr ( C hp ( 1 ) = 1 ❘ F , S , R , Ω ~ ) , Θ ( j ) using Θ · g = ∑ p = 1 n 1 F · p Pr ( C gp ( 1 ) = 1 ❘ F , S , R , Ω ~ ) ∑ p = 1 n 1 Pr ( C gp ( 1 ) = 1 ❘ F , S , R , Ω ~ ) , Γ ( j ) using Γ gh = ∑ p , q = 1 n 1 S pq Pr ( C gp ( 1 ) = 1 , C hq ( 1 ) = 1 ❘ F , S , R , Ω ~ ) ∑ p , q = 1 n 1 Pr ( C gp ( 1 ) = 1 , C hq ( 1 ) = 1 ❘ F , S , R , Ω ~ ) , Υ ( ij ) using Υ gh = ∑ p = 1 n 1 ∑ q = 1 n 2 R pq Pr ( C gp ( 1 ) = 1 , C hq ( 2 ) = 1 ❘ F , S , R , Ω ~ ) ∑ p = 1 n 1 ∑ q = 1 n 2 Pr ( C gp ( 1 ) = 1 , C hq ( 2 ) = 1 ❘ F , S , R , Ω ~ ) ; (d) comparing the stored set of matrices {tilde over (Ω)} with a resulting set of matrices Ω of an iteration of said computing to determine a convergence; and (e) outputting a converged result Ω. 11. The method according to claim 9 , further comprising using at least the converged result Ω to determine a clustering of objects represented in the input data set.
| 0.5 |
9,703,854 | 9 | 11 |
9. A device for determining criticality of a Structured Query Language (SQL) statement, comprising: a processor; and a memory coupled to the processor, wherein the memory comprises instructions which, when executed by the processor, cause the processor to: extract a plurality of elements in the SQL statement; calculate a score of the SQL statement based on a correlation relation among respective elements in the plurality of elements and base scores of the respective elements, wherein the instructions to calculate the score of the SQL statement based on a correlation relation among respective elements in the plurality of elements and base scores of the respective elements further causes the processor to: determine the score of the SQL statement as a first value in response to that the correlation relation does not exist between any two of the plurality of elements, the first value being a maximum one of the respective base scores of the plurality of elements; and determine the score of the SQL statement as a second value in response to that the correlation relation exists between at least two of the plurality of elements, the second value being greater than the maximum one of the respective base scores of the plurality of elements; and determine the criticality of the SQL statement based on the score of the SQL statement.
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9. A device for determining criticality of a Structured Query Language (SQL) statement, comprising: a processor; and a memory coupled to the processor, wherein the memory comprises instructions which, when executed by the processor, cause the processor to: extract a plurality of elements in the SQL statement; calculate a score of the SQL statement based on a correlation relation among respective elements in the plurality of elements and base scores of the respective elements, wherein the instructions to calculate the score of the SQL statement based on a correlation relation among respective elements in the plurality of elements and base scores of the respective elements further causes the processor to: determine the score of the SQL statement as a first value in response to that the correlation relation does not exist between any two of the plurality of elements, the first value being a maximum one of the respective base scores of the plurality of elements; and determine the score of the SQL statement as a second value in response to that the correlation relation exists between at least two of the plurality of elements, the second value being greater than the maximum one of the respective base scores of the plurality of elements; and determine the criticality of the SQL statement based on the score of the SQL statement. 11. The device of claim 9 , wherein the calculating apparatus obtains the base scores of the respective elements by referencing a base score table, the base score table including base scores of respective tables stored in the database, base scores of fields of the respective tables, and base scores of actions allowed to be executed on the database.
| 0.540682 |
9,507,861 | 8 | 12 |
8. A system for identifying related strings for search query rewriting, comprising: one or more processors coupled to a computer storage medium, the computer storage medium having stored thereon a plurality of computer software components executable by the one or more processors, the computer software components comprising: a database containing click log data; a session extraction component that accesses click log data and identifies session data for a user search query session in the click log data, the session data including: a first user search query, a first set of search results provided for the first user search query, one or more additional search queries, and search results provided for the one or more additional search queries including a second set of search results provided for a first additional search query; a query analysis component that determines that the first additional search query in the session data is related to the first user search query based on: (1) a similarity between the title or uniform resource locator (URL) of a web page included in the first set of search results and the title or URL of a web page included in the second set of search results exceeding a similarity threshold and (2) at least one of: (a) a comparison of dwell time between the first and second sets of search results, or (b) a comparison of a number of result links clicked on between the first and second sets of search results; a secondary analysis component that identifies one or more supplemental strings that are related to the first user search query, the supplemental strings including at least one of: the title or URL of a web page included in the search results provided for either the first search query or the first additional search query that was clicked on after the corresponding search results were provided, a snippet corresponding to a web page included in the search results provided for either the first user query or the first additional search query that was clicked on after the corresponding search results were provided, or a second user search query identified using random walk analysis such that the second user search query is identified when a URL included in the search results provided for the first user search query is clicked on and a similar URL included in the search results provided for the second user search query is also clicked on; and a list population component that: responsive to the first additional search query being determined to be related to the first user search query, incorporates the first additional search query into a list of strings related to the first user search query, incorporates the identified supplemental strings into the list of strings related to the first user search query, and from the list of strings, each string having at least one term, determines a probability of relatedness for a first term from a first string and a second term from a second string; and a search engine component that receives a search query during a current session, wherein the search query includes the first term from the first string in the list of strings, and rewrites the search query based at least in part on the determined probability of relatedness for the first term and the second term from the second string in the list of strings.
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8. A system for identifying related strings for search query rewriting, comprising: one or more processors coupled to a computer storage medium, the computer storage medium having stored thereon a plurality of computer software components executable by the one or more processors, the computer software components comprising: a database containing click log data; a session extraction component that accesses click log data and identifies session data for a user search query session in the click log data, the session data including: a first user search query, a first set of search results provided for the first user search query, one or more additional search queries, and search results provided for the one or more additional search queries including a second set of search results provided for a first additional search query; a query analysis component that determines that the first additional search query in the session data is related to the first user search query based on: (1) a similarity between the title or uniform resource locator (URL) of a web page included in the first set of search results and the title or URL of a web page included in the second set of search results exceeding a similarity threshold and (2) at least one of: (a) a comparison of dwell time between the first and second sets of search results, or (b) a comparison of a number of result links clicked on between the first and second sets of search results; a secondary analysis component that identifies one or more supplemental strings that are related to the first user search query, the supplemental strings including at least one of: the title or URL of a web page included in the search results provided for either the first search query or the first additional search query that was clicked on after the corresponding search results were provided, a snippet corresponding to a web page included in the search results provided for either the first user query or the first additional search query that was clicked on after the corresponding search results were provided, or a second user search query identified using random walk analysis such that the second user search query is identified when a URL included in the search results provided for the first user search query is clicked on and a similar URL included in the search results provided for the second user search query is also clicked on; and a list population component that: responsive to the first additional search query being determined to be related to the first user search query, incorporates the first additional search query into a list of strings related to the first user search query, incorporates the identified supplemental strings into the list of strings related to the first user search query, and from the list of strings, each string having at least one term, determines a probability of relatedness for a first term from a first string and a second term from a second string; and a search engine component that receives a search query during a current session, wherein the search query includes the first term from the first string in the list of strings, and rewrites the search query based at least in part on the determined probability of relatedness for the first term and the second term from the second string in the list of strings. 12. The system of claim 8 , wherein the second user search query is determined to be more related to the first user search query when additional URLs included in the search results provided for the first user search query are clicked on and similar URLs included in the search results provided for the second user search query are also clicked on.
| 0.5 |
10,162,882 | 9 | 11 |
9. A system for automatically linking text to concepts in a knowledge base, the system comprising: a memory having computer readable computer instructions; and one or more processors for executing the computer readable instructions, the computer readable instructions including: receiving a plurality of text strings; building a conceptual index that links the text strings to the knowledge base, the building comprising for each of the text strings: selecting a plurality of data sources that correspond to at least a subset of the concepts in the knowledge base, the selecting based on contents of the text string; calculating, for each of the selected data sources, a probability that the text string is output by a language model built using the selected data source; calculating a probability that the text string is output by a generic language model that is not related to any particular concept in the knowledge base; calculating link confidence scores for each of the at least a subset of the concepts based on a differential analysis of the probabilities; and creating an entry in the conceptual index that includes a link between the text string and one of the concepts in the knowledge base, the creating based at least in part on a link confidence score of the concept being more than a first threshold value away from a prescribed threshold; generating a conceptual inverted index based on entries in the conceptual index, each entry of the conceptual inverted index corresponding to a different one of the concepts in the knowledge base and comprising pointers to at least a subset of text strings of the plurality of text strings linked to the concept in the conceptual index; receiving a query from an agent external to the computer system, the query specifying a concept in the knowledge base; processing the query, the processing comprising searching the conceptual inverted index for the concept specified in the query and returning a pointer to a text string in an entry of the conceptual inverted index corresponding to the concept; and returning a set of documents to the external agent through the use of the conceptual inverted index, based on the received query.
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9. A system for automatically linking text to concepts in a knowledge base, the system comprising: a memory having computer readable computer instructions; and one or more processors for executing the computer readable instructions, the computer readable instructions including: receiving a plurality of text strings; building a conceptual index that links the text strings to the knowledge base, the building comprising for each of the text strings: selecting a plurality of data sources that correspond to at least a subset of the concepts in the knowledge base, the selecting based on contents of the text string; calculating, for each of the selected data sources, a probability that the text string is output by a language model built using the selected data source; calculating a probability that the text string is output by a generic language model that is not related to any particular concept in the knowledge base; calculating link confidence scores for each of the at least a subset of the concepts based on a differential analysis of the probabilities; and creating an entry in the conceptual index that includes a link between the text string and one of the concepts in the knowledge base, the creating based at least in part on a link confidence score of the concept being more than a first threshold value away from a prescribed threshold; generating a conceptual inverted index based on entries in the conceptual index, each entry of the conceptual inverted index corresponding to a different one of the concepts in the knowledge base and comprising pointers to at least a subset of text strings of the plurality of text strings linked to the concept in the conceptual index; receiving a query from an agent external to the computer system, the query specifying a concept in the knowledge base; processing the query, the processing comprising searching the conceptual inverted index for the concept specified in the query and returning a pointer to a text string in an entry of the conceptual inverted index corresponding to the concept; and returning a set of documents to the external agent through the use of the conceptual inverted index, based on the received query. 11. The system of claim 9 , wherein the generic language model is derived from a generic data source not specific to any of the concepts in the knowledge base.
