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9,857,947 | 6 | 7 |
6. The computer-implemented method of claim 1 wherein receiving a metadata representation comprises: receiving a markup language representation of the dialog.
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6. The computer-implemented method of claim 1 wherein receiving a metadata representation comprises: receiving a markup language representation of the dialog. 7. The computer-implemented method of claim 6 wherein receiving a markup language representation of the dialog comprises; receiving an XML representation of the dialog.
| 0.5 |
8,112,401 | 1 | 5 |
1. A computer implemented method for analyzing an externally generated document for use in a document management system of the type having a Native Template database including a list of templates for one or more types of documents having common characteristics and a Conversion Database including a list of one or more data points associated with each listed document type, wherein each data point is of a particular Data Type, for example, a “number”, “date”, ‘text” or the like, one or more Descriptive Text entries associated with each listed data point, and Proximity information which describes the location of the data point in relation to the Descriptive Text, the method comprising the steps of: (a) introducing the externally generated document into the system; (b) indexing the externally generated document by recording the locations of words, sentences, paragraphs, and sections within the document; (c) selecting a document type from the Native Template database that has characteristics in common with the externally generated document; (d) selecting a data point from the template; (e) searching the introduced document for Possible Data Points based on the Data Type of the selected data point in the Conversion Database; (f) obtaining Proximity range information from the Conversion Database for the Descriptive Text entries associated with the selected data point; (g) determining whether Possible Data Point values for the selected data point are located within the Proximity range for each Descriptive Text entry, using the index information created in (b); (h) if Possible Data Point values for the selected data point are located within the Proximity range for a Descriptive Text entry, indicating the probability that a Possible Data Point that is the sought Data Type and that the Possible Data Point is located within the Proximity range of a Descriptive Text is therefore likely to be the sought Data Point, calculating a cumulative Evaluation Score for each Possible Data Point value based on its proximity to each Descriptive Text entry, or if no Possible Data Point values for the selected data point are located within the Proximity range for a Descriptive Text entry, leaving the Evaluation Score for that Possible Data Point value unchanged; (i) recording the Possible Data Point with the highest score above zero that has been accepted by the user; (j) upon user acceptance of a Possible Data Point, storing additional Descriptive Text entries in the Conversion Database, said stored additional Descriptive Text entries improving the probability of finding a Possible Data Point in other externally generated documents because appropriate Descriptive Text values and Proximity range have been found in the introduced document; and (k) repeating steps (d)-(j) until each data point has been selected.
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1. A computer implemented method for analyzing an externally generated document for use in a document management system of the type having a Native Template database including a list of templates for one or more types of documents having common characteristics and a Conversion Database including a list of one or more data points associated with each listed document type, wherein each data point is of a particular Data Type, for example, a “number”, “date”, ‘text” or the like, one or more Descriptive Text entries associated with each listed data point, and Proximity information which describes the location of the data point in relation to the Descriptive Text, the method comprising the steps of: (a) introducing the externally generated document into the system; (b) indexing the externally generated document by recording the locations of words, sentences, paragraphs, and sections within the document; (c) selecting a document type from the Native Template database that has characteristics in common with the externally generated document; (d) selecting a data point from the template; (e) searching the introduced document for Possible Data Points based on the Data Type of the selected data point in the Conversion Database; (f) obtaining Proximity range information from the Conversion Database for the Descriptive Text entries associated with the selected data point; (g) determining whether Possible Data Point values for the selected data point are located within the Proximity range for each Descriptive Text entry, using the index information created in (b); (h) if Possible Data Point values for the selected data point are located within the Proximity range for a Descriptive Text entry, indicating the probability that a Possible Data Point that is the sought Data Type and that the Possible Data Point is located within the Proximity range of a Descriptive Text is therefore likely to be the sought Data Point, calculating a cumulative Evaluation Score for each Possible Data Point value based on its proximity to each Descriptive Text entry, or if no Possible Data Point values for the selected data point are located within the Proximity range for a Descriptive Text entry, leaving the Evaluation Score for that Possible Data Point value unchanged; (i) recording the Possible Data Point with the highest score above zero that has been accepted by the user; (j) upon user acceptance of a Possible Data Point, storing additional Descriptive Text entries in the Conversion Database, said stored additional Descriptive Text entries improving the probability of finding a Possible Data Point in other externally generated documents because appropriate Descriptive Text values and Proximity range have been found in the introduced document; and (k) repeating steps (d)-(j) until each data point has been selected. 5. The method of claim 1 further comprising the step of assigning a preference to the descriptive text with the highest Evaluation Score, if the text associated with the selected data point is located in more than one location in the introduced document, based on the expectation that the probability that a possible Data Point is of the sought Data Type and is within the Proximity range and is under the Section Topic Header.
| 0.5 |
5,579,416 | 18 | 19 |
18. An apparatus according to claim 8, further comprising means for designating the at least one desired inputted character and the desired character pattern from the graphic patterns corresponding to the character patterns.
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18. An apparatus according to claim 8, further comprising means for designating the at least one desired inputted character and the desired character pattern from the graphic patterns corresponding to the character patterns. 19. An apparatus according to claim 18, wherein the parameters comprise information for modifying inputted characters.
| 0.611842 |
9,013,581 | 12 | 14 |
12. The method of claim 11 further comprising: comparing the digital fingerprint to a digital fingerprint template; responsive to the comparing, determining that the digital fingerprint matches the digital fingerprint template; and determining name data associated with the digital fingerprint template.
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12. The method of claim 11 further comprising: comparing the digital fingerprint to a digital fingerprint template; responsive to the comparing, determining that the digital fingerprint matches the digital fingerprint template; and determining name data associated with the digital fingerprint template. 14. The method of claim 12 further comprising processing the artistic work to include the name data in a digital watermark.
| 0.5 |
9,836,452 | 15 | 17 |
15. A computer-implemented method comprising: receiving a natural language expression, wherein the natural language expression includes at least one of words, terms, and phrases of text; creating a dialog hypothesis set from the natural language expression by using contextual information, wherein the dialog hypothesis set has a first dialog hypothesis corresponding to a first domain and a second dialog hypothesis corresponding to a second domain; generating, from a first domain engine component and a second domain engine component, a plurality of dialog responses for the dialog hypothesis set; ranking, by machine learning techniques, the first domain engine component and the second domain engine component based on an analysis of the plurality of the dialog responses; and performing an action with the highest ranked domain engine component.
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15. A computer-implemented method comprising: receiving a natural language expression, wherein the natural language expression includes at least one of words, terms, and phrases of text; creating a dialog hypothesis set from the natural language expression by using contextual information, wherein the dialog hypothesis set has a first dialog hypothesis corresponding to a first domain and a second dialog hypothesis corresponding to a second domain; generating, from a first domain engine component and a second domain engine component, a plurality of dialog responses for the dialog hypothesis set; ranking, by machine learning techniques, the first domain engine component and the second domain engine component based on an analysis of the plurality of the dialog responses; and performing an action with the highest ranked domain engine component. 17. The computer-implemented method of claim 15 , wherein the contextual information includes at least one of information extracted from a previously received natural language expression, a response to a previously received natural language expression, client context, and knowledge content.
| 0.602459 |
9,894,193 | 1 | 4 |
1. A method for controlling an electronic device being executed by a processor of the electronic device, the method comprising: establishing a central control tree for the electronic device; obtaining voice data; recognizing the voice data to obtain recognized voice data; and controlling an operation to the electronic device according to the central control tree based upon the recognized voice data; wherein the central control tree is established by: establishing a single control tree for each of a plurality of applications of the electronic device, and obtaining a plurality of single control trees, wherein each node of each of the plurality of single control trees corresponds to a serial number, wherein root nodes of the plurality of single control trees correspond to a same serial number, and other nodes of the plurality of single control trees correspond to different serial numbers; and obtaining the central control tree by merging the plurality of single control trees based upon the serial number corresponding to each node of each of the plurality of single control trees, wherein nodes corresponding to the same serial number are merged to be one node, and the other nodes corresponding to different serial numbers are correspondingly added to the central control tree; wherein the method further comprises: displaying the central control tree on a display screen of the electronic device in response to user's input; deleting a single control tree corresponding to one of the plurality of applications from the central control tree in response to a first signal; and adding a single control tree corresponding to a new application to the central control tree in response to a second signal.
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1. A method for controlling an electronic device being executed by a processor of the electronic device, the method comprising: establishing a central control tree for the electronic device; obtaining voice data; recognizing the voice data to obtain recognized voice data; and controlling an operation to the electronic device according to the central control tree based upon the recognized voice data; wherein the central control tree is established by: establishing a single control tree for each of a plurality of applications of the electronic device, and obtaining a plurality of single control trees, wherein each node of each of the plurality of single control trees corresponds to a serial number, wherein root nodes of the plurality of single control trees correspond to a same serial number, and other nodes of the plurality of single control trees correspond to different serial numbers; and obtaining the central control tree by merging the plurality of single control trees based upon the serial number corresponding to each node of each of the plurality of single control trees, wherein nodes corresponding to the same serial number are merged to be one node, and the other nodes corresponding to different serial numbers are correspondingly added to the central control tree; wherein the method further comprises: displaying the central control tree on a display screen of the electronic device in response to user's input; deleting a single control tree corresponding to one of the plurality of applications from the central control tree in response to a first signal; and adding a single control tree corresponding to a new application to the central control tree in response to a second signal. 4. The method according to claim 1 , wherein plurality of single control trees are obtained by: (a) determining first text information corresponding to a first function of the electronic device, creating a root node of a single control tree and establishing a first relationship between the root node and the first text information; (b) determining second text information corresponding to one of a plurality of applications, creating a father node of the single control tree, and establishing a second relationship between the second text information and the father node; (C) determining third text information corresponding to each of a plurality of function options of the one of the plurality of applications, creating a plurality of child nodes for the father node according to a number of the plurality of function options, and establishing a third relationship between each of the plurality of child nodes and corresponding third text information; and establishing a single control tree for each of other applications of the plurality of applications according to the steps (a)-(c), to obtain the plurality of single control trees each of which corresponds to one of the plurality of the applications.
| 0.5 |
7,770,104 | 3 | 4 |
3. A method according to claim 2 , wherein the new text body includes HTML document hyperlink tag text prepended with link count text.
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3. A method according to claim 2 , wherein the new text body includes HTML document hyperlink tag text prepended with link count text. 4. A method according to claim 3 , wherein the new text body includes title HTML document text.
| 0.5 |
8,141,003 | 6 | 9 |
6. A method for improving user navigation of a multi-page document on a small screen user device, the method comprising: presenting one page of the multi-page document to a user on a screen of the small screen user device; caching one or more pages of the multi-page document as the user progresses through the multi-page document; deleting cached data associated with one or more successive pages when the user goes back to an earlier page of the multi-page document; receiving a request from the user to view the multi-page document as a full-page document; comparing text of the cached one or more pages to text of the full-page document to determine a first appearance of non-duplicative text in the full-page document; returning all pages of the full-page document together; and presenting the full-page document to the user with the screen of the small screen user device showing a page comprising the first appearance of non-duplicative text of the full-page document.
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6. A method for improving user navigation of a multi-page document on a small screen user device, the method comprising: presenting one page of the multi-page document to a user on a screen of the small screen user device; caching one or more pages of the multi-page document as the user progresses through the multi-page document; deleting cached data associated with one or more successive pages when the user goes back to an earlier page of the multi-page document; receiving a request from the user to view the multi-page document as a full-page document; comparing text of the cached one or more pages to text of the full-page document to determine a first appearance of non-duplicative text in the full-page document; returning all pages of the full-page document together; and presenting the full-page document to the user with the screen of the small screen user device showing a page comprising the first appearance of non-duplicative text of the full-page document. 9. The method of claim 6 , wherein the presentation of the full-page document on the screen of the small screen user device comprises the first line of the first appearance of non-duplicative text.
| 0.749364 |
8,892,441 | 9 | 14 |
9. A system comprising: a processor; a computer-readable storage medium having instructions stored which, when executed by the processor, cause the processor to perform operations comprising: overgenerating potential pronunciations based on symbolic input by converting portions of the symbolic input into a number of possible lexical pronunciation variants based on an established set of conversion rules, wherein the symbolic input comprises labeled speech data; identifying potential pronunciations in a speech recognition context to yield identified potential pronunciations; and storing the identified potential pronunciations in a lexicon.
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9. A system comprising: a processor; a computer-readable storage medium having instructions stored which, when executed by the processor, cause the processor to perform operations comprising: overgenerating potential pronunciations based on symbolic input by converting portions of the symbolic input into a number of possible lexical pronunciation variants based on an established set of conversion rules, wherein the symbolic input comprises labeled speech data; identifying potential pronunciations in a speech recognition context to yield identified potential pronunciations; and storing the identified potential pronunciations in a lexicon. 14. The system of claim 9 , the computer-readable storage medium having additional instructions which result in operations comprising iteratively retraining the established set of conversion rules based on improved pronunciations.
| 0.5 |
9,053,185 | 6 | 7 |
6. A system of generating a representative model for a plurality of different models identified by similar feature data, the system comprising: a memory; and one or more processors, the processors configured to receive a first model and a second model, each of the first model and the second model configured for use in identifying a second set of network user identifiers as similar to a first set of network user identifiers; receive feature data associated with each of the first model and the second model, each feature data having a corresponding weight data; select a network user identifier pool including a plurality of network user identifiers, a subset of the network user identifier pool including at least one network user identifier that is included in at least one of the second set of network user identifiers identified by at least one of the first model or the second model plurality of different models and at least one network user identifier that is not included in the at least one of the second set of network user identifiers identified by at least one of the plurality of different models; determine, for each model of the first model and the second model, from the network user identifier pool, a network user identifier identified as similar to the first set of network user identifiers of the model; determining an overlap between positive predictions and negative predictions of the first model and the second model, a positive prediction between the first model and the second model occurring when each of the first model and the second model identifies a network user identifier from the network user identifier pool as a similar network user and a negative prediction between the first model and the second model occurring when either the first model identifies a network user identifier from the network user identifier pool that is not identified by the second model or the second model identifies a network user identifier from the network user identifier pool that is not identified by the first model; and calculate, for the first model and the second model, a degree of overlap between the positive predictions and the negative predictions; identify that the first model and the second model are similar responsive to determining that the degree of overlap is greater than a threshold value; and generate the representative model to represent the first model and the second model, the representative model configured for use in generating a second set of network user identifiers associated with the representative model based on a first set of network user identifiers associated with the representative model.
