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9,491,207 | 1 | 13 | 1. A method comprising: maintaining a profile for the user at a social networking system including a processor, the profile including one or more information items associated with data describing characteristics of the user and a set of unknown information items not associated with data; obtaining a plurality of questions associated with one or more unknown information items from the set of unknown information items at the social networking system; determining, for each of the plurality of questions associated with the one or more unknown information items, a response probability based at least in part on one or a combination of a format and content of the question, the response probability indicating a likelihood of the social networking system receiving a response to the question when presented; determining a data acquisition value for each of the one or more unknown information items in the set of unknown information items, the data acquisition value of an unknown information item based at least in part on a value to the social networking system of associating data with the unknown information item and the determined response probability; selecting an unknown information item from the one or more unknown information items by the social networking system based at least in part on the data acquisition values; and selecting, by the social networking system, a question associated with the selected unknown information item for presentation to the user based at least in part on the response probabilities of one or more questions associated with the selected unknown information item. | 1. A method comprising: maintaining a profile for the user at a social networking system including a processor, the profile including one or more information items associated with data describing characteristics of the user and a set of unknown information items not associated with data; obtaining a plurality of questions associated with one or more unknown information items from the set of unknown information items at the social networking system; determining, for each of the plurality of questions associated with the one or more unknown information items, a response probability based at least in part on one or a combination of a format and content of the question, the response probability indicating a likelihood of the social networking system receiving a response to the question when presented; determining a data acquisition value for each of the one or more unknown information items in the set of unknown information items, the data acquisition value of an unknown information item based at least in part on a value to the social networking system of associating data with the unknown information item and the determined response probability; selecting an unknown information item from the one or more unknown information items by the social networking system based at least in part on the data acquisition values; and selecting, by the social networking system, a question associated with the selected unknown information item for presentation to the user based at least in part on the response probabilities of one or more questions associated with the selected unknown information item. 13. The method of claim 1 , wherein obtaining a plurality of questions associated with one or more information items comprises retrieving a stored question. | 0.915309 |
8,782,620 | 3 | 4 | 3. The system according to claim 1 , wherein the processor appends a special suffix to the general dispatch method name to generate each special dispatch method. | 3. The system according to claim 1 , wherein the processor appends a special suffix to the general dispatch method name to generate each special dispatch method. 4. The system according to claim 3 , wherein the processor accesses a look-up table from the memory device to determine a type symbol corresponding to the primitive return type associated with each special dispatch method as part of the special suffix. | 0.837419 |
9,342,496 | 1 | 14 | 1. A method for automatically completing a remainder portion of a name as it is being entered, comprising: receiving, at processing circuitry configured to execute instructions stored on a memory, at least a prescribed number of starting characters of a name being entered into a first cell, wherein: the first cell is disposed in a document; the document includes a first table having a first table name and a second table having a second table name; the first table includes a first row or column having a first row or column name and the second table includes a second row or column having a second row or column name; the first row or column name and the second row or column name are the same; and the starting characters of the name being entered into the first cell include a string of characters found in the first row or column name and the second row or column name; using the processing circuitry to determine a set of valid reference names that include the starting characters of the name being entered into the first cell, wherein: a first valid reference name of the set of valid reference names comprises the first row or column name; a second valid reference name of the set of valid reference names comprises the second row or column name; and the first valid reference name comprises the first table name or the second valid reference name comprises the second table name; and providing with the processing circuitry the set of valid reference names as selectable auto-completion options to enable selection between the first valid reference name and the second valid reference name. | 1. A method for automatically completing a remainder portion of a name as it is being entered, comprising: receiving, at processing circuitry configured to execute instructions stored on a memory, at least a prescribed number of starting characters of a name being entered into a first cell, wherein: the first cell is disposed in a document; the document includes a first table having a first table name and a second table having a second table name; the first table includes a first row or column having a first row or column name and the second table includes a second row or column having a second row or column name; the first row or column name and the second row or column name are the same; and the starting characters of the name being entered into the first cell include a string of characters found in the first row or column name and the second row or column name; using the processing circuitry to determine a set of valid reference names that include the starting characters of the name being entered into the first cell, wherein: a first valid reference name of the set of valid reference names comprises the first row or column name; a second valid reference name of the set of valid reference names comprises the second row or column name; and the first valid reference name comprises the first table name or the second valid reference name comprises the second table name; and providing with the processing circuitry the set of valid reference names as selectable auto-completion options to enable selection between the first valid reference name and the second valid reference name. 14. A method as recited in claim 1 , comprising providing with the processing circuitry an option to turn an associated auto-completion feature on or off. | 0.9277 |
9,483,577 | 1 | 6 | 1. A computer to adapt web content for display on a small form factor device having a display screen with a width, the computer comprising: a high level structure analysis module executed on one or more processors to receive a web page and to analyze a markup language tag tree of a markup language document representing the web page to identify: peripheral regions of the web page including a header, a footer, a left, and a right regions; and body regions enclosed by the peripheral regions; a low level structure analysis module executed on the one or more processors to analyze the markup language tag tree defining the peripheral regions and the body regions to: identify visual boundaries from analyzing properties of tags of the markup language tag tree; detect patterns in leaf markup language tags to find one or more basic semantic units each having a shape; project the shape of each basic semantic unit normal to perpendicular axes; and identify the visual boundaries of the web page based on projection values for each axis. | 1. A computer to adapt web content for display on a small form factor device having a display screen with a width, the computer comprising: a high level structure analysis module executed on one or more processors to receive a web page and to analyze a markup language tag tree of a markup language document representing the web page to identify: peripheral regions of the web page including a header, a footer, a left, and a right regions; and body regions enclosed by the peripheral regions; a low level structure analysis module executed on the one or more processors to analyze the markup language tag tree defining the peripheral regions and the body regions to: identify visual boundaries from analyzing properties of tags of the markup language tag tree; detect patterns in leaf markup language tags to find one or more basic semantic units each having a shape; project the shape of each basic semantic unit normal to perpendicular axes; and identify the visual boundaries of the web page based on projection values for each axis. 6. The computer according to claim 1 , wherein the markup language document is a Hyper Text Markup Language (HTML) document. | 0.915761 |
9,542,496 | 9 | 11 | 9. A computer program product comprising a non-transitory computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to: parse a received input question having a set of question characteristics; compare the set of question characteristics found in the received input question to question characteristics associated with a set of previous questions; responsive to the set of question characteristics found in the received input question matching the question characteristics associated with one or more previous questions in the set of previous questions above a related-question predetermined threshold, identify whether answers to the one or more previous questions were obtained from static information sources or real-time information sources; and responsive to the answers to the one or more previous questions being obtained from the real-time information sources above the predetermined real-time threshold, initially utilize real-time information sources related to the characteristics of the input question to answer the input question. | 9. A computer program product comprising a non-transitory computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to: parse a received input question having a set of question characteristics; compare the set of question characteristics found in the received input question to question characteristics associated with a set of previous questions; responsive to the set of question characteristics found in the received input question matching the question characteristics associated with one or more previous questions in the set of previous questions above a related-question predetermined threshold, identify whether answers to the one or more previous questions were obtained from static information sources or real-time information sources; and responsive to the answers to the one or more previous questions being obtained from the real-time information sources above the predetermined real-time threshold, initially utilize real-time information sources related to the characteristics of the input question to answer the input question. 11. The computer program product of claim 9 , wherein the computer readable program further causes the computing device to: responsive to the answers to the one or more previous questions failing to be obtained from the real-time information sources above the predetermined real-time threshold and responsive to the answers to the one or more previous questions failing to be obtained from the static information sources above a predetermined static threshold, initially utilize both real-time information sources and static information sources related to the characteristics of the input question to answer the input question. | 0.549569 |
9,116,672 | 12 | 16 | 12. A computer system, comprising a memory and at least one processor, the memory storing software executable by the at least one processor, the software comprising: a code editor component that receives input and is operable in an edit mode to allow text editing of the input, the input comprising source code, the source code comprising instructions for mapping of values from one or more fields of a data source to one or more fields of a data destination; a compiler component that automatically executes the source code by compiling the source code into object code and sending the object code to an interface engine, causing the interface engine to receive one or more structured messages from the data source containing values for the mapping, and to extract and collect from the structured messages, for each instruction of the source code from which the object code was derived, values of the one or more fields of the data source referenced by that instruction; a display component that receives the collected field values from the interface engine and during the text editing of the input, for each instruction in the source code, displays an annotation comprising the value of any fields referenced by that instruction as collected for that instruction. | 12. A computer system, comprising a memory and at least one processor, the memory storing software executable by the at least one processor, the software comprising: a code editor component that receives input and is operable in an edit mode to allow text editing of the input, the input comprising source code, the source code comprising instructions for mapping of values from one or more fields of a data source to one or more fields of a data destination; a compiler component that automatically executes the source code by compiling the source code into object code and sending the object code to an interface engine, causing the interface engine to receive one or more structured messages from the data source containing values for the mapping, and to extract and collect from the structured messages, for each instruction of the source code from which the object code was derived, values of the one or more fields of the data source referenced by that instruction; a display component that receives the collected field values from the interface engine and during the text editing of the input, for each instruction in the source code, displays an annotation comprising the value of any fields referenced by that instruction as collected for that instruction. 16. The computer system of claim 12 , wherein the input further comprises a search phrase, and wherein the software further comprises: an autocompletion suggestion generator component that generates a list of autocompletion suggestions for the search phrase, the list comprising variable identifiers and corresponding collected values of fields whose identifiers or collected field values contain the search phrase; and an autocompletion tool component that presents the list of autocompletion suggestions to a user and enables selection of an autocompletion suggestion. | 0.544728 |
7,813,929 | 13 | 22 | 13. A computer program product in a non-transitory computer readable storage medium for transforming an input sequence of unstructured speech recognition text into output structured document text, the product comprising: program code for performing transformation modeling of a source unstructured speech recognition text to create a most likely word sequence output structured document text, the program code for performing transformation modeling including: program code for providing a probabilistic word substitution model to establish association probabilities indicative of target structured document text correlating with source unstructured speech recognition text; program code for considering a set of candidate sequences of structured document text based on the word substitution model with respect to an input sequence of unstructured speech recognition text; program code for evaluating the likelihood of candidates corresponding to the input sequence of unstructured speech recognition text; program code for determining as an output a most likely sequence of structured document text. | 13. A computer program product in a non-transitory computer readable storage medium for transforming an input sequence of unstructured speech recognition text into output structured document text, the product comprising: program code for performing transformation modeling of a source unstructured speech recognition text to create a most likely word sequence output structured document text, the program code for performing transformation modeling including: program code for providing a probabilistic word substitution model to establish association probabilities indicative of target structured document text correlating with source unstructured speech recognition text; program code for considering a set of candidate sequences of structured document text based on the word substitution model with respect to an input sequence of unstructured speech recognition text; program code for evaluating the likelihood of candidates corresponding to the input sequence of unstructured speech recognition text; program code for determining as an output a most likely sequence of structured document text. 22. A computer program product according to claim 13 , wherein the word substitution model uses a combination of speaker dependent models and speaker independent models. | 0.589806 |
8,214,736 | 36 | 61 | 36. A method for identifying a plurality of critical textual passages and test methods that influence the pagination of electronic documents such that the modification of text in said plurality of critical passages by said test methods have the effect of decreasing or increasing the page count of the document, said method comprising: determining a first plurality of existent page break locations in the document; selection of a sub-portion of the document's text; performing a test method on said sub-portion of the document's text; recalculating changes in page break positions resulting from performing said test method; determining a second plurality of updated page break locations in the document; detecting and recording a change in page break positions between said first plurality of page break locations and said second plurality of page break locations; recursively performing the steps until a desired result or a maximum number of recursions is achieved. | 36. A method for identifying a plurality of critical textual passages and test methods that influence the pagination of electronic documents such that the modification of text in said plurality of critical passages by said test methods have the effect of decreasing or increasing the page count of the document, said method comprising: determining a first plurality of existent page break locations in the document; selection of a sub-portion of the document's text; performing a test method on said sub-portion of the document's text; recalculating changes in page break positions resulting from performing said test method; determining a second plurality of updated page break locations in the document; detecting and recording a change in page break positions between said first plurality of page break locations and said second plurality of page break locations; recursively performing the steps until a desired result or a maximum number of recursions is achieved. 61. The method of claim 36 , wherein said test method includes changing a margin of a line of text containing the said sub-portion. | 0.860043 |
9,317,557 | 14 | 16 | 14. The computer system of claim 13 , wherein the schema graph illustrates primary key and foreign key relationships between graph nodes. | 14. The computer system of claim 13 , wherein the schema graph illustrates primary key and foreign key relationships between graph nodes. 16. The computer system of claim 14 , wherein the primary key-foreign key relationships are converted to edges in the schema graph. | 0.970654 |
8,990,068 | 1 | 3 | 1. A method implemented by a data processing apparatus, the method comprising: selecting a mixture of old training data and new training data, the old training data comprising an old text message for which a correct translation to a different language is known, the new training data comprising a new text message for which a correct translation to the different language is not known; sending a plurality of respective requests at different times to a client device of a user, the requests comprising (i) a respective request for the user to translate at least one of the old training data and the new training data and (ii) a respective incentive for the translation; after sending a particular request, receiving a translation from the client device for the old training data of the particular request; comparing the received translation with the correct translation for the old training data; determining an accuracy of the received translation based on the comparison; detecting collusion between the user and a second user by identifying a pre-existing relationship between the user and the second user; and updating a confidence score for the user based on the translation using item response theory to identify a deviation from a norm in user translation accuracy, the confidence score representing a likelihood that the user will provide an accurate translation of a text message to the different language at a later time. | 1. A method implemented by a data processing apparatus, the method comprising: selecting a mixture of old training data and new training data, the old training data comprising an old text message for which a correct translation to a different language is known, the new training data comprising a new text message for which a correct translation to the different language is not known; sending a plurality of respective requests at different times to a client device of a user, the requests comprising (i) a respective request for the user to translate at least one of the old training data and the new training data and (ii) a respective incentive for the translation; after sending a particular request, receiving a translation from the client device for the old training data of the particular request; comparing the received translation with the correct translation for the old training data; determining an accuracy of the received translation based on the comparison; detecting collusion between the user and a second user by identifying a pre-existing relationship between the user and the second user; and updating a confidence score for the user based on the translation using item response theory to identify a deviation from a norm in user translation accuracy, the confidence score representing a likelihood that the user will provide an accurate translation of a text message to the different language at a later time. 3. The method of claim 1 , wherein the respective incentive comprises at least one of a virtual good and a virtual currency for an online game. | 0.844227 |
9,412,092 | 15 | 17 | 15. An electronic system comprising: one or more processing devices; and one or more machine-readable media storing instructions that are executable by the one or more processing devices to perform operations comprising: receiving, from a first user, information specifying one or more attributes of a story related to a second user; obtaining content items from a social network, with the content items comprising one or more content items in the social network related to the story and related to the second user and one or more content items in the social network unrelated to the story and related to the second user; filtering the obtained content items to include only one or more of the one or more content items related to the story and related to the second user, with a content item being related to the story when the content item satisfies one or more of the one or more attributes, and with the content item being related to the second user when a node representing the content item in a social graph of the social network is connected in the social graph to another node representing the second user; and generating, based on the filtered content, data for a graphical user interface that when rendered by a device used by the first user, comprises a visual representation of the story. | 15. An electronic system comprising: one or more processing devices; and one or more machine-readable media storing instructions that are executable by the one or more processing devices to perform operations comprising: receiving, from a first user, information specifying one or more attributes of a story related to a second user; obtaining content items from a social network, with the content items comprising one or more content items in the social network related to the story and related to the second user and one or more content items in the social network unrelated to the story and related to the second user; filtering the obtained content items to include only one or more of the one or more content items related to the story and related to the second user, with a content item being related to the story when the content item satisfies one or more of the one or more attributes, and with the content item being related to the second user when a node representing the content item in a social graph of the social network is connected in the social graph to another node representing the second user; and generating, based on the filtered content, data for a graphical user interface that when rendered by a device used by the first user, comprises a visual representation of the story. 17. The electronic system of claim 15 , wherein obtaining the content items comprises: traversing the social graph of the social network to identify content items associated with the second user. | 0.502551 |
9,864,744 | 1 | 13 | 1. A method, performed by a computing device, for mining translation pairs for training in-domain machine translation engines, comprising: obtaining one or more sources of potential translation pairs comprising one or more content items, wherein the one or more sources of potential translation pairs are in an identified domain for which a machine translation engine is to be trained; generating one or more potential translation pairs from the obtained one or more sources of potential translation pairs by applying one or more automated filtering techniques to the obtained one or more sources of potential translation pairs, wherein one of the one or more automated filtering techniques applied to a selected obtained source of potential translation pairs is configured based on a type of the selected obtained source of potential translation pairs, and wherein each of the one or more potential translation pairs comprises at least two language snippets; selecting at least one actual translation pair from the generated one or more potential translation pairs, said selecting comprising: extracting characteristics from each of the two language snippets of at least one of the one or more potential translation pairs; determining that the two language snippets of the at least one of the one or more potential translation pairs are translations of each other by comparing the extracted characteristics; and training the machine translation engine using the selected at least one actual translation pair. | 1. A method, performed by a computing device, for mining translation pairs for training in-domain machine translation engines, comprising: obtaining one or more sources of potential translation pairs comprising one or more content items, wherein the one or more sources of potential translation pairs are in an identified domain for which a machine translation engine is to be trained; generating one or more potential translation pairs from the obtained one or more sources of potential translation pairs by applying one or more automated filtering techniques to the obtained one or more sources of potential translation pairs, wherein one of the one or more automated filtering techniques applied to a selected obtained source of potential translation pairs is configured based on a type of the selected obtained source of potential translation pairs, and wherein each of the one or more potential translation pairs comprises at least two language snippets; selecting at least one actual translation pair from the generated one or more potential translation pairs, said selecting comprising: extracting characteristics from each of the two language snippets of at least one of the one or more potential translation pairs; determining that the two language snippets of the at least one of the one or more potential translation pairs are translations of each other by comparing the extracted characteristics; and training the machine translation engine using the selected at least one actual translation pair. 13. The method of claim 1 , wherein the extracted characteristics comprise data to compute, between the two language snippets, two or more of: a ratio of a number words; an IBM score, maximum fertility, a number of covered words, a length of a longest sequence of covered words, a length of a longest sequence of not-covered words; a set of three top fertility values; a maximal number of consequent source words which have corresponding consequent target words; or a maximum number of consequent not-covered words. | 0.812181 |