| 0.828294 |
8,086,440 | 7 | 9 |
7. A method of prioritizing for automated translation from a first human language to a second human language communications relating to at least one predetermined topic, the method comprising: capturing and inputting into a data processing system a translation-candidate communication rendered in the first human language and storing in computer memory associated with the data processing system, in a predetermined machine-readable format, a first data set representative of the contents of the translation-candidate communication in the first human language; parsing the first data set into first-data-set sub-portions correspondingly representative of communication sub-portions of the translation-candidate communication; maintaining in computer memory a set of relevancy thresholds including at least first and second relevancy thresholds indicative of the relatedness of a communication sub-portion to the at least one predetermined topic, wherein the first relevancy threshold indicates a greater degree of relatedness to the at least one predetermined topic than does the second relevancy threshold; maintaining in computer memory a first-language prioritization protocol including data indicative of first-language extraction rules according to which a selected first-data-set sub-portion is algorithmically one of (i) extracted and prioritized for translation; (ii) extracted and de-prioritized for translation and (iii) rejected for translation depending on whether the selected communication sub-portion, respectively, (a) exceeds the first relevancy threshold, (b) exceeds the second relevancy threshold, but not the first relevancy threshold, and (c) exceeds neither of the first and second relevancy thresholds; consulting the first-language prioritization protocol and algorithmically analyzing, in accordance with the first-language extraction rules, the first data set in order to determine whether at least one communication sub-portion of the translation-candidate communication associated with the first data set exceeds either of the first and second relevancy thresholds; selecting for translation to the second human language each communication sub-portion algorithmically determined to exceed either of the first and second relevancy thresholds; and rejecting for translation to the second human language each communication sub-portion algorithmically determined not to exceed either of the first and second relevancy thresholds; wherein as to each communication sub-portion determined to exceed at least one of the first and second relevancy thresholds, the method further comprises causing the first-data-set sub-portion representative of that communication sub-portion in the first human language to be translated to a translated-data-set sub-portion representative, in a machine-readable format, of that communication sub-portion in the second human language; a first-data-set sub-portion representative of a communication sub-portion determined to exceed the first relevancy threshold is translated to a translated-data-set sub-portion prior to a first-data-set sub-portion representative of a communication sub-portion determined to exceed the second relevancy threshold and not the first relevancy threshold; and as to a communication sub-portion rejected for translation, the method further comprises one of deleting and archiving in computer memory the first-data-set sub-portion representative of that communication sub-portion.
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7. A method of prioritizing for automated translation from a first human language to a second human language communications relating to at least one predetermined topic, the method comprising: capturing and inputting into a data processing system a translation-candidate communication rendered in the first human language and storing in computer memory associated with the data processing system, in a predetermined machine-readable format, a first data set representative of the contents of the translation-candidate communication in the first human language; parsing the first data set into first-data-set sub-portions correspondingly representative of communication sub-portions of the translation-candidate communication; maintaining in computer memory a set of relevancy thresholds including at least first and second relevancy thresholds indicative of the relatedness of a communication sub-portion to the at least one predetermined topic, wherein the first relevancy threshold indicates a greater degree of relatedness to the at least one predetermined topic than does the second relevancy threshold; maintaining in computer memory a first-language prioritization protocol including data indicative of first-language extraction rules according to which a selected first-data-set sub-portion is algorithmically one of (i) extracted and prioritized for translation; (ii) extracted and de-prioritized for translation and (iii) rejected for translation depending on whether the selected communication sub-portion, respectively, (a) exceeds the first relevancy threshold, (b) exceeds the second relevancy threshold, but not the first relevancy threshold, and (c) exceeds neither of the first and second relevancy thresholds; consulting the first-language prioritization protocol and algorithmically analyzing, in accordance with the first-language extraction rules, the first data set in order to determine whether at least one communication sub-portion of the translation-candidate communication associated with the first data set exceeds either of the first and second relevancy thresholds; selecting for translation to the second human language each communication sub-portion algorithmically determined to exceed either of the first and second relevancy thresholds; and rejecting for translation to the second human language each communication sub-portion algorithmically determined not to exceed either of the first and second relevancy thresholds; wherein as to each communication sub-portion determined to exceed at least one of the first and second relevancy thresholds, the method further comprises causing the first-data-set sub-portion representative of that communication sub-portion in the first human language to be translated to a translated-data-set sub-portion representative, in a machine-readable format, of that communication sub-portion in the second human language; a first-data-set sub-portion representative of a communication sub-portion determined to exceed the first relevancy threshold is translated to a translated-data-set sub-portion prior to a first-data-set sub-portion representative of a communication sub-portion determined to exceed the second relevancy threshold and not the first relevancy threshold; and as to a communication sub-portion rejected for translation, the method further comprises one of deleting and archiving in computer memory the first-data-set sub-portion representative of that communication sub-portion. 9. The method of claim 7 wherein the first data set is parsed into first-data-set sub-portions representative of equi-durational communication sub-portions of the translation-candidate communication.
| 0.947046 |
7,730,395 | 16 | 19 |
16. The method of claim 1 further comprising the step of: monitoring the one or more virtual tags and the one or more transformation rules.
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16. The method of claim 1 further comprising the step of: monitoring the one or more virtual tags and the one or more transformation rules. 19. The method of claim 16 wherein said monitoring step further comprises the step of: sending a message to a content provider after creation of predefined one or more of said virtual tags.
| 0.5 |
9,953,631 | 8 | 9 |
8. The computing device of claim 6 , wherein the operations further comprise displaying the text in the detected language in response to obtaining the text, wherein the text is displayed in a first area on a display of the computing device, and wherein the translated text is displayed in a separate second area on the display.
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8. The computing device of claim 6 , wherein the operations further comprise displaying the text in the detected language in response to obtaining the text, wherein the text is displayed in a first area on a display of the computing device, and wherein the translated text is displayed in a separate second area on the display. 9. The computing device of claim 8 , wherein the text is displayed in at least one of a first color and a first style, and wherein the translated text is displayed in at least one of a different second color and a different second style.
| 0.5 |
6,085,196 | 30 | 31 |
30. A computer program product according to claim 27, wherein the structured information includes markup language, the first structured information format includes a first markup language, the second structured information format includes a second markup language, and the means for interactively comprises: means for interactively a rule to transform an element of a first structured information format which includes a first markup language element into an element of a second structured information format which includes a second markup language element utilizing the interactive input from the user, the first structural description which includes a structural description of the first markup language, and the second structural description which includes a structural description of the second markup language.
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30. A computer program product according to claim 27, wherein the structured information includes markup language, the first structured information format includes a first markup language, the second structured information format includes a second markup language, and the means for interactively comprises: means for interactively a rule to transform an element of a first structured information format which includes a first markup language element into an element of a second structured information format which includes a second markup language element utilizing the interactive input from the user, the first structural description which includes a structural description of the first markup language, and the second structural description which includes a structural description of the second markup language. 31. A computer program product according to claim 30, wherein the first markup language includes SGML, the second markup language includes HTML, and the means for interactively further comprises: means for interactively a rule to transform an element of a first markup language which includes an SGML element into an element of a second markup language which includes an HTML element utilizing the interactive input from the user, the first structural description which includes an SGML DTD, and the second structural description which includes an HTML DTD.
| 0.5 |
9,037,897 | 1 | 4 |
1. A method for cloud-driven task execution, wherein the method comprises: determining a plurality of attributes of a task that is (i) requested by a client device and (ii) precluded from execution on a given operating system of the client device based on a policy consideration of the given operating system, wherein the plurality of attributes comprises at least one policy context attribute and multiple context attributes comprising location of a user associated with the client device, the type of client device, a data type associated with the task, a location of data associated with the task, a given operation to be performed on data associated with the task, and one or more content attributes of data associated with the task; correlating each of the plurality of attributes to at least one alternative asset in a cloud network, wherein the at least one alternative asset comprises at least one service that can execute tasks on the given operating system; provisioning the at least one alternative asset from the cloud network to the client device to enable execution of the task on the given operating system of the client device; wherein at least one of the steps is carried out by a computer device.
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1. A method for cloud-driven task execution, wherein the method comprises: determining a plurality of attributes of a task that is (i) requested by a client device and (ii) precluded from execution on a given operating system of the client device based on a policy consideration of the given operating system, wherein the plurality of attributes comprises at least one policy context attribute and multiple context attributes comprising location of a user associated with the client device, the type of client device, a data type associated with the task, a location of data associated with the task, a given operation to be performed on data associated with the task, and one or more content attributes of data associated with the task; correlating each of the plurality of attributes to at least one alternative asset in a cloud network, wherein the at least one alternative asset comprises at least one service that can execute tasks on the given operating system; provisioning the at least one alternative asset from the cloud network to the client device to enable execution of the task on the given operating system of the client device; wherein at least one of the steps is carried out by a computer device. 4. The method of claim 1 , wherein said provisioning comprises streaming the task to the client device.
| 0.799611 |
9,569,615 | 1 | 6 |
1. A computer implemented method for detecting cyber physical system behavior, comprising: utilizing one or more hardware processors and associated memory storing one or more programs for execution by the one or more hardware processors, the one or more programs including instructions for: receiving data from a plurality of sensors associated with a cyber physical system, wherein the receiving the data includes receiving time series data from the plurality of sensors monitoring the cyber physical system and wherein the cyber physical system is an electrical power grid system; constructing a metrization of the data utilizing a data structuring; determining at least one ensemble and at least one summary variable from the metrized data, wherein the at least one summary variable is based on automata model utilizing a probabilistic grammatical inference that includes discovering common subtrees of a string parse tree via a nonparametric Bayesian clustering method including a Dirichlet Process or a Beta Process or a diffusion map technique; applying a thermodynamic formalism to the at least one summary variable to classify a plurality of system behaviors; identifying the plurality of system behaviors based at least in part on the classified plurality of system behaviors; obtaining, by the one or more hardware processors, a baseline of the system behavior associated with the classified plurality of systems behaviors; and detecting an anomalous condition based on a deviation of the plurality of system behaviors from the baseline.
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1. A computer implemented method for detecting cyber physical system behavior, comprising: utilizing one or more hardware processors and associated memory storing one or more programs for execution by the one or more hardware processors, the one or more programs including instructions for: receiving data from a plurality of sensors associated with a cyber physical system, wherein the receiving the data includes receiving time series data from the plurality of sensors monitoring the cyber physical system and wherein the cyber physical system is an electrical power grid system; constructing a metrization of the data utilizing a data structuring; determining at least one ensemble and at least one summary variable from the metrized data, wherein the at least one summary variable is based on automata model utilizing a probabilistic grammatical inference that includes discovering common subtrees of a string parse tree via a nonparametric Bayesian clustering method including a Dirichlet Process or a Beta Process or a diffusion map technique; applying a thermodynamic formalism to the at least one summary variable to classify a plurality of system behaviors; identifying the plurality of system behaviors based at least in part on the classified plurality of system behaviors; obtaining, by the one or more hardware processors, a baseline of the system behavior associated with the classified plurality of systems behaviors; and detecting an anomalous condition based on a deviation of the plurality of system behaviors from the baseline. 6. The method for detecting cyber physical system behavior of claim 1 , wherein the at least one ensemble is determined empirically.
| 0.814085 |
10,089,295 | 12 | 13 |
12. The computer program product of claim 10 , wherein a weight associated with a characteristic of the page template is based on a number of page templates previously selected to present content from the digital magazine having the characteristic.