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6. A system of generating a representative model for a plurality of different models identified by similar feature data, the system comprising: a memory; and one or more processors, the processors configured to receive a first model and a second model, each of the first model and the second model configured for use in identifying a second set of network user identifiers as similar to a first set of network user identifiers; receive feature data associated with each of the first model and the second model, each feature data having a corresponding weight data; select a network user identifier pool including a plurality of network user identifiers, a subset of the network user identifier pool including at least one network user identifier that is included in at least one of the second set of network user identifiers identified by at least one of the first model or the second model plurality of different models and at least one network user identifier that is not included in the at least one of the second set of network user identifiers identified by at least one of the plurality of different models; determine, for each model of the first model and the second model, from the network user identifier pool, a network user identifier identified as similar to the first set of network user identifiers of the model; determining an overlap between positive predictions and negative predictions of the first model and the second model, a positive prediction between the first model and the second model occurring when each of the first model and the second model identifies a network user identifier from the network user identifier pool as a similar network user and a negative prediction between the first model and the second model occurring when either the first model identifies a network user identifier from the network user identifier pool that is not identified by the second model or the second model identifies a network user identifier from the network user identifier pool that is not identified by the first model; and calculate, for the first model and the second model, a degree of overlap between the positive predictions and the negative predictions; identify that the first model and the second model are similar responsive to determining that the degree of overlap is greater than a threshold value; and generate the representative model to represent the first model and the second model, the representative model configured for use in generating a second set of network user identifiers associated with the representative model based on a first set of network user identifiers associated with the representative model. 7. The system of claim 6 , wherein generating the representative model comprises selecting one of the first model and the second model to be the representative model.
| 0.766854 |
9,323,845 | 1 | 4 |
1. A portable communication device for extracting a user interest, the device comprising: a term vector generation unit for generating, based on types of text data stored in the portable communication device, a term vector representing each text data; a subject classification tree storage unit for storing a subject classification tree, which is a tree structure in which multiple nodes, each including at least one training data and representing a subject, are connected to one another; a subject classification tree generation unit for generating the subject classification tree by processing open directory data; a training data generation unit for generating the training data representing each directory based on text data information of a set of web sites included in the each directory of the open directory data; a classification unit for mapping the training data to a directory included in the subject classification tree; and a similarity calculation unit for calculating a similarity between the term vector and the training data for each node in the subject classification tree, wherein the similarity calculation unit extracts a node name representing the user interest from the subject classification tree based on the similarity.
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1. A portable communication device for extracting a user interest, the device comprising: a term vector generation unit for generating, based on types of text data stored in the portable communication device, a term vector representing each text data; a subject classification tree storage unit for storing a subject classification tree, which is a tree structure in which multiple nodes, each including at least one training data and representing a subject, are connected to one another; a subject classification tree generation unit for generating the subject classification tree by processing open directory data; a training data generation unit for generating the training data representing each directory based on text data information of a set of web sites included in the each directory of the open directory data; a classification unit for mapping the training data to a directory included in the subject classification tree; and a similarity calculation unit for calculating a similarity between the term vector and the training data for each node in the subject classification tree, wherein the similarity calculation unit extracts a node name representing the user interest from the subject classification tree based on the similarity. 4. The portable communication device for extracting a user interest of claim 1 , wherein the name of the node having the highest similarity in the subject classification tree is extracted as the user interest.
| 0.733418 |
9,852,130 | 11 | 14 |
11. A device for displaying caption data comprising: a video receiver configured to receive content data including video data; a display configured to display the video data; and a controller configured to: if original caption data is included in the content data, perform error checking for the original caption data and generate corrected caption data by correcting one or more errors of the original caption data, extract one or more high-difficulty words from the corrected caption data, generate a sign language animation corresponding to each of the one or more high-difficulty words, wherein the sign language animation includes explanation data for explaining each of the one or more high-difficulty words, determine at least one special effect corresponding to at least one word included in the original caption data or the explanation data, wherein the at least one special effect includes vibrations, color light, heat, chill, or fragrance corresponding to the at least one word included in the original caption data or the explanation data, and while displaying the video data, simultaneously provide the corrected caption data, the sign language animation, and at least one special effect.
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11. A device for displaying caption data comprising: a video receiver configured to receive content data including video data; a display configured to display the video data; and a controller configured to: if original caption data is included in the content data, perform error checking for the original caption data and generate corrected caption data by correcting one or more errors of the original caption data, extract one or more high-difficulty words from the corrected caption data, generate a sign language animation corresponding to each of the one or more high-difficulty words, wherein the sign language animation includes explanation data for explaining each of the one or more high-difficulty words, determine at least one special effect corresponding to at least one word included in the original caption data or the explanation data, wherein the at least one special effect includes vibrations, color light, heat, chill, or fragrance corresponding to the at least one word included in the original caption data or the explanation data, and while displaying the video data, simultaneously provide the corrected caption data, the sign language animation, and at least one special effect. 14. The device of claim 11 , where the controller is further configured to: if a user input for displaying an another language-based sign language animation is received, translate at least a portion of the corrected caption data into an another language-based sign language, generate the another language-based sign language animation based on the translated another language-based sign language, and control the display to display the translated another language-based sign language animation.
| 0.5 |
7,983,478 | 1 | 7 |
1. A method for handwritten character generation, implemented at least in part by a computing device, the method comprising: receiving a character; and generating a corresponding handwritten character using Hidden Markov Models trained to generate handwritten characters, the generating including using a Multi-Space Probability Distribution technique that includes a probability for determining whether a stroke is real or imaginary.
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1. A method for handwritten character generation, implemented at least in part by a computing device, the method comprising: receiving a character; and generating a corresponding handwritten character using Hidden Markov Models trained to generate handwritten characters, the generating including using a Multi-Space Probability Distribution technique that includes a probability for determining whether a stroke is real or imaginary. 7. The method of claim 1 , wherein the Hidden Markov Models comprise Hidden Markov Models trained using data from a standard writer.
| 0.759124 |
9,864,743 | 18 | 19 |
18. The method of claim 17 , wherein determining the emotion weights includes determining one or more emotion weights by topic based on a sum of the scalars assigned to the top words of the respective topic.
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18. The method of claim 17 , wherein determining the emotion weights includes determining one or more emotion weights by topic based on a sum of the scalars assigned to the top words of the respective topic. 19. The method of claim 18 , wherein determining the emotion weights includes determining one or more emotion weights by emotion based on the emotion weights by topic.
| 0.5 |
7,974,472 | 10 | 11 |
10. A method for online character recognition of East Asian characters, implemented at least in part by a computing device, the method comprising: acquiring time sequential, online ink data for a handwritten East Asian character; conditioning the ink data to produce conditioned ink data where the conditioned ink data comprises ink data frames and information as to writing sequence of the handwritten East Asian character; determining neighborhoods of ink data frames wherein the determining neighborhoods comprises: determining a turning angle between two adjacent ink data frames; determining at least one other turning angle between the two adjacent ink data frames; determining a cumulative angle based on the turning angle and the at least one other turning angle; and comparing the cumulative angle to a predetermined threshold to decide if the two adjacent ink data frames belong to the same neighborhood; and applying a Hidden Markov Model based character recognition system to recognize the handwritten East Asian character.
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10. A method for online character recognition of East Asian characters, implemented at least in part by a computing device, the method comprising: acquiring time sequential, online ink data for a handwritten East Asian character; conditioning the ink data to produce conditioned ink data where the conditioned ink data comprises ink data frames and information as to writing sequence of the handwritten East Asian character; determining neighborhoods of ink data frames wherein the determining neighborhoods comprises: determining a turning angle between two adjacent ink data frames; determining at least one other turning angle between the two adjacent ink data frames; determining a cumulative angle based on the turning angle and the at least one other turning angle; and comparing the cumulative angle to a predetermined threshold to decide if the two adjacent ink data frames belong to the same neighborhood; and applying a Hidden Markov Model based character recognition system to recognize the handwritten East Asian character. 11. The method of claim 10 further comprising determining a local length feature for each of the neighborhoods.
| 0.809932 |
8,401,837 | 10 | 11 |
10. A system for text discrimination, the system comprising: a word recognizer comprising a processor configured to operate in conjunction with a non-transitory computer readable storage medium, the word recognizer having language data compiled using a number of finite state automata; and control logic, stored on the non-transitory computer readable storage medium which, when executed by the processor, is configured to operate the word recognizer to accept or reject a number of characters as a word.
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10. A system for text discrimination, the system comprising: a word recognizer comprising a processor configured to operate in conjunction with a non-transitory computer readable storage medium, the word recognizer having language data compiled using a number of finite state automata; and control logic, stored on the non-transitory computer readable storage medium which, when executed by the processor, is configured to operate the word recognizer to accept or reject a number of characters as a word. 11. The system of claim 10 , wherein the control logic further applies input data to the word recognizer one byte at a time until each byte of input data has been applied to the word recognizer.
| 0.5 |
9,262,556 | 11 | 14 |
11. A system comprising: at least one processor; and at least one memory including code which when executed by the at least one processor causes operations comprising: receiving, by the at least one programmable processor, an indication and a search term to be identified in one or more steps of a process, the indication received from a user interface, the indication being representative of a request to provide one or more search results in a search result format for an embedded context search; initiating, by the at least one programmable processor, a search for the search term in one or more steps of the process; receiving, by the at least one programmable processor, the one or more search results including metadata describing where the one or more search results are located in the process; and generating, by the at least one programmable processor, a first page including the one or more steps of the process, the first page further including the one or more search results embedded within the one or more steps to provide a context by providing, on the user interface, a graphically distinct element to indicate the one or more search results are part of the one or more steps of the process being handled at the user interface.
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11. A system comprising: at least one processor; and at least one memory including code which when executed by the at least one processor causes operations comprising: receiving, by the at least one programmable processor, an indication and a search term to be identified in one or more steps of a process, the indication received from a user interface, the indication being representative of a request to provide one or more search results in a search result format for an embedded context search; initiating, by the at least one programmable processor, a search for the search term in one or more steps of the process; receiving, by the at least one programmable processor, the one or more search results including metadata describing where the one or more search results are located in the process; and generating, by the at least one programmable processor, a first page including the one or more steps of the process, the first page further including the one or more search results embedded within the one or more steps to provide a context by providing, on the user interface, a graphically distinct element to indicate the one or more search results are part of the one or more steps of the process being handled at the user interface. 14. The system of claim 11 , wherein the generating further comprises: generating a first window and a separate second window, wherein the first window includes the one or more search results, and the second window includes the one or more steps of the process including the graphically distinct element.
| 0.5 |
8,352,473 | 14 | 19 |
14. One or more computer-readable storage media comprising computer executable instructions for automatically synthesizing products into an on-line catalog, the computer executable instructions including instructions for automatically synthesizing information corresponding to existing products into the on-line catalog, comprising: obtaining, from a plurality of incoming data sources, incoming information corresponding to an existing product in the on-line catalog, the incoming information including attribute names and corresponding attribute values and excluding a product name and a product identifier of the existing product; determining a source-to-source attribute correspondence comprising determining a correspondence between a first incoming attribute name included in a first incoming product schema of a first incoming data source and a second incoming attribute name included in a second incoming product schema of a second incoming data source; segmenting and normalizing the incoming information; clustering the segmented and normalized incoming information according to, in part, the source-to-source attribute correspondence so that each cluster corresponds to a different product; extracting one or more attribute names and corresponding attribute values corresponding to a particular cluster corresponding to a particular existing product, and including the extracted one or more attribute names and attribute values in the catalog schema; fusing the extracted one or more attribute names and attribute values with other attribute names and attribute values in the catalog schema corresponding to the particular existing product, including determining one or more representative attribute names and corresponding representative attribute values; updating, with the representative catalog attribute value, a particular entry in the on-line catalog corresponding to the particular existing product; and displaying the one or more representative attribute names and corresponding representative attribute values in an entry of the on-line catalog corresponding to the particular existing product.
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14. One or more computer-readable storage media comprising computer executable instructions for automatically synthesizing products into an on-line catalog, the computer executable instructions including instructions for automatically synthesizing information corresponding to existing products into the on-line catalog, comprising: obtaining, from a plurality of incoming data sources, incoming information corresponding to an existing product in the on-line catalog, the incoming information including attribute names and corresponding attribute values and excluding a product name and a product identifier of the existing product; determining a source-to-source attribute correspondence comprising determining a correspondence between a first incoming attribute name included in a first incoming product schema of a first incoming data source and a second incoming attribute name included in a second incoming product schema of a second incoming data source; segmenting and normalizing the incoming information; clustering the segmented and normalized incoming information according to, in part, the source-to-source attribute correspondence so that each cluster corresponds to a different product; extracting one or more attribute names and corresponding attribute values corresponding to a particular cluster corresponding to a particular existing product, and including the extracted one or more attribute names and attribute values in the catalog schema; fusing the extracted one or more attribute names and attribute values with other attribute names and attribute values in the catalog schema corresponding to the particular existing product, including determining one or more representative attribute names and corresponding representative attribute values; updating, with the representative catalog attribute value, a particular entry in the on-line catalog corresponding to the particular existing product; and displaying the one or more representative attribute names and corresponding representative attribute values in an entry of the on-line catalog corresponding to the particular existing product. 19. The one or more computer-readable storage media of claim 14 , further comprising additional computer-executable instructions for obtaining updated incoming information from at least one of the plurality of incoming data sources or a new incoming data source at least twice daily and for obtaining updated information from all of the plurality of incoming data sources at least monthly.
| 0.739276 |
8,920,469 | 1 | 7 |
1. A spinal fixation system, comprising: a bone anchor having a bone-engaging portion and a spinal fixation element receiving portion with opposed arms configured to receive a spinal fixation element therebetween; a connecting member having first and second ends, the first end having an elongate opening extending therethrough; a washer configured to slide along the first end of the connecting member to adjust a distance between the bone anchor and the second end of the connecting member; and a locking mechanism configured to simultaneously lock the washer in a fixed position along the first end of the connecting member, and to lock the first end of the connecting member to the spinal fixation element receiving portion of the bone anchor.
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1. A spinal fixation system, comprising: a bone anchor having a bone-engaging portion and a spinal fixation element receiving portion with opposed arms configured to receive a spinal fixation element therebetween; a connecting member having first and second ends, the first end having an elongate opening extending therethrough; a washer configured to slide along the first end of the connecting member to adjust a distance between the bone anchor and the second end of the connecting member; and a locking mechanism configured to simultaneously lock the washer in a fixed position along the first end of the connecting member, and to lock the first end of the connecting member to the spinal fixation element receiving portion of the bone anchor. 7. The system of claim 1 , wherein the washer includes a pair of opposed rails configured to slidably engage the first end of the connecting member.
| 0.751678 |
5,583,988 | 36 | 40 |
36. A method for performing runtime checking on arguments passed to external libraries in a compiled programming environment, comprising the steps of: inserting argument restrictions into a source code file; generating argument runtime checking code based on said inserted argument restrictions; linking said argument runtime checking code to a library; executing a program which includes one or more calls to functions in said library, wherein said one or more calls to said functions in said library pass one or more arguments to said functions in said library; executing said argument runtime checking code to determine if said one or more arguments passed to said functions in said library violate said argument restrictions; reporting an error if said one or more arguments passed to said functions in said library violate said argument restrictions.
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36. A method for performing runtime checking on arguments passed to external libraries in a compiled programming environment, comprising the steps of: inserting argument restrictions into a source code file; generating argument runtime checking code based on said inserted argument restrictions; linking said argument runtime checking code to a library; executing a program which includes one or more calls to functions in said library, wherein said one or more calls to said functions in said library pass one or more arguments to said functions in said library; executing said argument runtime checking code to determine if said one or more arguments passed to said functions in said library violate said argument restrictions; reporting an error if said one or more arguments passed to said functions in said library violate said argument restrictions. 40. The method of claim 36, wherein said source code file includes declarations of said functions in said library.
| 0.897112 |
8,280,448 | 27 | 28 |
27. The method of claim 23 , wherein the choosing of the one menu icon is a first choosing, the method further comprising; choosing the chosen menu icon a second time; and entering a menu corresponding to the chosen menu icon when the chosen menu icon is chosen the second time.