9,304,787 | 15 | 16 | 15. The method of claim 11 , further comprising providing, on the user interface and in response to the selection of one of the regions, a second map of the selected region delineated into sub-regions, and wherein the received first response further includes a selection of one of the sub-regions. | 15. The method of claim 11 , further comprising providing, on the user interface and in response to the selection of one of the regions, a second map of the selected region delineated into sub-regions, and wherein the received first response further includes a selection of one of the sub-regions. 16. The method of claim 15 , wherein the second map of the selected region is delineated into sub-regions based on one or more languages spoken within the geographic sub-regions. | 0.854337 |
9,977,729 | 1 | 2 | 1. A method comprising: receiving a sequence of values of text elements; determining, with one or more computing devices, a score for a text element value of the sequence, where the score is related to the probability of a particular text element value equaling one or more given values, where said probability is based on sequences of text element values that are consistent with a defined format, and where determining the score with respect to the value of the particular text element in a particular sequence of text elements is related to how frequently the value of the particular text element follows same or similar sequences of the text element values that are consistent with the defined format; comparing, with the one or more computing devices, the score to a threshold; when the score is below a threshold, modifying, with the one or more computing devices, the value of the text element to form a modified sequence of text element values; processing, with the one or more computing devices, the modified sequence of text element values with a set of instructions; and testing, with the one or more computing devices, a performance characteristic of the set of instructions when the set of instructions process the modified sequence of text element values. | 1. A method comprising: receiving a sequence of values of text elements; determining, with one or more computing devices, a score for a text element value of the sequence, where the score is related to the probability of a particular text element value equaling one or more given values, where said probability is based on sequences of text element values that are consistent with a defined format, and where determining the score with respect to the value of the particular text element in a particular sequence of text elements is related to how frequently the value of the particular text element follows same or similar sequences of the text element values that are consistent with the defined format; comparing, with the one or more computing devices, the score to a threshold; when the score is below a threshold, modifying, with the one or more computing devices, the value of the text element to form a modified sequence of text element values; processing, with the one or more computing devices, the modified sequence of text element values with a set of instructions; and testing, with the one or more computing devices, a performance characteristic of the set of instructions when the set of instructions process the modified sequence of text element values. 2. The method of claim 1 , wherein determining the score comprises determining the score based on a recurrent neural network trained with the sequences of values of text elements that conform with the defined format. | 0.924211 |
9,098,541 | 1 | 5 | 1. A computer-implemented method comprising: receiving a semantic query that includes at least one of a user interest domain or a user intent; responsive to the receiving the semantic query, retrieving a set of indexed semantic user profiles resulting from a search based on the semantic query, each indexed semantic user profile associated with a particular user of a plurality of users; ranking the set of indexed semantic user profiles based at least in part on an index of each of the indexed semantic user profiles of the set; and providing the ranked set of indexed semantic user profiles to enable selection of a target user segment. | 1. A computer-implemented method comprising: receiving a semantic query that includes at least one of a user interest domain or a user intent; responsive to the receiving the semantic query, retrieving a set of indexed semantic user profiles resulting from a search based on the semantic query, each indexed semantic user profile associated with a particular user of a plurality of users; ranking the set of indexed semantic user profiles based at least in part on an index of each of the indexed semantic user profiles of the set; and providing the ranked set of indexed semantic user profiles to enable selection of a target user segment. 5. The computer-implemented method of claim 1 , wherein the ranking of the set of indexed semantic user profiles is further based on a correspondence between the semantic query and each of the set of indexed semantic user profiles. | 0.659292 |
8,812,540 | 1 | 2 | 1. A method comprising: based on first content that has been opened within a content presentation application executing on a client device, the client device automatically selecting context information to submit to a server; responsive to automatically selecting the context information for submission to the server, the client device automatically sending the context information from the client device to the server; responsive to sending the context information to the server, the client device receiving a first search result from the server; responsive to activation input activating a search interface, displaying the search interface; after receiving the activation input, and prior to receiving any user input of a query term via the activated search interface, displaying the first search result within a preview section of the search interface; subsequent to displaying the first search result in the preview section, receiving user input entering one or more query terms via the search interface, the one or more query terms including at least one term that is not found in the context information; sending the one or more query terms to the server; responsive to sending the one or more query terms to the server, the client device receiving a second search result from the server; displaying the second search result in the search interface at the client device. | 1. A method comprising: based on first content that has been opened within a content presentation application executing on a client device, the client device automatically selecting context information to submit to a server; responsive to automatically selecting the context information for submission to the server, the client device automatically sending the context information from the client device to the server; responsive to sending the context information to the server, the client device receiving a first search result from the server; responsive to activation input activating a search interface, displaying the search interface; after receiving the activation input, and prior to receiving any user input of a query term via the activated search interface, displaying the first search result within a preview section of the search interface; subsequent to displaying the first search result in the preview section, receiving user input entering one or more query terms via the search interface, the one or more query terms including at least one term that is not found in the context information; sending the one or more query terms to the server; responsive to sending the one or more query terms to the server, the client device receiving a second search result from the server; displaying the second search result in the search interface at the client device. 2. The method of claim 1 , wherein the method is performed entirely by the content presentation application. | 0.914422 |
7,770,104 | 2 | 5 | 2. A method according to claim 1 , wherein the text document is a Hypertext Markup Language (HTML) document. | 2. A method according to claim 1 , wherein the text document is a Hypertext Markup Language (HTML) document. 5. A method according to claim 2 , wherein the new text body includes HTML document text proceeding a first HTML tag in the HTML document. | 0.969948 |
7,953,679 | 8 | 9 | 8. The method of claim 1 , wherein the generating of a document index comprises: computing a similarity between each layout block in the provided document and each representative block; and generating a document index for each provided document based on the computed similarities. | 8. The method of claim 1 , wherein the generating of a document index comprises: computing a similarity between each layout block in the provided document and each representative block; and generating a document index for each provided document based on the computed similarities. 9. The method of claim 8 , wherein the generating of the document index based on the computed similarities comprises using one of the following functions: w j = ∑ i = 1 n v ij , and w j = max i = 1 ij v n where n is the number of layout blocks in the document, m is the number of representative blocks, i=1 to n, j=1 to m, v ij is a vector of values, with each value representing the computed similarity between a layout block and a representative block, and the document index is a vector comprising m elements, with each element equal to w j for j=1 to m. | 0.858624 |
8,566,347 | 1 | 9 | 1. A method for capturing ontologies in a relational database, comprising the steps of: storing to a non-transitory computer readable storage medium one or more instances as a first entity in a first table of the relational database; storing to the non-transitory computer readable storage medium one or more concepts as a second entity in a second table of the relational database; storing to the non-transitory computer readable storage medium one or more relationship definitions for defining one or more relationships between one or more entities in a third table of the relational database, each of the one or more relationship definitions being stored independent of any other entities; storing to the non-transitory computer readable storage medium one or more type entities with which each of the one or more of concepts and instances are associated in a fourth table of the relational database, each type entity defining one or more formats of entities associated therewith; storing to the non-transitory computer readable storage medium one or more cloud entities in a fifth table of the relational database, each cloud entity comprising one or more attributes with which all entities associated therewith also comprise; storing to the non-transitory computer readable storage medium one or more behavior entities in a sixth table of the relational database, each behavior entity comprising one or more behavior definitions with which all entities associated therewith also comprise; defining one or more domain entity table records of valid combinations of one or more of instance entities, concept entities, type entities and relationship entities in accordance with knowledgebase associated with one or more defined domains; wherein one or more documents of unstructured content may be referenced by one or more of the domain entity table records if a record in the domain entity table matches a portion of the knowledge representation of the unstructured content. | 1. A method for capturing ontologies in a relational database, comprising the steps of: storing to a non-transitory computer readable storage medium one or more instances as a first entity in a first table of the relational database; storing to the non-transitory computer readable storage medium one or more concepts as a second entity in a second table of the relational database; storing to the non-transitory computer readable storage medium one or more relationship definitions for defining one or more relationships between one or more entities in a third table of the relational database, each of the one or more relationship definitions being stored independent of any other entities; storing to the non-transitory computer readable storage medium one or more type entities with which each of the one or more of concepts and instances are associated in a fourth table of the relational database, each type entity defining one or more formats of entities associated therewith; storing to the non-transitory computer readable storage medium one or more cloud entities in a fifth table of the relational database, each cloud entity comprising one or more attributes with which all entities associated therewith also comprise; storing to the non-transitory computer readable storage medium one or more behavior entities in a sixth table of the relational database, each behavior entity comprising one or more behavior definitions with which all entities associated therewith also comprise; defining one or more domain entity table records of valid combinations of one or more of instance entities, concept entities, type entities and relationship entities in accordance with knowledgebase associated with one or more defined domains; wherein one or more documents of unstructured content may be referenced by one or more of the domain entity table records if a record in the domain entity table matches a portion of the knowledge representation of the unstructured content. 9. The method of claim 1 , wherein a knowledge representation of the unstructured content is obtained by querying all domain entity table records referencing the unstructured content. | 0.628049 |
8,542,096 | 2 | 3 | 2. The apparatus of claim 1 , further comprising sensitivity software in communication with said evolving database. | 2. The apparatus of claim 1 , further comprising sensitivity software in communication with said evolving database. 3. The apparatus of claim 2 , wherein said at least one computer or said at least one second computer is in communication with at least one external database. | 0.920603 |
9,535,945 | 1 | 10 | 1. A system comprising: network communications circuitry, configured to: receive a search query from a client device, over a network; communicate an entity search result to the client device over the network; search engine circuitry communicatively coupled to the network communications circuitry, the search engine circuitry comprising a processor, the processor configured to: execute the search query on an entity search database, wherein the entity search database comprises a plurality of entity circuitries, wherein individual ones of the entity circuitries comprises a single root object for a single person entity, a single place entity, or a single thing entity that is different from other individual ones of the entity circuitries; identify an entity indicator in the search query, according to the execution of the search query on the entity search database; identify the entity search result, according to the entity indicator; identify an additional query part besides the entity indicator in the search query, according to the execution of the search query on the entity search database; execute a non-entity query using the additional query part on a non-entity search database with respect to the entity search result, wherein the non-entity search database comprises multiple root objects for a single person entity, a single place entity, or a single thing entity; identify one or more non-entity search results, according to the execution of the non-entity query; alter a display of the entity search result to include the one or more non-entity search results; and emphasize the one or more non-entity search results in the entity search result. | 1. A system comprising: network communications circuitry, configured to: receive a search query from a client device, over a network; communicate an entity search result to the client device over the network; search engine circuitry communicatively coupled to the network communications circuitry, the search engine circuitry comprising a processor, the processor configured to: execute the search query on an entity search database, wherein the entity search database comprises a plurality of entity circuitries, wherein individual ones of the entity circuitries comprises a single root object for a single person entity, a single place entity, or a single thing entity that is different from other individual ones of the entity circuitries; identify an entity indicator in the search query, according to the execution of the search query on the entity search database; identify the entity search result, according to the entity indicator; identify an additional query part besides the entity indicator in the search query, according to the execution of the search query on the entity search database; execute a non-entity query using the additional query part on a non-entity search database with respect to the entity search result, wherein the non-entity search database comprises multiple root objects for a single person entity, a single place entity, or a single thing entity; identify one or more non-entity search results, according to the execution of the non-entity query; alter a display of the entity search result to include the one or more non-entity search results; and emphasize the one or more non-entity search results in the entity search result. 10. The system of claim 1 , wherein the processor is further configured to include a visual representation of the one or more non-entity search results in a most forefront part of a first GUI to appear after a user selects the entity search result on the client device. | 0.672749 |
9,336,295 | 11 | 15 | 11. A mobile device comprising: means for receiving sensor data from at least one data source associated with the mobile device, wherein the at least one data source comprises one or more sensors of the mobile device; means for determining a first context class for the received sensor data, the first context class corresponding to a context state indicated by the received sensor data; means for determining that a confidence value associated with the determination of the first context class is below a threshold value; means for creating a fusion class for the received sensor data at least in part by fusing the first context class for the received sensor data and at least a second context class for the received sensor data, the at least the second context class being different from the first context class, wherein the fusion class semantically encompasses the first context class and the at least the second context class and further wherein the fusion class is broader than each of the first context class and the at least the second context class; means for substituting the fusion class for the first context class; and means for outputting the fusion class as an inferred context for the mobile device. | 11. A mobile device comprising: means for receiving sensor data from at least one data source associated with the mobile device, wherein the at least one data source comprises one or more sensors of the mobile device; means for determining a first context class for the received sensor data, the first context class corresponding to a context state indicated by the received sensor data; means for determining that a confidence value associated with the determination of the first context class is below a threshold value; means for creating a fusion class for the received sensor data at least in part by fusing the first context class for the received sensor data and at least a second context class for the received sensor data, the at least the second context class being different from the first context class, wherein the fusion class semantically encompasses the first context class and the at least the second context class and further wherein the fusion class is broader than each of the first context class and the at least the second context class; means for substituting the fusion class for the first context class; and means for outputting the fusion class as an inferred context for the mobile device. 15. The mobile device of claim 11 wherein the means for substituting the fusion class is configured to substitute the fusion class in response to the confidence value associated with the determination of the first context class being below the threshold value. | 0.856195 |
6,131,082 | 1 | 8 | 1. A translation memory comprising: an aligned file having a number of source language text segments encoded in a computer readable format, each of the source language text segments positioned at a unique address and paired with a target language text segment encoded in the computer readable format; an inverted index comprising a listing of source language letter n-grams, wherein each listed letter n-gram includes an associated entry for an entropy weight for the listed letter n-gram, a count of the number of source language text segments in the aligned file that include an entry for the listed letter n-gram, and a pointer to a unique location in the translation memory; and a posting vector file having a posting vector associated with each listed letter n-gram in the inverted index, each posting vector positioned at one of the unique locations pointed to in the inverted index, each posting vector including: i) a plurality of document identification numbers each corresponding to a selected one of the source language text strings in the aligned file, and ii) a number of entropy weight values, each of the number of entropy weight values associated with one document identification number. | 1. A translation memory comprising: an aligned file having a number of source language text segments encoded in a computer readable format, each of the source language text segments positioned at a unique address and paired with a target language text segment encoded in the computer readable format; an inverted index comprising a listing of source language letter n-grams, wherein each listed letter n-gram includes an associated entry for an entropy weight for the listed letter n-gram, a count of the number of source language text segments in the aligned file that include an entry for the listed letter n-gram, and a pointer to a unique location in the translation memory; and a posting vector file having a posting vector associated with each listed letter n-gram in the inverted index, each posting vector positioned at one of the unique locations pointed to in the inverted index, each posting vector including: i) a plurality of document identification numbers each corresponding to a selected one of the source language text strings in the aligned file, and ii) a number of entropy weight values, each of the number of entropy weight values associated with one document identification number. 8. The translation memory of claim 1 wherein the listing of letter n-grams is provided in Unicode format. | 0.874402 |
8,725,721 | 8 | 14 | 8. A computer system for establishing personalized limits on a search responsive to a key word query to an enterprise search system, the computer system including one or more processors configured to perform operations including: receiving an object types access history for at least one=particular user; wherein the object types access history includes records of object types selected by the particular user from search results returning multiple object types and records of object types selected by the particular user via interfaces other than search results; and determining and storing in computer readable memory a personalized scope of object types by analyzing frequencies of individual object type selections in the object types access history; wherein the personalized scope limits individual object types initially returned by an enterprise search system to object types that have frequencies above a pre-determined threshold in response to key word queries by the particular user that do not specify object types to search. | 8. A computer system for establishing personalized limits on a search responsive to a key word query to an enterprise search system, the computer system including one or more processors configured to perform operations including: receiving an object types access history for at least one=particular user; wherein the object types access history includes records of object types selected by the particular user from search results returning multiple object types and records of object types selected by the particular user via interfaces other than search results; and determining and storing in computer readable memory a personalized scope of object types by analyzing frequencies of individual object type selections in the object types access history; wherein the personalized scope limits individual object types initially returned by an enterprise search system to object types that have frequencies above a pre-determined threshold in response to key word queries by the particular user that do not specify object types to search. 14. The computer system of claim 8 , wherein the processors configured to further perform operations including: determining and storing in computer readable memory a personalized ordering of object types for the at least one particular user using the object types access history; wherein the personalized ordering of object types sets an order in which to present search results from the search performed by the enterprise search system. | 0.653724 |
9,079,098 | 1 | 2 | 1. A computer implemented method of operating a wagering game system in real time, comprising the steps of: establishing a communication link using network infrastructure between a plurality of gaming devices and a central recommender processor, wherein each gaming device is operated by a player having a wagering game system registration, and wherein each gaming device is programmed to carry out game functions of receiving wagering and game instructions, determining a game outcome and determining an award; generating a gaming and play behavior model by partitioning stored play data into a plurality of game session patterns, each game session pattern corresponds to a time period and indicates occurrence of game factors over the time period; detecting, at the recommender processor, in real time or near real time that a current player begins to play a game or indicates a desire to play the game using a first gaming device of the plurality of gaming devices; collecting, by the recommender processor from the first gaming device using the communication link, at the moment of the detection that the current player begins to play the game or indicates the desire to play the game, a first set of real time game data defining occurrence of game factors for game play in an ongoing live game; determining, by the recommender processor, that the first set of real time game data has been collected for a minimum length of time of game play required to categorize game behaviour using the plurality of game session patterns; matching, by the recommender processor, the first set of real time game data to a first set of game session patterns of the plurality of game session patterns; determining, by the recommender processor, at least one game player type from among a set of predefined game player types for the current game player based on the first set of game session patterns; and transmitting from the recommender processor to the first gaming device using the communication link, a selection of games identified for the determined at least one game player type. | 1. A computer implemented method of operating a wagering game system in real time, comprising the steps of: establishing a communication link using network infrastructure between a plurality of gaming devices and a central recommender processor, wherein each gaming device is operated by a player having a wagering game system registration, and wherein each gaming device is programmed to carry out game functions of receiving wagering and game instructions, determining a game outcome and determining an award; generating a gaming and play behavior model by partitioning stored play data into a plurality of game session patterns, each game session pattern corresponds to a time period and indicates occurrence of game factors over the time period; detecting, at the recommender processor, in real time or near real time that a current player begins to play a game or indicates a desire to play the game using a first gaming device of the plurality of gaming devices; collecting, by the recommender processor from the first gaming device using the communication link, at the moment of the detection that the current player begins to play the game or indicates the desire to play the game, a first set of real time game data defining occurrence of game factors for game play in an ongoing live game; determining, by the recommender processor, that the first set of real time game data has been collected for a minimum length of time of game play required to categorize game behaviour using the plurality of game session patterns; matching, by the recommender processor, the first set of real time game data to a first set of game session patterns of the plurality of game session patterns; determining, by the recommender processor, at least one game player type from among a set of predefined game player types for the current game player based on the first set of game session patterns; and transmitting from the recommender processor to the first gaming device using the communication link, a selection of games identified for the determined at least one game player type. 2. The method of claim 1 , wherein the step of generating the gaming and play behavior model comprises: performing a cluster analysis of the plurality of game session patterns to group game session patterns into different clusters, wherein each game cluster is linked to one or more games, and wherein the method further comprises identifying a cluster of game session patterns based on the first set of game session patterns. | 0.862047 |