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12. The computer program product of claim 10 , wherein a weight associated with a characteristic of the page template is based on a number of page templates previously selected to present content from the digital magazine having the characteristic. 13. The computer program product of claim 12 , wherein the weight associated with the characteristic of the page template is further based at least in part on a number of page templates selected to subsequently present content from the digital magazine having the characteristic.
| 0.5 |
8,832,104 | 4 | 5 |
4. A method for an advertiser to associate an advertisement published on a plurality of Internet websites on the world wide web as recited in claim 3 wherein determining one of more predefined categories includes associating the advertisement with the at least one content in one or more pre-defined categories from the set of pre-defined categories.
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4. A method for an advertiser to associate an advertisement published on a plurality of Internet websites on the world wide web as recited in claim 3 wherein determining one of more predefined categories includes associating the advertisement with the at least one content in one or more pre-defined categories from the set of pre-defined categories. 5. A method for an advertiser to associate an advertisement published on a plurality of Internet websites on the world wide web as recited in claim 4 wherein associating the advertisement is at least one of automatically associating the advertisement with the at least one content and manually associating the advertisement with at least one content.
| 0.5 |
8,756,252 | 11 | 12 |
11. A system, comprising: a namespace database; and a processor coupled to the namespace data and executing a parsing framework component associated with execution of an automated software build tool, the parsing framework component including: a category determination component to determine categories and an associated category graph for a parsing framework associated with execution of an automated software build tool; a type determination component to determine a plurality of types, wherein each type implements at least one category; a set of types determination component to determine a set of types for each category, wherein a type is included a set of types for a particular category when that type implements that category or any descendant of that category in the category graph; a namespace definition component to (i) automatically define namespaces for the categories based at least in part on sets of types associated with each category, wherein the categories and namespaces are associated with a description, and (ii) store information about the defined namespaces in the namespace database; an automated software build tool component to simultaneously determine, using reflection of a single Java bean and associated Java bean method, both: (i) a name of a parameter in accordance a method name, and (ii) a category and said associated automatically defined namespace in accordance with a parameter type, and further to automatically parse two nesting levels using both (i) the name of the parameter and (ii) the category and said associated namespace, wherein the category and said associated namespace are identified by a first nested element level and the parameter type is identified by a second nested element level within the first nested element level, and further wherein said automated software build tool component is to execute the automated software build tool in accordance with the defined namespaces such that (i) a first parameter name at a first location in the automated software build tool refers to first namespace and is resolved to a first parameter type and (ii) the first parameter name at a second location in the automated software build tool refers to a second namespace and is resolved to a second parameter type; and a graph creation component to create a graph structure based on the description in accordance with parsing by the parsing framework.
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11. A system, comprising: a namespace database; and a processor coupled to the namespace data and executing a parsing framework component associated with execution of an automated software build tool, the parsing framework component including: a category determination component to determine categories and an associated category graph for a parsing framework associated with execution of an automated software build tool; a type determination component to determine a plurality of types, wherein each type implements at least one category; a set of types determination component to determine a set of types for each category, wherein a type is included a set of types for a particular category when that type implements that category or any descendant of that category in the category graph; a namespace definition component to (i) automatically define namespaces for the categories based at least in part on sets of types associated with each category, wherein the categories and namespaces are associated with a description, and (ii) store information about the defined namespaces in the namespace database; an automated software build tool component to simultaneously determine, using reflection of a single Java bean and associated Java bean method, both: (i) a name of a parameter in accordance a method name, and (ii) a category and said associated automatically defined namespace in accordance with a parameter type, and further to automatically parse two nesting levels using both (i) the name of the parameter and (ii) the category and said associated namespace, wherein the category and said associated namespace are identified by a first nested element level and the parameter type is identified by a second nested element level within the first nested element level, and further wherein said automated software build tool component is to execute the automated software build tool in accordance with the defined namespaces such that (i) a first parameter name at a first location in the automated software build tool refers to first namespace and is resolved to a first parameter type and (ii) the first parameter name at a second location in the automated software build tool refers to a second namespace and is resolved to a second parameter type; and a graph creation component to create a graph structure based on the description in accordance with parsing by the parsing framework. 12. The system of claim 11 , further comprising at least one of: (i) an input device, (ii) a communication device, or (iii) an output device.
| 0.765781 |
8,190,684 | 1 | 6 |
1. A method of intelligently distributing, in a networked environment, an object that represents an offering, the method comprising: receiving a request from a user initiated at a computing device to create the object to represent the offering; creating the object to represent the offering to be made available for access in the networked environment, the object being created to include metadata specifying criteria of target recipients of the offering; wherein, the criteria of target recipients are received in the networked environment from the user that requested creation of the object, matching the criteria specified in the metadata of the object to users in the networked environment, to find recipients who satisfy the criteria; automatically sending the offering represented by the object to the recipients who satisfy the criteria specified in the metadata; automatically generating multiple versions of the object to represent the offering based on an example object specified by the user; test-posting the multiple versions to compute price-performance attributes of each of the multiple versions.
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1. A method of intelligently distributing, in a networked environment, an object that represents an offering, the method comprising: receiving a request from a user initiated at a computing device to create the object to represent the offering; creating the object to represent the offering to be made available for access in the networked environment, the object being created to include metadata specifying criteria of target recipients of the offering; wherein, the criteria of target recipients are received in the networked environment from the user that requested creation of the object, matching the criteria specified in the metadata of the object to users in the networked environment, to find recipients who satisfy the criteria; automatically sending the offering represented by the object to the recipients who satisfy the criteria specified in the metadata; automatically generating multiple versions of the object to represent the offering based on an example object specified by the user; test-posting the multiple versions to compute price-performance attributes of each of the multiple versions. 6. The method of claim 1 , further comprising, performing semantic matching to identify another set of recipients who satisfy the criteria.
| 0.750896 |
9,741,348 | 1 | 4 |
1. A computer-implemented method comprising: obtaining training acoustic data corresponding to a user's utterance of one or more words that include one or more particular subwords; dynamically generating a verification phrase based at least on one or more of the particular subwords included in the words uttered by the user in the training acoustic data; prompting, by a mobile device that is in a locked mode, the user to speak the dynamically generated verification phrase; and in response to determining that the user has likely spoken the dynamically generated verification phrase, determining, by the mobile device, whether to exit the locked mode.
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1. A computer-implemented method comprising: obtaining training acoustic data corresponding to a user's utterance of one or more words that include one or more particular subwords; dynamically generating a verification phrase based at least on one or more of the particular subwords included in the words uttered by the user in the training acoustic data; prompting, by a mobile device that is in a locked mode, the user to speak the dynamically generated verification phrase; and in response to determining that the user has likely spoken the dynamically generated verification phrase, determining, by the mobile device, whether to exit the locked mode. 4. The method of claim 1 , wherein dynamically generating a verification phrase based at least on one or more of the particular subwords included in the words uttered by the user in the training acoustic data comprises: generating a verification phrase that includes (i) at least one or more of the particular subwords and (ii) one or more subwords that are not any of the one or more particular subwords.
| 0.5 |
9,105,267 | 5 | 8 |
5. The speech recognition apparatus according to claim 1 , wherein the threshold level is referred to as a first predetermined level, wherein, when the control unit determines that the likelihood level of the selected candidate is lower than the first predetermined level and equal to or higher than a second predetermined level, the control unit controls the output unit to output both the first recognition result and the second recognition result, and wherein the second predetermined level is lower than the first predetermined level.
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5. The speech recognition apparatus according to claim 1 , wherein the threshold level is referred to as a first predetermined level, wherein, when the control unit determines that the likelihood level of the selected candidate is lower than the first predetermined level and equal to or higher than a second predetermined level, the control unit controls the output unit to output both the first recognition result and the second recognition result, and wherein the second predetermined level is lower than the first predetermined level. 8. The speech recognition apparatus according to claim 5 , wherein the control unit controls the output unit to output a first candidate group obtained based on the first recognition result and a second candidate group obtained based on the second recognition result, wherein the first candidate group includes one or more first candidates including the selected candidate, and the second candidate group includes one or more second candidates, and wherein the control unit controls the output unit to respectively output the first candidate group and the second candidate group irrespective of likelihood levels of the first candidates and the second candidates.
| 0.5 |
9,251,279 | 37 | 59 |
37. A computer system comprising: a database having a plurality of records in respective categories of information, each record having one or more facets to the respective category of information; means for receiving user input of a first search term formed of a first parameter indicative of at least one category of information of the database; a search engine to implement a search of the database for records of the at least one category of information; means for displaying, in response to the user input of the first search term, simultaneously displaying both in a same screen view: (a) a set of search results, including records from the database of the at least one category of information, and (b) a listing of any one or combination of facets and facet values of the records in the set of search results, the listing serving as suggested additional parameters for further refining the first search term upon user selection of the any one or combination of facets and facet values, wherein at least one of the any one or combination of facets and facet values is defined by a community of users and corresponds to content generated by the community of users; means for enabling user input of a second search term formed of a second parameter; and a refinement engine for refining the search based on the listing and additional user input or the second search term.
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37. A computer system comprising: a database having a plurality of records in respective categories of information, each record having one or more facets to the respective category of information; means for receiving user input of a first search term formed of a first parameter indicative of at least one category of information of the database; a search engine to implement a search of the database for records of the at least one category of information; means for displaying, in response to the user input of the first search term, simultaneously displaying both in a same screen view: (a) a set of search results, including records from the database of the at least one category of information, and (b) a listing of any one or combination of facets and facet values of the records in the set of search results, the listing serving as suggested additional parameters for further refining the first search term upon user selection of the any one or combination of facets and facet values, wherein at least one of the any one or combination of facets and facet values is defined by a community of users and corresponds to content generated by the community of users; means for enabling user input of a second search term formed of a second parameter; and a refinement engine for refining the search based on the listing and additional user input or the second search term. 59. A system as claimed in claim 37 , wherein facet values with facet value count of zero are not listed adjacent search results.
| 0.881651 |
8,260,619 | 12 | 13 |
12. A computer readable medium having stored thereon a set of data operable to configure a computer to: a) identify a plurality of relevant phrases in a website, wherein said plurality of relevant phrases comprises a first phrase from a first page from said website and a second phrase from a second page from said website; b) determine a plurality of weights wherein each weight from said plurality of weights corresponds to a relevant phrase from said plurality of relevant phrases and is based at least in part on a relationship between the corresponding relevant phrase and a leaf page from the website; c) using a set of information comprising said plurality of weights and said plurality of relevant phrases, train a grammar to categorize an input according to a class corresponding to the leaf page from the website; and d) store said grammar in a computer memory in a format readable by an interactive voice response system; wherein at least one relevant phrase from the plurality of relevant phrases is a phrase in an ancestor page separated from the leaf page by a number of links, and wherein the relationship between the at least one relevant phrase from the ancestor page comprises the number of links separating the ancestor page from the leaf page.