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27. The method of claim 23 , wherein the choosing of the one menu icon is a first choosing, the method further comprising; choosing the chosen menu icon a second time; and entering a menu corresponding to the chosen menu icon when the chosen menu icon is chosen the second time. 28. The method of claim 27 , further comprising outputting a second control signal for controlling the haptic module to generate a haptic effect corresponding to the menu.
| 0.5 |
7,873,509 | 1 | 5 |
1. A method of processing natural language in an apparatus, which comprises steps providing in memory associated with said apparatus a data base of purpose relation data associated with clause implying word sense numbers such that said purpose relation is a concept that labels one clause implying word sense number or more than one related clause implying word sense number, utilizing a natural language processor to provide natural language with associated clause implying word sense numbers in memory associated with said apparatus, purpose relation identification processing with said apparatus of said clause implying word sense numbers from said natural language and said purpose relation data associated with said clause implying word sense numbers such that said purpose relations which are associated with said clause implying word sense numbers are identified, providing criteria for selecting purpose relations in memory associated with said apparatus, utilizing said criteria for selecting purpose relations to select one or more of said identified purpose relations with said apparatus.
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1. A method of processing natural language in an apparatus, which comprises steps providing in memory associated with said apparatus a data base of purpose relation data associated with clause implying word sense numbers such that said purpose relation is a concept that labels one clause implying word sense number or more than one related clause implying word sense number, utilizing a natural language processor to provide natural language with associated clause implying word sense numbers in memory associated with said apparatus, purpose relation identification processing with said apparatus of said clause implying word sense numbers from said natural language and said purpose relation data associated with said clause implying word sense numbers such that said purpose relations which are associated with said clause implying word sense numbers are identified, providing criteria for selecting purpose relations in memory associated with said apparatus, utilizing said criteria for selecting purpose relations to select one or more of said identified purpose relations with said apparatus. 5. A method of processing as defined in claim 1 which comprises selecting a purpose relation from said identified purpose relations such that said identified purpose relation is a response.
| 0.917395 |
9,858,345 | 7 | 9 |
7. A tangible computer-readable medium storing medium instructions which, executed by a processor, cause the processor to perform operations for processing query, the operations comprising: receiving the query from a user; processing the query to locate a plurality of documents in accordance with a search engine having a discriminative classifier, wherein the discriminative classifier is trained with a plurality of artificial multi-label query examples, wherein the artificial multi-label query examples comprise simulated queries automatically generated from terms in the plurality of documents, wherein each of the artificial multi-label query examples further comprises a name and metadata information associated with the name, wherein one of the simulated queries comprises a plurality of the terms selected from the plurality of documents; retraining, the discriminative classifier based on an update to at least one the plurality of documents and based on an example derived from a log of previous searches; and presenting a result of the processing to the user.
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7. A tangible computer-readable medium storing medium instructions which, executed by a processor, cause the processor to perform operations for processing query, the operations comprising: receiving the query from a user; processing the query to locate a plurality of documents in accordance with a search engine having a discriminative classifier, wherein the discriminative classifier is trained with a plurality of artificial multi-label query examples, wherein the artificial multi-label query examples comprise simulated queries automatically generated from terms in the plurality of documents, wherein each of the artificial multi-label query examples further comprises a name and metadata information associated with the name, wherein one of the simulated queries comprises a plurality of the terms selected from the plurality of documents; retraining, the discriminative classifier based on an update to at least one the plurality of documents and based on an example derived from a log of previous searches; and presenting a result of the processing to the user. 9. The tangible computer-readable medium of claim 7 , wherein the discriminative classifier comprises a support vector machine classifier.
| 0.67757 |
9,922,098 | 1 | 2 |
1. A computing system, comprising: at least one processor; and memory storing instructions executable by the at least one processor, wherein the instructions, when executed, configure the computing system to provide: a context identification system configured to detect a plurality of different sensor inputs, indicative of a plurality of different items of context, from a plurality of different sources of context; a cross-source search component configured to detect a trigger input and search a plurality of different content sources, to identify a set of documents, based on the trigger input; a relevancy generator configured to generate a relevancy metric corresponding to each document in the identified set of documents, based on the plurality of different items of context; and a user interface component configured to generate a user interface display that selectively displays the documents in the set of documents based on the corresponding relevancy metric.
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1. A computing system, comprising: at least one processor; and memory storing instructions executable by the at least one processor, wherein the instructions, when executed, configure the computing system to provide: a context identification system configured to detect a plurality of different sensor inputs, indicative of a plurality of different items of context, from a plurality of different sources of context; a cross-source search component configured to detect a trigger input and search a plurality of different content sources, to identify a set of documents, based on the trigger input; a relevancy generator configured to generate a relevancy metric corresponding to each document in the identified set of documents, based on the plurality of different items of context; and a user interface component configured to generate a user interface display that selectively displays the documents in the set of documents based on the corresponding relevancy metric. 2. The computing system of claim 1 wherein the context identification system is configured to detect whether a user is using the computing system in a work context or a non-work context, and the relevancy generator is configured to generate the relevancy metric for the documents based on the detected work context or non-work context.
| 0.5 |
8,429,183 | 1 | 3 |
1. A search engine server comprising: processing circuitry; memory coupled to the processing circuitry; and network circuitry, the processing circuitry, memory, and network circuitry are operable to: receive an international search string; determine the language of the international search string via correlation with a conjugate terms database; retrieve a conjugate target language term from the conjugate terms database; in response to determining that international terms support is enabled, facilitate searching of websites having international language support using the conjugate target language term; in response to determining that international terms support is disabled, facilitate searching of websites having international language support using the international search string; and deliver a search results page via the network circuitry to a web browser associated with a client device, wherein the search results page comprises links of a few search results that link to a few websites.
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1. A search engine server comprising: processing circuitry; memory coupled to the processing circuitry; and network circuitry, the processing circuitry, memory, and network circuitry are operable to: receive an international search string; determine the language of the international search string via correlation with a conjugate terms database; retrieve a conjugate target language term from the conjugate terms database; in response to determining that international terms support is enabled, facilitate searching of websites having international language support using the conjugate target language term; in response to determining that international terms support is disabled, facilitate searching of websites having international language support using the international search string; and deliver a search results page via the network circuitry to a web browser associated with a client device, wherein the search results page comprises links of a few search results that link to a few websites. 3. The search engine server of claim 1 , wherein the processing circuitry, memory, and network circuitry are further operable to access an international language support website database comprising lists of websites that provide information in the language of the international search string.
| 0.570588 |
8,818,932 | 14 | 15 |
14. The computer readable medium of claim 13 , wherein the computer readable medium further comprises the steps of: linking algorithms with a user interface; and, parsing each sentence based on parts of speech and relative positions, using a semantic role labeling algorithm, to extract entities and discover relationships between entities.
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14. The computer readable medium of claim 13 , wherein the computer readable medium further comprises the steps of: linking algorithms with a user interface; and, parsing each sentence based on parts of speech and relative positions, using a semantic role labeling algorithm, to extract entities and discover relationships between entities. 15. The computer readable medium of claim 14 , wherein the computer readable medium further comprises the steps of: utilizing a Bayesian network learning algorithm to analyze causes and effects of observed evidence using Bayesian Networks; and, creating real-time mathematical models to predict actions.
| 0.5 |
7,765,271 | 69 | 70 |
69. A method comprising the steps of: a) generating a display including a display portion with a view of a scanned document within a browser of a client device based on document data derived from a scan of a document in print form; b) inputting predetermined index data into at least one field of an index field portion of the display within the browser of the client device, the index field portion defined in the display within the browser separately from the display portion; c) generating a send data signal from within the browser of the client device using a control element of a control portion defined separately from the index field portion and the display portion in the display within the browser; d) transmitting the document data and index data from the client device to the server over an internetwork with the control element of the control portion using a destination address of a server identified in an address field of the browser in response to the send data signal generated in said step (c); e) receiving the document data and index data at the server; and f) storing the document data in association with the index data received from the server in a database of a data storage unit separate from the server.
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69. A method comprising the steps of: a) generating a display including a display portion with a view of a scanned document within a browser of a client device based on document data derived from a scan of a document in print form; b) inputting predetermined index data into at least one field of an index field portion of the display within the browser of the client device, the index field portion defined in the display within the browser separately from the display portion; c) generating a send data signal from within the browser of the client device using a control element of a control portion defined separately from the index field portion and the display portion in the display within the browser; d) transmitting the document data and index data from the client device to the server over an internetwork with the control element of the control portion using a destination address of a server identified in an address field of the browser in response to the send data signal generated in said step (c); e) receiving the document data and index data at the server; and f) storing the document data in association with the index data received from the server in a database of a data storage unit separate from the server. 70. A method as claimed in claim 69 wherein the display of the scanned document is included in a hypertext mark-up language (HTML) document displayed by the browser of the client device's user interface.
| 0.5 |
7,818,179 | 6 | 8 |
6. The device of claim 4 , wherein the speech processing system comprises a word spotting system adapted to analyze the speech input of a user for detecting one or more words or expressions or sounds, if any, which are specified in a vocabulary list, in the speech input of the user.
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6. The device of claim 4 , wherein the speech processing system comprises a word spotting system adapted to analyze the speech input of a user for detecting one or more words or expressions or sounds, if any, which are specified in a vocabulary list, in the speech input of the user. 8. The device of claim 6 , wherein an identified speech habit comprises a repetitive use of a word or expression specified in the vocabulary list.
| 0.647343 |
9,053,210 | 1 | 11 |
1. A computer-implemented process for responding to graph queries, comprising: using a hardware computer comprising one or more processors and memory to perform the following process actions: receiving a graph query that is submitted to a graph database which is modeled by an attributed graph comprising a plurality of nodes each of which is labeled with one or more attributes, and a plurality of edges each of which is labeled with another one or more attributes, each of the nodes representing a different entity instance, each of the edges connecting a different pair of nodes, the attributes of each of the edges specifying the type of relationship that exists between the pair of nodes connected by the edge, the graph query comprising predicates based on one or more of, the topology of the attributed graph, or the attributes of the nodes and edges therein; decomposing the graph query into a plurality of query components; for each of the query components, identifying a one of a plurality of independent query execution engines that is available to process the query component, generating a sub-query that represents the query component, sending the sub-query to the identified query execution engine for processing, and receiving results for the sub-query from the identified query execution engine; and combining the results received to generate a response to the graph query.
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1. A computer-implemented process for responding to graph queries, comprising: using a hardware computer comprising one or more processors and memory to perform the following process actions: receiving a graph query that is submitted to a graph database which is modeled by an attributed graph comprising a plurality of nodes each of which is labeled with one or more attributes, and a plurality of edges each of which is labeled with another one or more attributes, each of the nodes representing a different entity instance, each of the edges connecting a different pair of nodes, the attributes of each of the edges specifying the type of relationship that exists between the pair of nodes connected by the edge, the graph query comprising predicates based on one or more of, the topology of the attributed graph, or the attributes of the nodes and edges therein; decomposing the graph query into a plurality of query components; for each of the query components, identifying a one of a plurality of independent query execution engines that is available to process the query component, generating a sub-query that represents the query component, sending the sub-query to the identified query execution engine for processing, and receiving results for the sub-query from the identified query execution engine; and combining the results received to generate a response to the graph query. 11. The process of claim 1 , wherein the process action of identifying a one of a plurality of independent query execution engines that is available to process the query component comprises an action of using at least a monetary-cost-based engine selection rule to perform said identification, said rule specifying that the query component is to be executed by a one of the plurality of independent query execution engines that is the least expensive to use.
| 0.573557 |
8,661,069 | 1 | 4 |
1. An automated method, comprising: clustering documents into a first subset and a second subset, where documents in the first subset are duplicative, and where documents in the second subset are duplicative to one another and differ from the documents in the first subset; selecting a first representative document and a second representative document, where the first representative document is selected from the first subset, and where the second representative document is selected from the second subset; determining a first redirect target document and a second redirect target document, where the first redirect target document is referenced by the first representative document, and where the second redirect target document is referenced by the second representative document; determining that the first redirect target document and the second redirect target document are duplicative; re-clustering the documents in response to determining that the first redirect target and the second redirect target are duplicative, where re-clustering the documents include combining the first subset and the second subset; and indexing, based on a document included in the combined first and second subsets, the combined first and second subsets.
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1. An automated method, comprising: clustering documents into a first subset and a second subset, where documents in the first subset are duplicative, and where documents in the second subset are duplicative to one another and differ from the documents in the first subset; selecting a first representative document and a second representative document, where the first representative document is selected from the first subset, and where the second representative document is selected from the second subset; determining a first redirect target document and a second redirect target document, where the first redirect target document is referenced by the first representative document, and where the second redirect target document is referenced by the second representative document; determining that the first redirect target document and the second redirect target document are duplicative; re-clustering the documents in response to determining that the first redirect target and the second redirect target are duplicative, where re-clustering the documents include combining the first subset and the second subset; and indexing, based on a document included in the combined first and second subsets, the combined first and second subsets. 4. The method of claim 1 , where determining the first redirect target document and the second redirect target document comprises: determining redirect information associated, respectively, with the first representative document and the second representative document; determining the first redirect target document based on the redirect information associated with the first representative document; and determining the second redirect target document based on the redirect information associated with the second representative document.
| 0.513562 |
6,094,197 | 1 | 5 |
1. A user interface apparatus for a computing system, said apparatus comprising: a display device displaying a graphical keyboard image; an input source comprising a designating device, said input source providing an input comprising key information and path information, said key information representing a key of said graphical keyboard image, said path information representing a series of points designated by said designating device, wherein said path information comprises a direction of displacement of said designating device; an output generator, responsive to said key information and said path information provided by said input source, for producing a keycode data defing an output character by applying a modifier corresponding to said path information to an input character corresponding to said key information, with said modifier being determined according to said direction of displacement; and a buffer, wherein said keycode data produced by said output generator is stored in said buffer.
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1. A user interface apparatus for a computing system, said apparatus comprising: a display device displaying a graphical keyboard image; an input source comprising a designating device, said input source providing an input comprising key information and path information, said key information representing a key of said graphical keyboard image, said path information representing a series of points designated by said designating device, wherein said path information comprises a direction of displacement of said designating device; an output generator, responsive to said key information and said path information provided by said input source, for producing a keycode data defing an output character by applying a modifier corresponding to said path information to an input character corresponding to said key information, with said modifier being determined according to said direction of displacement; and a buffer, wherein said keycode data produced by said output generator is stored in said buffer. 5. The apparatus of claim 1 wherein said designating device comprises a pointing device selected from the group consisting of a pen, a light pen, a stylus, a pencil, a pointer, a mouse, a trackball, a joystick, or a finger.
| 0.689415 |
10,114,897 | 1 | 6 |
1. A method comprising: identifying a most recent interest from user device submitted data; searching a database for instances of the most recent interest; creating a new category based on the most recent interest; storing the new category in a memory; combining the new category with weighted query search terms and submitting a combined query, separate weights assigned to query search terms according to validity of information found in each of local and remote memories, information found in local memories contributing to higher weights than information found in remote memories in response to private browsing not enabled on the user device, information found in remote memories contributing to higher weights than information found in local memories in response to private browsing being enabled on the user device; receiving combined query results; and creating a modified user interface based on the results of the combined query.