8,126,912 | 1 | 5 | 1. A computer-implemented method for tagging content, comprising: providing a graphical user interface (GUI) configured to receive input; providing, via the GUI, a prompt for an initial taxonomy category from among a plurality of taxonomy categories and a prompt for an initial metadata tag for the object; receiving, via the GUI, a selection of the initial taxonomy category from among the plurality of taxonomy categories, and associating the object with the initial taxonomy category; receiving, via the GUI, a selection of the initial metadata tag and associating the initial metadata tag with the object; based on the initial taxonomy category and the initial metadata tag, accessing a metadata tag knowledgebase to derive a plurality of suggested metadata tags; visually depicting, via the GUI, the plurality of suggested metadata tags, the visually depicting comprising continually updating a suggested tags area of the GUI with new metadata tags based on a prior trend of tag selections; receiving, via the GUI, a selected metadata tag from among the plurality of suggested metadata tags, and associating the selected metadata tag with the object; and updating the metadata tag knowledgebase to reflect tags associated with the object. | 1. A computer-implemented method for tagging content, comprising: providing a graphical user interface (GUI) configured to receive input; providing, via the GUI, a prompt for an initial taxonomy category from among a plurality of taxonomy categories and a prompt for an initial metadata tag for the object; receiving, via the GUI, a selection of the initial taxonomy category from among the plurality of taxonomy categories, and associating the object with the initial taxonomy category; receiving, via the GUI, a selection of the initial metadata tag and associating the initial metadata tag with the object; based on the initial taxonomy category and the initial metadata tag, accessing a metadata tag knowledgebase to derive a plurality of suggested metadata tags; visually depicting, via the GUI, the plurality of suggested metadata tags, the visually depicting comprising continually updating a suggested tags area of the GUI with new metadata tags based on a prior trend of tag selections; receiving, via the GUI, a selected metadata tag from among the plurality of suggested metadata tags, and associating the selected metadata tag with the object; and updating the metadata tag knowledgebase to reflect tags associated with the object. 5. The computer-implemented method of claim 1 , wherein the object is a document, an image file, a video file, or a URL. | 0.916201 |
9,263,048 | 17 | 18 | 17. The computer program product of claim 16 , wherein the hierarchical word lattice comprises nodes corresponding to the one or more words of the first transcription of the utterance and words of the second transcription of the utterance, and edges between the nodes that identify possible paths through the word lattice, wherein each path has an associated probability of being correct. | 17. The computer program product of claim 16 , wherein the hierarchical word lattice comprises nodes corresponding to the one or more words of the first transcription of the utterance and words of the second transcription of the utterance, and edges between the nodes that identify possible paths through the word lattice, wherein each path has an associated probability of being correct. 18. The computer program product of claim 17 , wherein providing the one or more alternate words for the selected word comprises: identifying one or more alternate words for the selected word for which an edge exists to the other words of the first transcription in the hierarchical word lattice; and providing the one or more alternate words for the selected word for which an edge exists to the other words of the first transcription as the one or more alternate words for the selected word, without providing other words from the hierarchical word lattice for which an edge does not exist to the other words of the first transcription as alternate words for the selected word. | 0.813769 |
8,255,572 | 1 | 2 | 1. A computing system implemented process for identifying 419 messages in a live message stream comprising: subjecting a message from a live message stream directed to a user computing system to an anti-spam pipeline, the anti-spam pipeline including: a dynamic feedback-based heuristic filter stage, the dynamic feedback-based heuristic filter stage using one or more processors associated with one or more computing systems to analyze the message using heuristics utilizing one or more feedback-based potential 419 message parameters obtained from actual 419 messages identified by historical applications of the process for identifying 419 messages in a live message stream; if, as a result of the analysis of the message by the dynamic feedback-based heuristic filter stage, the message is determined to be a potential 419 message, removing the message from further processing by the computing system implemented process for identifying 419 messages in a live message stream; if, as a result of the analysis of the message by the dynamic feedback-based heuristic filter stage, the message is not determined to be a potential 419 message, transferring the message to a 419 text-based heuristic filter stage, the 419 text-based heuristic filter stage using one or more processors associated with one or more computing systems to analyze the message using heuristics utilizing one or more text based 419 identification parameters; if, as a result of the analysis of the message by the 419 text-based heuristic filter stage, the message is determined to be a potential 419 message, removing the message from further processing by the computing system implemented process for identifying 419 messages in a live message stream; if, as a result of the analysis of the message by 419 text-based heuristic filter stage, the message is not determined to be a potential 419 message, transferring the message to one or more metadata creating heuristic filter stages, the one or more metadata creating heuristic filter stages using one or more processors associated with one or more computing systems to analyze the message and generate a metadata set including one or more metadata entries associated with the message; transferring the message and the metadata set including one or more metadata entries associated with the message to a metadata analysis stage, the metadata analysis stage using one or more processors associated with one or more computing systems to analyze the metadata set including one or more metadata entries associated with the message using heuristics utilizing one or more metadata-based 419 message identification parameters; if, as a result of the analysis of the message by the metadata analysis stage, the message is not determined to be a potential 419 message, removing the message from further processing by the computing system implemented process for identifying 419 messages in a live message stream; if, as a result of the analysis of the message by the metadata analysis stage, the message is determined to be a potential 419 message, using one or more processors associated with one or more computing systems to transform data indicating a status of the message determined to be a potential 419 message to data indicating the message is a potential 419 message; using one or more processors associated with one or more computing systems to analyze the potential 419 message to identify one or more potential 419 message parameters associated with the message; using one or more processors associated with one or more computing systems to transform data representing the one or more potential 419 message parameters associated with the message to data representing one or more feedback-based 419 message parameters; and using one or more processors associated with one or more computing systems to transfer the data representing one or more feedback-based 419 message parameters to the dynamic feedback-based heuristic filter stage of the anti-spam pipeline for use with one or more heuristics. | 1. A computing system implemented process for identifying 419 messages in a live message stream comprising: subjecting a message from a live message stream directed to a user computing system to an anti-spam pipeline, the anti-spam pipeline including: a dynamic feedback-based heuristic filter stage, the dynamic feedback-based heuristic filter stage using one or more processors associated with one or more computing systems to analyze the message using heuristics utilizing one or more feedback-based potential 419 message parameters obtained from actual 419 messages identified by historical applications of the process for identifying 419 messages in a live message stream; if, as a result of the analysis of the message by the dynamic feedback-based heuristic filter stage, the message is determined to be a potential 419 message, removing the message from further processing by the computing system implemented process for identifying 419 messages in a live message stream; if, as a result of the analysis of the message by the dynamic feedback-based heuristic filter stage, the message is not determined to be a potential 419 message, transferring the message to a 419 text-based heuristic filter stage, the 419 text-based heuristic filter stage using one or more processors associated with one or more computing systems to analyze the message using heuristics utilizing one or more text based 419 identification parameters; if, as a result of the analysis of the message by the 419 text-based heuristic filter stage, the message is determined to be a potential 419 message, removing the message from further processing by the computing system implemented process for identifying 419 messages in a live message stream; if, as a result of the analysis of the message by 419 text-based heuristic filter stage, the message is not determined to be a potential 419 message, transferring the message to one or more metadata creating heuristic filter stages, the one or more metadata creating heuristic filter stages using one or more processors associated with one or more computing systems to analyze the message and generate a metadata set including one or more metadata entries associated with the message; transferring the message and the metadata set including one or more metadata entries associated with the message to a metadata analysis stage, the metadata analysis stage using one or more processors associated with one or more computing systems to analyze the metadata set including one or more metadata entries associated with the message using heuristics utilizing one or more metadata-based 419 message identification parameters; if, as a result of the analysis of the message by the metadata analysis stage, the message is not determined to be a potential 419 message, removing the message from further processing by the computing system implemented process for identifying 419 messages in a live message stream; if, as a result of the analysis of the message by the metadata analysis stage, the message is determined to be a potential 419 message, using one or more processors associated with one or more computing systems to transform data indicating a status of the message determined to be a potential 419 message to data indicating the message is a potential 419 message; using one or more processors associated with one or more computing systems to analyze the potential 419 message to identify one or more potential 419 message parameters associated with the message; using one or more processors associated with one or more computing systems to transform data representing the one or more potential 419 message parameters associated with the message to data representing one or more feedback-based 419 message parameters; and using one or more processors associated with one or more computing systems to transfer the data representing one or more feedback-based 419 message parameters to the dynamic feedback-based heuristic filter stage of the anti-spam pipeline for use with one or more heuristics. 2. The computing system implemented process for identifying 419 messages in a live message stream of claim 1 , wherein: the message is an e-mail. | 0.944656 |
8,478,702 | 5 | 6 | 5. The method of claim 4 , wherein the set of differences comprises a first set of differences, the method comprising: obtaining a third set of information atoms; computing a fourth set of relationships between the second and third sets of information atoms; predicting a second set of differences related to the descriptor, based on the third and fourth sets of relationships; and outputting a difference from the second set of differences. | 5. The method of claim 4 , wherein the set of differences comprises a first set of differences, the method comprising: obtaining a third set of information atoms; computing a fourth set of relationships between the second and third sets of information atoms; predicting a second set of differences related to the descriptor, based on the third and fourth sets of relationships; and outputting a difference from the second set of differences. 6. The method of claim 5 , comprising: computing a fifth set of relationships between the first set of differences and the second set of differences; predicting a fourth set of information atoms related to the descriptor, based on the fifth set of relationships; and outputting an information atom from the fourth set of information atoms. | 0.804498 |
9,384,175 | 10 | 11 | 10. An apparatus comprising: a non-transitory machine-readable storage medium to store a first electronic document and a second electronic document; and a document comparison component to: determine a mode type for the first and second electronic documents, the mode type being one of single-page mode or continuous mode, wherein determining a mode type includes: determining a type of application that created the first and second electronic documents; and determining if there is a repeated pattern for one or more data or attributes for a number of pages of the first and second electronic documents; determine a difference between the first electronic document and the second electronic document according to the determined mode type based on an operation to match of a component of the first electronic document to a component of the second electronic document in a top-down hierarchical order. | 10. An apparatus comprising: a non-transitory machine-readable storage medium to store a first electronic document and a second electronic document; and a document comparison component to: determine a mode type for the first and second electronic documents, the mode type being one of single-page mode or continuous mode, wherein determining a mode type includes: determining a type of application that created the first and second electronic documents; and determining if there is a repeated pattern for one or more data or attributes for a number of pages of the first and second electronic documents; determine a difference between the first electronic document and the second electronic document according to the determined mode type based on an operation to match of a component of the first electronic document to a component of the second electronic document in a top-down hierarchical order. 11. The apparatus of claim 10 , wherein the component of the first electronic document and the component of the second electronic document are of a same type. | 0.791005 |
9,009,296 | 6 | 8 | 6. A method for providing content to a network browser based on a reported latency comprising: storing, in a memory, a first item of content for a web page and a second item of content for the web page; receiving, with a processor, a request for the web page from a network browser; transmitting the web page, the first item of content, and the second item of content to the network browser; receiving a latency measurement for the previously transmitted web page and the second item of content, the latency measurement representing a difference between a first time when the first item of content began executing and a second time when a predefined event occurred associated with the second item of content; and modifying data associated with the second item of content for the web page with a different form of the data to account for the received latency measurement. | 6. A method for providing content to a network browser based on a reported latency comprising: storing, in a memory, a first item of content for a web page and a second item of content for the web page; receiving, with a processor, a request for the web page from a network browser; transmitting the web page, the first item of content, and the second item of content to the network browser; receiving a latency measurement for the previously transmitted web page and the second item of content, the latency measurement representing a difference between a first time when the first item of content began executing and a second time when a predefined event occurred associated with the second item of content; and modifying data associated with the second item of content for the web page with a different form of the data to account for the received latency measurement. 8. The method of claim 6 , wherein the second item of content is of a first complexity, and the method further comprises: storing a third item of content of a second complexity that is less complex than the first complexity; and modifying the second item of content by replacing the second item of content with the third item of content. | 0.554233 |
10,147,050 | 1 | 5 | 1. A method to provide selective supporting evidence in a question answering (QA) system, comprising: initiating processing of a first question using an execution pipeline of the QA system, wherein the execution pipeline comprises (i) a first stage configured to determine candidate answers to the first question without consideration of any items of supporting evidence and (ii) a second stage, downstream from the first stage, configured to process items of supporting evidence relating to the candidate answers in order to generate updated confidence scores; upon determining a first candidate answer for a first question during the first stage of the execution pipeline and prior to executing the second stage of the execution pipeline: generating, by the QA system, using a first machine learning (ML) model, a first confidence score value for the first candidate answer, wherein the first confidence score value reflects a degree to which the first candidate answer is a correct response to the first question, wherein the first ML model does not consider supporting evidence features for the first candidate answer; generating, by the QA system, using a second ML model, a measure of expected change to the first confidence score value based at least in part on supporting evidence features for the first candidate answer; and upon determining that the measure of expected change does not exceed a first threshold, returning, by the QA system, at least the first candidate answer in response to the first question, without processing the first question using the second stage of the execution pipeline. | 1. A method to provide selective supporting evidence in a question answering (QA) system, comprising: initiating processing of a first question using an execution pipeline of the QA system, wherein the execution pipeline comprises (i) a first stage configured to determine candidate answers to the first question without consideration of any items of supporting evidence and (ii) a second stage, downstream from the first stage, configured to process items of supporting evidence relating to the candidate answers in order to generate updated confidence scores; upon determining a first candidate answer for a first question during the first stage of the execution pipeline and prior to executing the second stage of the execution pipeline: generating, by the QA system, using a first machine learning (ML) model, a first confidence score value for the first candidate answer, wherein the first confidence score value reflects a degree to which the first candidate answer is a correct response to the first question, wherein the first ML model does not consider supporting evidence features for the first candidate answer; generating, by the QA system, using a second ML model, a measure of expected change to the first confidence score value based at least in part on supporting evidence features for the first candidate answer; and upon determining that the measure of expected change does not exceed a first threshold, returning, by the QA system, at least the first candidate answer in response to the first question, without processing the first question using the second stage of the execution pipeline. 5. The method of claim 1 , further comprising: generating, by the QA system, using the first machine learning (ML) model, a second confidence score value for a second candidate answer; generating, by the QA system, using the second ML model, a second measure of expected change to the first confidence score value based at least in part on supporting evidence features for the second candidate answer; determining that the second measure of expected change exceeds a second threshold; processing items of supporting evidence for the second candidate answer; scoring the second candidate answer; and ranking the second candidate answer relative to a set of other candidate answers based on a respective score for each candidate answer. | 0.50068 |
9,563,667 | 2 | 3 | 2. The method of claim 1 further comprising, after the determining that the reference to the first color is intended to identify any actual color: determining that a respective second color associated with a particular product of the plurality of products is identical or similar to the first color; increasing a ranking score associated with the particular product; and generating the updated ranking of the plurality of products based on the increased ranking score associated with the particular product. | 2. The method of claim 1 further comprising, after the determining that the reference to the first color is intended to identify any actual color: determining that a respective second color associated with a particular product of the plurality of products is identical or similar to the first color; increasing a ranking score associated with the particular product; and generating the updated ranking of the plurality of products based on the increased ranking score associated with the particular product. 3. The method of claim 2 wherein the determining, by the one or more processors of the server, whether the reference to the first color is intended to identify any actual color, comprises: parsing the initial search query as a query vector; deriving a cosine similarity measurement of a cosine of an angle between the query vector and a title vector of an identified title; and determining that the first color is intended to identify the actual color when the cosine similarity measurement is less than a threshold value. | 0.910555 |
8,966,456 | 14 | 26 | 14. A computer-readable storage medium storing instructions, the instructions comprising: one or more instructions which, when executed by a processor, cause the processor to: define a meta-data class having meta-data used to describe a class and constituent components of the class, the meta-data class having a property; create a compiled version of the class, the compiled version of the class being embedded with the meta-data class property; create an instance of the class; receive, from an entity, a request to access the instance of the class; access, based on receiving the request, the property of the meta-data class embedded in the compiled version of the class to determine how to access data associated with the instance of the class; receive, based on accessing the property of the meta-data class, information regarding how to access the data associated with the instance of the class, the meta-data providing the information, the meta-data providing a value to the entity, and the value being used to identify the instance of the class; and access, based on the information regarding how to access the data associated with the instance of the class, the data associated with the instance of the class. | 14. A computer-readable storage medium storing instructions, the instructions comprising: one or more instructions which, when executed by a processor, cause the processor to: define a meta-data class having meta-data used to describe a class and constituent components of the class, the meta-data class having a property; create a compiled version of the class, the compiled version of the class being embedded with the meta-data class property; create an instance of the class; receive, from an entity, a request to access the instance of the class; access, based on receiving the request, the property of the meta-data class embedded in the compiled version of the class to determine how to access data associated with the instance of the class; receive, based on accessing the property of the meta-data class, information regarding how to access the data associated with the instance of the class, the meta-data providing the information, the meta-data providing a value to the entity, and the value being used to identify the instance of the class; and access, based on the information regarding how to access the data associated with the instance of the class, the data associated with the instance of the class. 26. The storage medium of claim 14 , where the class is defined by a class definition that includes a specification of constituent methods, each specified constituent method, of the constituent methods, corresponds to an instance of a meta-method class, the specification of the constituent methods includes a specification of an attribute and a value of the attribute, the attribute is represented by a property of the meta-method class, and the specification of the constituent methods includes a specification of a particular meta-method class. | 0.677856 |
8,250,074 | 7 | 8 | 7. The document processing method of claim 1 , wherein the Step (e) further comprises the steps of: (e1) giving a first weight and a second weight to the preliminary key term and the advanced key term respectively; (e2) calculating a score of the preliminary key terms sequence containing the preliminary key term according to the first weight, and then calculating a score of a sentence containing the preliminary key terms sequence according to the score of the preliminary key terms sequence, and selecting a plurality of sentences according to the calculated score of each sentence; and (e3) combining at least one of the selected sentences into the core abstract. | 7. The document processing method of claim 1 , wherein the Step (e) further comprises the steps of: (e1) giving a first weight and a second weight to the preliminary key term and the advanced key term respectively; (e2) calculating a score of the preliminary key terms sequence containing the preliminary key term according to the first weight, and then calculating a score of a sentence containing the preliminary key terms sequence according to the score of the preliminary key terms sequence, and selecting a plurality of sentences according to the calculated score of each sentence; and (e3) combining at least one of the selected sentences into the core abstract. 8. The document processing method of claim 7 , further comprising the steps of: (e21) calculating a proportion of the preliminary key term in each the sentence; and (e31) picking the preliminary key term with a lower proportion in the sentence as the core candidate sentence to form the core abstract. | 0.939534 |
9,355,174 | 1 | 5 | 1. A computer-implemented process, comprising: identifying, by a computer server system, a base topic of a personalized media stream; identifying, with the computer server system, a first media item associated with the base topic based on first data from a first source, wherein the first data is assigned a first weighting of the first source, and wherein the first weighting of the first source is based on a first plurality of factors, wherein the first plurality of factors comprises an identified level of familiarity associated with the base topic and an identified level of popularity in another personalized media stream; identifying, with the computer server system, a second media item associated with the base topic based on second data from a second source, wherein the second data is assigned a second weighting of the second source, and wherein the second weighting of the second source is based on a second plurality of factors; building a collection of candidate media items that includes the first and second media items; ordering the collection of candidate media items to form the personalized media stream, wherein ordering the collection includes ordering the first media item within the collection based on the first weighting and ordering the second media item within the collection based on the second weighting; and communicating the personalized media stream to a client device for playback. | 1. A computer-implemented process, comprising: identifying, by a computer server system, a base topic of a personalized media stream; identifying, with the computer server system, a first media item associated with the base topic based on first data from a first source, wherein the first data is assigned a first weighting of the first source, and wherein the first weighting of the first source is based on a first plurality of factors, wherein the first plurality of factors comprises an identified level of familiarity associated with the base topic and an identified level of popularity in another personalized media stream; identifying, with the computer server system, a second media item associated with the base topic based on second data from a second source, wherein the second data is assigned a second weighting of the second source, and wherein the second weighting of the second source is based on a second plurality of factors; building a collection of candidate media items that includes the first and second media items; ordering the collection of candidate media items to form the personalized media stream, wherein ordering the collection includes ordering the first media item within the collection based on the first weighting and ordering the second media item within the collection based on the second weighting; and communicating the personalized media stream to a client device for playback. 5. The process of claim 1 , further comprising identifying that the collection of candidate media items is deficient and applying a media selection override to identify a second plurality of candidate media items. | 0.81446 |