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12. A computer readable medium having stored thereon a set of data operable to configure a computer to: a) identify a plurality of relevant phrases in a website, wherein said plurality of relevant phrases comprises a first phrase from a first page from said website and a second phrase from a second page from said website; b) determine a plurality of weights wherein each weight from said plurality of weights corresponds to a relevant phrase from said plurality of relevant phrases and is based at least in part on a relationship between the corresponding relevant phrase and a leaf page from the website; c) using a set of information comprising said plurality of weights and said plurality of relevant phrases, train a grammar to categorize an input according to a class corresponding to the leaf page from the website; and d) store said grammar in a computer memory in a format readable by an interactive voice response system; wherein at least one relevant phrase from the plurality of relevant phrases is a phrase in an ancestor page separated from the leaf page by a number of links, and wherein the relationship between the at least one relevant phrase from the ancestor page comprises the number of links separating the ancestor page from the leaf page. 13. The computer readable medium of claim 12 , wherein determining the plurality of weights comprises, for each weight from the plurality of weights, determining a value for that weight which is inversely related to the number of links separating the ancestor page from the home page.
| 0.790251 |
8,612,970 | 12 | 15 |
12. A method comprising: receiving, by a virtual machine monitor of a computing device, a request to start an applet from a first virtual domain of a set of virtual domains provided by the virtual machine monitor; creating, by the virtual machine monitor, a second virtual domain for running the applet; incorporate a byte code interpreter in address space of a kernel of an operating system of the second virtual domain, the operating system of the second virtual domain, to run byte code interpreted applications comprising at least the applet using the byte code interpreter, wherein the kernel to access hardware of the computer through a platform independent interface provided by the virtual machine monitor; and starting, by the virtual machine monitor, the applet in the second virtual domain, wherein the applet runs in the operating system of the second virtual domain.
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12. A method comprising: receiving, by a virtual machine monitor of a computing device, a request to start an applet from a first virtual domain of a set of virtual domains provided by the virtual machine monitor; creating, by the virtual machine monitor, a second virtual domain for running the applet; incorporate a byte code interpreter in address space of a kernel of an operating system of the second virtual domain, the operating system of the second virtual domain, to run byte code interpreted applications comprising at least the applet using the byte code interpreter, wherein the kernel to access hardware of the computer through a platform independent interface provided by the virtual machine monitor; and starting, by the virtual machine monitor, the applet in the second virtual domain, wherein the applet runs in the operating system of the second virtual domain. 15. The method of claim 12 , wherein the operating system is a special purpose operating system that is to provide only functions requested for executing the applet.
| 0.5 |
9,240,969 | 8 | 11 |
8. A computer program product comprising a non-transitory computer usable medium including a computer readable program, wherein the computer readable program when executed on a computer causes the computer to: determine one or more topics associated with a message based at least in part on message data included in the message; determine knowledge data describing the one or more topics associated with the message; determine social activity data describing one or more user activities associated with a group of one or more social users based at least in part on the knowledge data, the one or more user activities describing the one or more topics; generate a selectable tag based at least in part on the social activity data describing the one or more user activities, a selection of the selectable tag causing a display of the social activity data associated with the message; and generate graphical user interface data for displaying the social activity data associated with the message in response to the selectable tag being selected.
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8. A computer program product comprising a non-transitory computer usable medium including a computer readable program, wherein the computer readable program when executed on a computer causes the computer to: determine one or more topics associated with a message based at least in part on message data included in the message; determine knowledge data describing the one or more topics associated with the message; determine social activity data describing one or more user activities associated with a group of one or more social users based at least in part on the knowledge data, the one or more user activities describing the one or more topics; generate a selectable tag based at least in part on the social activity data describing the one or more user activities, a selection of the selectable tag causing a display of the social activity data associated with the message; and generate graphical user interface data for displaying the social activity data associated with the message in response to the selectable tag being selected. 11. The computer program product of claim 8 , wherein the computer readable program when executed on the computer causes the computer to also rank the social activity data based at least in part on the one or more topics.
| 0.74 |
5,422,992 | 10 | 12 |
10. A system for processing a hierarchically structured document, comprising: means for creating a first state variable table having a first set of state variables; means for creating a first reference to the first state variable table, said first reference to the first state variable table being associated with a structure portion of a predetermined hierarchical level of the document; means for creating, for said predetermined hierarchical level of the document when a content portion of said predetermined hierarchical level is processed, a second state variable table and copying the first set of state variables to a second set of state variables of the second state variable table; means for processing a structure portion of a subsequent hierarchical level, wherein said subsequent hierarchical level is lower in the hierarchical structure of the document than the predetermined hierarchical level; means for determining if processing of content of said predetermined hierarchical level is in process; means for copying the second reference to a third reference associated with structure of the subsequent hierarchical level, when said processing of content for said predetermined hierarchical level is determined to be in process; means for copying the first reference to the third reference associated with the structure of the subsequent hierarchical level, when said processing of content for said predetermined hierarchical level is determined not to be in process; means for creating, for said subsequent hierarchical level of the document when a content portion of said subsequent hierarchical level is processed, a third state variable table referenced by a fourth reference and copying the set of state variables referred to by the third reference to the third state variable table; means for processing said content portion of said subsequent hierarchical level using said fourth reference to the third state variable table; and means for continuing processing of said content portion of said predetermined hierarchical level after processing of said content portion of said subsequent hierarchical level is complete, using said second reference to said second state variable table, when said processing of content for said predetermined hierarchical level is determined to be in process.
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10. A system for processing a hierarchically structured document, comprising: means for creating a first state variable table having a first set of state variables; means for creating a first reference to the first state variable table, said first reference to the first state variable table being associated with a structure portion of a predetermined hierarchical level of the document; means for creating, for said predetermined hierarchical level of the document when a content portion of said predetermined hierarchical level is processed, a second state variable table and copying the first set of state variables to a second set of state variables of the second state variable table; means for processing a structure portion of a subsequent hierarchical level, wherein said subsequent hierarchical level is lower in the hierarchical structure of the document than the predetermined hierarchical level; means for determining if processing of content of said predetermined hierarchical level is in process; means for copying the second reference to a third reference associated with structure of the subsequent hierarchical level, when said processing of content for said predetermined hierarchical level is determined to be in process; means for copying the first reference to the third reference associated with the structure of the subsequent hierarchical level, when said processing of content for said predetermined hierarchical level is determined not to be in process; means for creating, for said subsequent hierarchical level of the document when a content portion of said subsequent hierarchical level is processed, a third state variable table referenced by a fourth reference and copying the set of state variables referred to by the third reference to the third state variable table; means for processing said content portion of said subsequent hierarchical level using said fourth reference to the third state variable table; and means for continuing processing of said content portion of said predetermined hierarchical level after processing of said content portion of said subsequent hierarchical level is complete, using said second reference to said second state variable table, when said processing of content for said predetermined hierarchical level is determined to be in process. 12. A system according to claim 10, wherein said hierarchically structured document is a document which conforms to rules of a Standard Page Description Language.
| 0.864775 |
8,307,038 | 11 | 12 |
11. The method of claim 8 wherein the step of determining includes marking any email address as relevant and applying a subtraction algorithm.
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11. The method of claim 8 wherein the step of determining includes marking any email address as relevant and applying a subtraction algorithm. 12. The method of claim 11 wherein the subtraction algorithm is at least one of: marking the email address as not relevant if a new email containing said address is not received in a pre-determined amount of time; and/or marking the email address as not relevant if a new email containing said address is not received in a pre-determined number of emails.
| 0.5 |
6,085,196 | 20 | 23 |
20. A system according to claim 15, wherein the user interface object further comprises: a reference to a software object for an interactive user input of a source for inputting the second structured information format attribute value; a reference to a software object for an interactive user input of the second structured information format attribute value; an object method for obtaining interactive input from the user of the source for inputting the second structured information format attribute value using the software object for the interactive user input of the source for inputting the second structured information format attribute value; and an object method for obtaining interactive input from the user of the second structured information format attribute value using the software object for the interactive user input of the second structured information format attribute value.
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20. A system according to claim 15, wherein the user interface object further comprises: a reference to a software object for an interactive user input of a source for inputting the second structured information format attribute value; a reference to a software object for an interactive user input of the second structured information format attribute value; an object method for obtaining interactive input from the user of the source for inputting the second structured information format attribute value using the software object for the interactive user input of the source for inputting the second structured information format attribute value; and an object method for obtaining interactive input from the user of the second structured information format attribute value using the software object for the interactive user input of the second structured information format attribute value. 23. A system according to claim 20, further comprising: a reference to a software object for a rule to be created; an object method for examining the source which has been input by the user; and an object method for assigning a first structured information format attribute value to the second structured information format attribute value using the software object for the rule to be created, when the source which has been input by the user indicates a first structured information format attribute source is to be used.
| 0.601527 |
9,817,887 | 1 | 6 |
1. A method comprising: receiving a document in a first format, wherein the document comprises a text supported non-text data, and unsupported non-text data; creating, using a first filter associated with the first format, a universal text representation of the document, wherein the universal text representation presents the text and the supported non-text data, wherein the universal text representation preserves the unsupported non-text data by storing an association of the unsupported non-text data with supported data from the document, wherein the universal text representation comprises a text tree, wherein the text tree comprises nodes that comprise one or more words and locations of the words, and wherein one or more of the nodes comprise attributes associated with formatting of the words; modifying the universal text representation based upon input from a user of a program in a what you see is what you get (WYSIWYG) mode, wherein a location of where the supported data and the unsupported non-text data are kept is presented to the user, wherein modifying the universal text representation comprises translating the text presented in the universal text representation from a first language to a second language, wherein the first language is different than the second language, and wherein translating the text comprises: receiving a translation table representing the text in the second language, wherein the translation table comprises a correspondence between the words in the first language and translated words in the second language; creating a copy of the text tree, wherein the copy of the text tree preserves the formatting of the words; and for each node among the nodes in the copy of the text tree, replacing the words in the first language with the translated words based upon the translation table; and exporting, by at least one processor, the modified universal text representation using a second filter associated with a second format, wherein the supported data and the unsupported non-text data are exported.