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1. A method comprising: identifying a most recent interest from user device submitted data; searching a database for instances of the most recent interest; creating a new category based on the most recent interest; storing the new category in a memory; combining the new category with weighted query search terms and submitting a combined query, separate weights assigned to query search terms according to validity of information found in each of local and remote memories, information found in local memories contributing to higher weights than information found in remote memories in response to private browsing not enabled on the user device, information found in remote memories contributing to higher weights than information found in local memories in response to private browsing being enabled on the user device; receiving combined query results; and creating a modified user interface based on the results of the combined query. 6. The method of claim 1 , further comprising: creating alias keyword terms from the most recent interest; and applying the alias keyword terms to the combined query.
| 0.741433 |
4,633,430 | 3 | 5 |
3. The document processing system of claim 2, further comprising: control interface means, including execution pointer means for storing said operation vectors, said processor means being responsive to said keyboard inputs and to said execution of said current routine for writing said operation vectors into said execution pointer means and reading said operation vectors from said execution pointer means, and said state storing means.
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3. The document processing system of claim 2, further comprising: control interface means, including execution pointer means for storing said operation vectors, said processor means being responsive to said keyboard inputs and to said execution of said current routine for writing said operation vectors into said execution pointer means and reading said operation vectors from said execution pointer means, and said state storing means. 5. The document processing means of claim 3, wherein said supervisory control means for providing said operation vectors further comprises: means for accepting and classifying keystroke inputs from said keyboard means, and state table means for storing said operation vectors, said state table means responsive to said state information and to operation of said classifying means for relating said state information and said keystroke classifications to said corresponding operation vectors and providing said corresponding operation vectors to be written into said execution pointer means.
| 0.5 |
9,361,341 | 9 | 10 |
9. The computing system of claim 7 , wherein the functional-form query framework further comprises a plurality of functions, each function mapping one of the objects to another of the objects, each function being one of a query function, a relation valued function, a construct primitive, and a combination of one or more of a query function, a relation valued function, and a construction primitive, wherein a query function is applied to a sequence of relations, and wherein a construction primitive defines how a function is applied to one or more variables.
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9. The computing system of claim 7 , wherein the functional-form query framework further comprises a plurality of functions, each function mapping one of the objects to another of the objects, each function being one of a query function, a relation valued function, a construct primitive, and a combination of one or more of a query function, a relation valued function, and a construction primitive, wherein a query function is applied to a sequence of relations, and wherein a construction primitive defines how a function is applied to one or more variables. 10. The computing system of claim 9 , wherein the functional-form query framework further comprises: an apply meta-operator to apply a function to an object; a set of functional form primitives to combine existing functions to create new functions; and, a set of definitions that define the existing functions.
| 0.5 |
9,160,548 | 1 | 3 |
1. A computer-implemented method of matching users to user communities, the method comprising: for each of a plurality of users of an electronic catalog, storing event data representing user-generated events that reflect user affinities for particular items represented in the electronic catalog; storing information about a plurality of user communities, each of which corresponds to a respective subset of the plurality of users and comprises multiple users; identifying a first plurality of items that characterize item preferences of a first user community of the plurality of user communities; calculating a degree to which the first plurality of items are related to a set of items in the electronic catalog identified by the first user; and based at least in part on the degree, determining whether to suggest the first user community to the first user; wherein the method is performed by one or more computers.
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1. A computer-implemented method of matching users to user communities, the method comprising: for each of a plurality of users of an electronic catalog, storing event data representing user-generated events that reflect user affinities for particular items represented in the electronic catalog; storing information about a plurality of user communities, each of which corresponds to a respective subset of the plurality of users and comprises multiple users; identifying a first plurality of items that characterize item preferences of a first user community of the plurality of user communities; calculating a degree to which the first plurality of items are related to a set of items in the electronic catalog identified by the first user; and based at least in part on the degree, determining whether to suggest the first user community to the first user; wherein the method is performed by one or more computers. 3. The method of claim 1 , wherein the event data identifies at least one of the following: (a) items purchased by particular users, (b) items rented by particular users, (c) items added to an electronic shopping cart by particular users, (d) items added to a rental queue by particular users, (e) items rated by particular users, (f) items selected for viewing by particular users, (g) search queries submitted by particular users to search the electronic catalog.
| 0.5 |
8,954,368 | 6 | 9 |
6. A computer-implemented method comprising: receiving data representing a paralinguistic indicator associated with a gaming platform, wherein the paralinguistic indicator is described using a first data format associated with the gaming platform; receiving at least some voice data with the data representing the paralinguistic indicator; translating the paralinguistic indicator to a second data format which is different from the first data format, wherein the second data format is associated with a real time messaging platform; and sending the translated paralinguistic indicator to the real time messaging platform, wherein the gaming platform is a virtual world platform and the real time messaging platform is an instant messaging platform.
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6. A computer-implemented method comprising: receiving data representing a paralinguistic indicator associated with a gaming platform, wherein the paralinguistic indicator is described using a first data format associated with the gaming platform; receiving at least some voice data with the data representing the paralinguistic indicator; translating the paralinguistic indicator to a second data format which is different from the first data format, wherein the second data format is associated with a real time messaging platform; and sending the translated paralinguistic indicator to the real time messaging platform, wherein the gaming platform is a virtual world platform and the real time messaging platform is an instant messaging platform. 9. The computer-implemented method of claim 6 further comprising: receiving data describing a paralinguistic indicator from the real-time messaging platform; converting the data describing the paralinguistic indicator from the real-time messaging platform to intermediate paralinguistic data; translating the intermediate paralinguistic data into gaming platform paralinguistic data, wherein the gaming platform paralinguistic data comprises a data format associated with the gaming platform; and forwarding the gaming platform paralinguistic data to the gaming platform.
| 0.5 |
7,826,979 | 1 | 29 |
1. A computer-based method for generating 3-D structural models of complex formation between a query ligand and a target macromolecule, the method comprising: a) providing a structural model of a query ligand and a structural model of a target macromolecule; wherein the structural model of the query ligand is based on data from X-ray crystallography or NMR spectroscopy, and the structural model of the target macromolecule is based on data from X-ray crystallography; b) identifying a substructure of the query ligand; c) identifying comparison ligands in a set of 3-D structural models that each share an identical substructure with the query ligand, wherein each 3-D structural model comprises a comparison ligand and a comparison macromolecule, and wherein the comparison macromolecule has structural features homologous to structural features of the target macromolecule of 20% or greater nucleic acid and/or amino acid homology; d) mapping spatial relationships between the substructure atoms of the query ligand and a comparison ligand identified in c) such that corresponding atoms are identified; e) assigning atomic coordinates to the corresponding atoms of the query ligand; f) generating and displaying one or more output models, each model comprising a 3-D structural model of the query ligand substructure and the target macromolecule, wherein the 3-D model of the query ligand substructure comprises the atomic coordinates of the query ligand from step (e).
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1. A computer-based method for generating 3-D structural models of complex formation between a query ligand and a target macromolecule, the method comprising: a) providing a structural model of a query ligand and a structural model of a target macromolecule; wherein the structural model of the query ligand is based on data from X-ray crystallography or NMR spectroscopy, and the structural model of the target macromolecule is based on data from X-ray crystallography; b) identifying a substructure of the query ligand; c) identifying comparison ligands in a set of 3-D structural models that each share an identical substructure with the query ligand, wherein each 3-D structural model comprises a comparison ligand and a comparison macromolecule, and wherein the comparison macromolecule has structural features homologous to structural features of the target macromolecule of 20% or greater nucleic acid and/or amino acid homology; d) mapping spatial relationships between the substructure atoms of the query ligand and a comparison ligand identified in c) such that corresponding atoms are identified; e) assigning atomic coordinates to the corresponding atoms of the query ligand; f) generating and displaying one or more output models, each model comprising a 3-D structural model of the query ligand substructure and the target macromolecule, wherein the 3-D model of the query ligand substructure comprises the atomic coordinates of the query ligand from step (e). 29. The method of claim 1 , wherein the set of 3-D structural models is contained in a database.
| 0.746032 |
8,385,652 | 18 | 19 |
18. The image processing apparatus of claim 17 wherein the Western word recognizer engine provides a Western word recognition result and a confidence level associated therewith, wherein the confidence level represents a probability that recognized words have been recognized correctly and wherein the Western and hieroglyphic text classifier component is further configured to reclassify the Western text segment as a hieroglyphic text segment if the confidence level is below a threshold level.
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18. The image processing apparatus of claim 17 wherein the Western word recognizer engine provides a Western word recognition result and a confidence level associated therewith, wherein the confidence level represents a probability that recognized words have been recognized correctly and wherein the Western and hieroglyphic text classifier component is further configured to reclassify the Western text segment as a hieroglyphic text segment if the confidence level is below a threshold level. 19. The image processing apparatus of claim 18 wherein the Western and hieroglyphic text classifier component is configured to place inter-word breaks around any hieroglyphic characters within the reclassified Western text segments.
| 0.5 |
4,597,057 | 1 | 2 |
1. A method of compressing information by coding and storing eight-bit binary coded alphabetic characters forming an individual word as a string of four-bit units, comprising: storing in a first table a plurality of commonly used words, searching for the individual word being compressed in the first table, generating a group of units in response to a match with a word in the first table, coding the generated units to indicate with one unit that the word is in the first table and with at least one additional unit the location of the word in the first table, storing in a second table a plurality of common word prefixes, comparing a first group of characters starting at the beginning of said individual word being compressed with each of the prefixes in the second table when the full word is not present in the first table, generating a string of at least two four-bit units in response to a match between an initial group of characters in the word being compressed and a prefix in the second table, and coding one of the generated units to indicate with one unit that the prefix is in the second table and with at least one additional unit the location of the prefix in the second table, and comparing a group of characters of said word being compressed with each of the prefixes in the second table each time the preceding group matches one of the stored prefixes, and generating additional four-bit units in said string of units coded to indicate an additional prefix is in the second table and its location, and generating additional four-bit units codes to indicate respectively the number of remaining characters and the value of each remaining character in the word not part of a group of characters matched with a prefix in the second table.
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1. A method of compressing information by coding and storing eight-bit binary coded alphabetic characters forming an individual word as a string of four-bit units, comprising: storing in a first table a plurality of commonly used words, searching for the individual word being compressed in the first table, generating a group of units in response to a match with a word in the first table, coding the generated units to indicate with one unit that the word is in the first table and with at least one additional unit the location of the word in the first table, storing in a second table a plurality of common word prefixes, comparing a first group of characters starting at the beginning of said individual word being compressed with each of the prefixes in the second table when the full word is not present in the first table, generating a string of at least two four-bit units in response to a match between an initial group of characters in the word being compressed and a prefix in the second table, and coding one of the generated units to indicate with one unit that the prefix is in the second table and with at least one additional unit the location of the prefix in the second table, and comparing a group of characters of said word being compressed with each of the prefixes in the second table each time the preceding group matches one of the stored prefixes, and generating additional four-bit units in said string of units coded to indicate an additional prefix is in the second table and its location, and generating additional four-bit units codes to indicate respectively the number of remaining characters and the value of each remaining character in the word not part of a group of characters matched with a prefix in the second table. 2. The method of claim 1 futher including the steps of: storing in a third table a plurality of suffixes, comparing successive groups of characters at the end of a word being compressed with suffixes in the third table when the full word is not composed of prefixes present in the second table, generating at least two units in response to a match between a group of characters in the word being compressed and a suffix in the third table, and coding the generated units to indicate the suffix is in the third table and the location of the suffix in the third table.
| 0.5 |
8,626,507 | 17 | 18 |
17. The method of claim 14 wherein the generating the first mode recognition lattice is further based on a first mode feature lattice.
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17. The method of claim 14 wherein the generating the first mode recognition lattice is further based on a first mode feature lattice. 18. The method of claim 17 further comprising: generating the first mode feature lattice based on the multimodal input.
| 0.5 |
7,836,436 | 2 | 3 |
2. A method in accordance with claim 1 , wherein said web services compatible instructions are compatible with a remote procedure call protocol.
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2. A method in accordance with claim 1 , wherein said web services compatible instructions are compatible with a remote procedure call protocol. 3. A method in accordance with claim 2 , wherein said remote procedure call protocol utilizes hypertext transport protocol (HTTP) as a transport and extensible markup language (XML) as a data format.
| 0.5 |
8,127,270 | 10 | 17 |
10. Software stored in one or more computer-readable storage media for execution and when executed operable to: accept, as input at a transformation engine, a data file containing an implementation independent model written in a modeling language, wherein the implementation independent model includes one or more inheritable classes each of which includes zero or more attributes and zero or more relationships to another class; accept, as input at the transformation engine, a configuration file designating as a manageable resource one or more of the inheritable classes included in the implementation independent model, wherein the manageable resource represents a device having manageable capabilities, the manageable capabilities comprising connectivity and identity, and wherein the configuration file identifies one or more of the inheritable classes for exclusion; and output, at the transformation engine, each designated class as a manageable resource, wherein the manageable resource includes any subclasses by inheritance from the designated class unless excluded in the configuration file.
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10. Software stored in one or more computer-readable storage media for execution and when executed operable to: accept, as input at a transformation engine, a data file containing an implementation independent model written in a modeling language, wherein the implementation independent model includes one or more inheritable classes each of which includes zero or more attributes and zero or more relationships to another class; accept, as input at the transformation engine, a configuration file designating as a manageable resource one or more of the inheritable classes included in the implementation independent model, wherein the manageable resource represents a device having manageable capabilities, the manageable capabilities comprising connectivity and identity, and wherein the configuration file identifies one or more of the inheritable classes for exclusion; and output, at the transformation engine, each designated class as a manageable resource, wherein the manageable resource includes any subclasses by inheritance from the designated class unless excluded in the configuration file. 17. The software of claim 10 , wherein the transformation engine comprises the AndroMDA transformation engine.
| 0.796296 |
7,974,989 | 10 | 11 |
10. The computerized method according to claim 1 , further comprises: merging results between a plurality of seed keywords.
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10. The computerized method according to claim 1 , further comprises: merging results between a plurality of seed keywords. 11. The computerized method according to claim 10 , further comprises: reusing results as secondary seeds for further refinement.
| 0.5 |
8,578,330 | 1 | 11 |
1. A computer-implemented method comprising: transmitting instructions to a computing device to display a user interface of a web-based widget composition platform; receiving, via the user interface, first user input comprising a selection of at least a first service and a second service; receiving, via the user interface, second user input comprising a selection of a widget engine; automatically generating code based on the first service, the second service and the widget engine, the code, when invoked by the widget engine, providing a widget that includes a first service window associated with the first service and a second service window associated with the second service, and that is operable to communicate with the first service and the second service to connect an output of the first service to an input of the second service such that data presented in the second service window is contextualized based upon a user-selection of one of a plurality of data items displayed in the first service window; and creating a service mediator instance based on receiving the selection, the service mediator instance comprising: a service requestor component operable to: gather input parameters, transmit a service call to the first service based on the input parameters, and transmit raw data results of the service call to the service mediator instance; an interpreter component operable to: process the raw data results into an interpreted S ERVICE D ATA M ODEL object, and transmit the S ERVICE D ATA M ODEL object to the service mediator instance; and a renderer component operable to render a visual representation of data in the S ERVICE D ATA M ODEL object.