7,987,169 | 45 | 46 | 45. The method of claim 42 , wherein the plurality of recursively embedded sub-expressions comprise first and second sub-expressions specifying first and second matching criteria for first and second content pages respectively, and the search expression further comprises directive directing return or non-return of the first or second matching content page. | 45. The method of claim 42 , wherein the plurality of recursively embedded sub-expressions comprise first and second sub-expressions specifying first and second matching criteria for first and second content pages respectively, and the search expression further comprises directive directing return or non-return of the first or second matching content page. 46. The method of claim 45 wherein the search expression comprises an explicit directive directing return of the first content page when the first content is also associated with the second content page. | 0.956494 |
8,990,074 | 1 | 18 | 1. A method of noise-robust speech classification, comprising: inputting classification parameters to a speech classifier from external components; generating, in the speech classifier, internal classification parameters from at least one of the input classification parameters; setting a Normalized Auto-correlation Coefficient Function threshold, wherein setting the Normalized Auto-correlation Coefficient Function threshold comprises: increasing a first voicing threshold for classifying a current frame as unvoiced when a signal-to-noise ratio (SNR) fails to exceed a first SNR threshold, wherein the first voicing threshold is not adjusted if the SNR is above the first SNR threshold, and increasing an energy threshold for classifying the current frame as unvoiced when the noise estimate exceeds a noise estimate threshold, wherein the energy threshold is not adjusted if the noise estimate is below the noise estimate threshold; and determining a speech mode classification based on a the first voicing threshold and the energy threshold. | 1. A method of noise-robust speech classification, comprising: inputting classification parameters to a speech classifier from external components; generating, in the speech classifier, internal classification parameters from at least one of the input classification parameters; setting a Normalized Auto-correlation Coefficient Function threshold, wherein setting the Normalized Auto-correlation Coefficient Function threshold comprises: increasing a first voicing threshold for classifying a current frame as unvoiced when a signal-to-noise ratio (SNR) fails to exceed a first SNR threshold, wherein the first voicing threshold is not adjusted if the SNR is above the first SNR threshold, and increasing an energy threshold for classifying the current frame as unvoiced when the noise estimate exceeds a noise estimate threshold, wherein the energy threshold is not adjusted if the noise estimate is below the noise estimate threshold; and determining a speech mode classification based on a the first voicing threshold and the energy threshold. 18. The method of claim 1 , wherein the setting a Normalized Auto-correlation Coefficient Function threshold comprises comparing the noise estimate to a pre-determined Signal to a noise estimate threshold. | 0.848597 |
8,660,973 | 1 | 10 | 1. A system for knowledge representation and application development comprising: a processor; a memory unit coupled to the processor; a plurality of information units stored in the memory units, the information units including: an information unit element or set of information unit elements that reference at least one separate information unit, either directly or indirectly; an operator that defines one of a plurality of actions the information unit performs when it is shocked or activated; and at least one path that describes a relationship that exists between the information unit and a separate information unit; and a dynamic engine module executable by the processor for shocking or activating the operator of an information unit and causing further shocks, or activations, to flow to separate information units to which the shocked information unit is connected via a path, and wherein the information units and the paths embody a knowledge representation schema, the information unit model, whereby an instantiation or instance of the information unit model refers to a particular collection and assembly of model elements to correspond to at least one of a domain, a set of observations, actual knowledge and data. | 1. A system for knowledge representation and application development comprising: a processor; a memory unit coupled to the processor; a plurality of information units stored in the memory units, the information units including: an information unit element or set of information unit elements that reference at least one separate information unit, either directly or indirectly; an operator that defines one of a plurality of actions the information unit performs when it is shocked or activated; and at least one path that describes a relationship that exists between the information unit and a separate information unit; and a dynamic engine module executable by the processor for shocking or activating the operator of an information unit and causing further shocks, or activations, to flow to separate information units to which the shocked information unit is connected via a path, and wherein the information units and the paths embody a knowledge representation schema, the information unit model, whereby an instantiation or instance of the information unit model refers to a particular collection and assembly of model elements to correspond to at least one of a domain, a set of observations, actual knowledge and data. 10. The system of claim 1 , wherein the shock, or activation, of the operator enables the selective initiation of a flow of a dynamic property, or energy, through a predetermined set of information units that are connected by the paths. | 0.619355 |
8,205,152 | 1 | 8 | 1. A method for displaying a markup language document on a display screen of an electronic terminal, the markup language document including a frameset whose contents are arranged in a plurality of frames, the method comprising: reading by a processor, instructions in the markup language document; calculating by a processor, pixel dimensions for each frame in the markup language document based on the instructions, wherein the pixel dimensions are calculated in such a manner to allow all of the frames to fit within the pixel dimensions of the display screen; determining by a processor, whether the contents of each frame will fit within the pixel dimensions for that frame; adjusting by a processor, each frame in which the contents do not fit within the pixel dimensions for that frame by expanding at least one of the pixel dimensions for that frame; and displaying the frameset on the display screen based on the adjusted pixel dimensions, wherein the adjusting step is performed in such a manner that no more than one scrollbar is required for viewing the contents of all of the frames on the display screen. | 1. A method for displaying a markup language document on a display screen of an electronic terminal, the markup language document including a frameset whose contents are arranged in a plurality of frames, the method comprising: reading by a processor, instructions in the markup language document; calculating by a processor, pixel dimensions for each frame in the markup language document based on the instructions, wherein the pixel dimensions are calculated in such a manner to allow all of the frames to fit within the pixel dimensions of the display screen; determining by a processor, whether the contents of each frame will fit within the pixel dimensions for that frame; adjusting by a processor, each frame in which the contents do not fit within the pixel dimensions for that frame by expanding at least one of the pixel dimensions for that frame; and displaying the frameset on the display screen based on the adjusted pixel dimensions, wherein the adjusting step is performed in such a manner that no more than one scrollbar is required for viewing the contents of all of the frames on the display screen. 8. The method according to claim 1 , further comprising: reformatting the contents of a particular frame in the frameset, wherein the adjusting step is performed on the particular frame if the reformatted contents are determined not to fit within the pixel dimensions of the particular frame. | 0.857838 |
7,904,298 | 1 | 16 | 1. A method of multi-modal text prediction, comprising: (a) receiving a speech waveform corresponding to text to be recognized; (b) receiving at least one letter corresponding to a portion of the text, the at least one letter being received from an unambiguous data source; (c) dynamically determining a search network based on the at least one letter, the search network including units of sound based on the at least one letter; (d) applying speech recognition techniques that pattern-match the speech waveform against the search network to generate a list of matching text choices; (e) re-ordering the list of matching text choices using a statistical language model, the statistical language model using usage statistics; (f) providing the re-ordered list to a user interface for a determination whether one of the words in the list is the text to be recognized; and (g) reiterating steps (b) to (f) until one of the words in the list is the text to be recognized. | 1. A method of multi-modal text prediction, comprising: (a) receiving a speech waveform corresponding to text to be recognized; (b) receiving at least one letter corresponding to a portion of the text, the at least one letter being received from an unambiguous data source; (c) dynamically determining a search network based on the at least one letter, the search network including units of sound based on the at least one letter; (d) applying speech recognition techniques that pattern-match the speech waveform against the search network to generate a list of matching text choices; (e) re-ordering the list of matching text choices using a statistical language model, the statistical language model using usage statistics; (f) providing the re-ordered list to a user interface for a determination whether one of the words in the list is the text to be recognized; and (g) reiterating steps (b) to (f) until one of the words in the list is the text to be recognized. 16. The method recited in claim 1 , wherein the usage statistics is associated with a specific user. | 0.728261 |
8,249,351 | 11 | 12 | 11. The logical structure model creation assistance device according to claim 10 , wherein, the logical structure of the logical structure model stored in the logical structure model has a hierarchical structure, the logical element selection unit assigns the order of priority and selects several logical elements among the plurality of logical elements, according to the degrees of similarity between the extracted character strings in the input image, and the character strings associated respectively with the plurality of logical elements stored in the logical structure model, letting the logical element with the highest priority to be a reference logical element, and further comprising, a logical structure similarity estimation unit to determine, for the several logical elements selected by the logical element selection unit, according to the priority, the degrees of similarity between the character strings associated with a logical element at an upper level, a logical element at a lower level or a logical element at the same level in the hierarchical structure of the reference logical element and the character strings associated with the plurality of extracted logical elements, and estimates, based on the result of the determination, the degree of similarity between the logical structure of the logical structure model and the logical structure among the extracted character strings in the input image. | 11. The logical structure model creation assistance device according to claim 10 , wherein, the logical structure of the logical structure model stored in the logical structure model has a hierarchical structure, the logical element selection unit assigns the order of priority and selects several logical elements among the plurality of logical elements, according to the degrees of similarity between the extracted character strings in the input image, and the character strings associated respectively with the plurality of logical elements stored in the logical structure model, letting the logical element with the highest priority to be a reference logical element, and further comprising, a logical structure similarity estimation unit to determine, for the several logical elements selected by the logical element selection unit, according to the priority, the degrees of similarity between the character strings associated with a logical element at an upper level, a logical element at a lower level or a logical element at the same level in the hierarchical structure of the reference logical element and the character strings associated with the plurality of extracted logical elements, and estimates, based on the result of the determination, the degree of similarity between the logical structure of the logical structure model and the logical structure among the extracted character strings in the input image. 12. The logical structure model creation assistance device according to claim 11 , wherein, the character string extraction unit extracts character strings associated with the logical elements included in the logical structure of the logical structure model for which the degree of similarity to the logical structure among the extracted character strings in the input image has been estimated, and character strings in the input image associated with the logical elements based on the logical structure among the extracted character strings. | 0.836056 |
8,938,757 | 19 | 21 | 19. A non-transitory machine readable medium encoded with machine-readable instructions for presenting program information, the machine-readable instructions comprising: retrieving program data; and generating, for display, the retrieved program data in a format including buttons, wherein the buttons represent program listings, and wherein at least one button is selectable and comprises a plurality of graphical logos, a first of the plurality of graphical logos representative of an associated program, and a second of the plurality of graphical logos representative of a provider of the associated program. | 19. A non-transitory machine readable medium encoded with machine-readable instructions for presenting program information, the machine-readable instructions comprising: retrieving program data; and generating, for display, the retrieved program data in a format including buttons, wherein the buttons represent program listings, and wherein at least one button is selectable and comprises a plurality of graphical logos, a first of the plurality of graphical logos representative of an associated program, and a second of the plurality of graphical logos representative of a provider of the associated program. 21. The non-transitory machine readable medium encoded with machine-readable instructions of claim 19 , wherein at least one button comprises a graphically displayed television program listing. | 0.57489 |
9,921,853 | 1 | 3 | 1. A computer-implemented method, comprising: at a first time, identifying a first event on a computing device; in response to identifying the first event on the computing device, storing a first state of the computing device including storing a log of the first event; at a second time after the first time, identifying a second event on the computing device; determining, from the log of the first event, that the second event is similar to the first event; in response to determining that the second event is similar to the first event, providing for display on the computing device, a list of one or more operating system objects associated with the first state of the computing device; receiving a user selection of one of the one or more operating system objects associated with the first state of the computing device; and causing the one of the one or more operating system objects associated with the first state of the computing device to be instantiated on the computing device. | 1. A computer-implemented method, comprising: at a first time, identifying a first event on a computing device; in response to identifying the first event on the computing device, storing a first state of the computing device including storing a log of the first event; at a second time after the first time, identifying a second event on the computing device; determining, from the log of the first event, that the second event is similar to the first event; in response to determining that the second event is similar to the first event, providing for display on the computing device, a list of one or more operating system objects associated with the first state of the computing device; receiving a user selection of one of the one or more operating system objects associated with the first state of the computing device; and causing the one of the one or more operating system objects associated with the first state of the computing device to be instantiated on the computing device. 3. The computer-implemented method of claim 1 , wherein the second event comprises instantiating a particular operating system object associated with the first event. | 0.755882 |
9,015,653 | 2 | 4 | 2. A method for implementing executable software in one or more display equipment systems according to claim 1 , wherein the executable software, the graphical functions and their functionalities conform to an ARINC 661 aeronautical standard. | 2. A method for implementing executable software in one or more display equipment systems according to claim 1 , wherein the executable software, the graphical functions and their functionalities conform to an ARINC 661 aeronautical standard. 4. A method for implementing executable software in one or more display equipment systems according to claim 2 , wherein the aeronautical software application development workbench further includes means for generating human machine interface software handling the following functions: translating the structured functional description language into a programming language of C or C++ or ADA type; activating flow control in code generated by using activation “input plugs” controlling application of ARINC 661 commands; and activating flow control in code generated by controlling modification of the “input plugs” associated with the modifiable attributes. | 0.785528 |
8,422,787 | 21 | 25 | 21. The computer-readable recording medium storing the program according to claim 20 , wherein the parameter estimation processing estimates a parameter used by the change point detection topic segmentation processing to segment the segmentation target interval, using the result of segmentation by the model based topic segmentation processing for a range of the segmentation target interval plus a preset range extended from the segmentation target interval of the text by the change point detection topic segmentation processing as training data. | 21. The computer-readable recording medium storing the program according to claim 20 , wherein the parameter estimation processing estimates a parameter used by the change point detection topic segmentation processing to segment the segmentation target interval, using the result of segmentation by the model based topic segmentation processing for a range of the segmentation target interval plus a preset range extended from the segmentation target interval of the text by the change point detection topic segmentation processing as training data. 25. The computer-readable recording medium storing the program according to claim 21 , wherein the model based topic segmentation processing computes the segmentation confidence by a likelihood of the topic model or by an entropy of an a posteriori probability of the topic model for at least one interval obtained on segmentation of the text in association with a topic. | 0.878201 |
8,312,042 | 1 | 2 | 1. A non-transitory computer-readable medium encoded with a computer program comprising instructions that, when executed, operate to cause a computer to perform operations comprising: receiving, by a mobile device, a voice input; encoding, by the mobile device, the voice input in an audio signal; identifying, by the mobile device, information either identifying a current location of the mobile device or usable to determine a current location of the mobile device; submitting, by the mobile device, the audio signal and the information to a search engine, as part of a voice query; receiving, by the mobile device and from the search engine, a search engine results page that identifies multiple search results in response to the voice query; identifying, by the mobile device, a telephone number associated with a particular one of the search results identified on the search engine results page; and automatically placing, by the mobile device in response to a determination that the mobile device is to automatically place a telephone call, said telephone call using the telephone number, without requiring a user of the mobile device to select the particular search result from among the multiple search results identified on the search engine results page. | 1. A non-transitory computer-readable medium encoded with a computer program comprising instructions that, when executed, operate to cause a computer to perform operations comprising: receiving, by a mobile device, a voice input; encoding, by the mobile device, the voice input in an audio signal; identifying, by the mobile device, information either identifying a current location of the mobile device or usable to determine a current location of the mobile device; submitting, by the mobile device, the audio signal and the information to a search engine, as part of a voice query; receiving, by the mobile device and from the search engine, a search engine results page that identifies multiple search results in response to the voice query; identifying, by the mobile device, a telephone number associated with a particular one of the search results identified on the search engine results page; and automatically placing, by the mobile device in response to a determination that the mobile device is to automatically place a telephone call, said telephone call using the telephone number, without requiring a user of the mobile device to select the particular search result from among the multiple search results identified on the search engine results page. 2. The medium of claim 1 , comprising displaying on the search engine results page a notification indicating that the telephone number is to be dialed unless a cancel control on the search engine results page is selected. | 0.80614 |
9,367,742 | 1 | 8 | 1. An object monitoring apparatus, comprising: an image receiver configured to receive at least one frame of captured images; an edge image generator configured to generate an edge image by detecting edges of objects appearing in the frame; a reference image generator configured to generate a reference image by detecting a part corresponding to a background in the frame to thereby define the detected part as a background edge, the reference image generated by repeatedly detecting as many as or more than a predetermined number an edge commonly appearing over a plurality of the frames; a candidate object extractor configured to extract one or more candidate object pixels by comparing the edge image with the reference image, and to extract a candidate object by grouping the extracted candidate object pixels into the candidate object; and an object-of-interest determiner configured to determine whether the candidate object is an object-of-interest based on a size of the candidate object and a duration time of detection of the candidate object. | 1. An object monitoring apparatus, comprising: an image receiver configured to receive at least one frame of captured images; an edge image generator configured to generate an edge image by detecting edges of objects appearing in the frame; a reference image generator configured to generate a reference image by detecting a part corresponding to a background in the frame to thereby define the detected part as a background edge, the reference image generated by repeatedly detecting as many as or more than a predetermined number an edge commonly appearing over a plurality of the frames; a candidate object extractor configured to extract one or more candidate object pixels by comparing the edge image with the reference image, and to extract a candidate object by grouping the extracted candidate object pixels into the candidate object; and an object-of-interest determiner configured to determine whether the candidate object is an object-of-interest based on a size of the candidate object and a duration time of detection of the candidate object. 8. The object monitoring apparatus of claim 1 , wherein the edge image generator is configured to detect the edges by using gradient information of the frame. | 0.797436 |
8,600,166 | 25 | 26 | 25. The apparatus according to claim 18 , where, in being programmed to approximate the hand pose using IK optimization, the processor is programmed to: partition the at least one input image into a plurality of processing regions; determine a centroid of features within each of the plurality of processing regions; and map a location of each feature centroid onto three dimensional (3D) pose data associated with a motion capture data set. | 25. The apparatus according to claim 18 , where, in being programmed to approximate the hand pose using IK optimization, the processor is programmed to: partition the at least one input image into a plurality of processing regions; determine a centroid of features within each of the plurality of processing regions; and map a location of each feature centroid onto three dimensional (3D) pose data associated with a motion capture data set. 26. The apparatus according to claim 25 , where, in being programmed to determine the centroid of features within each of the plurality of processing regions, the processor is programmed to: compare variances from each feature centroid to a closest match within the 3D pose data; and determine which of a plurality of joint constraints affect the IK optimization. | 0.909612 |
7,873,666 | 4 | 5 | 4. The method of claim 1 , wherein the converting step comprises: reading the data from the conversion source file; and applying the set of rules to the data. | 4. The method of claim 1 , wherein the converting step comprises: reading the data from the conversion source file; and applying the set of rules to the data. 5. The method according to claim 4 , wherein the conversion source file and the conversion target file are separate files stored in a file system. | 0.97118 |
9,245,254 | 10 | 11 | 10. The method of claim 1 , wherein the presenting at least some of the conference history information includes: presenting the conference history information to a participant in the voice conference, the participant having rejoined the voice conference after having not participated in the voice conference for a period of time. | 10. The method of claim 1 , wherein the presenting at least some of the conference history information includes: presenting the conference history information to a participant in the voice conference, the participant having rejoined the voice conference after having not participated in the voice conference for a period of time. 11. The method of claim 10 , wherein the participant rejoins the voice conference after at least one of: pausing the voice conference, muting the voice conference, holding the voice conference, voluntarily leaving the voice conference, and/or involuntarily leaving the voice conference. | 0.931349 |