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1. A method comprising: receiving a document in a first format, wherein the document comprises a text supported non-text data, and unsupported non-text data; creating, using a first filter associated with the first format, a universal text representation of the document, wherein the universal text representation presents the text and the supported non-text data, wherein the universal text representation preserves the unsupported non-text data by storing an association of the unsupported non-text data with supported data from the document, wherein the universal text representation comprises a text tree, wherein the text tree comprises nodes that comprise one or more words and locations of the words, and wherein one or more of the nodes comprise attributes associated with formatting of the words; modifying the universal text representation based upon input from a user of a program in a what you see is what you get (WYSIWYG) mode, wherein a location of where the supported data and the unsupported non-text data are kept is presented to the user, wherein modifying the universal text representation comprises translating the text presented in the universal text representation from a first language to a second language, wherein the first language is different than the second language, and wherein translating the text comprises: receiving a translation table representing the text in the second language, wherein the translation table comprises a correspondence between the words in the first language and translated words in the second language; creating a copy of the text tree, wherein the copy of the text tree preserves the formatting of the words; and for each node among the nodes in the copy of the text tree, replacing the words in the first language with the translated words based upon the translation table; and exporting, by at least one processor, the modified universal text representation using a second filter associated with a second format, wherein the supported data and the unsupported non-text data are exported. 6. The method of claim 1 , wherein the association of the unsupported non-text data with the supported data comprises a description of a desired behavior of the unsupported non-text data.
| 0.700321 |
7,941,395 | 12 | 14 |
12. A system according to claim 10 wherein said data indicating a relationship to said one or more different documents comprises data identifying the received document as well as data identifying one of said one or more different documents and data identifying the relationship between said received document and said one of said one or more different documents.
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12. A system according to claim 10 wherein said data indicating a relationship to said one or more different documents comprises data identifying the received document as well as data identifying one of said one or more different documents and data identifying the relationship between said received document and said one of said one or more different documents. 14. A system according to claim 12 wherein said data identifying said received document and said data identifying said one of said one or more documents comprises a unique name for said document.
| 0.533493 |
8,275,621 | 10 | 11 |
10. The method of claim 8 , wherein the act of generating the audio output comprises using a statistical model to generate the audio output.
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10. The method of claim 8 , wherein the act of generating the audio output comprises using a statistical model to generate the audio output. 11. The method of claim 10 , further comprising acts of: storing a first weight for use by the statistical model, the first weight associated with the first type of pronunciation; and storing a second weight for use by the statistical model, the second weight associated with the second type of pronunciation.
| 0.5 |
8,971,644 | 10 | 11 |
10. A non-transitory computer readable medium having program instructions stored thereon, that, in response to execution by a processor, cause a computing device to perform operations comprising: determining a plurality of images related to the particular image; identifying a plurality of annotations associated with the plurality of images; generating an ontology for the particular image wherein the ontology comprises: a plurality of terms, the plurality of annotations, and the plurality of images arranged in a hierarchy with the plurality of annotations being downstream from the plurality of terms associated with a highest level of the hierarchy, and the plurality of images being downstream from the plurality of annotations, and a plurality of links defining relationships between respective terms, annotations, or images, wherein each link is associated with a respective relevance value indicating a measure of relevance between two respective terms, annotations, or images connected by a respective link; determining a total relevance value for each term associated with the highest level, wherein for each term associated with the highest level, the total relevance value is a sum of relevance values of links downstream from the term; and associating one of the plurality of terms having a highest total relevance value with the particular image as an image annotation.
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10. A non-transitory computer readable medium having program instructions stored thereon, that, in response to execution by a processor, cause a computing device to perform operations comprising: determining a plurality of images related to the particular image; identifying a plurality of annotations associated with the plurality of images; generating an ontology for the particular image wherein the ontology comprises: a plurality of terms, the plurality of annotations, and the plurality of images arranged in a hierarchy with the plurality of annotations being downstream from the plurality of terms associated with a highest level of the hierarchy, and the plurality of images being downstream from the plurality of annotations, and a plurality of links defining relationships between respective terms, annotations, or images, wherein each link is associated with a respective relevance value indicating a measure of relevance between two respective terms, annotations, or images connected by a respective link; determining a total relevance value for each term associated with the highest level, wherein for each term associated with the highest level, the total relevance value is a sum of relevance values of links downstream from the term; and associating one of the plurality of terms having a highest total relevance value with the particular image as an image annotation. 11. The non-transitory computer readable medium of claim 10 , further comprising instructions defining the steps of: generating a fingerprint associated with the particular image; and determining the plurality of images related to the particular image, based on the fingerprint associated with the particular image and fingerprints associated with the plurality of images.
| 0.5 |
5,546,107 | 7 | 15 |
7. A method according to claim 1 wherein said matching step b comprises the step of: building an isomorphism between two subsets of 0-cells by several successive runs of different filtering procedures which reject certain potential matches if they are recognized as invalid.
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7. A method according to claim 1 wherein said matching step b comprises the step of: building an isomorphism between two subsets of 0-cells by several successive runs of different filtering procedures which reject certain potential matches if they are recognized as invalid. 15. A method according to claim 7, comprising the step of: rejecting all 0-cell matches at which chains on either map A or B intersect in their internal parts.
| 0.847992 |
8,458,701 | 8 | 10 |
8. The method of claim 7 , said first context being an application virtualization context.
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8. The method of claim 7 , said first context being an application virtualization context. 10. The method of claim 8 , said second virtual context being a virtual machine context.
| 0.555556 |
9,082,040 | 2 | 3 |
2. A method as recited in claim 1 , further comprising: extracting a visual word from the image; expanding the visual word to a plurality of visual contextual synonyms; and producing a panel of patches representing a weighted combination of the plurality of visual contextual synonyms.
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2. A method as recited in claim 1 , further comprising: extracting a visual word from the image; expanding the visual word to a plurality of visual contextual synonyms; and producing a panel of patches representing a weighted combination of the plurality of visual contextual synonyms. 3. A method as recited in claim 2 , further comprising providing the panel of patches to a search application.
| 0.5 |
8,700,577 | 19 | 23 |
19. A computer system comprising: a rule repository operable for storing data quality rules; a graphical user interface comprising a display window and capable of receiving a data set, said data set comprising a plurality of records comprising a plurality of attributes and a plurality of values for said attributes, said plurality of attributes comprising attributes having multiple values, an ontology comprising links that indicate which of said attributes are related, and a set of rule generation parameters; a data quality rules discovery engine capable of receiving said data set, said ontology, and said set of rule generation parameters from said user interface, generating said set of data quality rules, and sending said set of data quality rules to said rule repository, wherein data quality rules generated by said data quality rules discovery engine are displayed in said display window; wherein said data quality rules discovery engine formulates a set of candidate conditional functional dependencies based on a set of candidate seeds by using said ontology, said candidate seeds comprising instances of related attributes; and wherein said data quality rules discovery engine refines said set of candidate conditional functional dependencies by: incrementing a first count of records in a first subset of said plurality of records that are consistent with a conditional functional dependency, wherein all values in a pattern tuple of said conditional functional dependency match respective values in a record that is consistent with said conditional functional dependency; incrementing a second count of records in said first subset of said plurality of records that are inconsistent with said conditional functional dependency, wherein all values in a pattern tuple of the antecedent of said conditional functional dependency match respective values, but values in said pattern tuple of the consequent of said conditional functional dependency do not match respective values, in a record that is inconsistent with said conditional functional dependency; incrementing a third count of records in said first subset of said plurality of records that are not consistent with said conditional functional dependency and are not inconsistent with said conditional functional dependency; determining whether a first measure based on said first and third counts satisfies a first threshold value, wherein if said first measure fails to satisfy said first threshold value then a condition is removed from said antecedent of said conditional functional dependency and said refining then continues for a second subset of said plurality of records; and determining whether a second measure based on said second and third counts satisfies a second threshold value, wherein if said second measure fails to satisfy said second threshold value then said first measure is reduced and said refining then continues for said second subset of said plurality of records; wherein said data quality rules discovery engine terminates refining of said set of candidate conditional functional dependencies when said set of conditional functional dependencies reaches a quiescent state and becomes said data quality rules.
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19. A computer system comprising: a rule repository operable for storing data quality rules; a graphical user interface comprising a display window and capable of receiving a data set, said data set comprising a plurality of records comprising a plurality of attributes and a plurality of values for said attributes, said plurality of attributes comprising attributes having multiple values, an ontology comprising links that indicate which of said attributes are related, and a set of rule generation parameters; a data quality rules discovery engine capable of receiving said data set, said ontology, and said set of rule generation parameters from said user interface, generating said set of data quality rules, and sending said set of data quality rules to said rule repository, wherein data quality rules generated by said data quality rules discovery engine are displayed in said display window; wherein said data quality rules discovery engine formulates a set of candidate conditional functional dependencies based on a set of candidate seeds by using said ontology, said candidate seeds comprising instances of related attributes; and wherein said data quality rules discovery engine refines said set of candidate conditional functional dependencies by: incrementing a first count of records in a first subset of said plurality of records that are consistent with a conditional functional dependency, wherein all values in a pattern tuple of said conditional functional dependency match respective values in a record that is consistent with said conditional functional dependency; incrementing a second count of records in said first subset of said plurality of records that are inconsistent with said conditional functional dependency, wherein all values in a pattern tuple of the antecedent of said conditional functional dependency match respective values, but values in said pattern tuple of the consequent of said conditional functional dependency do not match respective values, in a record that is inconsistent with said conditional functional dependency; incrementing a third count of records in said first subset of said plurality of records that are not consistent with said conditional functional dependency and are not inconsistent with said conditional functional dependency; determining whether a first measure based on said first and third counts satisfies a first threshold value, wherein if said first measure fails to satisfy said first threshold value then a condition is removed from said antecedent of said conditional functional dependency and said refining then continues for a second subset of said plurality of records; and determining whether a second measure based on said second and third counts satisfies a second threshold value, wherein if said second measure fails to satisfy said second threshold value then said first measure is reduced and said refining then continues for said second subset of said plurality of records; wherein said data quality rules discovery engine terminates refining of said set of candidate conditional functional dependencies when said set of conditional functional dependencies reaches a quiescent state and becomes said data quality rules. 23. The computer system from claim 19 , wherein said data quality rules discovery engine also refines said candidate conditional functional dependencies by identifying and eliminating a high entropy attribute from a subset of said plurality of attributes, said subset comprising multiple attributes and associated with a candidate conditional functional dependency, said high entropy attribute having the most different values relative to any of the other attributes in said subset of said plurality of attributes.
| 0.53442 |
8,713,534 | 1 | 3 |
1. A method for guiding correction of semantic errors in code in an integrated development environment, said method comprising the steps of: inputting, using a code editor, code being developed by a developer in an integrated development environment; constructing, using a code parser, a syntax tree representation of said code inputted; identifying, using one or more collaboration records located for a node in a syntax tree constructed for said code inputted, any semantic errors pertaining to use of a third-party library in said code inputted by said developer, wherein said one or more collaboration records contain suggestions offered by peer developers for correcting semantic errors related to a specific third-party library class; displaying said code on a user interface; displaying an error warning icon adjacent to any lines of said code that contain one or more of said semantic errors; in response to receiving a signal indicating that a user has clicked a specific error warning icon, displaying one or more suggestions associated with said one or more collaboration records located to correct said any semantic errors identified for said node, wherein said developer can choose a suggestion of said one or more suggestions displayed to guide correction of said any semantic errors; inquiring whether or not said developer wishes to add a new suggestion for handling a semantic error in a particular node in the syntax tree representation of said code inputted; in response to determining that said developer does not wish to add said new suggestion for handling said semantic error in said particular node in the syntax tree representation of said code inputted, inquiring whether or not said developer wishes to contribute feedback with respect to said one or more suggestions displayed for said node; and if said developer wishes to contribute said feedback pertaining to said one or more suggestions displayed, receiving said feedback for updating said one or more suggestions displayed.