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1. A computer-implemented method comprising: transmitting instructions to a computing device to display a user interface of a web-based widget composition platform; receiving, via the user interface, first user input comprising a selection of at least a first service and a second service; receiving, via the user interface, second user input comprising a selection of a widget engine; automatically generating code based on the first service, the second service and the widget engine, the code, when invoked by the widget engine, providing a widget that includes a first service window associated with the first service and a second service window associated with the second service, and that is operable to communicate with the first service and the second service to connect an output of the first service to an input of the second service such that data presented in the second service window is contextualized based upon a user-selection of one of a plurality of data items displayed in the first service window; and creating a service mediator instance based on receiving the selection, the service mediator instance comprising: a service requestor component operable to: gather input parameters, transmit a service call to the first service based on the input parameters, and transmit raw data results of the service call to the service mediator instance; an interpreter component operable to: process the raw data results into an interpreted S ERVICE D ATA M ODEL object, and transmit the S ERVICE D ATA M ODEL object to the service mediator instance; and a renderer component operable to render a visual representation of data in the S ERVICE D ATA M ODEL object. 11. The method of claim 1 , further comprising outputting the widget.
| 0.897626 |
8,401,987 | 12 | 16 |
12. A system, comprising: a data quality model builder to cause operations, the operations comprising: receiving a schema definition describing a structure and a format of at least one column in a first data set having a plurality of columns and records providing data for each of the columns; generating at least one model, wherein each model asserts conditions for the at least one column in the first data set whose structure is described in the schema, wherein the rules for each model validate values in the at least one column; storing the schema definition and the at least one model in a data quality model, wherein the data quality model comprises a data structure; and a data quality validator to cause operations, the operations comprising: receiving selection of a second data set and the data quality model; determining whether a structure of the at least one column in the second data set is compatible with at least one column described in the schema definition in the selected data quality model; applying the rules in each model in the data quality model to records in the at least one column of the second data set to validate the records in the at least one column in the second data set in response to determining that the structure of the second data set and the schema definition are compatible.
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12. A system, comprising: a data quality model builder to cause operations, the operations comprising: receiving a schema definition describing a structure and a format of at least one column in a first data set having a plurality of columns and records providing data for each of the columns; generating at least one model, wherein each model asserts conditions for the at least one column in the first data set whose structure is described in the schema, wherein the rules for each model validate values in the at least one column; storing the schema definition and the at least one model in a data quality model, wherein the data quality model comprises a data structure; and a data quality validator to cause operations, the operations comprising: receiving selection of a second data set and the data quality model; determining whether a structure of the at least one column in the second data set is compatible with at least one column described in the schema definition in the selected data quality model; applying the rules in each model in the data quality model to records in the at least one column of the second data set to validate the records in the at least one column in the second data set in response to determining that the structure of the second data set and the schema definition are compatible. 16. The system of claim 12 , wherein the data quality model builder generates the at least one model by: using a first data mining algorithm to generate at least one model to validate values in at least one column in the records of the first data set; using a second data mining algorithm to generate at least one model to validate values in at least one column in the records of the first data set; and wherein the data quality validator applies each model in the data quality model to the records in the second data set by: determining a metric for each result of applying the models generated by the first and second data mining algorithms for each record in the second data set; and generating a summary metric for each record in the second data set that is a function of the metrics resulting from applying the models to the record.
| 0.5 |
7,937,657 | 1 | 2 |
1. A computer readable storage medium containing a software program which, when executed by a processor, causes the processor to perform an operation for providing user-specific error analysis to identify as problem words any correctly spelled words of a document that are improperly used, the operation comprising: recording each word contained in a first document as pre-edited contents; receiving user edits replacing each problem word contained in the first document with a respective replacement word; after receiving the user edits, recording the each word contained in a first document as post-edited contents; comparing the pre-edited contents to the post-edited contents to identify the problem words and the respective replacement words; storing the user-replaced problem words and respective replacement words to a first data structure, wherein each user-replaced problem word is associated with the respective replacement word in an individual record of the first data structure and wherein each individual record includes a field indicating a number of times a respective user-replaced problem word has been replaced by its associated replacement word; assigning a formatting definition to each problem word for use in identifying problem words on a display device, wherein the formatting definition is reflective, on a display device displaying the respective problem word, of the number of times the respective problem word has been replaced by its associated replacement word; determining whether one or more problem words are present in a second document utilizing the first data structure; and indicating each problem word present in the second document with its respective formatting definition, wherein respective problems words are displayed in respective visually distinguishable formats.
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1. A computer readable storage medium containing a software program which, when executed by a processor, causes the processor to perform an operation for providing user-specific error analysis to identify as problem words any correctly spelled words of a document that are improperly used, the operation comprising: recording each word contained in a first document as pre-edited contents; receiving user edits replacing each problem word contained in the first document with a respective replacement word; after receiving the user edits, recording the each word contained in a first document as post-edited contents; comparing the pre-edited contents to the post-edited contents to identify the problem words and the respective replacement words; storing the user-replaced problem words and respective replacement words to a first data structure, wherein each user-replaced problem word is associated with the respective replacement word in an individual record of the first data structure and wherein each individual record includes a field indicating a number of times a respective user-replaced problem word has been replaced by its associated replacement word; assigning a formatting definition to each problem word for use in identifying problem words on a display device, wherein the formatting definition is reflective, on a display device displaying the respective problem word, of the number of times the respective problem word has been replaced by its associated replacement word; determining whether one or more problem words are present in a second document utilizing the first data structure; and indicating each problem word present in the second document with its respective formatting definition, wherein respective problems words are displayed in respective visually distinguishable formats. 2. The computer readable storage medium of claim 1 , wherein the steps of recording comprise separately storing the pre-edited contents and post-edited contents to a second data structure, wherein each record of the second data structure includes a pre-edited word field containing pre-edited content, a post-edited word field containing corresponding post-edited content and a changed indication field containing an indicator indicating whether the pre-edited and the corresponding post-edited content are different.
| 0.5 |
9,747,365 | 12 | 19 |
12. A search engine comprising: a storage device that stores a datastore, the datastore storing a plurality of records, wherein each record includes state data related to at least one of (i) a function of a corresponding application and (ii) a state of the corresponding application resulting from performing the function; a processing device that executes computer readable instructions, the computer readable instructions, when executed by the processing device, causing the processing device to: receive a search query from a remote device, wherein the search query includes one or more query terms; generate a collection of one or more analyzed tokens based on the search query, wherein the collection of one or more analyzed tokens indicates at least a subset of the one or more query terms; analyze the collection of one or more analyzed tokens with a plurality of different parsers, wherein: each parser of the plurality of different parsers is configured to (i) according to a parsing operation specific to the parser, parse at least one of (a) the collection of one or more analyzed tokens and (b) the search query and (iii) output parsed query that includes a list of one or more parsed tokens, each of the parsed tokens include a corresponding string, and at least one parsed token of at least one of the parsed queries includes one or more properties of the corresponding string; generate a retrieval query based on the parsed queries output by the plurality of different parsers, wherein: the retrieval query is a data structure that includes a plurality of retrieval leaf nodes, and each leaf node of the plurality of retrieval leaf nodes stores at least one of (i) a property of a string from a respective one of the parsed tokens and (ii) the string from the respective one of the parsed tokens; identify a consideration set of records based on the plurality of retrieval leaf nodes; determine, for each record in the consideration set of records, a result score for the record, wherein the result score for the record is determined based on a matching between the retrieval leaf node and the state data of the record; generate search results based on a subset of the consideration set of records, wherein the subset is selected based on the result scores of the consideration set of records; and provide the search results to the remote device.
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12. A search engine comprising: a storage device that stores a datastore, the datastore storing a plurality of records, wherein each record includes state data related to at least one of (i) a function of a corresponding application and (ii) a state of the corresponding application resulting from performing the function; a processing device that executes computer readable instructions, the computer readable instructions, when executed by the processing device, causing the processing device to: receive a search query from a remote device, wherein the search query includes one or more query terms; generate a collection of one or more analyzed tokens based on the search query, wherein the collection of one or more analyzed tokens indicates at least a subset of the one or more query terms; analyze the collection of one or more analyzed tokens with a plurality of different parsers, wherein: each parser of the plurality of different parsers is configured to (i) according to a parsing operation specific to the parser, parse at least one of (a) the collection of one or more analyzed tokens and (b) the search query and (iii) output parsed query that includes a list of one or more parsed tokens, each of the parsed tokens include a corresponding string, and at least one parsed token of at least one of the parsed queries includes one or more properties of the corresponding string; generate a retrieval query based on the parsed queries output by the plurality of different parsers, wherein: the retrieval query is a data structure that includes a plurality of retrieval leaf nodes, and each leaf node of the plurality of retrieval leaf nodes stores at least one of (i) a property of a string from a respective one of the parsed tokens and (ii) the string from the respective one of the parsed tokens; identify a consideration set of records based on the plurality of retrieval leaf nodes; determine, for each record in the consideration set of records, a result score for the record, wherein the result score for the record is determined based on a matching between the retrieval leaf node and the state data of the record; generate search results based on a subset of the consideration set of records, wherein the subset is selected based on the result scores of the consideration set of records; and provide the search results to the remote device. 19. The search engine of claim 12 , wherein the plurality of different parsers includes a location parser that outputs location parsed tokens and a synonym parser that outputs synonym parsed tokens.
| 0.884211 |
9,236,047 | 15 | 19 |
15. A computer-readable storage device which stores a set of instructions which when executed performs a method for providing voice stream augmented note taking, the method executed by the set of instructions comprising: recording a voice stream into a buffer; converting the voice stream into a text stream, wherein the text stream includes at least one text chunk comprising a sentence or phrase, the at least one text chunk comprising content-laden text that is defined by logical breaks identifying phrase or sentence boundaries in the text; receiving a text input to an electronic document from a user; determining whether the text input at least partially matches the at least one text chunk; in response to determining that the text input at least partially matches the at least one text chunk, displaying the at least one text chunk to the user as a selectable element; receiving a selection of the displayed at least one text chunk from the user; and inserting the at least one text chunk into the electronic document.
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15. A computer-readable storage device which stores a set of instructions which when executed performs a method for providing voice stream augmented note taking, the method executed by the set of instructions comprising: recording a voice stream into a buffer; converting the voice stream into a text stream, wherein the text stream includes at least one text chunk comprising a sentence or phrase, the at least one text chunk comprising content-laden text that is defined by logical breaks identifying phrase or sentence boundaries in the text; receiving a text input to an electronic document from a user; determining whether the text input at least partially matches the at least one text chunk; in response to determining that the text input at least partially matches the at least one text chunk, displaying the at least one text chunk to the user as a selectable element; receiving a selection of the displayed at least one text chunk from the user; and inserting the at least one text chunk into the electronic document. 19. The computer-readable storage device of claim 15 , further comprising: in response to receiving the selection of the displayed at least one text chunk from the user, displaying at least one subsequent text chunk to the user as a selectable element.
| 0.715576 |
8,078,517 | 20 | 21 |
20. The system of claim 15 , further comprising executable instructions configured to aggregate cash equivalent transactions associated with the individual over a predetermined period of time to determine the suspicious financial activity.
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20. The system of claim 15 , further comprising executable instructions configured to aggregate cash equivalent transactions associated with the individual over a predetermined period of time to determine the suspicious financial activity. 21. The system of claim 20 , further comprising executable instructions configured to report an amount associated with the cash equivalent transactions associated with the individual if the amount exceeds a threshold.
| 0.5 |
8,639,509 | 6 | 17 |
6. The method of claim 5 , wherein, in addition to the history score, the at least one feature includes at least one of a set of features, including: a parse-tree-word-level confidence score calculated based on respective word-level confidence scores of a plurality of words of the respective sub-tree; a POS-tag confidence score based on respective POS-tag scores computed for the POS tag assignments of the plurality of words of the respective sub-tree; a linking score representing a conditional probability of a link of a highest level of the respective sub-tree, the link including a dependency relation and a directionality; each of a plurality of words of the respective sub-tree's corresponding section of the interpreted text; each of the plurality of POS tags corresponding to the plurality of words of the respective sub-tree's corresponding section of the interpreted text; each of a plurality of multi-level hierarchical POS tags corresponding to the plurality of words of the respective sub-tree's corresponding section of the interpreted text; a dependency relation characteristic; a single level joint head and dependency relation (SL-JHD) characteristic; a single level joint mod and dependency relation (SL-JMD) characteristic; a single level joint head, mod, and dependency relation (SL-JHMD) characteristic; a joint dependency relation (JDR) characteristic; a multi-level joint head and dependency relation (ML-JHD) characteristic; a multi-level joint mod and dependency relation (ML-JMD) characteristic; a multi-level joint head, mod, and dependency relation (ML-JHMD) characteristic; a head, dependency, and left and right neighbors (HDLRN) characteristic; a sub-tree size characteristic; and a semantic slot feature.
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6. The method of claim 5 , wherein, in addition to the history score, the at least one feature includes at least one of a set of features, including: a parse-tree-word-level confidence score calculated based on respective word-level confidence scores of a plurality of words of the respective sub-tree; a POS-tag confidence score based on respective POS-tag scores computed for the POS tag assignments of the plurality of words of the respective sub-tree; a linking score representing a conditional probability of a link of a highest level of the respective sub-tree, the link including a dependency relation and a directionality; each of a plurality of words of the respective sub-tree's corresponding section of the interpreted text; each of the plurality of POS tags corresponding to the plurality of words of the respective sub-tree's corresponding section of the interpreted text; each of a plurality of multi-level hierarchical POS tags corresponding to the plurality of words of the respective sub-tree's corresponding section of the interpreted text; a dependency relation characteristic; a single level joint head and dependency relation (SL-JHD) characteristic; a single level joint mod and dependency relation (SL-JMD) characteristic; a single level joint head, mod, and dependency relation (SL-JHMD) characteristic; a joint dependency relation (JDR) characteristic; a multi-level joint head and dependency relation (ML-JHD) characteristic; a multi-level joint mod and dependency relation (ML-JMD) characteristic; a multi-level joint head, mod, and dependency relation (ML-JHMD) characteristic; a head, dependency, and left and right neighbors (HDLRN) characteristic; a sub-tree size characteristic; and a semantic slot feature. 17. The method of claim 6 , wherein: the POS-tag confidence score is calculated as a geometric mean of the respective POS-tag scores.
| 0.887858 |
8,229,753 | 3 | 4 |
3. The computer readable storage medium of claim 1 wherein the grammar is for one of speech recognition, handwriting recognition, gesture recognition and visual recognition.