8,844,033 | 1 | 10 | 1. A method for detecting network anomalies, the method comprising: receiving a training dataset of communication protocol messages having argument strings; determining a content and a structure associated with each of the argument strings; receiving a mixture size that specifies a number of Markov chains to use in a probabilistic model; training the probabilistic model using the determined content and structure of each of the argument strings and using a mixture of Markov chains specified by the received mixture size; receiving a communication protocol message having an argument string that is transmitted from a first processor to a second processor across a computer network; applying the probabilistic model to the received communication protocol message to determine whether the communication protocol message is anomalous; and performing a predetermined action in response to determining that the communication protocol message is anomalous. | 1. A method for detecting network anomalies, the method comprising: receiving a training dataset of communication protocol messages having argument strings; determining a content and a structure associated with each of the argument strings; receiving a mixture size that specifies a number of Markov chains to use in a probabilistic model; training the probabilistic model using the determined content and structure of each of the argument strings and using a mixture of Markov chains specified by the received mixture size; receiving a communication protocol message having an argument string that is transmitted from a first processor to a second processor across a computer network; applying the probabilistic model to the received communication protocol message to determine whether the communication protocol message is anomalous; and performing a predetermined action in response to determining that the communication protocol message is anomalous. 10. The method of claim 1 , wherein the predetermined action comprises issuing an alert. | 0.917448 |
8,972,370 | 3 | 10 | 3. The repetitive fusion search system according to claim 1 , wherein: the searcher information database is configured to store a history of input information and target selections received from the searcher; and the search engine is configured to estimate, with respect to the input information input by the searcher, search tendency information for the at least one instinct based search parameter and a search parameter significance level based on the history of input information, wherein the search engine is also configured to search the search target database by adjusting the input information using the estimation result or by adjusting a matching degree calculation between the input information and the one or more relevant targets. | 3. The repetitive fusion search system according to claim 1 , wherein: the searcher information database is configured to store a history of input information and target selections received from the searcher; and the search engine is configured to estimate, with respect to the input information input by the searcher, search tendency information for the at least one instinct based search parameter and a search parameter significance level based on the history of input information, wherein the search engine is also configured to search the search target database by adjusting the input information using the estimation result or by adjusting a matching degree calculation between the input information and the one or more relevant targets. 10. The repetitive fusion search system according to claim 3 , wherein: the search engine is configured to obtain, in response to a selection of a search target in the search interface, a setting criterion deviation of the numerical value received for the at least one instinct based search parameter for the search target from a set value for the at least one instinct based search parameter, and to store the setting criterion deviation for the at least one instinct based search parameter in the searcher information database; and the search engine is configured to correct a newly-input numerical value setting for the at least one instinct based search parameter based on the setting criterion deviation, and to search the search target database with the corrected numerical value setting for the at least one instinct based search parameter as a search condition. | 0.789384 |
9,491,179 | 1 | 4 | 1. A method for detecting unauthorized user account communications, comprising: sampling messages associated with an authorized user of an account to provide a plurality of message samples; creating an authorized profile based on language patterns extracted from the plurality of message samples; comparing by a processor a language pattern extracted from a recent message with the authorized profile to determine a deviation between the language pattern extracted from the recent message and the authorized profile; determining that the recent message is an unauthorized user account communication when the deviation is not within an allowable amount of deviation, the allowable amount of deviation being based on an amount of samples in the plurality of message samples; and generating an alert indicating that the recent message is an unauthorized user account communication in response to the determining that the recent message is an unauthorized user account communication. | 1. A method for detecting unauthorized user account communications, comprising: sampling messages associated with an authorized user of an account to provide a plurality of message samples; creating an authorized profile based on language patterns extracted from the plurality of message samples; comparing by a processor a language pattern extracted from a recent message with the authorized profile to determine a deviation between the language pattern extracted from the recent message and the authorized profile; determining that the recent message is an unauthorized user account communication when the deviation is not within an allowable amount of deviation, the allowable amount of deviation being based on an amount of samples in the plurality of message samples; and generating an alert indicating that the recent message is an unauthorized user account communication in response to the determining that the recent message is an unauthorized user account communication. 4. The method as recited in claim 1 , wherein sampling the messages comprises: sampling the messages associated with the authorized user until a standard deviation among the language patterns extracted from the plurality of message samples is within a predetermined range. | 0.683721 |
6,161,092 | 25 | 43 | 25. A processor readable storage medium having processor readable code embodied on said processor readable storage medium, said processor readable code for programming a processor to perform a method comprising the steps of: receiving data for a set of traffic incidents, said data including a set of parameters; identifying groups of files that store speech for describing said incidents, each group of files is associated with at least one of said incidents, said step of identifying comprises the steps of: accessing a first parameter having a first value, accessing information directly correlating values of said first parameter to references to audio files, determining whether said information directly correlates said first value to a first reference to an audio file, identifying, as part of a first group of files, a first audio file if said information directly correlates said first value to said first reference to said first audio file, and identifying, as part of said first group of files, a second audio file if said information does not direct correlate said first value to any reference to an audio file; and automatically presenting said stored speech from each group of files. | 25. A processor readable storage medium having processor readable code embodied on said processor readable storage medium, said processor readable code for programming a processor to perform a method comprising the steps of: receiving data for a set of traffic incidents, said data including a set of parameters; identifying groups of files that store speech for describing said incidents, each group of files is associated with at least one of said incidents, said step of identifying comprises the steps of: accessing a first parameter having a first value, accessing information directly correlating values of said first parameter to references to audio files, determining whether said information directly correlates said first value to a first reference to an audio file, identifying, as part of a first group of files, a first audio file if said information directly correlates said first value to said first reference to said first audio file, and identifying, as part of said first group of files, a second audio file if said information does not direct correlate said first value to any reference to an audio file; and automatically presenting said stored speech from each group of files. 43. A method according to claim 25, wherein: said step of automatically presenting includes continuously presenting an audio/visual program that includes said stored speech. | 0.80692 |
9,311,499 | 1 | 8 | 1. A computerized method of securing data in a plurality of security controlled data stores with access controls thereat, each data store having a defined security level, said data potentially having sensitive content defined as sensitive words, data objects, characters, images, data elements or icons, comprising: separately storing sensitive content in secure data stores of said plurality of security data stores at the respective defined security level for said sensitive content; permitting reconstruction of some or all of said data with appropriate access controls applied to respective secure data stores; and said storing or reconstruction based upon (i) territorial protocol and a geographic location signal or (ii) a triggering event; and at least one of: prior to storing, at least one or more of tagging, labeling, or classifying said sensitive content in said secure data stores; or concurrent with storing, at least one or more of tagging, labeling, or classifying said sensitive content in said secure data stores. | 1. A computerized method of securing data in a plurality of security controlled data stores with access controls thereat, each data store having a defined security level, said data potentially having sensitive content defined as sensitive words, data objects, characters, images, data elements or icons, comprising: separately storing sensitive content in secure data stores of said plurality of security data stores at the respective defined security level for said sensitive content; permitting reconstruction of some or all of said data with appropriate access controls applied to respective secure data stores; and said storing or reconstruction based upon (i) territorial protocol and a geographic location signal or (ii) a triggering event; and at least one of: prior to storing, at least one or more of tagging, labeling, or classifying said sensitive content in said secure data stores; or concurrent with storing, at least one or more of tagging, labeling, or classifying said sensitive content in said secure data stores. 8. A computerized method of securing data as claimed in claim 1 wherein said storing of sensitive content in said secure data stores includes at least one or more of storing in predetermined security data stores, storing in a predetermined manner by random selection of security data stores, storing by data class in said security data stores, storing data by data type in said security data stores, or storing by level of security in said security data stores. | 0.720606 |
7,899,816 | 1 | 7 | 1. A computer implemented method for classifying a document comprising the steps of: providing a single lexicon comprising one or more lexicon terms wherein the one or more lexicon terms are associated with category information, and wherein the category information comprises weights indicating a likelihood that each of the one or more lexicon terms corresponds to each of one or more categories, identifying a document comprising one or more document terms, comparing the one or more document terms with the one or more lexicon terms, determining which, if any, of the one or more document terms match the one or more lexicon terms, calculating a score indicating a likelihood that the document belongs to each of the one or more categories using the weights for each of the matched terms, outputting a result of the calculating step, and calculating additional moments based upon relative distances of subsequent terms matched with additional terms whose moment meets the requirements of the threshold. | 1. A computer implemented method for classifying a document comprising the steps of: providing a single lexicon comprising one or more lexicon terms wherein the one or more lexicon terms are associated with category information, and wherein the category information comprises weights indicating a likelihood that each of the one or more lexicon terms corresponds to each of one or more categories, identifying a document comprising one or more document terms, comparing the one or more document terms with the one or more lexicon terms, determining which, if any, of the one or more document terms match the one or more lexicon terms, calculating a score indicating a likelihood that the document belongs to each of the one or more categories using the weights for each of the matched terms, outputting a result of the calculating step, and calculating additional moments based upon relative distances of subsequent terms matched with additional terms whose moment meets the requirements of the threshold. 7. The method of claim 1 , wherein an exact match between a document term and a lexicon term are not required. | 0.906463 |
9,436,287 | 11 | 12 | 11. The system of claim 1 , wherein the one or more processors are configured to enable a gesture detection procedure using the gesture detection sensor when the microphone detects an audio waveform. | 11. The system of claim 1 , wherein the one or more processors are configured to enable a gesture detection procedure using the gesture detection sensor when the microphone detects an audio waveform. 12. The system of claim 11 , wherein the gesture detection sensor is inactive until the detected audio waveform is determined to be a human voice. | 0.959127 |
9,503,228 | 10 | 14 | 10. A server implementing an application to detect, diagnose, and mitigate issues in a network, the server comprising: a network interface, a processor, and memory, each communicatively coupled therebetween; wherein the memory stores instructions that, when executed, cause the processor to obtain Operations, Administration, and Maintenance (OAM) data related to the network, the OAM data related to current operation of the network, receive external data related to the network, the external data describing events related to any one or more of construction, weather, natural disasters, and planned outages, instantiate a rule engine to evaluate one or more rules based on any one of the OAM data, an event, policy, and an anomaly, perform one or more actions based on an evaluation of the one or more rules, and analyze the external data to determine a relationship between the events and the network elements in the network, wherein the relationship comprises any one or more of distance, amount of time the event exists, a number of events in a shared area, reputation of an event based on historical data, and a magnitude of collateral damage an event may cause to generate an associated risk level. | 10. A server implementing an application to detect, diagnose, and mitigate issues in a network, the server comprising: a network interface, a processor, and memory, each communicatively coupled therebetween; wherein the memory stores instructions that, when executed, cause the processor to obtain Operations, Administration, and Maintenance (OAM) data related to the network, the OAM data related to current operation of the network, receive external data related to the network, the external data describing events related to any one or more of construction, weather, natural disasters, and planned outages, instantiate a rule engine to evaluate one or more rules based on any one of the OAM data, an event, policy, and an anomaly, perform one or more actions based on an evaluation of the one or more rules, and analyze the external data to determine a relationship between the events and the network elements in the network, wherein the relationship comprises any one or more of distance, amount of time the event exists, a number of events in a shared area, reputation of an event based on historical data, and a magnitude of collateral damage an event may cause to generate an associated risk level. 14. The server of claim 10 , wherein the one or more actions comprise biasing a Path Computation Element (PCE) or other path computation component away from nodes and/or links in the network with detected issues. | 0.862159 |
9,171,079 | 33 | 38 | 33. One or more computer-readable non-transitory storage media embodying software that is configured when executed to: building a profile for an end user based on collectively learned preferences of the end user, the profile comprising a set of keywords and a weight assigned to each keyword in the set of keywords; receive information identifying sensor data associated with a specific sensor among a plurality of sensor data from a plurality of sensors, the plurality of sensor data being indexed according to a multi-dimensional array, one or more first ones of the dimensions comprising time and one or more second ones of the dimensions comprising one or more pre-determined sensor-data attributes, the translated query having been translated from an original query for particular sensor data, the translated query comprising an unique resource locator specifying the specific sensor of the plurality of sensors, the translated query comprising one or more values for one or more of the dimensions of the multi-dimensional array; generate one or more multi-dimensional-array filters based on the translated query; determine that the translated query is not accurate; modify the translated query based on the profile for the end user to provide a more relevant response to the translated query; apply the one or more multi-dimensional-array filters to the plurality of sensor data as indexed to identify the sensor data associated with the specific sensor among a plurality of sensor data for a response to the modified translated query; sort the information based on one or more preferences in the profile for the end user to provide a representation expected by the end user, wherein sorting the information comprises ordering the information such that the information that is most relevant to the user is displayed first; and communicate the information as sorted for display to the end user, the display of the information providing a response to the original query, wherein communicating the sorted information for display comprises: rendering a page comprising multiple panels on the user's display; determining, for each sensor data in the sorted information, which panel the sensor data is most relevant to; and rendering each sensor data in the determined most relevant panel. | 33. One or more computer-readable non-transitory storage media embodying software that is configured when executed to: building a profile for an end user based on collectively learned preferences of the end user, the profile comprising a set of keywords and a weight assigned to each keyword in the set of keywords; receive information identifying sensor data associated with a specific sensor among a plurality of sensor data from a plurality of sensors, the plurality of sensor data being indexed according to a multi-dimensional array, one or more first ones of the dimensions comprising time and one or more second ones of the dimensions comprising one or more pre-determined sensor-data attributes, the translated query having been translated from an original query for particular sensor data, the translated query comprising an unique resource locator specifying the specific sensor of the plurality of sensors, the translated query comprising one or more values for one or more of the dimensions of the multi-dimensional array; generate one or more multi-dimensional-array filters based on the translated query; determine that the translated query is not accurate; modify the translated query based on the profile for the end user to provide a more relevant response to the translated query; apply the one or more multi-dimensional-array filters to the plurality of sensor data as indexed to identify the sensor data associated with the specific sensor among a plurality of sensor data for a response to the modified translated query; sort the information based on one or more preferences in the profile for the end user to provide a representation expected by the end user, wherein sorting the information comprises ordering the information such that the information that is most relevant to the user is displayed first; and communicate the information as sorted for display to the end user, the display of the information providing a response to the original query, wherein communicating the sorted information for display comprises: rendering a page comprising multiple panels on the user's display; determining, for each sensor data in the sorted information, which panel the sensor data is most relevant to; and rendering each sensor data in the determined most relevant panel. 38. The media of claim 33 , wherein the relevance of each sensor data is determined at least in part by the reputation of the sensor data originator. | 0.797003 |
9,251,185 | 10 | 11 | 10. A system comprising one or more processors that performs a method comprising: providing a set of search results returned in response to a search query composed and issued by a user; determining a ranking of the set of returned search results against each other as a function of relevance to the issued search query; selecting for evaluation one or more search results from the returned set of search results based on the ranking; determining a level of quality, that is independent of the terms of the search query, for each of the one or more selected search results from the returned set of search results by employing a classification process, wherein the classification process comprises: targeting features demonstrated by the one or more selected search results to be evaluated; evaluating the targeted features to generate a level-of-quality score for each of the one or more selected search results, wherein the level-of-quality score is evaluated using a cross-result entropy that quantifies a level of similarity between a plurality of the search results, and wherein the search engine is configured to decrease the level-of-quality score based on determining that the cross-result entropy is sufficiently high; comparing the level-of-quality score against a predefined threshold value; and based on the comparison, assigning each of the one or more selected search results an absolute measurement, wherein the absolute measurement indicates good quality when the level-of-quality score is greater than the threshold value, and wherein the absolute measurement indicates poor quality when the level-of-quality score is less than the threshold value; calculating a level of confidence that a rewritten search query will provide new search results that improve the set of search results; automatically rewriting the search query by the search engine to generate the rewritten search query, wherein each key word of the rewritten search query is automatically selected for inclusion in the rewritten search query; and based on the calculated level of confidence indicating that the rewritten search query will provide the new search results that improve the set of search results, and based on identifying that the absolute measurement assigned to at least one of the one or more selected search results indicates the poor quality, automatically conducting a search with the rewritten search query. | 10. A system comprising one or more processors that performs a method comprising: providing a set of search results returned in response to a search query composed and issued by a user; determining a ranking of the set of returned search results against each other as a function of relevance to the issued search query; selecting for evaluation one or more search results from the returned set of search results based on the ranking; determining a level of quality, that is independent of the terms of the search query, for each of the one or more selected search results from the returned set of search results by employing a classification process, wherein the classification process comprises: targeting features demonstrated by the one or more selected search results to be evaluated; evaluating the targeted features to generate a level-of-quality score for each of the one or more selected search results, wherein the level-of-quality score is evaluated using a cross-result entropy that quantifies a level of similarity between a plurality of the search results, and wherein the search engine is configured to decrease the level-of-quality score based on determining that the cross-result entropy is sufficiently high; comparing the level-of-quality score against a predefined threshold value; and based on the comparison, assigning each of the one or more selected search results an absolute measurement, wherein the absolute measurement indicates good quality when the level-of-quality score is greater than the threshold value, and wherein the absolute measurement indicates poor quality when the level-of-quality score is less than the threshold value; calculating a level of confidence that a rewritten search query will provide new search results that improve the set of search results; automatically rewriting the search query by the search engine to generate the rewritten search query, wherein each key word of the rewritten search query is automatically selected for inclusion in the rewritten search query; and based on the calculated level of confidence indicating that the rewritten search query will provide the new search results that improve the set of search results, and based on identifying that the absolute measurement assigned to at least one of the one or more selected search results indicates the poor quality, automatically conducting a search with the rewritten search query. 11. The system of claim 10 , wherein the automatically conducting the search with the rewritten search query is selected as a first corrective action over rendering a reformulation user interface (UI) for the search query based on determining that the calculated level of confidence is sufficiently high. | 0.694165 |
7,515,751 | 1 | 5 | 1. A computer-implemented method, comprising: a. receiving a search term comprising a plurality of word units; b. conducting a search for an instance of the search term in a set of target data to be searched beginning at a target stream, the search including: i. selecting a first word unit in the search term as a selected search word unit, and selecting a corresponding word unit in the target stream as a selected target word unit; ii. comparing the selected search word unit with the selected target word unit and providing a match result indicative of whether a sufficient matching level is achieved, wherein when the selected target word unit is recognized handwritten word having alternates, comparing includes evaluating the alternates; and iii. determining based on the match result whether the search term sufficiently matches the target stream, and 1. if so, considering the search a success with respect to the target stream and advancing to step (C), and 2. if not, determining whether additional word units in the search stream need to be compared to determine whether the search term sufficiently matches the target stream, and a. if so, selecting a next word unit in the search term as the selected search word unit, and selecting a next corresponding word unit in the target stream as the selected target word unit and returning to step (B) (ii); and b. if not, considering the search a failure with respect to the target stream and advancing to step (C); and (C) returning information indicative of the success or failure of the search and concluding the search with respect to the target stream. | 1. A computer-implemented method, comprising: a. receiving a search term comprising a plurality of word units; b. conducting a search for an instance of the search term in a set of target data to be searched beginning at a target stream, the search including: i. selecting a first word unit in the search term as a selected search word unit, and selecting a corresponding word unit in the target stream as a selected target word unit; ii. comparing the selected search word unit with the selected target word unit and providing a match result indicative of whether a sufficient matching level is achieved, wherein when the selected target word unit is recognized handwritten word having alternates, comparing includes evaluating the alternates; and iii. determining based on the match result whether the search term sufficiently matches the target stream, and 1. if so, considering the search a success with respect to the target stream and advancing to step (C), and 2. if not, determining whether additional word units in the search stream need to be compared to determine whether the search term sufficiently matches the target stream, and a. if so, selecting a next word unit in the search term as the selected search word unit, and selecting a next corresponding word unit in the target stream as the selected target word unit and returning to step (B) (ii); and b. if not, considering the search a failure with respect to the target stream and advancing to step (C); and (C) returning information indicative of the success or failure of the search and concluding the search with respect to the target stream. 5. The method of claim 1 wherein receiving the search term includes receiving data recognized from handwritten ink data. | 0.810127 |