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1. A method for guiding correction of semantic errors in code in an integrated development environment, said method comprising the steps of: inputting, using a code editor, code being developed by a developer in an integrated development environment; constructing, using a code parser, a syntax tree representation of said code inputted; identifying, using one or more collaboration records located for a node in a syntax tree constructed for said code inputted, any semantic errors pertaining to use of a third-party library in said code inputted by said developer, wherein said one or more collaboration records contain suggestions offered by peer developers for correcting semantic errors related to a specific third-party library class; displaying said code on a user interface; displaying an error warning icon adjacent to any lines of said code that contain one or more of said semantic errors; in response to receiving a signal indicating that a user has clicked a specific error warning icon, displaying one or more suggestions associated with said one or more collaboration records located to correct said any semantic errors identified for said node, wherein said developer can choose a suggestion of said one or more suggestions displayed to guide correction of said any semantic errors; inquiring whether or not said developer wishes to add a new suggestion for handling a semantic error in a particular node in the syntax tree representation of said code inputted; in response to determining that said developer does not wish to add said new suggestion for handling said semantic error in said particular node in the syntax tree representation of said code inputted, inquiring whether or not said developer wishes to contribute feedback with respect to said one or more suggestions displayed for said node; and if said developer wishes to contribute said feedback pertaining to said one or more suggestions displayed, receiving said feedback for updating said one or more suggestions displayed. 3. The method according to claim 1 , further comprising: establishing a separate collaboration record for each of said semantic errors related to said specific third-party library class.
| 0.877309 |
7,849,144 | 17 | 19 |
17. The server of claim 16 , wherein the presence resource is configured for detecting a presence of each of the sending party and the destination party for transfer of instant messages in response to a corresponding registration with the presence resource, the presence resource configured for determining the language preference for the destination party in response to detecting the corresponding presence.
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17. The server of claim 16 , wherein the presence resource is configured for detecting a presence of each of the sending party and the destination party for transfer of instant messages in response to a corresponding registration with the presence resource, the presence resource configured for determining the language preference for the destination party in response to detecting the corresponding presence. 19. The server of claim 17 , wherein the network interface is configured for receiving a Session Initiation Protocol (SIP) message that includes the instant message.
| 0.678988 |
8,943,043 | 1 | 8 |
1. A method for providing query search results for mobile communications devices, comprising steps for: for each of two or more different communities of mobile communications devices, constructing at least one query cache from contents of one or more historical query logs of each of the communities of mobile communications devices; wherein constructing at least one query cache for each of the two or more different communities of mobile communications devices inherently results in the construction of a set of two or more different query caches; wherein the contents of each query cache include a set of query terms and corresponding links from the historical query logs; selecting one or more of the query caches from the set of two or more query caches by determining which of the corresponding communities are similar to a community associated with one or more particular mobile communications devices and providing the selected query caches to the one or more particular mobile communications devices; and on one or more of the mobile communications devices, locally servicing queries using one or more of the query caches provided to each particular mobile communications device without accessing an external query search service.
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1. A method for providing query search results for mobile communications devices, comprising steps for: for each of two or more different communities of mobile communications devices, constructing at least one query cache from contents of one or more historical query logs of each of the communities of mobile communications devices; wherein constructing at least one query cache for each of the two or more different communities of mobile communications devices inherently results in the construction of a set of two or more different query caches; wherein the contents of each query cache include a set of query terms and corresponding links from the historical query logs; selecting one or more of the query caches from the set of two or more query caches by determining which of the corresponding communities are similar to a community associated with one or more particular mobile communications devices and providing the selected query caches to the one or more particular mobile communications devices; and on one or more of the mobile communications devices, locally servicing queries using one or more of the query caches provided to each particular mobile communications device without accessing an external query search service. 8. The method of claim 1 wherein each query cache is constructed using a “cache saturation threshold” that limits the contents of the query cache to query/link pairs having a normalized volume greater than a predetermined threshold.
| 0.849741 |
7,577,879 | 10 | 13 |
10. A system for patching a software error in a mobile computing device application, comprising: a mobile computing device configured to use a wireless cellular link as its primary mode of communication that is arranged to: execute the application, detect when the error occurs, wherein the error is an error that occurred during execution of the application such that a process that is executing the application crashes, determine whether crash data related to the error has been previously transmitted; and only in response to determining that the error has not been previously transmitted, compile crash data when the error is detected, wherein the crash data identifies the state of the mobile computing device and the application when the error occurred; a crash server coupled to the mobile computing device, wherein the crash server is arranged to: receive the crash data from the mobile computing device, determine the availability of an application fix that addresses the error, wherein the application fix is determined to be available when the crash data corresponds to registration information that identifies the mobile computing device, the application fix and the error; a patch detection server coupled to the crash server, wherein the patch detection server is arranged to: receive crash information from the crash server, the crash information based on the registration information and the crash data, process the crash information to determine the type of error and the location of the corresponding application fix, and generate an Extensible Markup Language (XML) file based on the crash information, wherein the XML file identifies the application fix and locates the application fix; and a patch server coupled to the patch detection server through a web services interface, wherein the patch server is arranged to receive the XML file from the patch detection server, locate the application fix based on the XML file and a patch identifier associated with the application fix, and send the application fix to the mobile computing device; wherein the application is modified on the mobile computing device using the application fix such that the modified application overcomes the error.
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10. A system for patching a software error in a mobile computing device application, comprising: a mobile computing device configured to use a wireless cellular link as its primary mode of communication that is arranged to: execute the application, detect when the error occurs, wherein the error is an error that occurred during execution of the application such that a process that is executing the application crashes, determine whether crash data related to the error has been previously transmitted; and only in response to determining that the error has not been previously transmitted, compile crash data when the error is detected, wherein the crash data identifies the state of the mobile computing device and the application when the error occurred; a crash server coupled to the mobile computing device, wherein the crash server is arranged to: receive the crash data from the mobile computing device, determine the availability of an application fix that addresses the error, wherein the application fix is determined to be available when the crash data corresponds to registration information that identifies the mobile computing device, the application fix and the error; a patch detection server coupled to the crash server, wherein the patch detection server is arranged to: receive crash information from the crash server, the crash information based on the registration information and the crash data, process the crash information to determine the type of error and the location of the corresponding application fix, and generate an Extensible Markup Language (XML) file based on the crash information, wherein the XML file identifies the application fix and locates the application fix; and a patch server coupled to the patch detection server through a web services interface, wherein the patch server is arranged to receive the XML file from the patch detection server, locate the application fix based on the XML file and a patch identifier associated with the application fix, and send the application fix to the mobile computing device; wherein the application is modified on the mobile computing device using the application fix such that the modified application overcomes the error. 13. The system of claim 10 , wherein the crash server is further arranged to associate the registration information with the mobile computing device, the application fix and the error.
| 0.568075 |
4,882,759 | 4 | 5 |
4. The method of claim 3 wherein step (e) further includes the step of: (iv) for said word not spoken during the training session, repeating steps (i), (ii), and (iii) for each piece thereof; each word piece having a string of output-related models associated therewith.
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4. The method of claim 3 wherein step (e) further includes the step of: (iv) for said word not spoken during the training session, repeating steps (i), (ii), and (iii) for each piece thereof; each word piece having a string of output-related models associated therewith. 5. The method of claim 4 wherein step (e) further includes the step of: (v) concatenating the strings in the same order as the word pieces said strings represent to form a word baseform of output related models.
| 0.5 |
7,624,422 | 1 | 16 |
1. A network auditing method comprising: retrieving network information gathered by a plurality of heterogeneous information sources; identifying a network policy to be applied to the retrieved information, utilizing a policy and vulnerability engine; identifying semantic equivalencies in the information gathered by the plurality of heterogeneous information sources, utilizing the policy and vulnerability engine; uniformly applying the network policy to the information identified as being semantically equivalent, utilizing the policy and vulnerability engine; determining compliance with the network policy, utilizing the policy and vulnerability engine; and making a recommendation for modifying a network feature based on the compliance determination, utilizing the policy and vulnerability engine; wherein the identifying semantic equivalencies comprises: identifying a list of facts gathered by each information source; identifying for each fact on the list one or more equivalent facts gathered by each of the other information sources; and storing the semantic equivalences; wherein the recommendation is a list of network policy rules to include in the network policy; wherein the network policy rules are ranked based on a number of times that a network policy rule was applied, a severity meter set for the network policy rule, and assets that are affected; wherein an identifier is used for generating the network policy rule independently of a source type.
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1. A network auditing method comprising: retrieving network information gathered by a plurality of heterogeneous information sources; identifying a network policy to be applied to the retrieved information, utilizing a policy and vulnerability engine; identifying semantic equivalencies in the information gathered by the plurality of heterogeneous information sources, utilizing the policy and vulnerability engine; uniformly applying the network policy to the information identified as being semantically equivalent, utilizing the policy and vulnerability engine; determining compliance with the network policy, utilizing the policy and vulnerability engine; and making a recommendation for modifying a network feature based on the compliance determination, utilizing the policy and vulnerability engine; wherein the identifying semantic equivalencies comprises: identifying a list of facts gathered by each information source; identifying for each fact on the list one or more equivalent facts gathered by each of the other information sources; and storing the semantic equivalences; wherein the recommendation is a list of network policy rules to include in the network policy; wherein the network policy rules are ranked based on a number of times that a network policy rule was applied, a severity meter set for the network policy rule, and assets that are affected; wherein an identifier is used for generating the network policy rule independently of a source type. 16. The method of claim 1 , wherein the making of the recommendation includes generating a remediation task for the network policy, the remediation task including information indicating the remediation task is for a network policy violation, a name of the network policy or a name of the network policy rule, a severity measure for the network policy rule, an address of a host in which the network policy violation was noted, and a date in which the network policy violation was detected.
| 0.5 |
6,065,000 | 1 | 2 |
1. A computer-implemented process of reporting injured-worker safety information, comprising the steps of: creating a system database stored in computer memory, the system database including a plurality of defined lists of entries for selected injured-worker-related items including information necessary to comply with governmental injured-worker-reporting regulations, and a plurality of defined formats for selected injured-worker incident reports including an OSHA 200 report; creating an injured-worker incident database stored in computer memory by selecting an entry from one or more of the defined lists in the system database, and inserting the selected entry or entries into a data record; and creating an injured-worker incident report that includes regulatory-required injured-worker information by: selecting one of the defined formats from the system database; extracting and manipulating information from the incident database as defined in the selected format; and producing the report on a computer output medium.