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3. The computer readable storage medium of claim 1 wherein the grammar is for one of speech recognition, handwriting recognition, gesture recognition and visual recognition. 4. The computer readable storage medium of claim 3 wherein the controls relate to one of HTML, XHTML, cHTML, XML and WML.
| 0.5 |
8,302,084 | 1 | 2 |
1. A method for determining a structure of a computer program project, the project having one or more compilable units, the method comprising: a) receiving program structure information from a user, the program structure information being described by program structure parameters that are used to provide navigational and syntax functionality, the program structures parameters including a symbol database and file database that keeps track of the interrelationships between project components; b) analyzing at least one of the compilable units to identify compiler commentary contained therein; c) discovering information about the program structure from the compiler commentary; and d) reconciling the information obtained from steps a) and c).
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1. A method for determining a structure of a computer program project, the project having one or more compilable units, the method comprising: a) receiving program structure information from a user, the program structure information being described by program structure parameters that are used to provide navigational and syntax functionality, the program structures parameters including a symbol database and file database that keeps track of the interrelationships between project components; b) analyzing at least one of the compilable units to identify compiler commentary contained therein; c) discovering information about the program structure from the compiler commentary; and d) reconciling the information obtained from steps a) and c). 2. The method of claim 1 wherein step d) is accomplish by a reconciliation strategy.
| 0.784615 |
7,549,119 | 12 | 14 |
12. A computer implemented method according to claim 11 wherein processing step (g3b) further comprises the steps of: (g3b1) checking a first character of a first of said plurality of strings against a predefined alias character list, wherein each of said alias characters is a predefined character mapping where more than one character in an ordered sequence is mapped to a single character; (g3b2) when a match is found in said step (g3b1) for said first character of said first of said plurality of strings, building a temporary alias character list for said first character; (g3b3) comparing said first character against a first character of all said previously identified undesirable terms in said secondary database of undesirable terms for a match; (g3b4) when a match is found, counting the match toward a predetermined total of counted matches needed to identify said at least one matching phrase; (g3b5) for said match, determining if a flag has been set in a current position of said previously identified undesirable term in said secondary database of undesirable terms, indicating an end of said previously identified undesirable term; (g3b6) when said determining step (g3b5) result is no, passing control to step (g3b10); (g3b7) when said determining step (g3b5) result is yes, determining if a total of said counted matches is equal to said predetermined total of counted matches needed to identify said at least one matching phrase; (g3b8) when said determining step (g3b7) result is yes, saving said first of said plurality of strings as said at least one matching phrase; (g3b9) when said determining step (g3b7) result is no, passing control to step (g3b10); (g3b10) determining if said recursive matching comparison subroutine has reached an end of said first of said plurality of strings; (g3b11) when said determining step (g3b10) result is no, calling, by said recursive matching comparison subroutine, said recursive matching comparison subroutine recursively; (g3b12) moving to a next character in said first of said plurality of strings and passing control to said checking step (g3b1) for said next character; (g3b13) repeating steps (g3b1) through (g3b12) for said next character; and (g3b14) repeating step (g3b13) for each remaining character in said first of said plurality of strings.
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12. A computer implemented method according to claim 11 wherein processing step (g3b) further comprises the steps of: (g3b1) checking a first character of a first of said plurality of strings against a predefined alias character list, wherein each of said alias characters is a predefined character mapping where more than one character in an ordered sequence is mapped to a single character; (g3b2) when a match is found in said step (g3b1) for said first character of said first of said plurality of strings, building a temporary alias character list for said first character; (g3b3) comparing said first character against a first character of all said previously identified undesirable terms in said secondary database of undesirable terms for a match; (g3b4) when a match is found, counting the match toward a predetermined total of counted matches needed to identify said at least one matching phrase; (g3b5) for said match, determining if a flag has been set in a current position of said previously identified undesirable term in said secondary database of undesirable terms, indicating an end of said previously identified undesirable term; (g3b6) when said determining step (g3b5) result is no, passing control to step (g3b10); (g3b7) when said determining step (g3b5) result is yes, determining if a total of said counted matches is equal to said predetermined total of counted matches needed to identify said at least one matching phrase; (g3b8) when said determining step (g3b7) result is yes, saving said first of said plurality of strings as said at least one matching phrase; (g3b9) when said determining step (g3b7) result is no, passing control to step (g3b10); (g3b10) determining if said recursive matching comparison subroutine has reached an end of said first of said plurality of strings; (g3b11) when said determining step (g3b10) result is no, calling, by said recursive matching comparison subroutine, said recursive matching comparison subroutine recursively; (g3b12) moving to a next character in said first of said plurality of strings and passing control to said checking step (g3b1) for said next character; (g3b13) repeating steps (g3b1) through (g3b12) for said next character; and (g3b14) repeating step (g3b13) for each remaining character in said first of said plurality of strings. 14. A computer implemented method according to claim 12 wherein step (g3b7) further comprises requiring said predetermined total of counted matches to be a hard coded number that is based upon the total number of characters in said previously identified undesirable term, said hard coded number being less than or equal to the number of characters in said previously identified undesirable term, in order to identify said at least one matching term.
| 0.735882 |
7,814,127 | 12 | 13 |
12. The method of claim 5 , wherein the translation information further describes translations of each of the plurality of logical fields from the first natural language expression to a third natural language expression, and further comprising: displaying, to a user, at least a portion of the data abstraction model using only one of the first natural language expression, one of the two or more second natural language expression and the third natural language expression.
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12. The method of claim 5 , wherein the translation information further describes translations of each of the plurality of logical fields from the first natural language expression to a third natural language expression, and further comprising: displaying, to a user, at least a portion of the data abstraction model using only one of the first natural language expression, one of the two or more second natural language expression and the third natural language expression. 13. The method of claim 12 , wherein which language expression is used to display the portion of the data abstraction model is based on user parameters.
| 0.5 |
9,092,636 | 24 | 26 |
24. A system for preventing unauthorized disclosure of secure information, the system having a processor and comprising: a receiving module to receive digital information including a first text, the first text including a plurality of words; a candidate ID module to identify a first candidate entity, the first candidate entity corresponding to a particular word of the plurality of words; a comparison module to compare the first candidate entity against a plurality of compressed registered entities stored in a lightweight entity database (LWED) stored locally; a communication module to transmit the first candidate entity to a remote server that can access a global entity database (GED), wherein the GED includes the plurality of registered entities in an uncompressed form, and wherein the remote server automatically generates a confirmation on whether the first candidate entity matches against a second registered entity in the GED; the communication module to receive from the remote server the confirmation; and a security action module to perform a security action when the first candidate entity matches against a particular registered entity of the registered entities in the GED, wherein the security action is performed on the first text before the first text is disclosed through the system.
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24. A system for preventing unauthorized disclosure of secure information, the system having a processor and comprising: a receiving module to receive digital information including a first text, the first text including a plurality of words; a candidate ID module to identify a first candidate entity, the first candidate entity corresponding to a particular word of the plurality of words; a comparison module to compare the first candidate entity against a plurality of compressed registered entities stored in a lightweight entity database (LWED) stored locally; a communication module to transmit the first candidate entity to a remote server that can access a global entity database (GED), wherein the GED includes the plurality of registered entities in an uncompressed form, and wherein the remote server automatically generates a confirmation on whether the first candidate entity matches against a second registered entity in the GED; the communication module to receive from the remote server the confirmation; and a security action module to perform a security action when the first candidate entity matches against a particular registered entity of the registered entities in the GED, wherein the security action is performed on the first text before the first text is disclosed through the system. 26. The system of claim 24 , wherein the candidate ID module includes: a heuristics engine to skip over one or more words from the plurality of words based on a heuristic rule.
| 0.821862 |
8,370,117 | 13 | 14 |
13. A proofing tool for a computer-aided design (CAD) object having at least one drawing note, the tool stored in a memory of a computer system having a processor, the memory storing the CAD object, the tool comprising: an extractor to extract the drawing note from the CAD object; a rule module to obtain a plurality of rules from the memory, the plurality of rules relating to acceptable drawing notes for the CAD object, the plurality of rules including a first rule having a first plurality of keywords and a second rule having a second plurality of keywords; a comparator to compare the extracted note with the first rule and with the second rule; and a tagging module to generate a result based on the comparisons.
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13. A proofing tool for a computer-aided design (CAD) object having at least one drawing note, the tool stored in a memory of a computer system having a processor, the memory storing the CAD object, the tool comprising: an extractor to extract the drawing note from the CAD object; a rule module to obtain a plurality of rules from the memory, the plurality of rules relating to acceptable drawing notes for the CAD object, the plurality of rules including a first rule having a first plurality of keywords and a second rule having a second plurality of keywords; a comparator to compare the extracted note with the first rule and with the second rule; and a tagging module to generate a result based on the comparisons. 14. The tool of claim 13 , wherein the tagging module is further operable to tag the extracted note when the extracted note satisfies at least one of the first rule and the second rule.
| 0.5 |
9,454,582 | 8 | 14 |
8. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving a search query; obtaining search results that satisfy the search query, wherein the search results identify a plurality of web pages, wherein each web page is a web page on a corresponding website of a plurality of websites; computing a respective global ranking score for each website of the plurality of websites, wherein the global ranking score represents an indication of relevance of the website to the search query relative to other websites of the plurality of websites; computing an onsite ranking score for each of the plurality of web pages, wherein the onsite ranking score is computed from onsite data that is controlled by a webmaster or a developer of the corresponding website for the web page, wherein the onsite ranking score represents an indication of relevance of the web page as responsive to the search query relative to other web pages within the corresponding web site; selecting, as a representative web page for a particular website from among a plurality of web pages for the particular website, a particular web page having a highest onsite ranking score among the plurality of web pages for the particular website; comparing the onsite ranking score for the representative web page to the global ranking score for the particular website; determining that the onsite ranking score for the representative web page is not consistent with the global ranking score for the particular website; in response to determining that the onsite ranking score for the representative web page is not consistent with the global ranking score for the particular website, assigning a new global ranking score for the particular website including modifying the global ranking score for the particular website; computing a combined ranking score for each web page of the plurality of web pages including combining a respective global ranking score for a website associated with the web page and an onsite ranking score for the web page, including using the new global ranking score for the particular website when computing the combined ranking score for web pages on the particular website; and ranking the search results according to the combined ranking scores computed for respective web pages identified by the search results.
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8. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving a search query; obtaining search results that satisfy the search query, wherein the search results identify a plurality of web pages, wherein each web page is a web page on a corresponding website of a plurality of websites; computing a respective global ranking score for each website of the plurality of websites, wherein the global ranking score represents an indication of relevance of the website to the search query relative to other websites of the plurality of websites; computing an onsite ranking score for each of the plurality of web pages, wherein the onsite ranking score is computed from onsite data that is controlled by a webmaster or a developer of the corresponding website for the web page, wherein the onsite ranking score represents an indication of relevance of the web page as responsive to the search query relative to other web pages within the corresponding web site; selecting, as a representative web page for a particular website from among a plurality of web pages for the particular website, a particular web page having a highest onsite ranking score among the plurality of web pages for the particular website; comparing the onsite ranking score for the representative web page to the global ranking score for the particular website; determining that the onsite ranking score for the representative web page is not consistent with the global ranking score for the particular website; in response to determining that the onsite ranking score for the representative web page is not consistent with the global ranking score for the particular website, assigning a new global ranking score for the particular website including modifying the global ranking score for the particular website; computing a combined ranking score for each web page of the plurality of web pages including combining a respective global ranking score for a website associated with the web page and an onsite ranking score for the web page, including using the new global ranking score for the particular website when computing the combined ranking score for web pages on the particular website; and ranking the search results according to the combined ranking scores computed for respective web pages identified by the search results. 14. The system of claim 8 , wherein determining that the onsite ranking score for the representative web page is not consistent with the global ranking score for the particular website comprises determining that the global ranking score for the website indicates high relevance to the query and the onsite ranking score for the representative web page indicates low relevance to the query, and wherein modifying the global ranking score for the website comprises decreasing the global ranking score for the website.
| 0.5 |
8,218,849 | 1 | 3 |
1. A method for detecting anatomic landmarks of a left ventricle (LV) in a magnetic resonance (MR) long axis image slice, comprising: detecting a plurality of apex candidates in the MR long axis image slice using a trained apex detector; detecting a plurality of base plane candidates in the MR long axis image slice using a trained base plane detector; generating a joint context for each apex-base plane candidate pair; and determining a best apex-base plane candidate pair based on the generated joint context using a trained joint context detector.
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1. A method for detecting anatomic landmarks of a left ventricle (LV) in a magnetic resonance (MR) long axis image slice, comprising: detecting a plurality of apex candidates in the MR long axis image slice using a trained apex detector; detecting a plurality of base plane candidates in the MR long axis image slice using a trained base plane detector; generating a joint context for each apex-base plane candidate pair; and determining a best apex-base plane candidate pair based on the generated joint context using a trained joint context detector. 3. The method of claim 1 , wherein the apex candidates and the base plane candidates are detected using marginal space learning (MSL).
| 0.877737 |
8,972,415 | 1 | 7 |
1. A similarity search initialization system comprising: a leaf selector implemented by a processor to select a leaf of a suffix tree generated from a target string representing a target sequence, the selected leaf being associated with a prefix in the suffix tree having a longest match to a suffix of a query string representing a query; and a distance module to determine a distance between the query and a subsequence of the target sequence that is represented by a candidate substring of the target string, the candidate substring comprising the prefix associated with the selected leaf, wherein the determined distance is to provide an initial upper bound in a similarity search of the target sequence using the query.
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1. A similarity search initialization system comprising: a leaf selector implemented by a processor to select a leaf of a suffix tree generated from a target string representing a target sequence, the selected leaf being associated with a prefix in the suffix tree having a longest match to a suffix of a query string representing a query; and a distance module to determine a distance between the query and a subsequence of the target sequence that is represented by a candidate substring of the target string, the candidate substring comprising the prefix associated with the selected leaf, wherein the determined distance is to provide an initial upper bound in a similarity search of the target sequence using the query. 7. The similarity search initialization system claim 1 , further comprising a string quantizer to convert the target sequence into the target string and to convert the query into the query string, the target string and the query string each comprising a different termination symbol, wherein the termination symbols are not present in a finite symbol alphabet of the string quantizer.
| 0.695238 |
7,917,847 | 76 | 79 |
76. The computer program product according to claim 67 , further comprising instructions for: storing information regarding a focus position and a scrolling position in the first browsing mode; and restoring the focus position and the scrolling position, based on the stored information, in the second browsing mode.
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76. The computer program product according to claim 67 , further comprising instructions for: storing information regarding a focus position and a scrolling position in the first browsing mode; and restoring the focus position and the scrolling position, based on the stored information, in the second browsing mode. 79. The computer program product according to claim 76 , wherein the restoring the focus position and the scrolling position is performed so that an item adjacent to the focus position to be restored is used as a focus target in the second browsing mode if it is judged that a focus target in the first browsing mode does not exist at a position to he restored in the second browsing mode.
| 0.501282 |
8,935,251 | 7 | 8 |
7. A system according to claim 1 , further comprising: an angle determination module to determine one or more angles for placing one of the remaining cluster spines on one of the clusters of one of the unique cluster spines, comprising at least one of: a first assignment module to assign a primary angle as σ + Π 3 and a secondary angle as σ - Π 3 , where σ is the angle of the unique cluster spine and 0≦σ<II; and a further assignment module to assign the primary angle as σ - Π 3 and the secondary angle as σ + Π 3 , where σ is the angle of the unique cluster spine and 0≦σ<II.