4,513,395 | 9 | 10 | 9. A method of selectively storing, in a plurality of successive storage locations contained in a data storage device, a plurality of groups of successive data words which are present on logic circuitry of a system under test, each said group comprising at least one particular data word and no more than a predetermined number of data words which precede said particular data word on said logic circuitry, each said storage location having a storage address, said method comprising the steps of: (a) providing predetermined most significant bits to form a first portion of each said address; (b) providing a predetermined number of least significant bits to form a second portion of each said address; (c) changing the value of said second portion in response to the presence of said data words on said data bus; (d) storing successive data words at storage locations having addresses determined according to (a), (b), and (c); and (e) changing the value of said first portion in response to the presence of said particular data word on said data bus. | 9. A method of selectively storing, in a plurality of successive storage locations contained in a data storage device, a plurality of groups of successive data words which are present on logic circuitry of a system under test, each said group comprising at least one particular data word and no more than a predetermined number of data words which precede said particular data word on said logic circuitry, each said storage location having a storage address, said method comprising the steps of: (a) providing predetermined most significant bits to form a first portion of each said address; (b) providing a predetermined number of least significant bits to form a second portion of each said address; (c) changing the value of said second portion in response to the presence of said data words on said data bus; (d) storing successive data words at storage locations having addresses determined according to (a), (b), and (c); and (e) changing the value of said first portion in response to the presence of said particular data word on said data bus. 10. The method of claim 9 wherein steps (a)-(e) are performed repetitively. | 0.894366 |
6,154,156 | 1 | 14 | 1. A message processing device for sending and receiving a message composed of one or more elements between different kinds of devices, comprising: parse tree holding means having a function of holding a parse tree generated from syntax descriptions defining structure of a message sent or received; parse tree scanning means connected to said parse tree holding means and having a function of, as message data to be processed is applied, scanning said parse tree to encode and decode a value and outputting encoded and decoded values; preamble processing means connected to said parse tree scanning means and having a function of processing a preamble field of a message for use in indicating whether an element exists or not in message data sent or received; and index processing means connected to said parse tree scanning means and having a function of processing an index field of a message for use in indicating what number of element is selected in message data whose one of a plurality of elements is selected to be sent or received. | 1. A message processing device for sending and receiving a message composed of one or more elements between different kinds of devices, comprising: parse tree holding means having a function of holding a parse tree generated from syntax descriptions defining structure of a message sent or received; parse tree scanning means connected to said parse tree holding means and having a function of, as message data to be processed is applied, scanning said parse tree to encode and decode a value and outputting encoded and decoded values; preamble processing means connected to said parse tree scanning means and having a function of processing a preamble field of a message for use in indicating whether an element exists or not in message data sent or received; and index processing means connected to said parse tree scanning means and having a function of processing an index field of a message for use in indicating what number of element is selected in message data whose one of a plurality of elements is selected to be sent or received. 14. The message processing device as set forth in claim 1, wherein said parse tree holding means holds such a parse tree generated from syntax descriptions defining structure of a message sent or received as having a node format made up of six fields, "type of element", "tag attached to element", "constraints on element", "whether element is omissible", "pointer to subsequent node in the same nesting hierarchy" and "pointer to node in lower nesting hierarchy". | 0.901695 |
10,157,426 | 27 | 30 | 27. The method of claim 26 , the prioritization data comprising: ranking data, wherein each question or topic is assigned a ranking or score; and categorization data, wherein each question or topic is assigned a category from a plurality of categories. | 27. The method of claim 26 , the prioritization data comprising: ranking data, wherein each question or topic is assigned a ranking or score; and categorization data, wherein each question or topic is assigned a category from a plurality of categories. 30. The method of claim 27 , wherein the first paginated screen is structured such that groups of questions of a pre-determined category are sorted according to respective rankings. | 0.977676 |
7,908,329 | 1 | 4 | 1. A computer-readable storage medium that does not consist of a signal, the computer-readable medium having computer-executable instructions for causing a computer to perform steps comprising: providing, in electronic mail storage of an electronic mail application, a junk folder of a user to store a plurality of junk electronic mail messages that have been sent to the user from un-trusted sources and that have been identified by the electronic mail application as spam messages or phishing messages, wherein each junk message in the junk folder of the user is stored in its original format as sent by an un-trusted source and received by the electronic mail application; providing, in the electronic mail storage of the electronic mail application, an inbox folder of the user to store a selected message having at least one hyperlink included in a body of the selected message in its original format upon being moved by the user from the junk folder to the inbox folder, wherein when the selected message is displayed in its original format, text of a uniform resource locator associated with the at least one hyperlink is hidden from view of the user; in response to receiving a request from the user to view the selected message when in the junk folder: reformatting the selected message having the at least one hyperlink included in the body of the selected message from its original format into a modified format, the modified format comprising a plain text format for reformatting the body of the selected message with the at least one hyperlink disabled, and displaying the selected message in the modified format with the at least one hyperlink disabled and the text of the uniform resource locator associated with the at least one hyperlink visible to the user as plain text in the body of the selected message, and in response to receiving a request from the user to view the selected message when in the inbox folder after being moved by the user from the junk folder to the inbox folder: displaying the selected message in its original format with the at least one hyperlink enabled if the selected message has been identified as a spam message, and displaying the selected message in its original format with the at least one hyperlink disabled if the selected message has been identified as a phishing message. | 1. A computer-readable storage medium that does not consist of a signal, the computer-readable medium having computer-executable instructions for causing a computer to perform steps comprising: providing, in electronic mail storage of an electronic mail application, a junk folder of a user to store a plurality of junk electronic mail messages that have been sent to the user from un-trusted sources and that have been identified by the electronic mail application as spam messages or phishing messages, wherein each junk message in the junk folder of the user is stored in its original format as sent by an un-trusted source and received by the electronic mail application; providing, in the electronic mail storage of the electronic mail application, an inbox folder of the user to store a selected message having at least one hyperlink included in a body of the selected message in its original format upon being moved by the user from the junk folder to the inbox folder, wherein when the selected message is displayed in its original format, text of a uniform resource locator associated with the at least one hyperlink is hidden from view of the user; in response to receiving a request from the user to view the selected message when in the junk folder: reformatting the selected message having the at least one hyperlink included in the body of the selected message from its original format into a modified format, the modified format comprising a plain text format for reformatting the body of the selected message with the at least one hyperlink disabled, and displaying the selected message in the modified format with the at least one hyperlink disabled and the text of the uniform resource locator associated with the at least one hyperlink visible to the user as plain text in the body of the selected message, and in response to receiving a request from the user to view the selected message when in the inbox folder after being moved by the user from the junk folder to the inbox folder: displaying the selected message in its original format with the at least one hyperlink enabled if the selected message has been identified as a spam message, and displaying the selected message in its original format with the at least one hyperlink disabled if the selected message has been identified as a phishing message. 4. The computer-readable storage medium of claim 1 , wherein reformatting the selected message comprises removing formatting and images from the body of the selected message. | 0.870536 |
7,836,038 | 20 | 29 | 20. The method of claim 1 , wherein obtaining, based on the search query, at least one price for the product and at least one image of the product from each of the identified articles further comprises: selecting a plurality of potential prices and a plurality of potential images for the product from the first article, the prices selected based on terms of the search query, the images selected based on the terms of the search query and based on the prices selected, wherein the selection includes a distance between a location of each of the prices within the first article and a location of each of the images within the first article; and making a ranked list of the potential prices and of the potential images selected for the product from the first article, wherein prices and images that are located nearer to each other within the first article are ranked higher than prices and images that are farther apart from each other. | 20. The method of claim 1 , wherein obtaining, based on the search query, at least one price for the product and at least one image of the product from each of the identified articles further comprises: selecting a plurality of potential prices and a plurality of potential images for the product from the first article, the prices selected based on terms of the search query, the images selected based on the terms of the search query and based on the prices selected, wherein the selection includes a distance between a location of each of the prices within the first article and a location of each of the images within the first article; and making a ranked list of the potential prices and of the potential images selected for the product from the first article, wherein prices and images that are located nearer to each other within the first article are ranked higher than prices and images that are farther apart from each other. 29. The method of claim 20 further comprising: identifying a highest ranked price from the potential prices on the ranked list for the first article; and automatically selecting and extracting the highest ranked price from the first article. | 0.915557 |
8,745,019 | 16 | 18 | 16. The method of claim 11 , wherein said generating the first pseudo-document similarity score Score pdsim (s e →r e ) for the individual candidate strings comprises: receiving a set of pseudo-documents PD, individual pseudo-documents pdoc in the set of pseudo-documents PD corresponding to a document d in the set of documents D, and individual pseudo-documents pdoc containing a set of terms found in queries that have been submitted and which have resulted in the selection of the document d; determining, for the individual candidate strings, a number of the individual pseudo-documents pdoc that contain all of the terms in the individual candidate strings; and generating the first pseudo-document similarity score Score pdsim (s e →r e ) for the individual candidate strings using said number. | 16. The method of claim 11 , wherein said generating the first pseudo-document similarity score Score pdsim (s e →r e ) for the individual candidate strings comprises: receiving a set of pseudo-documents PD, individual pseudo-documents pdoc in the set of pseudo-documents PD corresponding to a document d in the set of documents D, and individual pseudo-documents pdoc containing a set of terms found in queries that have been submitted and which have resulted in the selection of the document d; determining, for the individual candidate strings, a number of the individual pseudo-documents pdoc that contain all of the terms in the individual candidate strings; and generating the first pseudo-document similarity score Score pdsim (s e →r e ) for the individual candidate strings using said number. 18. The method of claim 16 , wherein said determining the number of the individual pseudo-documents pdoc comprises: generating a PD inverted index that relates the individual pseudo-documents pdoc to terms found in the set of candidate strings S e ; generating a s e inverted index that relates the individual candidate strings to terms found in the set of candidate strings S e ; generating a CD matrix that identifies, for individual pairs of a particular pseudo-document pdoc and a particular candidate string s e , a number of terms in the particular candidate string s e that are found in the particular pseudo-document pdoc; and determining the number of the individual pseudo-documents pdoc using the CD matrix. | 0.516173 |
8,032,532 | 1 | 3 | 1. A computer-implemented method of querying multifaceted information in an information retrieval system, comprising: constructing, by said information retrieval (IR) system, an inverted index having a plurality of unique indexed tokens associated with a plurality of posting lists in a one-to-one correspondence, each posting list including one or more documents of a plurality of documents, wherein an indexed token of said plurality of unique indexed tokens is one of a facet token included as an annotation in a document of said plurality of documents and a path prefix of said facet token, wherein said annotation indicates a path within a tree structure representing a facet that includes said document, said tree structure including a plurality of nodes representing a category and one or more sub-categories that categorize said document; receiving, by said IR system, a query that includes a plurality of constraints on said plurality of documents, said plurality of constraints being associated with multiple indexed tokens of said plurality of unique indexed tokens and multiple posting lists corresponding to said multiple indexed tokens; and executing said query by said IR system, said executing including: identifying said multiple posting lists via a utilization of said plurality of constraints and said inverted index, and intersecting said multiple posting lists to obtain a result of said query. | 1. A computer-implemented method of querying multifaceted information in an information retrieval system, comprising: constructing, by said information retrieval (IR) system, an inverted index having a plurality of unique indexed tokens associated with a plurality of posting lists in a one-to-one correspondence, each posting list including one or more documents of a plurality of documents, wherein an indexed token of said plurality of unique indexed tokens is one of a facet token included as an annotation in a document of said plurality of documents and a path prefix of said facet token, wherein said annotation indicates a path within a tree structure representing a facet that includes said document, said tree structure including a plurality of nodes representing a category and one or more sub-categories that categorize said document; receiving, by said IR system, a query that includes a plurality of constraints on said plurality of documents, said plurality of constraints being associated with multiple indexed tokens of said plurality of unique indexed tokens and multiple posting lists corresponding to said multiple indexed tokens; and executing said query by said IR system, said executing including: identifying said multiple posting lists via a utilization of said plurality of constraints and said inverted index, and intersecting said multiple posting lists to obtain a result of said query. 3. The method of claim 1 , wherein said constructing said inverted index comprises: generating a full path token and a full path token posting list associated therewith by said inverted index, said full path token posting list including a plurality of identifiers representing said plurality of documents, wherein an identifier of said plurality of identifiers represents said document and includes a payload value, said payload value identifying a full path of said document in said tree structure, and said payload value including a set of full path indicators provided by a scheme that uniquely labels each sibling node of said tree structure. | 0.753623 |
9,436,758 | 13 | 16 | 13. A non-transitory computer-readable storage device having encoded thereon computer readable instructions, which when executed by a processor, cause the processor to: retrieve a plurality of documents from a database, at least a portion of the plurality of documents including a set of content representing an interaction between a user and a support entity, wherein the set of content includes both customer feedback content received from a user, and support content provided by the support entity and to the user responsive to the customer feedback content; identify, within each document of the portion of the received plurality of documents, the customer feedback content received from a user and the support content provided to the user responsive to the customer feedback; filter the portion of the plurality of documents, including removing the customer feedback content and retaining the support content; partition, after the filtering, the plurality of filtered documents, in which the customer feedback has been removed, into multiple clusters based on the support content of each of the plurality of filtered documents; after the partitioning, for each filtered document in each cluster of the multiple clusters, associate the customer feedback content that was filtered from the filtered document with the cluster to which the filtered document belongs and the retained support content from that filtered document, and storing association information in memory; receive a new document including customer feedback related to an issue; determine, using the association information the in memory, that the customer feedback of the new document matches the customer feedback content associated with one of the clusters; and provide, over a communication connection with a user associated with the new document, the retained support content associated with the cluster based on the match between the customer feedback of the new document and the customer feedback content that was associated with the cluster after the partitioning. | 13. A non-transitory computer-readable storage device having encoded thereon computer readable instructions, which when executed by a processor, cause the processor to: retrieve a plurality of documents from a database, at least a portion of the plurality of documents including a set of content representing an interaction between a user and a support entity, wherein the set of content includes both customer feedback content received from a user, and support content provided by the support entity and to the user responsive to the customer feedback content; identify, within each document of the portion of the received plurality of documents, the customer feedback content received from a user and the support content provided to the user responsive to the customer feedback; filter the portion of the plurality of documents, including removing the customer feedback content and retaining the support content; partition, after the filtering, the plurality of filtered documents, in which the customer feedback has been removed, into multiple clusters based on the support content of each of the plurality of filtered documents; after the partitioning, for each filtered document in each cluster of the multiple clusters, associate the customer feedback content that was filtered from the filtered document with the cluster to which the filtered document belongs and the retained support content from that filtered document, and storing association information in memory; receive a new document including customer feedback related to an issue; determine, using the association information the in memory, that the customer feedback of the new document matches the customer feedback content associated with one of the clusters; and provide, over a communication connection with a user associated with the new document, the retained support content associated with the cluster based on the match between the customer feedback of the new document and the customer feedback content that was associated with the cluster after the partitioning. 16. The computer-readable storage device of claim 13 , wherein the plurality of different communication channels includes at least one of an email thread, a chat thread, and a help forum thread between a user and a customer service representative. | 0.828234 |
8,271,436 | 3 | 7 | 3. The system of claim 1 , wherein the shadowing system is configured to control the applying using modified information of the component of the near-line server. | 3. The system of claim 1 , wherein the shadowing system is configured to control the applying using modified information of the component of the near-line server. 7. The system of claim 3 , wherein the shadowing system is configured to modify the component. | 0.970384 |
9,473,463 | 1 | 8 | 1. A method comprising: caching concurrently, by a computing device, a first set of control words and a first set of entitlement control messages (ECMs) associated with the first set of control words; encrypting a transport stream, for a first service, using a first control word of the first set of control words during a first cryptographic period; encrypting the transport stream, for the first service, using a second control word of the first set of control words different from the first control word during a second cryptographic period, wherein the second cryptographic period occurs after the first cryptographic period; encrypting the transport stream, for the first service, using the first control word of the first set of control words during a third cryptographic period, wherein the third cryptographic period occurs after the first cryptographic period and the second cryptographic period; inserting into the transport stream, for the first service, a first ECM, of the first set of ECMs, corresponding to the first control word; and sending, to a device downstream from the computing device, the transport stream. | 1. A method comprising: caching concurrently, by a computing device, a first set of control words and a first set of entitlement control messages (ECMs) associated with the first set of control words; encrypting a transport stream, for a first service, using a first control word of the first set of control words during a first cryptographic period; encrypting the transport stream, for the first service, using a second control word of the first set of control words different from the first control word during a second cryptographic period, wherein the second cryptographic period occurs after the first cryptographic period; encrypting the transport stream, for the first service, using the first control word of the first set of control words during a third cryptographic period, wherein the third cryptographic period occurs after the first cryptographic period and the second cryptographic period; inserting into the transport stream, for the first service, a first ECM, of the first set of ECMs, corresponding to the first control word; and sending, to a device downstream from the computing device, the transport stream. 8. The method of claim 1 , further comprising: determining to reuse one or more control words, of the first set of control words, and one or more ECMs, of the first set of ECMs, in response to a network failure between the computing device and an ECM generator, wherein the one or more ECMs are associated with the one or more control words. | 0.772363 |
10,031,981 | 1 | 8 | 1. A method for converting both a tabbed table in an XML format and a collapsible section in the XML format to forms configured for storage in a relational database and use by a web-based application, said method comprising: converting, by a processor of a computing device, unstructured rich text information to XML files in the XML format, wherein the unstructured rich text information comprises the tabbed table as unstructured rich text and the collapsible section as unstructured rich text, wherein the tabbed table is a first type of unstructured rich text information that is tabbed table specific, wherein the collapsible section is a second type of unstructured rich text information that is collapsible section specific, and wherein the XML files in the XML format comprise the tabbed table in the XML format and the collapsible section in the XML format; transforming, by the processor using a first reusable stylesheet that is specific to the first type of unstructured rich text information that is tabbed table specific, the tabbed table in the XML format to an XHTML format configured for storage in the relational database by creating a parent, table object, creating a body for the parent table object to form a container including cells for the tabbed table and creating children of the parent, table object to record information contents of the cells in the container; initiating, by the processor, storage of the tabbed table object, including the body of the parent table object and the children of the parent table object, in the XHTML format in the relational database; subsequently exporting, by the processor, the tabbed table, including the body of the parent table object and the children of the parent table object, in the XHMTL format from the relational database to the web-based application; transforming, by the processor using a second reusable stylesheet that is specific to the second type of unstructured rich text information that is collapsible section specific, the collapsible section in the XML format to an XHTML format configured for storage in the relational database by creating a parent, collapsed object and creating children of the parent, collapsed object to record information content of an uncollapsed form of the collapsed parent object; initiating, by the processor, storage of the collapsible section in the XHTML format in the relational database; and subsequently exporting, by the processor, the collapsible section in the XHMTL format from the relational database to the web-based application. | 1. A method for converting both a tabbed table in an XML format and a collapsible section in the XML format to forms configured for storage in a relational database and use by a web-based application, said method comprising: converting, by a processor of a computing device, unstructured rich text information to XML files in the XML format, wherein the unstructured rich text information comprises the tabbed table as unstructured rich text and the collapsible section as unstructured rich text, wherein the tabbed table is a first type of unstructured rich text information that is tabbed table specific, wherein the collapsible section is a second type of unstructured rich text information that is collapsible section specific, and wherein the XML files in the XML format comprise the tabbed table in the XML format and the collapsible section in the XML format; transforming, by the processor using a first reusable stylesheet that is specific to the first type of unstructured rich text information that is tabbed table specific, the tabbed table in the XML format to an XHTML format configured for storage in the relational database by creating a parent, table object, creating a body for the parent table object to form a container including cells for the tabbed table and creating children of the parent, table object to record information contents of the cells in the container; initiating, by the processor, storage of the tabbed table object, including the body of the parent table object and the children of the parent table object, in the XHTML format in the relational database; subsequently exporting, by the processor, the tabbed table, including the body of the parent table object and the children of the parent table object, in the XHMTL format from the relational database to the web-based application; transforming, by the processor using a second reusable stylesheet that is specific to the second type of unstructured rich text information that is collapsible section specific, the collapsible section in the XML format to an XHTML format configured for storage in the relational database by creating a parent, collapsed object and creating children of the parent, collapsed object to record information content of an uncollapsed form of the collapsed parent object; initiating, by the processor, storage of the collapsible section in the XHTML format in the relational database; and subsequently exporting, by the processor, the collapsible section in the XHMTL format from the relational database to the web-based application. 8. The method of claim 1 , said method further comprising: specifying, by the processor, in a GUI interface, a first Stylesheet Directory property and a first Stylesheet Name property; generating, by the processor, a first reference pointing to a first file stored in a file system by concatenating a first value of the first Stylesheet Directory property and a first value of the first Stylesheet Name property; retrieving, by the processor, the first stylesheet from the first file, using the first reference pointing to the first file, to transform the tabbed table in the XML format to an XHTML format; specifying, in the GUI interface, a second Stylesheet Directory property and a second Stylesheet Name property; generating, by the processor, a second reference pointing to a second file stored in the file system by concatenating a second value of the second Stylesheet Directory property and a second value of the second Stylesheet Name property; and retrieving, by the processor, the second stylesheet from the second file, using the second reference pointing to the second file, to transform the collapsible section in the XML format to an XHTML format. | 0.581655 |