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1. A computer-implemented process of reporting injured-worker safety information, comprising the steps of: creating a system database stored in computer memory, the system database including a plurality of defined lists of entries for selected injured-worker-related items including information necessary to comply with governmental injured-worker-reporting regulations, and a plurality of defined formats for selected injured-worker incident reports including an OSHA 200 report; creating an injured-worker incident database stored in computer memory by selecting an entry from one or more of the defined lists in the system database, and inserting the selected entry or entries into a data record; and creating an injured-worker incident report that includes regulatory-required injured-worker information by: selecting one of the defined formats from the system database; extracting and manipulating information from the incident database as defined in the selected format; and producing the report on a computer output medium. 2. The computer-implemented process according to claim 1, wherein the defined lists include a defined list of employees.
| 0.851485 |
10,089,901 | 1 | 7 |
1. An apparatus for bi-directional sign language/speech translation in real time comprising: a processor; and a non-transitory computer readable medium storing program instructions, when executed, causing the processor to: analyze a used pattern of sign language by a user in view of current surrounding environment information of the user to generate sign category information via a pattern analyzer; recognize a speech externally made through a microphone via a speech-sign outputter and output a sign corresponding to the speech via a display of the speech-sign outputter; and recognize a sign sensed through a camera via a sign-speech outputter and output a speech corresponding to the sign via a speaker of the sign-speech outputter, wherein the pattern analyzer transmits the sign category information to the speech-sign outputter to output a text or a sign corresponding to the sign category information via the display or to the sign-speech outputter to output a speech corresponding to the sign category information via the speaker, wherein the pattern analyzer comprises a sign category keyword comparator configured to compare the sign category information with the sign corresponding to the speech or the speech corresponding to the sign to determine whether the sign category information is correct, and wherein the sign category keyword comparator transmits a signal related to whether the sign category information is correct to the speech-sign outputter or the sign-speech outputter to block the text, the sign, or the speech corresponding to the sign category information.
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1. An apparatus for bi-directional sign language/speech translation in real time comprising: a processor; and a non-transitory computer readable medium storing program instructions, when executed, causing the processor to: analyze a used pattern of sign language by a user in view of current surrounding environment information of the user to generate sign category information via a pattern analyzer; recognize a speech externally made through a microphone via a speech-sign outputter and output a sign corresponding to the speech via a display of the speech-sign outputter; and recognize a sign sensed through a camera via a sign-speech outputter and output a speech corresponding to the sign via a speaker of the sign-speech outputter, wherein the pattern analyzer transmits the sign category information to the speech-sign outputter to output a text or a sign corresponding to the sign category information via the display or to the sign-speech outputter to output a speech corresponding to the sign category information via the speaker, wherein the pattern analyzer comprises a sign category keyword comparator configured to compare the sign category information with the sign corresponding to the speech or the speech corresponding to the sign to determine whether the sign category information is correct, and wherein the sign category keyword comparator transmits a signal related to whether the sign category information is correct to the speech-sign outputter or the sign-speech outputter to block the text, the sign, or the speech corresponding to the sign category information. 7. The apparatus for bi-directional sign language/speech translation in real time of claim 1 , wherein the pattern analyzer is configured to analyze the used pattern of sign language by the user by analyzing at least one of location information of the user, surrounding environment information of the user corresponding to the location information, a life pattern of the user, or a behavior pattern of the user.
| 0.548352 |
8,799,251 | 8 | 12 |
8. A method comprising: populating fields within a template associated with a document application based on data stored in a database associated with a data-based application by: providing connection information associated with the template to a database integration engine, wherein the connection information identifies a data set within the database from which data entities are to be extracted, wherein the connection information identifies a schema definition to be used by the database integration engine to generate a structured data document that includes the extracted data entities, wherein the connection information defines a link between the fields within the template and the data set within the database and wherein the connection information facilitates automated updates of the fields within the template with the latest data stored in the data set within the database; receiving the structured data document from the database integration engine; and transferring the extracted data entities from the structured data document to the template fields in accordance with a map associated with the template.
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8. A method comprising: populating fields within a template associated with a document application based on data stored in a database associated with a data-based application by: providing connection information associated with the template to a database integration engine, wherein the connection information identifies a data set within the database from which data entities are to be extracted, wherein the connection information identifies a schema definition to be used by the database integration engine to generate a structured data document that includes the extracted data entities, wherein the connection information defines a link between the fields within the template and the data set within the database and wherein the connection information facilitates automated updates of the fields within the template with the latest data stored in the data set within the database; receiving the structured data document from the database integration engine; and transferring the extracted data entities from the structured data document to the template fields in accordance with a map associated with the template. 12. The method of claim 8 , wherein the map associated with the template maps each of the template fields to an element or attribute of the schema definition.
| 0.754658 |
9,772,993 | 1 | 6 |
1. A computer-implemented method of recording utterances from unmanaged crowds for natural language processing, the method being implemented in a user device having one or more physical processors programmed with computer program instructions that, when executed by the one or more physical processors, cause the user device to perform the method, the method comprising: obtaining, by the user device, a token; transmitting, by the user device, the token to a remote device via a network; receiving, at the user device, from the remote device, one or more utterances to be uttered by a user and one or more campaign configuration parameters based on the token, wherein the one or more utterances and the one or more campaign configuration parameters are associated with a campaign that is associated with a natural language processing data collection effort; configuring, by the user device, the computer program instructions based on the one or more campaign configuration parameters; presenting to the user, by the user device, the one or more utterances to be uttered by the user; and recording, by the user device, audio of the user uttering the one or more utterances.
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1. A computer-implemented method of recording utterances from unmanaged crowds for natural language processing, the method being implemented in a user device having one or more physical processors programmed with computer program instructions that, when executed by the one or more physical processors, cause the user device to perform the method, the method comprising: obtaining, by the user device, a token; transmitting, by the user device, the token to a remote device via a network; receiving, at the user device, from the remote device, one or more utterances to be uttered by a user and one or more campaign configuration parameters based on the token, wherein the one or more utterances and the one or more campaign configuration parameters are associated with a campaign that is associated with a natural language processing data collection effort; configuring, by the user device, the computer program instructions based on the one or more campaign configuration parameters; presenting to the user, by the user device, the one or more utterances to be uttered by the user; and recording, by the user device, audio of the user uttering the one or more utterances. 6. The method of claim 1 , wherein the one or more campaign configuration parameters include a calibration parameter that indicates that a calibration test should be performed, the method further comprising: configuring, by the user device, the computer program instructions to perform a calibration test prior to presenting to the user the one or more utterances, by: determining, by the user device, a level of ambient noise; and determining, by the user device, whether the level of ambient noise satisfies the calibration test; and presenting to the user, by the user device, the one or more utterances to be uttered by the user responsive to a determination that the ambient level of audio satisfies the calibration test.
| 0.5 |
8,032,374 | 13 | 14 |
13. A method of recognizing continuous speech using search space restriction based on phoneme recognition, the method comprising the steps of: (a) extracting a feature vector from an input speech signal; (b) recognizing phonemes based on the extracted feature vector; and (c) recognizing a first word having corresponding first phoneme sequences from among the phonemes recognized in (b) and constructing a connection word search network that includes connection words for the first word, wherein the connection word search network has a search space that is restricted based on the phoneme recognition result, and (d) converting, into a phoneme code, N phoneme sequences that are subsequent to the first phoneme sequences in the phonemes recognized in (b), and calculating degrees of similarity between the phoneme code of the converted N phoneme sequences and phoneme codes of the respective connection words of the restricted connection word search network.
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13. A method of recognizing continuous speech using search space restriction based on phoneme recognition, the method comprising the steps of: (a) extracting a feature vector from an input speech signal; (b) recognizing phonemes based on the extracted feature vector; and (c) recognizing a first word having corresponding first phoneme sequences from among the phonemes recognized in (b) and constructing a connection word search network that includes connection words for the first word, wherein the connection word search network has a search space that is restricted based on the phoneme recognition result, and (d) converting, into a phoneme code, N phoneme sequences that are subsequent to the first phoneme sequences in the phonemes recognized in (b), and calculating degrees of similarity between the phoneme code of the converted N phoneme sequences and phoneme codes of the respective connection words of the restricted connection word search network. 14. The method according to claim 13 , wherein calculating the degree of similarity comprises performing a logic AND operation on the phoneme code of the N phoneme sequences and the phoneme code of the connection word of the restricted connection word search network, and summing respective code values of the result of the AND operation to calculate the degree of similarity.
| 0.5 |
7,752,442 | 1 | 9 |
1. In a distributed computing system environment that includes a plurality of computing devices that each comprise a processor and system memory, a method of transmitting a secure message from a first party to a second party, the first party using a first cryptographic technology and the second party using a second cryptographic technology, wherein the first and second parties are within a generic security framework and wherein the generic security framework abstracts cryptographic technologies and license formats, the method comprising: determining that a message is to be sent to the second party; a processor creating at least one security credential using a modular security policy and creating an encrypted message from the message, wherein the modular security policy: establishes security rules and procedures of the generic security framework; implements a security policy of the generic security framework with one or more protocols and transports; and describes security aspects, including properties, capabilities, requirements and interaction semantics, of a plurality of modular security components which define behaviors corresponding to the first and second cryptographic technologies used by the first and second parties, and which are written in a security policy language as selectable, deployable and combinable security modules and which enables the security components to be negotiated, partitioned and modified, and rather than being hard-coded, and which include: a store component for storing, retrieving, encrypting, and managing credentials; an integrity component for signing portions of a message and for verifying integrity and signatures of received messages; and a confidentiality component for encrypting and decrypting portions of a message; and formatting a second message with a markup language wherein the markup language comprises at least one header and wherein the second message contains the encrypted message; inserting at least the one security credential into the at least one header in the markup language in the second message; and transmitting the second message to the second party and wherein the second party can use the modular security policy to decrypt and verify the message.