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7. A system according to claim 1 , further comprising: an angle determination module to determine one or more angles for placing one of the remaining cluster spines on one of the clusters of one of the unique cluster spines, comprising at least one of: a first assignment module to assign a primary angle as σ + Π 3 and a secondary angle as σ - Π 3 , where σ is the angle of the unique cluster spine and 0≦σ<II; and a further assignment module to assign the primary angle as σ - Π 3 and the secondary angle as σ + Π 3 , where σ is the angle of the unique cluster spine and 0≦σ<II. 8. A system according to claim 7 , further comprising: an angle selection module to select the primary angle for grafting the remaining cluster spine when no overlap between the clusters exist and to further select the secondary angle when the primary angle is not selected.
| 0.5 |
9,684,498 | 1 | 7 |
1. A file processing method applied to an operating system, the file processing method comprising: packaging a first package file which supports a plurality of language versions into a plurality of first single-language package files, wherein the plurality of first single-language package files correspond to the plurality of language versions, respectively; wherein names of the plurality of first single-language package files are strings corresponding to a name of the first package file and language codes related to the plurality of language versions; adding the plurality of first single-language package files to a plurality of language packages, respectively; when changing a language version of the application to a specific language, searching a system language package corresponding to the specific language in a system file hi a framework layer; when it is unable to find the system language package corresponding the specific language in the system file in the framework layer, searching the language package corresponding to the specific language in the plurality of language packages; and loading the language package corresponding to the specific language.
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1. A file processing method applied to an operating system, the file processing method comprising: packaging a first package file which supports a plurality of language versions into a plurality of first single-language package files, wherein the plurality of first single-language package files correspond to the plurality of language versions, respectively; wherein names of the plurality of first single-language package files are strings corresponding to a name of the first package file and language codes related to the plurality of language versions; adding the plurality of first single-language package files to a plurality of language packages, respectively; when changing a language version of the application to a specific language, searching a system language package corresponding to the specific language in a system file hi a framework layer; when it is unable to find the system language package corresponding the specific language in the system file in the framework layer, searching the language package corresponding to the specific language in the plurality of language packages; and loading the language package corresponding to the specific language. 7. The file processing method of claim 1 , wherein the plurality of language packages are in a language package set.
| 0.869955 |
4,592,085 | 25 | 26 |
25. An apparatus for generating a transition signal as in claim 24 wherein said apparatus further comprises peak evaluation means for evaluating said transition signal to detect peaks therein by time-sampling said transition signal using a predetermined time interval and identifying as a peak level each maximum of said transition signal occurring in the middle of a said time interval and to thereby locate transitions in said voice signal.
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25. An apparatus for generating a transition signal as in claim 24 wherein said apparatus further comprises peak evaluation means for evaluating said transition signal to detect peaks therein by time-sampling said transition signal using a predetermined time interval and identifying as a peak level each maximum of said transition signal occurring in the middle of a said time interval and to thereby locate transitions in said voice signal. 26. An apparatus for generating a transition signal as in claim 25 further comprising log circuit means for calculating the logarithms of said respective average power levels and said respective power levels, and wherein said first difference levels represent the differences between said respective logarithms, whereby the influence on said first difference levels of variations in emphasis from phoneme to phoneme of a particular speaker is minimized.
| 0.5 |
7,979,840 | 5 | 8 |
5. The method of claim 1 , further comprising the step of: deriving an SOA-method model to provide a generic framework for service modeling in an SOA solution.
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5. The method of claim 1 , further comprising the step of: deriving an SOA-method model to provide a generic framework for service modeling in an SOA solution. 8. The method of claim 5 , further comprising the step of: instantiating said SOA-method model to facilitate at least one industry-specific application.
| 0.616162 |
7,930,180 | 2 | 3 |
2. The speech recognition system, according to claim 1 , further comprising: distance value buffers which store distance values generated by the distance calculation unit; and acoustic lookahead value buffers which store acoustic lookahead values generated by the acoustic lookahead unit, wherein in each of the distance value buffers, operations of writing the distance value from the distance calculation unit, reading out the distance value to the acoustic lookahead unit, and reading out the distance value to the word string matching unit are performed, in each of the acoustic lookahead value buffers, operations of writing the acoustic lookahead value from the acoustic lookahead unit and reading out the acoustic lookahead value to the word string matching unit are performed, wherein, at any point in time, the distance value buffer in which the distance value from the distance calculation unit is written, the distance value buffer from which the distance value to the acoustic lookahead unit is read out, and the distance value buffer from which the distance value to the word string matching unit are read out, are different from one another, and the acoustic lookahead value buffer in which the acoustic lookahead value from the acoustic lookahead unit is written and the acoustic lookahead value buffer from which the acoustic lookahead value to the word string matching unit is read out are different from each other.
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2. The speech recognition system, according to claim 1 , further comprising: distance value buffers which store distance values generated by the distance calculation unit; and acoustic lookahead value buffers which store acoustic lookahead values generated by the acoustic lookahead unit, wherein in each of the distance value buffers, operations of writing the distance value from the distance calculation unit, reading out the distance value to the acoustic lookahead unit, and reading out the distance value to the word string matching unit are performed, in each of the acoustic lookahead value buffers, operations of writing the acoustic lookahead value from the acoustic lookahead unit and reading out the acoustic lookahead value to the word string matching unit are performed, wherein, at any point in time, the distance value buffer in which the distance value from the distance calculation unit is written, the distance value buffer from which the distance value to the acoustic lookahead unit is read out, and the distance value buffer from which the distance value to the word string matching unit are read out, are different from one another, and the acoustic lookahead value buffer in which the acoustic lookahead value from the acoustic lookahead unit is written and the acoustic lookahead value buffer from which the acoustic lookahead value to the word string matching unit is read out are different from each other. 3. The speech recognition system according to claim 2 , further comprising a buffer length determination unit which determines a buffer length of the distance value buffer or the acoustic lookahead value buffer so that processing amounts of the distance calculation unit, the acoustic lookahead unit and the word string matching unit become uniform.
| 0.882015 |
8,412,650 | 1 | 5 |
1. A text analysis device having a physical processor, the device comprising: storage means configured to store opinions of users who participate in a discussion about a predetermined theme as text data and author information for specifying authors of the text data; feature quantity data generation means performed by the processor and configured to generate feature quantity data of the text data stored in the storage means; observation time-series signal generation means performed by the processor and configured to generate observation time-series signals based on information obtained by performing a predetermined process with respect to the feature quantity data; time-series node pattern generation means performed by the processor and configured to learn the observation time-series signals to specify node labels of the text data and to generate a time-series node pattern in which the node labels are arranged in order corresponding to the observation time-series signals; change point detection means performed by the processor and configured to detect a change point of the discussion based on the time-series node pattern; and influence specifying means performed by the processor and configured to specify an opinion having influence on an opinion corresponding to the specified text data out of opinions of the discussion based on the detected change point and the author information.
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1. A text analysis device having a physical processor, the device comprising: storage means configured to store opinions of users who participate in a discussion about a predetermined theme as text data and author information for specifying authors of the text data; feature quantity data generation means performed by the processor and configured to generate feature quantity data of the text data stored in the storage means; observation time-series signal generation means performed by the processor and configured to generate observation time-series signals based on information obtained by performing a predetermined process with respect to the feature quantity data; time-series node pattern generation means performed by the processor and configured to learn the observation time-series signals to specify node labels of the text data and to generate a time-series node pattern in which the node labels are arranged in order corresponding to the observation time-series signals; change point detection means performed by the processor and configured to detect a change point of the discussion based on the time-series node pattern; and influence specifying means performed by the processor and configured to specify an opinion having influence on an opinion corresponding to the specified text data out of opinions of the discussion based on the detected change point and the author information. 5. The text analysis device according to claim 1 , wherein the observation time-series signal generation means calculates polarity information of an object to be evaluated, which is set in advance, with respect to the opinions of the text data corresponding to the feature quantity data, based on the feature quantity data, and generates the observation time-series signals based on the calculated polarity information.
| 0.550429 |
8,577,668 | 13 | 16 |
13. A system comprising: one or more computing devices configured to perform operations including: sending, using one or more processors, a request to translate a source web document from a first language text to a second language text; receiving a translated web document for display containing a translation of the source web document into the second language text; displaying the translated web document; and in response to interacting with a portion of the translated web document, displaying the first language text that corresponds to a portion of the translated web document in a graphical element overlaying the translated web document.
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13. A system comprising: one or more computing devices configured to perform operations including: sending, using one or more processors, a request to translate a source web document from a first language text to a second language text; receiving a translated web document for display containing a translation of the source web document into the second language text; displaying the translated web document; and in response to interacting with a portion of the translated web document, displaying the first language text that corresponds to a portion of the translated web document in a graphical element overlaying the translated web document. 16. The system of claim 13 , where sending the request includes entering a uniform resource locator into a field of a presented translation interface and identifying the second language.
| 0.845258 |
7,743,054 | 4 | 5 |
4. The information retrieval system according to claim 1 , wherein said screen generator is further configured to generate a detailed search screen for prompting the user to select a second search condition for a detailed search from options; a screen to be displayed on said display is changed from a predetermined selection display screen included in said selection display screen group, to said detailed search screen in response to a predetermined second screen transition instruction; and said option is extracted from information displayed on said predetermined selection display screen.
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4. The information retrieval system according to claim 1 , wherein said screen generator is further configured to generate a detailed search screen for prompting the user to select a second search condition for a detailed search from options; a screen to be displayed on said display is changed from a predetermined selection display screen included in said selection display screen group, to said detailed search screen in response to a predetermined second screen transition instruction; and said option is extracted from information displayed on said predetermined selection display screen. 5. The information retrieval system according to claim 4 , wherein information displayed on said predetermined selection display screen is bibliographical information of said selected data.
| 0.860825 |
9,015,134 | 27 | 32 |
27. A computer based method of searching for documents, comprising the steps of: receiving a search query to search for documents, the search query including at least two terms; searching for related stored queries in a database of stored queries which were stored so to enable retrieval of said stored query as they were submitted; wherein a related stored query is a stored query including at least two terms and includes at least one term included in the received search query; performing a search for documents based on a query from said related stored queries that includes at least one term of said received query; displaying at least one of said related stored queries; and displaying at least one representation of a document that is related to one of said displayed related stored queries, the representation comprising at least one of a) a title, b) summary and c) a URL of said document.
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27. A computer based method of searching for documents, comprising the steps of: receiving a search query to search for documents, the search query including at least two terms; searching for related stored queries in a database of stored queries which were stored so to enable retrieval of said stored query as they were submitted; wherein a related stored query is a stored query including at least two terms and includes at least one term included in the received search query; performing a search for documents based on a query from said related stored queries that includes at least one term of said received query; displaying at least one of said related stored queries; and displaying at least one representation of a document that is related to one of said displayed related stored queries, the representation comprising at least one of a) a title, b) summary and c) a URL of said document. 32. The method of claim 27 , further comprising the step of ranking said document.
| 0.876133 |
6,134,532 | 37 | 40 |
37. A system for selecting advertisements in a computer environment, comprising: a database of electronic advertisements; and an electronic advertisement management system, comprising: a converter capable of converting an observed behavior of a user computing device in the computer environment to a behavior vector, a comparater capable of comparing the behavior vector to a plurality of entity vectors, the entity vectors indicative of the electronic advertisements, so as to identify at least one entity vector closely associated with the observed behavior, and a selector accessing the electronic database with the identified entity vector so as to select at least one electronic advertisement to communicate to the user computing device.
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37. A system for selecting advertisements in a computer environment, comprising: a database of electronic advertisements; and an electronic advertisement management system, comprising: a converter capable of converting an observed behavior of a user computing device in the computer environment to a behavior vector, a comparater capable of comparing the behavior vector to a plurality of entity vectors, the entity vectors indicative of the electronic advertisements, so as to identify at least one entity vector closely associated with the observed behavior, and a selector accessing the electronic database with the identified entity vector so as to select at least one electronic advertisement to communicate to the user computing device. 40. The system as defined in claim 37, wherein the observed behavior is selected from the group consisting of: a user query, a page view, or a purchase of a product.
| 0.720339 |
5,412,384 | 3 | 4 |
3. In a computer-implemented system for compressing input data arranged in a data stream of one or more records consisting of sequences of source symbols selected from a source alphabet to form output data consisting of sequences of code symbols selected from a code alphabet according to a static dictionary stored in a memory, said dictionary representing a static parse-tree having nodes representing said code symbols, said nodes being linked into paths representing said source symbol sequences, a method for creating said static dictionary comprising the steps of: (a) initializing a parse-tree with a plurality of said paths representing a set of said source symbol strings, each said path having at least one node with a unity use count value;. (b) setting a current input pointer at the beginning of said data stream; performing the steps of (c.1) determining the longest said source symbol sequence S, represented by a path P in said parse-tree, that matches a current said source symbol sequence in said data stream beginning at said current input pointer, (c.2) incrementing said use count value for all nodes in said path P, (c.3) adding a new node N having a unity use count value to the end of said path P to form a new path P' representing a new source symbol sequence S' consisting of said string S extended by at least one immediately subsequent source symbol in said data stream, and (c.4) advancing said current input pointer to immediately after said sequence S' in said data stream; (s) if said parse-tree contains less than a first predetermined plurality of nodes, repeating said performing step (c); (e) combining with its parent node one or more child nodes in said parse-tree, said child nodes each having a single-child parent node for which said use count value differs by no more than one from said use count value for each child node, thereby forming one or more new leaf nodes; (f) assembling said nodes with the associated said paths to form said static dictionary; and (g) storing said static dictionary in said memory.