7,546,334 | 69 | 70 | 69. An information processing system as claimed in claim 68 wherein said means for retrieving includes means for retrieving taxonomic words, characters or data objects from said compilation of additional data related to said security sensitive words, characters or data objects and based upon categorization and classification as reflected in said compilation of additional data and as related to said security sensitive words, characters or data objects. | 69. An information processing system as claimed in claim 68 wherein said means for retrieving includes means for retrieving taxonomic words, characters or data objects from said compilation of additional data related to said security sensitive words, characters or data objects and based upon categorization and classification as reflected in said compilation of additional data and as related to said security sensitive words, characters or data objects. 70. An information processing system as claimed in claim 69 wherein said means for storing the extracted data separately from said remainder data is based upon multiple security levels. | 0.946408 |
9,262,397 | 1 | 8 | 1. A computer-implemented error correction system, comprising: an error detection component that collects scores based on word usage in a sequence of multiple words intended to form a fluent language expression, generates a signal for presence of an error in a subsequence of two or more words of the sequence based on the scores, and detects an erroneous subsequence of two or more words in the sequence of multiple words; wherein the error detection component operates as a sliding window covering subsequences of two or more words of the sequence of multiple words; a potential candidate generation component that generates potential candidate strings of words related to the erroneous subsequence of words from a corpus, each of the candidate strings having a variable number of words compared a number of words of the erroneous subsequence; a candidate selection component that selects and presents one or more of the potential candidate strings as candidate strings for correction of language fluency introduced by the erroneous subsequence of words; and a processor that executes computer-executable instructions associated with at least one of the error detection component, potential candidate generation component, or candidate selection component. | 1. A computer-implemented error correction system, comprising: an error detection component that collects scores based on word usage in a sequence of multiple words intended to form a fluent language expression, generates a signal for presence of an error in a subsequence of two or more words of the sequence based on the scores, and detects an erroneous subsequence of two or more words in the sequence of multiple words; wherein the error detection component operates as a sliding window covering subsequences of two or more words of the sequence of multiple words; a potential candidate generation component that generates potential candidate strings of words related to the erroneous subsequence of words from a corpus, each of the candidate strings having a variable number of words compared a number of words of the erroneous subsequence; a candidate selection component that selects and presents one or more of the potential candidate strings as candidate strings for correction of language fluency introduced by the erroneous subsequence of words; and a processor that executes computer-executable instructions associated with at least one of the error detection component, potential candidate generation component, or candidate selection component. 8. The system of claim 1 , wherein the potential candidate generation component generates the potential candidate strings based on number of words in the potential candidate strings that match words in the erroneous subsequence. | 0.669565 |
7,545,809 | 9 | 10 | 9. The method of claim 7 , further comprising: for each rule set, adding a rule set identifier to the search key to generate a final search key for that rule set; and performing the final search for each rule set using the final search key for that rule set, wherein the final searches are performed using a combined search structure comprising the rule data for all rule sets, the rule data for each rule set being associated in the search structure with the rule set identifier for that rule set. | 9. The method of claim 7 , further comprising: for each rule set, adding a rule set identifier to the search key to generate a final search key for that rule set; and performing the final search for each rule set using the final search key for that rule set, wherein the final searches are performed using a combined search structure comprising the rule data for all rule sets, the rule data for each rule set being associated in the search structure with the rule set identifier for that rule set. 10. The method of claim 9 , wherein, for each rule set identifier, the search structure relates possible combinations of range identifiers for data items corresponding to rule ranges defined in the rule set associated with that rule set identifier to applicable rules in that rule set. | 0.915077 |
8,259,124 | 1 | 2 | 1. A computer-implemented method for enhancing the visibility of keywords in search results, the method comprising: receiving search results from a search engine based on a search query, wherein the search query contains one or more keywords; identifying occurrences of keywords in the search results; displaying the search results in a user interface; applying a first highlight effect to identified occurrences of keywords in the displayed search results; detecting an occurrence of a highlighting change event; and after detecting the highlighting change event, animating a transition between the first highlight effect and a second highlight effect; and applying the second highlight effect to the displayed search results, such that a user receives an initial noticeable highlight that fades to a level that is still noticeable but is less likely to distract the user from other elements of the user interface. | 1. A computer-implemented method for enhancing the visibility of keywords in search results, the method comprising: receiving search results from a search engine based on a search query, wherein the search query contains one or more keywords; identifying occurrences of keywords in the search results; displaying the search results in a user interface; applying a first highlight effect to identified occurrences of keywords in the displayed search results; detecting an occurrence of a highlighting change event; and after detecting the highlighting change event, animating a transition between the first highlight effect and a second highlight effect; and applying the second highlight effect to the displayed search results, such that a user receives an initial noticeable highlight that fades to a level that is still noticeable but is less likely to distract the user from other elements of the user interface. 2. The method of claim 1 wherein receiving search results comprises receiving results asynchronously as the search engine finds matching results. | 0.779635 |
7,969,457 | 13 | 14 | 13. The method of claim 12 , wherein the housing has a memory which stores the information inputted via the input element, the memory in communication with the input element. | 13. The method of claim 12 , wherein the housing has a memory which stores the information inputted via the input element, the memory in communication with the input element. 14. The method of claim 13 , wherein the housing has a CPU in communication with the memory and the clock that transfers the information from the memory to the stamp. | 0.929721 |
8,175,872 | 1 | 5 | 1. A system comprising: one or more computers; and a computer-readable medium coupled to the one or more computers having instructions stored thereon which, when executed by the one or more computers, cause the one or more computers to perform operations comprising: receiving geotagged audio signals that correspond to environmental audio recorded by multiple mobile devices in multiple geographic locations, receiving an audio signal that corresponds to an utterance recorded by a particular mobile device, determining a particular geographic location associated with the particular mobile device, selecting a subset of geotagged audio signals, and weighting each geotagged audio signal of the subset based on whether the respective audio signal was manually uploaded or automatically updated, generating a noise model for the particular geographic location using the subset of weighted geotagged audio signals, and performing noise compensation on the audio signal that corresponds to the utterance using the noise model that has been generated for the particular geographic location. | 1. A system comprising: one or more computers; and a computer-readable medium coupled to the one or more computers having instructions stored thereon which, when executed by the one or more computers, cause the one or more computers to perform operations comprising: receiving geotagged audio signals that correspond to environmental audio recorded by multiple mobile devices in multiple geographic locations, receiving an audio signal that corresponds to an utterance recorded by a particular mobile device, determining a particular geographic location associated with the particular mobile device, selecting a subset of geotagged audio signals, and weighting each geotagged audio signal of the subset based on whether the respective audio signal was manually uploaded or automatically updated, generating a noise model for the particular geographic location using the subset of weighted geotagged audio signals, and performing noise compensation on the audio signal that corresponds to the utterance using the noise model that has been generated for the particular geographic location. 5. The system of claim 1 , wherein the operations further comprise: determining, for each of the geotagged audio signals, a distance between the particular geographic location and a geographic location associated the geotagged audio signal; and selecting, as the subset of the geotagged audio signals, the geotagged audio signals that are associated with geographic locations which are within a predetermined distance of the particular geographic location, or that are associated with geographic locations which are among the N closest geographic locations to the particular geographic location. | 0.500839 |
8,171,084 | 21 | 26 | 21. A computing device storage media containing instructions that are executable by a computer to perform actions comprising: creating a custom graphical emoticon by selecting an image associated with the custom graphical emoticon by a sender; representing the image as a single array grid of pixels for the custom graphical emoticon; assigning a character sequence to the custom graphical emoticon, wherein the character sequence is assignable by the sender; and transmitting a text message by the sender along with the character sequence to a destination to allow for reconstruction of the custom graphical emoticon at the destination, wherein the custom graphical emoticon is substituted within the text message for the character sequence within the text message, and both the text message and the custom graphical emoticon are to be received in the same dialog. | 21. A computing device storage media containing instructions that are executable by a computer to perform actions comprising: creating a custom graphical emoticon by selecting an image associated with the custom graphical emoticon by a sender; representing the image as a single array grid of pixels for the custom graphical emoticon; assigning a character sequence to the custom graphical emoticon, wherein the character sequence is assignable by the sender; and transmitting a text message by the sender along with the character sequence to a destination to allow for reconstruction of the custom graphical emoticon at the destination, wherein the custom graphical emoticon is substituted within the text message for the character sequence within the text message, and both the text message and the custom graphical emoticon are to be received in the same dialog. 26. The computing device storage media as recited in claim 21 , further comprising instructions to retrieve the custom graphical emoticon. | 0.922732 |
7,644,058 | 2 | 4 | 2. The method of claim 1 , wherein said step of parsing said user input to create a boolean representation of said system includes the step of parsing at least one one-sided link. | 2. The method of claim 1 , wherein said step of parsing said user input to create a boolean representation of said system includes the step of parsing at least one one-sided link. 4. The method of claim 2 , wherein said step of parsing at least one one-sided link includes the step of making substitutions according to the following:
“always” translates to “([X])”; and
“never” translates to “(not [X])”; where X is a natural state of said system. | 0.930155 |
8,543,577 | 12 | 13 | 12. One or more machine-readable media configured to store instructions that are executable by one or more processing devices to perform operations comprising: receiving first information from a first type of channel and second information from a second type of channel, with the first type of channel differing from the second type of channel; merging the first information received from the first type of channel with the second information received from the second type of channel that differs from the first type of channel; applying an unsupervised clustering model to the merged information; and generating, based on results of the applying, a cross-channel cluster, the cross-channel cluster comprising (i) a portion of the first information received from the first type of channel associated with a subject matter, and (ii) a portion of the second information received from the second type of channel that differs from the first type of channel associated with the subject matter, wherein the cross-channel cluster comprises a cluster of information that is received from at least two different types of channels. | 12. One or more machine-readable media configured to store instructions that are executable by one or more processing devices to perform operations comprising: receiving first information from a first type of channel and second information from a second type of channel, with the first type of channel differing from the second type of channel; merging the first information received from the first type of channel with the second information received from the second type of channel that differs from the first type of channel; applying an unsupervised clustering model to the merged information; and generating, based on results of the applying, a cross-channel cluster, the cross-channel cluster comprising (i) a portion of the first information received from the first type of channel associated with a subject matter, and (ii) a portion of the second information received from the second type of channel that differs from the first type of channel associated with the subject matter, wherein the cross-channel cluster comprises a cluster of information that is received from at least two different types of channels. 13. The one or more machine-readable of claim 12 , wherein the operations further comprise: applying a supervised labeling model to identify a label for the cross-channel cluster. | 0.709416 |
9,330,065 | 15 | 16 | 15. Apparatus, comprising: a computer-readable memory storing computer-readable instructions; and a data processor coupled to the memory, operable to execute the instructions, and based at least in part on the execution of the instructions operable to perform operations comprising: displaying a graphical user interface comprising a template design area; based on one or more commands received through the graphical user interface from a user, arranging a layout of template elements with respective relative positions and sizes in the template design area, wherein at least one of the template elements is designated for receiving variable document content, receiving values of parameters characterizing one or more associated adaptive layout variables that constrain adaptability of respective ones of the template elements to different document content, wherein the parameters characterizes a degree of variability about a mean value of the associated adaptive layout variable, grouping respective ones of the template elements into a template element group, and receiving a specification of an optimization path and path groups that define the logical relationships between the template elements; encoding the relative positions of the template elements in the template design area in association with the respective parameter values and the optimization path and path groups in a variable document template data structures; and displaying a graphic representation of the optimization path and path groups in the template design area. | 15. Apparatus, comprising: a computer-readable memory storing computer-readable instructions; and a data processor coupled to the memory, operable to execute the instructions, and based at least in part on the execution of the instructions operable to perform operations comprising: displaying a graphical user interface comprising a template design area; based on one or more commands received through the graphical user interface from a user, arranging a layout of template elements with respective relative positions and sizes in the template design area, wherein at least one of the template elements is designated for receiving variable document content, receiving values of parameters characterizing one or more associated adaptive layout variables that constrain adaptability of respective ones of the template elements to different document content, wherein the parameters characterizes a degree of variability about a mean value of the associated adaptive layout variable, grouping respective ones of the template elements into a template element group, and receiving a specification of an optimization path and path groups that define the logical relationships between the template elements; encoding the relative positions of the template elements in the template design area in association with the respective parameter values and the optimization path and path groups in a variable document template data structures; and displaying a graphic representation of the optimization path and path groups in the template design area. 16. The apparatus of claim 15 , further comprising solving for an optimal layout of the variable document content over multiple pages by simultaneously allocating the variable document content to a sequence of multiple variable document template data structures in a table library and solving for template parameters that allow maximal page fill of the variable document content by assigning a probability to the multiple variable document template data structures for each page wherein the probability is indicative of maximal page fill and selecting a respective document template data structure for each page that has the highest probability. | 0.500774 |
7,809,668 | 4 | 7 | 4. The method of claim 1 , wherein the step of determining the degree of oscillation of the oscillatory behavior response and determining the leading cause of the oscillatory behavior response from the calculated modified damped natural frequency and the calculated modified damping ratio of the oscillatory behavior response further comprises utilizing fuzzy inference rules to identify a change factor vector to adjust the ultimate set of tuning parameters. | 4. The method of claim 1 , wherein the step of determining the degree of oscillation of the oscillatory behavior response and determining the leading cause of the oscillatory behavior response from the calculated modified damped natural frequency and the calculated modified damping ratio of the oscillatory behavior response further comprises utilizing fuzzy inference rules to identify a change factor vector to adjust the ultimate set of tuning parameters. 7. The method of claim 4 , wherein the fuzzy inference rules further comprises five linguistic values for each one of the tuning parameters' change factors, including decrease-a-lot, decrease-a-few, do-not-change, increase-a-few, and increase-a-lot. | 0.898863 |
9,804,862 | 1 | 3 | 1. A data processing method in a software-as-a-service platform of in context localization permitting a translator to view the appearance of textual language translations of a first application in real-time, comprising: a. substituting by a remote translation server, pseudo-language identifiers for an application text that is to be translated from a first language to a second language, wherein the pseudo-language identifiers comprise a sequence of characters indicating a string identifier at the remote translation server, or in a resource file on the end user electronic computing device that the application text was imported from; b. replacing by a translator computing device in response to the translator's running of the application, the pseudo-language identifiers with the text in the first language and making the text user selectable or clickable; c. launching by the translator computing device, a localization editor in response to the translator selecting a translated text, the editor comprising a graphical user interface simultaneously displaying in real-time inputted textual translations or machine translations and the appearance of the translation in the application; d. storing the translated text within a memory of the remote translation server; e. wherein the in context localization is performable in web based applications, content websites, and non-web based applications comprising Android and iOS applications; and f. wherein the pseudo-language identifier comprises the format of: {reserved-word|string-identifier-at-the-server|:plural-form-of-the-source-string}. | 1. A data processing method in a software-as-a-service platform of in context localization permitting a translator to view the appearance of textual language translations of a first application in real-time, comprising: a. substituting by a remote translation server, pseudo-language identifiers for an application text that is to be translated from a first language to a second language, wherein the pseudo-language identifiers comprise a sequence of characters indicating a string identifier at the remote translation server, or in a resource file on the end user electronic computing device that the application text was imported from; b. replacing by a translator computing device in response to the translator's running of the application, the pseudo-language identifiers with the text in the first language and making the text user selectable or clickable; c. launching by the translator computing device, a localization editor in response to the translator selecting a translated text, the editor comprising a graphical user interface simultaneously displaying in real-time inputted textual translations or machine translations and the appearance of the translation in the application; d. storing the translated text within a memory of the remote translation server; e. wherein the in context localization is performable in web based applications, content websites, and non-web based applications comprising Android and iOS applications; and f. wherein the pseudo-language identifier comprises the format of: {reserved-word|string-identifier-at-the-server|:plural-form-of-the-source-string}. 3. The data processing method of claim 1 , wherein the pseudo-language identifiers are generated by the remote translation server, then transmitted to and stored on the translator electronic computing device in a separate integration library. | 0.934595 |
8,719,192 | 12 | 14 | 12. A computer implemented method, comprising: computing similarity distances between a plurality of source domains and a new domain; discarding at least one of the plurality of source domains with a similarity distance that is greater than a predefined distance threshold to generate a remaining source domain; extracting seed query patterns for the remaining source domain from a search engine log; labeling each of one or more seed query patterns with an associated task classification and an associated task probability that represent a probability that a corresponding seed query pattern belongs to the associated task classification; and generating additional query patterns for the remaining source domain based at least on the seed query patterns to form an expanded set of query patterns, each of the additional query patterns having a corresponding task classification and a corresponding task probability; and transferring one or more resultant query patterns from the expanded set of query patterns to a new domain, the transferring comprising: selecting one or more resultant query patterns of the remaining source domain for the new domain based at least on consistency between the one or more resultant query patterns and the seed query patterns; and transferring the one or more resultant query patterns that are selected to the new domain. | 12. A computer implemented method, comprising: computing similarity distances between a plurality of source domains and a new domain; discarding at least one of the plurality of source domains with a similarity distance that is greater than a predefined distance threshold to generate a remaining source domain; extracting seed query patterns for the remaining source domain from a search engine log; labeling each of one or more seed query patterns with an associated task classification and an associated task probability that represent a probability that a corresponding seed query pattern belongs to the associated task classification; and generating additional query patterns for the remaining source domain based at least on the seed query patterns to form an expanded set of query patterns, each of the additional query patterns having a corresponding task classification and a corresponding task probability; and transferring one or more resultant query patterns from the expanded set of query patterns to a new domain, the transferring comprising: selecting one or more resultant query patterns of the remaining source domain for the new domain based at least on consistency between the one or more resultant query patterns and the seed query patterns; and transferring the one or more resultant query patterns that are selected to the new domain. 14. The computer implemented method of claim 12 , further comprising preparing the new domain to receive a plurality of query patterns from the expanded set of query patterns from the remaining source domain by populating the new domain with seed query patterns extracted from the a search engine log. | 0.762243 |
9,582,230 | 24 | 25 | 24. The system of claim 19 , wherein: the image capturing device is a component of a multifunction device that further comprises an image scanner and a print device; the programming instructions are further configured to: cause the image scanner to capture the image of the printed form, and cause the processing device to generate the image file from the captured image; the instructions to cause the document generation device to generate the document comprising the form with the selected candidate value comprise instructions to cause the print device to print, on a substrate, the form with the selected candidate value displayed in the identified fill-in field. | 24. The system of claim 19 , wherein: the image capturing device is a component of a multifunction device that further comprises an image scanner and a print device; the programming instructions are further configured to: cause the image scanner to capture the image of the printed form, and cause the processing device to generate the image file from the captured image; the instructions to cause the document generation device to generate the document comprising the form with the selected candidate value comprise instructions to cause the print device to print, on a substrate, the form with the selected candidate value displayed in the identified fill-in field. 25. The system of claim 24 , wherein: the substrate comprises the printed form; and the instructions to cause the print device to print the form on the substrate also comprise instructions to cause the print device to remove the handwritten symbol from the fill-in field by erasing or overwriting the handwritten symbol. | 0.890934 |