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1. In a distributed computing system environment that includes a plurality of computing devices that each comprise a processor and system memory, a method of transmitting a secure message from a first party to a second party, the first party using a first cryptographic technology and the second party using a second cryptographic technology, wherein the first and second parties are within a generic security framework and wherein the generic security framework abstracts cryptographic technologies and license formats, the method comprising: determining that a message is to be sent to the second party; a processor creating at least one security credential using a modular security policy and creating an encrypted message from the message, wherein the modular security policy: establishes security rules and procedures of the generic security framework; implements a security policy of the generic security framework with one or more protocols and transports; and describes security aspects, including properties, capabilities, requirements and interaction semantics, of a plurality of modular security components which define behaviors corresponding to the first and second cryptographic technologies used by the first and second parties, and which are written in a security policy language as selectable, deployable and combinable security modules and which enables the security components to be negotiated, partitioned and modified, and rather than being hard-coded, and which include: a store component for storing, retrieving, encrypting, and managing credentials; an integrity component for signing portions of a message and for verifying integrity and signatures of received messages; and a confidentiality component for encrypting and decrypting portions of a message; and formatting a second message with a markup language wherein the markup language comprises at least one header and wherein the second message contains the encrypted message; inserting at least the one security credential into the at least one header in the markup language in the second message; and transmitting the second message to the second party and wherein the second party can use the modular security policy to decrypt and verify the message. 9. The method of claim 1 , wherein: the security policy identifies access rights of the security system.
| 0.539823 |
9,641,528 | 15 | 16 |
15. One or more non-transitory computer readable media comprising computer-executable instructions that, when executed by one or more processors, cause the one or more processors to perform a method comprising: receiving a set of identity information supplied by a subject; querying one or more public or private databases with at least a portion of the set of identity information; receiving, in response to the querying, independent information, wherein the independent information is not received from the subject; responsive to receiving the independent information, producing, with the one or more processors, one or more identity proofing queries, wherein at least a portion of the one or more identity proofing queries is based on identity information derived from the independent information; receiving, in response to sending the one or more identity proofing queries, at least one query response; comparing with the one or more processors, the one or more proofing queries and the at least one query response; determining, by querying one or more watchlists with a combination of the identity information and the received independent information, whether the combination matches information in the one or more watchlists; and initiating, based at least in part on the comparing and the determining, one or more of authentication enrollment of the subject and multi-factor authentication of the subject.
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15. One or more non-transitory computer readable media comprising computer-executable instructions that, when executed by one or more processors, cause the one or more processors to perform a method comprising: receiving a set of identity information supplied by a subject; querying one or more public or private databases with at least a portion of the set of identity information; receiving, in response to the querying, independent information, wherein the independent information is not received from the subject; responsive to receiving the independent information, producing, with the one or more processors, one or more identity proofing queries, wherein at least a portion of the one or more identity proofing queries is based on identity information derived from the independent information; receiving, in response to sending the one or more identity proofing queries, at least one query response; comparing with the one or more processors, the one or more proofing queries and the at least one query response; determining, by querying one or more watchlists with a combination of the identity information and the received independent information, whether the combination matches information in the one or more watchlists; and initiating, based at least in part on the comparing and the determining, one or more of authentication enrollment of the subject and multi-factor authentication of the subject. 16. The non-transitory computer readable of claim 15 , wherein receiving the set of identity information comprises receiving, as applicable, one or more of: a phone number, an IP address, a location, a unique identifier, and a communication device electronic fingerprint.
| 0.865575 |
8,954,837 | 16 | 21 |
16. A computer program product for inserting delimiters into a formula, the computer program product being embodied in a non-transitory computer readable medium and comprising computer instructions for: successively receiving a plurality of selections of cells while in a formula editing mode in a host cell into which a formula is being entered; automatically determining a current context of the formula with respect to which the selections are received; and automatically inserting a first delimiter type between references to the cells inserted into the formula in response to receiving the plurality of selections when the current context comprises a first context and automatically inserting a second delimiter type between references inserted into the formula in response to receiving the plurality of selections when the current context comprises a second context, wherein each first and second delimiter type spatially separates the references to the cells from each other within the formula by being inserted between the references to the cells.
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16. A computer program product for inserting delimiters into a formula, the computer program product being embodied in a non-transitory computer readable medium and comprising computer instructions for: successively receiving a plurality of selections of cells while in a formula editing mode in a host cell into which a formula is being entered; automatically determining a current context of the formula with respect to which the selections are received; and automatically inserting a first delimiter type between references to the cells inserted into the formula in response to receiving the plurality of selections when the current context comprises a first context and automatically inserting a second delimiter type between references inserted into the formula in response to receiving the plurality of selections when the current context comprises a second context, wherein each first and second delimiter type spatially separates the references to the cells from each other within the formula by being inserted between the references to the cells. 21. A computer program product as recited in claim 16 , wherein the first context and the second context are included in a set of contexts of formulas and wherein each context in the set is associated with a default delimiter type.
| 0.51875 |
7,668,865 | 1 | 11 |
1. A computer implemented method of formatting a document comprising: a) providing a database comprising: (i) a plurality of document element classes; (ii) a set of document section formatting rules for each document element class; and (iii) a set of document elements for each document element class; and in a computer system comprising at least one processor operatively associated with the database: b) obtaining an electronic document; c) identifying at least one of the document elements within the electronic document; d) for one or more of the document elements identified within the electronic document: (i) identifying a document section within the electronic document that is associated with the respective identified document element; (ii) classifying the respective identified document element into one of the document element classes; (iii) determining the document section formatting rules associated with the classified document element class; (iv) determining whether said document section is formatted in accordance with the document section formatting rules of the classified document element class; and (v) where the document section is not formatted in accordance with the document section formatting rules of the classified document element class, selecting one or more of the document section formatting rules of the classified document element class and applying the selected one or more document section formatting rules to the document section to re-format the document section to a re-formatted document section that accords with the selected one or more document section formatting rules; and e) displaying a re-formatted version of the electronic document including one or more of the re-formatted document sections.
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1. A computer implemented method of formatting a document comprising: a) providing a database comprising: (i) a plurality of document element classes; (ii) a set of document section formatting rules for each document element class; and (iii) a set of document elements for each document element class; and in a computer system comprising at least one processor operatively associated with the database: b) obtaining an electronic document; c) identifying at least one of the document elements within the electronic document; d) for one or more of the document elements identified within the electronic document: (i) identifying a document section within the electronic document that is associated with the respective identified document element; (ii) classifying the respective identified document element into one of the document element classes; (iii) determining the document section formatting rules associated with the classified document element class; (iv) determining whether said document section is formatted in accordance with the document section formatting rules of the classified document element class; and (v) where the document section is not formatted in accordance with the document section formatting rules of the classified document element class, selecting one or more of the document section formatting rules of the classified document element class and applying the selected one or more document section formatting rules to the document section to re-format the document section to a re-formatted document section that accords with the selected one or more document section formatting rules; and e) displaying a re-formatted version of the electronic document including one or more of the re-formatted document sections. 11. A computer implemented method according to claim 1 wherein steps (d)(i) to (d)(v) are repeated for each document element identified within the electronic document.
| 0.866613 |
6,063,133 | 8 | 12 |
8. A system for compiling source code to p-code or machine-language instructions, wherein the source code contains high level source code with embedded SQL statements, comprising: reading means to read the source code one line at a time and generate a read line of source code; determining means to identify said read line of source code as a line of high level source code or an SQL statement; and compiling means to parse said read line of source code and generate p-code or machine-language instructions, wherein if said read line of source code is an SQL statement, said compiling means occurs in a one-pass parsing mechanism.
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8. A system for compiling source code to p-code or machine-language instructions, wherein the source code contains high level source code with embedded SQL statements, comprising: reading means to read the source code one line at a time and generate a read line of source code; determining means to identify said read line of source code as a line of high level source code or an SQL statement; and compiling means to parse said read line of source code and generate p-code or machine-language instructions, wherein if said read line of source code is an SQL statement, said compiling means occurs in a one-pass parsing mechanism. 12. A system for compiling source code to p-code or machine-language instructions according to claim 8, wherein said one-pass parsing mechanism of said compiling means comprises translating said SQL statement to a plurality of API statements.
| 0.546816 |
8,078,554 | 8 | 9 |
8. In a computer readable storage medium having stored therein data representing instructions executable by a programmed processor for knowledge-based interpretable predictive modeling of patients, the instructions comprising: receiving diagram information representing relationships between variables of lung cancer, wherein the variables comprise at least two of tumor load, T-stage, N-stage, number of lymph node stations, WHO performance, and survival, the predictive model trained to predict the survival; seeding a predictive model with the diagram information; training the predictive model, as seeded with the diagram information, with training data, the data comprising values for the variables of lung cancer; and displaying a graphical representation of the predictive model after the training, the graphical representation showing at least one of the relationships.
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8. In a computer readable storage medium having stored therein data representing instructions executable by a programmed processor for knowledge-based interpretable predictive modeling of patients, the instructions comprising: receiving diagram information representing relationships between variables of lung cancer, wherein the variables comprise at least two of tumor load, T-stage, N-stage, number of lymph node stations, WHO performance, and survival, the predictive model trained to predict the survival; seeding a predictive model with the diagram information; training the predictive model, as seeded with the diagram information, with training data, the data comprising values for the variables of lung cancer; and displaying a graphical representation of the predictive model after the training, the graphical representation showing at least one of the relationships. 9. The computer readable storage medium of claim 8 further comprising instructions for: applying the predictive model after the training to patient information for a patient; and displaying a prediction output by the predictive model as a result of the applying and displaying the graphical representation.
| 0.679245 |
8,280,918 | 13 | 14 |
13. A method for generating information to facilitate suggesting related queries for search queries based on a linking structure among a plurality of electronic documents in a document set, the method comprising: identifying document titles from the plurality of electronic documents, wherein the plurality of documents are in the document set having a linking structure between the plurality of documents; identifying each document title of the plurality of electronic documents, the title of each document designated as a potential search query; identifying links among the plurality of electronic documents, wherein at least a portion of the links among the plurality of electronic documents comprise explicit links between electronic documents; and storing information regarding the document titles and the links in a data structure that identifies connections between document titles based on links between electronic documents, wherein each document title represents a designated potential related query that may be returned in response to a search query.
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13. A method for generating information to facilitate suggesting related queries for search queries based on a linking structure among a plurality of electronic documents in a document set, the method comprising: identifying document titles from the plurality of electronic documents, wherein the plurality of documents are in the document set having a linking structure between the plurality of documents; identifying each document title of the plurality of electronic documents, the title of each document designated as a potential search query; identifying links among the plurality of electronic documents, wherein at least a portion of the links among the plurality of electronic documents comprise explicit links between electronic documents; and storing information regarding the document titles and the links in a data structure that identifies connections between document titles based on links between electronic documents, wherein each document title represents a designated potential related query that may be returned in response to a search query. 14. The method of claim 13 , wherein the electronic documents comprise a high-quality document set from a trusted data source.
| 0.686567 |
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