8,861,796 | 10 | 13 | 10. A method of displaying one or more sentences of a patent document that include a first element name, said method comprising: providing a patent document, wherein said patent document includes text data; identifying a first element number within said text data; identifying a first element name within said text data associated with said first element number, wherein said step of identifying a first element name comprises: providing a non-element database; identifying a plurality of first words to the left of said first element number within said text data; and identifying a preceding identifier within said plurality of first words, wherein said preceding identifier is within said non-element database, wherein said first element name is comprised of one or more words between said first element number and said preceding identifier; and displaying a corresponding text portion of said text data that includes said first element name, wherein said corresponding text portion is comprised of at least one sentence that includes said first element name and said first element number; wherein said text data includes a plurality of element numbers associated with a plurality of element names, wherein said plurality of element numbers includes said first element number associated with said first element name. | 10. A method of displaying one or more sentences of a patent document that include a first element name, said method comprising: providing a patent document, wherein said patent document includes text data; identifying a first element number within said text data; identifying a first element name within said text data associated with said first element number, wherein said step of identifying a first element name comprises: providing a non-element database; identifying a plurality of first words to the left of said first element number within said text data; and identifying a preceding identifier within said plurality of first words, wherein said preceding identifier is within said non-element database, wherein said first element name is comprised of one or more words between said first element number and said preceding identifier; and displaying a corresponding text portion of said text data that includes said first element name, wherein said corresponding text portion is comprised of at least one sentence that includes said first element name and said first element number; wherein said text data includes a plurality of element numbers associated with a plurality of element names, wherein said plurality of element numbers includes said first element number associated with said first element name. 13. The method of claim 10 , wherein said step of identifying a first element name comprises: providing said non-element database comprised of proper nouns, verbs, punctuation marks, symbols and/or months; identifying at least one word to the left of said first element number within said text data; determining if a first word to the left of said first element number is a non-element word included in said non-element database; and identifying said first word as part of said first element name if said first word is not within said non-element database. | 0.500898 |
5,530,794 | 13 | 14 | 13. A method for enabling a word processing system to properly identify and display paragraphs in a document that includes pasted text, said pasted text using a different type of paragraph delimiter than that used by the word processing system, comprising the steps of: (a) assigning each character in the document a coordinate in a piece table, said piece table comprising a character position array and an array of data records, said character position array including a plurality of pieces, each piece comprising at least one character, all characters in each piece having common formatting properties and being stored in a contiguous string within a common file, said array of data records including a separate record corresponding to one of the pieces in the array of character positions; (b) in each record of the array of data records, including a pointer to a file control block for the file in which all of the characters of the text in the piece are stored; (c) providing delimiter data bits in each of the file control blocks that indicate the type of paragraph delimiter used for the text stored in each file; (d) when a character of the document is selected by a user for display, determining a specific file control block for the file in which the character is stored, based upon the position of the character in the piece table; (e) identifying the paragraph delimiter that was used in the text, as a function of the delimiter data bits in the specific file control block determined in step (c); and (f) properly displaying the paragraph in which the character selected by the user is contained by translating any paragraph delimiters in the text displayed to the type of paragraph delimiter used by the word processing system. | 13. A method for enabling a word processing system to properly identify and display paragraphs in a document that includes pasted text, said pasted text using a different type of paragraph delimiter than that used by the word processing system, comprising the steps of: (a) assigning each character in the document a coordinate in a piece table, said piece table comprising a character position array and an array of data records, said character position array including a plurality of pieces, each piece comprising at least one character, all characters in each piece having common formatting properties and being stored in a contiguous string within a common file, said array of data records including a separate record corresponding to one of the pieces in the array of character positions; (b) in each record of the array of data records, including a pointer to a file control block for the file in which all of the characters of the text in the piece are stored; (c) providing delimiter data bits in each of the file control blocks that indicate the type of paragraph delimiter used for the text stored in each file; (d) when a character of the document is selected by a user for display, determining a specific file control block for the file in which the character is stored, based upon the position of the character in the piece table; (e) identifying the paragraph delimiter that was used in the text, as a function of the delimiter data bits in the specific file control block determined in step (c); and (f) properly displaying the paragraph in which the character selected by the user is contained by translating any paragraph delimiters in the text displayed to the type of paragraph delimiter used by the word processing system. 14. The method of claim 13, wherein the delimiter data bits include a bit indicating whether the paragraph delimiter includes a carriage return character and a bit indicating whether the paragraph delimiter includes two characters instead of one character. | 0.860262 |
9,721,241 | 1 | 2 | 1. A system for activating a document handler having a document validator comprising: an inlet for receiving documents; a validation passageway for guiding a document inserted into the inlet; a validation conveyor for transporting the document along the validation passageway; a validation sensor for detecting optical or magnetic features of the document transported along the validation passageway to produce detection signals; a control device for receiving detection signals from the validation sensor to decide whether the document is genuine or not; a clock in electronic communication with the control device for tracking a time period; and a memory in electronic communication with the control device for storing a set of unique time codes; wherein a timing ticket that provides an activation code is inserted into the inlet and transported along the passageway to detect the activation code on the timing ticket by the validation sensor that forwards the activation code to the control device; the control device activates to operate the document handler for a predefined time period tracked by the clock in accordance with: (a) the activation code matching one of the unique time codes in the memory; (b) the activation code including a specified time period within the activation code; or (c) a separate time value being detected on or corresponding to the timing ticket. | 1. A system for activating a document handler having a document validator comprising: an inlet for receiving documents; a validation passageway for guiding a document inserted into the inlet; a validation conveyor for transporting the document along the validation passageway; a validation sensor for detecting optical or magnetic features of the document transported along the validation passageway to produce detection signals; a control device for receiving detection signals from the validation sensor to decide whether the document is genuine or not; a clock in electronic communication with the control device for tracking a time period; and a memory in electronic communication with the control device for storing a set of unique time codes; wherein a timing ticket that provides an activation code is inserted into the inlet and transported along the passageway to detect the activation code on the timing ticket by the validation sensor that forwards the activation code to the control device; the control device activates to operate the document handler for a predefined time period tracked by the clock in accordance with: (a) the activation code matching one of the unique time codes in the memory; (b) the activation code including a specified time period within the activation code; or (c) a separate time value being detected on or corresponding to the timing ticket. 2. The system of claim 1 , wherein the predefined time period tracked by the clock corresponding to the activation code corresponds to a predefined financial payment. | 0.905359 |
8,321,371 | 14 | 15 | 14. The method of claim 13 wherein performing an initialization comprises: expanding “that” patterns; arranging “that” patterns alphabetically; assigning a specificity rank to each “that” pattern; and adding each “that” pattern to a “that” trie. | 14. The method of claim 13 wherein performing an initialization comprises: expanding “that” patterns; arranging “that” patterns alphabetically; assigning a specificity rank to each “that” pattern; and adding each “that” pattern to a “that” trie. 15. The method of claim 14 wherein performing an initialization comprises: expanding megacategories into conjunctions; ordering the conjunctions alphabetically; and adding each megacategory to a megacategory trie. | 0.846542 |
8,612,447 | 3 | 4 | 3. The method of claim 2 , wherein automatically generating a plurality of first feature bins further comprises, prior to generating the plurality of first feature bins: performing peak detection on the probability distribution of values of the first feature, and wherein generating the plurality of first feature bins based on the probability distribution for the values of the first feature comprises generating the plurality of first feature bins based on the peaks. | 3. The method of claim 2 , wherein automatically generating a plurality of first feature bins further comprises, prior to generating the plurality of first feature bins: performing peak detection on the probability distribution of values of the first feature, and wherein generating the plurality of first feature bins based on the probability distribution for the values of the first feature comprises generating the plurality of first feature bins based on the peaks. 4. The method of claim 3 , wherein generating the plurality of first feature bins based on the peaks comprises: performing k-means clustering at the detected peaks. | 0.937213 |
8,001,119 | 10 | 11 | 10. The method of claim 6 , wherein the step of adaptive information selection based on evolving analytic context persisted in the analytic action graph further comprises permitting an observer to assess the analytic context for information gathering based on the analytic action graph. | 10. The method of claim 6 , wherein the step of adaptive information selection based on evolving analytic context persisted in the analytic action graph further comprises permitting an observer to assess the analytic context for information gathering based on the analytic action graph. 11. The method of claim 10 , wherein the step of adaptive information selection based on evolving analytic context persisted in the analytic action graph further comprises permitting the observer to recommend one or more changes to the analytic action graph to improve information gathering. | 0.868919 |
9,244,905 | 1 | 2 | 1. A method comprising: determining text that recurs in one or more past communications associated with at least one context attribute; storing a text entry in a first text suggestion dictionary, the text entry comprising the text and first metadata associating the text with the at least a first context attribute; using the first text suggestion dictionary, determining at least one predicted-text suggestion for a current communication associated with a communication context that comprises the at least first context attribute, the at least one predicted-text suggestion including the text; and using the first text suggestion dictionary for email messages and further including using a second text suggestion dictionary for text messages; storing second metadata in the second text suggestion dictionary, the second metadata associating text with other context attributes than the first context attribute so that separate metadata is stored for different contexts and text in each of the first and second text suggestion dictionaries is associated with the metadata through edges. | 1. A method comprising: determining text that recurs in one or more past communications associated with at least one context attribute; storing a text entry in a first text suggestion dictionary, the text entry comprising the text and first metadata associating the text with the at least a first context attribute; using the first text suggestion dictionary, determining at least one predicted-text suggestion for a current communication associated with a communication context that comprises the at least first context attribute, the at least one predicted-text suggestion including the text; and using the first text suggestion dictionary for email messages and further including using a second text suggestion dictionary for text messages; storing second metadata in the second text suggestion dictionary, the second metadata associating text with other context attributes than the first context attribute so that separate metadata is stored for different contexts and text in each of the first and second text suggestion dictionaries is associated with the metadata through edges. 2. The method of claim 1 further comprising receiving input for the current communication; and wherein the determining the at least one predicted-text suggestion comprises: based on the input and the communication context, including the text of the text entry in the at least one predicted-text suggestion. | 0.712946 |
7,685,512 | 37 | 45 | 37. A computer readable medium for use in validating an eXtensible Markup Language (XML) message, said computer readable medium containing computer-executable instructions which, when performed by a processor in a computing device, cause the computing device to: from a custom XML schema based message model having at least one logical model extension, generate an XML schema fragment for use in validating said XML message at a node which lacks said custom XML schema based message model, wherein said custom XML schema based message model comprises a logical model of said XML message that complies with a standard XML schema, and wherein said custom XML schema based message model comprises a physical model that customizes said message using a logical model extension that is unsupported by said standard XML schema, wherein said custom XML schema based message model has an original group representing at least a portion of said XML message, said logical model extension being associated with said original group, and wherein said original group has an original sequence declaration containing a set of original subordinate entities, and wherein generating said XML schema fragment comprises: generating a new group; generating a sequence declaration within said new group, said sequence declaration having a minimum occurrence attribute with a value equivalent to a total number of said original subordinate entities in said set; and generating a choice declaration within said sequence declaration, said choice declaration containing said set of original subordinate entities. | 37. A computer readable medium for use in validating an eXtensible Markup Language (XML) message, said computer readable medium containing computer-executable instructions which, when performed by a processor in a computing device, cause the computing device to: from a custom XML schema based message model having at least one logical model extension, generate an XML schema fragment for use in validating said XML message at a node which lacks said custom XML schema based message model, wherein said custom XML schema based message model comprises a logical model of said XML message that complies with a standard XML schema, and wherein said custom XML schema based message model comprises a physical model that customizes said message using a logical model extension that is unsupported by said standard XML schema, wherein said custom XML schema based message model has an original group representing at least a portion of said XML message, said logical model extension being associated with said original group, and wherein said original group has an original sequence declaration containing a set of original subordinate entities, and wherein generating said XML schema fragment comprises: generating a new group; generating a sequence declaration within said new group, said sequence declaration having a minimum occurrence attribute with a value equivalent to a total number of said original subordinate entities in said set; and generating a choice declaration within said sequence declaration, said choice declaration containing said set of original subordinate entities. 45. The computer readable medium of claim 37 , wherein generating said XML schema fragment comprises: generating a new group; generating a sequence declaration within said new group; and generating an “any” element declaration within said sequence declaration, said “any” element declaration having a process content attribute specifying lax validation, a minimum occurrence attribute with a value of zero, and a maximum occurrence attribute with a value of “unbounded”. | 0.682003 |
9,563,399 | 22 | 24 | 22. The method of claim 1 , wherein the plurality of node types includes a variable count node type and the variable count node type indicates that the node is a greedy node, lazy node, possessive node, or all match node. | 22. The method of claim 1 , wherein the plurality of node types includes a variable count node type and the variable count node type indicates that the node is a greedy node, lazy node, possessive node, or all match node. 24. The method of claim 22 , wherein the variable count node type indicating the lazy node indicates matching for the shortest possible match in the payload. | 0.952309 |
10,123,028 | 7 | 9 | 7. A syntax parsing apparatus comprising: a plurality of syntax parsing circuits, each having at least entropy decoding capability, wherein the syntax parsing circuits are arranged to generate a plurality of entropy decoding results of a plurality of frames, respectively; and a dispatcher, arranged to assign bitstream start points of the frames to the syntax parsing circuits and trigger the syntax parsing circuits to start entropy decoding, respectively; wherein the syntax parsing circuits comprise: a first syntax parsing circuit, arranged to perform entropy decoding upon a first frame included in the frames during a first processing time period; and a second syntax parsing circuit, arranged to perform entropy decoding upon a second frame included in the frames during a second processing time period, wherein the second processing time period is overlapped with the first processing time period. | 7. A syntax parsing apparatus comprising: a plurality of syntax parsing circuits, each having at least entropy decoding capability, wherein the syntax parsing circuits are arranged to generate a plurality of entropy decoding results of a plurality of frames, respectively; and a dispatcher, arranged to assign bitstream start points of the frames to the syntax parsing circuits and trigger the syntax parsing circuits to start entropy decoding, respectively; wherein the syntax parsing circuits comprise: a first syntax parsing circuit, arranged to perform entropy decoding upon a first frame included in the frames during a first processing time period; and a second syntax parsing circuit, arranged to perform entropy decoding upon a second frame included in the frames during a second processing time period, wherein the second processing time period is overlapped with the first processing time period. 9. The syntax parsing apparatus of claim 7 , further comprising: a monitoring circuit, shared by the syntax parsing circuits and arranged to monitor availability of temporal reference data required by each of the syntax parsing circuits; wherein when temporal reference data required by entropy decoding of a processing unit is not available to a specific syntax parsing circuit, the monitoring circuit stalls the entropy decoding performed by the specific syntax parsing circuit. | 0.747634 |
7,539,619 | 1 | 2 | 1. A system providing cross-linguistic communication, comprising: a client component capturing speech and tactile inputs, the client component providing a user interface configured to display and correct the inputs and meanings for the inputs, a translation of the input term into a different language than an input language, and a back translation of the translation, i.e., a retranslation from the target language back into the source language, intended to help the user to judge the translation quality; and a server component providing the meanings, the translation and the back translation to the client component based upon the input term, the server component including, an interaction manager requesting the translation of the inputs, the interaction manager accessing a database of Meaning Cues corresponding to the inputs, the corresponding Meaning Cues being displayed through the client component to facilitate word sense selection for the inputs. | 1. A system providing cross-linguistic communication, comprising: a client component capturing speech and tactile inputs, the client component providing a user interface configured to display and correct the inputs and meanings for the inputs, a translation of the input term into a different language than an input language, and a back translation of the translation, i.e., a retranslation from the target language back into the source language, intended to help the user to judge the translation quality; and a server component providing the meanings, the translation and the back translation to the client component based upon the input term, the server component including, an interaction manager requesting the translation of the inputs, the interaction manager accessing a database of Meaning Cues corresponding to the inputs, the corresponding Meaning Cues being displayed through the client component to facilitate word sense selection for the inputs. 2. The system of claim 1 further comprising: at least two lexical resources, the first lexical resource including a dictionary database containing a plurality of words, wherein each of the plurality of words is associated with a meaning, the second lexical resource containing a plurality of word meanings in a central repository, each word meaning mapped to a corresponding word sense of the database of the Meaning Cues. | 0.501182 |
9,002,877 | 13 | 21 | 13. A system for determining a suitable font for displaying text, comprising: a non-transitory computer-readable storage medium; matrix generating means for generating a two-dimensional link matrix to link a plurality of available fonts for displaying text each identified in the matrix with one or more font attributes identified in the matrix via one or more attribute values identified in the matrix; receiving means for receiving a font match query to determine a suitable font for text to be displayed from the plurality of available fonts including one or more specified attribute-attribute value pairs; and font match determining means for determining a font satisfying the received font match query from the available fonts based on a match between the one or more specified attribute-attribute value pairs and the one or more attribute values of the two-dimensional link matrix to determine which font can correctly display the text wherein the font match determining means is further configured to: retrieve the two-dimensional link matrix for one or more of the attribute-attribute value pairs in the font match query to generate a set of fonts returned results. | 13. A system for determining a suitable font for displaying text, comprising: a non-transitory computer-readable storage medium; matrix generating means for generating a two-dimensional link matrix to link a plurality of available fonts for displaying text each identified in the matrix with one or more font attributes identified in the matrix via one or more attribute values identified in the matrix; receiving means for receiving a font match query to determine a suitable font for text to be displayed from the plurality of available fonts including one or more specified attribute-attribute value pairs; and font match determining means for determining a font satisfying the received font match query from the available fonts based on a match between the one or more specified attribute-attribute value pairs and the one or more attribute values of the two-dimensional link matrix to determine which font can correctly display the text wherein the font match determining means is further configured to: retrieve the two-dimensional link matrix for one or more of the attribute-attribute value pairs in the font match query to generate a set of fonts returned results. 21. The system according to claim 13 , wherein the font match query is generated by a text processing application. | 0.905785 |
5,466,072 | 6 | 10 | 6. A shorthand machine for recording and translating shorthand notes including a non-redundant binary tree look-up table comprising: a keyboard having keys representing letter symbols of a language; conversion means connected to said keyboard for generating a particular electric shorthand signal for each key or combination of keys pressed by an operator; a look-up table having a plurality of entries for electronically storing a translation dictionary; said look-up table further having main level and sublevel memory locations for storing said electric shorthand signals representing groups of one or more entry symbols, and electric signals representing groups of one or more translation symbols corresponding to the groups of entry symbols, said main level and sublevel memory locations containing no more than one of the groups of entry symbols; control means connected to said conversion means and to said look-up table; said control means capable of searching said look-up table entries to locate and read a particular electric translation signal corresponding to the particular electric shorthand signal generated at said conversion means; and display means connected to said control means for converting a predetermined number of said read translation signals into groups of display characters representing language words and displaying said words on a plurality of lines. | 6. A shorthand machine for recording and translating shorthand notes including a non-redundant binary tree look-up table comprising: a keyboard having keys representing letter symbols of a language; conversion means connected to said keyboard for generating a particular electric shorthand signal for each key or combination of keys pressed by an operator; a look-up table having a plurality of entries for electronically storing a translation dictionary; said look-up table further having main level and sublevel memory locations for storing said electric shorthand signals representing groups of one or more entry symbols, and electric signals representing groups of one or more translation symbols corresponding to the groups of entry symbols, said main level and sublevel memory locations containing no more than one of the groups of entry symbols; control means connected to said conversion means and to said look-up table; said control means capable of searching said look-up table entries to locate and read a particular electric translation signal corresponding to the particular electric shorthand signal generated at said conversion means; and display means connected to said control means for converting a predetermined number of said read translation signals into groups of display characters representing language words and displaying said words on a plurality of lines. 10. The device defined in claim 6 further comprising: a clock/calendar chip connected to said control means for generating at least one time parameter; and said control means being capable of transferring said at least one time parameter to said display means for displaying said time parameter. | 0.885035 |
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