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3. In a computer-implemented system for compressing input data arranged in a data stream of one or more records consisting of sequences of source symbols selected from a source alphabet to form output data consisting of sequences of code symbols selected from a code alphabet according to a static dictionary stored in a memory, said dictionary representing a static parse-tree having nodes representing said code symbols, said nodes being linked into paths representing said source symbol sequences, a method for creating said static dictionary comprising the steps of: (a) initializing a parse-tree with a plurality of said paths representing a set of said source symbol strings, each said path having at least one node with a unity use count value;. (b) setting a current input pointer at the beginning of said data stream; performing the steps of (c.1) determining the longest said source symbol sequence S, represented by a path P in said parse-tree, that matches a current said source symbol sequence in said data stream beginning at said current input pointer, (c.2) incrementing said use count value for all nodes in said path P, (c.3) adding a new node N having a unity use count value to the end of said path P to form a new path P' representing a new source symbol sequence S' consisting of said string S extended by at least one immediately subsequent source symbol in said data stream, and (c.4) advancing said current input pointer to immediately after said sequence S' in said data stream; (s) if said parse-tree contains less than a first predetermined plurality of nodes, repeating said performing step (c); (e) combining with its parent node one or more child nodes in said parse-tree, said child nodes each having a single-child parent node for which said use count value differs by no more than one from said use count value for each child node, thereby forming one or more new leaf nodes; (f) assembling said nodes with the associated said paths to form said static dictionary; and (g) storing said static dictionary in said memory. 4. The method of claim 3 further comprising the steps of: (e.1) initializing a use count value threshold to unity; (e.2) performing the steps of (e.2.1) deleting each said childless node having a use count value less than or equal to said use count value threshold, and (e.2.2) incrementing said use count value threshold; and (e.3) if said parse-tree contains more than a second predetermined plurality of said nodes, repeating said performing step (e.2).
| 0.770161 |
8,674,855 | 7 | 8 |
7. The method of claim 6 , further comprising using a letter, a symbol, and/or a space as the one or more characters.
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7. The method of claim 6 , further comprising using a letter, a symbol, and/or a space as the one or more characters. 8. The method of claim 7 , further comprising using different arrangements of the letter, the symbol, and/or the space as the one or more characters.
| 0.5 |
9,630,109 | 7 | 11 |
7. A non-transitory computer readable storage medium embodying a computer program for performing a method, said method comprising: causing a context engine comprising an in-memory database engine to collect data from a first source comprising a gamification platform regarding a first event comprising an action taken in an enterprise by an actor; causing the context engine to collect first context data over an asynchronous message broker from a second source comprising a machine-to-machine stack including a hygroscopic sensor from a wearable of the actor; causing the context engine to collect second context data over the asynchronous message broker from a third source comprising a second gamification platform regarding a second event involving the actor; causing the context engine to perform a first aggregation of the first context data from the second source, and then to perform a second aggregation to process the data and aggregated first context data to create context enriched data by calculating a defined trust metric from the data and the second context data; causing the context engine to store the context enriched data in an in-memory database; causing the context engine to provide the context enriched data in a view within the in-memory database; determining from the context enriched data that the actor has achieved a predetermined goal; based upon achievement of the predetermined goal, triggering the asynchronous message broker to communicate a message to assign an additional role to the actor.
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7. A non-transitory computer readable storage medium embodying a computer program for performing a method, said method comprising: causing a context engine comprising an in-memory database engine to collect data from a first source comprising a gamification platform regarding a first event comprising an action taken in an enterprise by an actor; causing the context engine to collect first context data over an asynchronous message broker from a second source comprising a machine-to-machine stack including a hygroscopic sensor from a wearable of the actor; causing the context engine to collect second context data over the asynchronous message broker from a third source comprising a second gamification platform regarding a second event involving the actor; causing the context engine to perform a first aggregation of the first context data from the second source, and then to perform a second aggregation to process the data and aggregated first context data to create context enriched data by calculating a defined trust metric from the data and the second context data; causing the context engine to store the context enriched data in an in-memory database; causing the context engine to provide the context enriched data in a view within the in-memory database; determining from the context enriched data that the actor has achieved a predetermined goal; based upon achievement of the predetermined goal, triggering the asynchronous message broker to communicate a message to assign an additional role to the actor. 11. The non-transitory computer readable storage medium of claim 7 wherein the context engine processes the data and the first context data by filtering.
| 0.809701 |
9,311,285 | 19 | 27 |
19. A method of curating chat transcripts into webpages, the method comprising: automatically generating a transcript of a chat between an expert human agent of an online retailer website and a potential customer of the online retailer website, the chat being a synchronous online dialog, the chat transcript being separate from the chat itself; selecting the chat transcript for curation; curating the chat transcript by adding inline preferable information to the transcript and/or deleting non-preferable information from the transcript; automatically generating a static new webpage separate from the chat itself that includes: a transcript portion which includes the curated chat transcript; and a value-added portion which includes value-added information beyond inline information contained in the curated chat transcript; and automatically publishing the static new webpage separately from the chat itself and on the online retailer web site in order to drive traffic to the online retailer web site and in a location to be indexed by an indexing service.
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19. A method of curating chat transcripts into webpages, the method comprising: automatically generating a transcript of a chat between an expert human agent of an online retailer website and a potential customer of the online retailer website, the chat being a synchronous online dialog, the chat transcript being separate from the chat itself; selecting the chat transcript for curation; curating the chat transcript by adding inline preferable information to the transcript and/or deleting non-preferable information from the transcript; automatically generating a static new webpage separate from the chat itself that includes: a transcript portion which includes the curated chat transcript; and a value-added portion which includes value-added information beyond inline information contained in the curated chat transcript; and automatically publishing the static new webpage separately from the chat itself and on the online retailer web site in order to drive traffic to the online retailer web site and in a location to be indexed by an indexing service. 27. The method as recited in claim 19 , wherein the chat is a voice chat and the transcript is an automatically generated text transcript of the voice chat.
| 0.668085 |
9,792,373 | 1 | 6 |
1. A computer-implemented method comprising: determining, by a computing system, at least one topic for potential presentation to a user based on an interaction of a connection of the user on a social networking system; determining, by the computing system, a degree of separation between the user and the connection within the social networking system; determining, by the computing system, a value of affinity between the user and the connection; determining, by the computing system, a weight reflecting a value of interest similarity between the user and the connection; calculating, by the computing system, a term based on the degree of separation, the value of affinity, and the weight reflecting a value of interest similarity; and combining, by the computing system, terms associated with the at least one topic to generate a composite score associated with the at least one topic to determine whether to present the at least one topic to the user.
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1. A computer-implemented method comprising: determining, by a computing system, at least one topic for potential presentation to a user based on an interaction of a connection of the user on a social networking system; determining, by the computing system, a degree of separation between the user and the connection within the social networking system; determining, by the computing system, a value of affinity between the user and the connection; determining, by the computing system, a weight reflecting a value of interest similarity between the user and the connection; calculating, by the computing system, a term based on the degree of separation, the value of affinity, and the weight reflecting a value of interest similarity; and combining, by the computing system, terms associated with the at least one topic to generate a composite score associated with the at least one topic to determine whether to present the at least one topic to the user. 6. The computer-implemented method of claim 1 , wherein the interaction is within a threshold value of a number of interactions of connections from which topics can be determined.
| 0.639113 |
9,471,890 | 1 | 3 |
1. A system for managing and executing interpreted language code comprising: a parser configured to: parse controlled language code against a language grammar to build an execution model comprising parsed known concepts; parse a statement to determine that the statement includes a term; and determine that the term is not part of the language grammar; a pattern matching engine configured to: execute pattern matching to determine that a first pattern associated with the term in the statement matches a second pattern associated with one or more of the parsed known concepts; and determine, based at least in part on the first pattern matching the second pattern, that the term represents a new concept; and a concept engine configured to: create the new concept; and save the new concept into an object model and into the language grammar so that current and future parsing errors will not occur when recognizing the new concept.
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1. A system for managing and executing interpreted language code comprising: a parser configured to: parse controlled language code against a language grammar to build an execution model comprising parsed known concepts; parse a statement to determine that the statement includes a term; and determine that the term is not part of the language grammar; a pattern matching engine configured to: execute pattern matching to determine that a first pattern associated with the term in the statement matches a second pattern associated with one or more of the parsed known concepts; and determine, based at least in part on the first pattern matching the second pattern, that the term represents a new concept; and a concept engine configured to: create the new concept; and save the new concept into an object model and into the language grammar so that current and future parsing errors will not occur when recognizing the new concept. 3. The system of claim 1 , wherein the interpreted language code comprises rules and actions for making decisions and the system is a decision management system.
| 0.895725 |
8,751,232 | 1 | 9 |
1. A method comprising: detecting that a frequency of occurrence of a particular type of utterance satisfies a threshold; and in response to detecting that the frequency satisfies the threshold, tuning a speech recognition system with respect to the particular type of utterance.
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1. A method comprising: detecting that a frequency of occurrence of a particular type of utterance satisfies a threshold; and in response to detecting that the frequency satisfies the threshold, tuning a speech recognition system with respect to the particular type of utterance. 9. The method of claim 1 , wherein the utterance is one of a single word spoken by a speaker, a phrase spoken by the speaker, or a sentence spoken by the speaker.
| 0.614286 |
9,179,278 | 20 | 22 |
20. The method of claim 16 , further comprising: displaying a modified price of said particular menu item on a display device of said mobile device.
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20. The method of claim 16 , further comprising: displaying a modified price of said particular menu item on a display device of said mobile device. 22. The method of claim 20 , wherein said modified price is based, at least in part, on a profile and/or loyalty of said customer.
| 0.5 |
9,734,986 | 8 | 19 |
8. A method of spectrum data analysis, the method comprising: acquiring a spectrum of an unknown mineral sample; selecting a first mineral definition, the first mineral definition including an element list of the elements comprising the first mineral; decomposing the spectrum of the unknown mineral sample using elements from the first mineral definition; calculating an overall match probability between the unknown mineral sample and the first mineral definition; and selecting a second mineral definition, the second mineral definition including an element list of the elements comprising the second mineral; decomposing the spectrum of the unknown mineral sample using elements from the second mineral definition; calculating an overall match probability between the unknown mineral sample and the second mineral definition; and in response to the first mineral definition having a greater match probability than the second mineral definition, classifying the acquired spectrum as being composed of the first mineral; and in response to the second mineral definition having a greater match probability than the first mineral definition, classifying the acquired spectrum as being composed of the second mineral.
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8. A method of spectrum data analysis, the method comprising: acquiring a spectrum of an unknown mineral sample; selecting a first mineral definition, the first mineral definition including an element list of the elements comprising the first mineral; decomposing the spectrum of the unknown mineral sample using elements from the first mineral definition; calculating an overall match probability between the unknown mineral sample and the first mineral definition; and selecting a second mineral definition, the second mineral definition including an element list of the elements comprising the second mineral; decomposing the spectrum of the unknown mineral sample using elements from the second mineral definition; calculating an overall match probability between the unknown mineral sample and the second mineral definition; and in response to the first mineral definition having a greater match probability than the second mineral definition, classifying the acquired spectrum as being composed of the first mineral; and in response to the second mineral definition having a greater match probability than the first mineral definition, classifying the acquired spectrum as being composed of the second mineral. 19. The method of claim 8 in which calculating an overall probability match for the unknown mineral sample to the mineral definition includes calculating individual element probability matches from the decomposed spectrum to the mineral definition and calculating an overall similarity metric by multiplying the individual element probability matches.
| 0.559045 |
10,089,557 | 10 | 11 |
10. The electronic device of claim 9 , wherein the controller is further configured to: control to combine the recognized characters identified through a character-recognition-processing of the at least one image obtained while the preview image is displayed and having a predetermined maximum number of characters as a first word, control to search a dictionary database that stores dictionary information on various languages using the first word, and control to output a word corresponding to the first word in at least one of the various languages according to a result of the search of the dictionary database using the first word.
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10. The electronic device of claim 9 , wherein the controller is further configured to: control to combine the recognized characters identified through a character-recognition-processing of the at least one image obtained while the preview image is displayed and having a predetermined maximum number of characters as a first word, control to search a dictionary database that stores dictionary information on various languages using the first word, and control to output a word corresponding to the first word in at least one of the various languages according to a result of the search of the dictionary database using the first word. 11. The electronic device of claim 10 , wherein the controller is further configured to: control to exclude characters from the first word one by one, control to identify new words from characters remaining in the first word after excluding characters from the first word, control to search the dictionary database using the respective new words, and control to output words corresponding to the respective new words according to a result of the search of the dictionary database using the respective new words.
| 0.5 |
9,183,294 | 11 | 12 |
11. The system of claim 10 , wherein the high-level properties are based on a set of questions the system is expected to answer.
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11. The system of claim 10 , wherein the high-level properties are based on a set of questions the system is expected to answer. 12. The system of claim 11 , wherein the high-level properties are mapped to specific properties that provide answers to the expected questions.
| 0.5 |
8,170,880 | 1 | 7 |
1. In a system where an annotation guide is used to label utterances in speech data with a call type, a method for monitoring a labeler of the speech data, the method comprising: presenting, via a processor of a computing device, a test utterance to the labeler; generating a determination indicating whether the labeler correctly labeled the test utterance as one call type from a list of call types; and based on the determination, performing at least one of revising the annotation guide, retraining the labeler, and altering the test utterance.
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1. In a system where an annotation guide is used to label utterances in speech data with a call type, a method for monitoring a labeler of the speech data, the method comprising: presenting, via a processor of a computing device, a test utterance to the labeler; generating a determination indicating whether the labeler correctly labeled the test utterance as one call type from a list of call types; and based on the determination, performing at least one of revising the annotation guide, retraining the labeler, and altering the test utterance. 7. The method of claim 1 , wherein if the test utterance is altered, the method further comprises presenting a second test utterance that tests for more utterances within a same call type.
| 0.536946 |
6,073,146 | 1 | 7 |
1. A computer system for processing Chinese language text comprising: a computer memory; an input apparatus for entering a plurality of Chinese phonetic language syllables into the system, each syllable having one or more characters, the input apparatus marking one or more of the accented syllables with diacritic that indicates a tone of the accented syllable; an input unit that provides a character code for each character and a tone code for each diacritic entered by the input apparatus, the input unit recognizing a syllable as a string of character codes marked by tone code; a syllable list having a plurality of syllable strings, each syllable string being associated with one or more syllable string codes, the input unit matching the string of character codes marked by the diacritic code to one of the syllable string codes and storing in the computer memory a syllable representation for the syllable string associated with the syllable string code that matches the string of character codes marked by the tone code.
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1. A computer system for processing Chinese language text comprising: a computer memory; an input apparatus for entering a plurality of Chinese phonetic language syllables into the system, each syllable having one or more characters, the input apparatus marking one or more of the accented syllables with diacritic that indicates a tone of the accented syllable; an input unit that provides a character code for each character and a tone code for each diacritic entered by the input apparatus, the input unit recognizing a syllable as a string of character codes marked by tone code; a syllable list having a plurality of syllable strings, each syllable string being associated with one or more syllable string codes, the input unit matching the string of character codes marked by the diacritic code to one of the syllable string codes and storing in the computer memory a syllable representation for the syllable string associated with the syllable string code that matches the string of character codes marked by the tone code. 7. A system, as in claim 1, wherein the system further comprises a graphical user interface and the syllable representations are converted to a syllable text that is displayed on the graphical user interface.
| 0.5 |
9,070,036 | 8 | 17 |
8. A note recognition device, comprising: A sensor configured to capture an image comprising a visual representation of a scene having a plurality of physical notes, each of the physical notes comprising a separate physical object having a predefined boundary and recognizable content thereon; a note recognition module coupled to the sensor, the note recognition module configured to process image data associated with the image to identify a predefined boundary of one of the plurality of physical notes from the visual representation; note extraction module configured to extract from the image data the recognizable content from within the predefined boundary of the one of the plurality of physical notes based on identifying the predefined boundary of the one of the plurality of physical notes, and based at least in part on a contrast between the recognizable content and a background of the one of the plurality of physical notes from the visual representation; and wherein the note extraction module associate the recognizable content with a digital representative of the one of the plurality of physical notes.
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8. A note recognition device, comprising: A sensor configured to capture an image comprising a visual representation of a scene having a plurality of physical notes, each of the physical notes comprising a separate physical object having a predefined boundary and recognizable content thereon; a note recognition module coupled to the sensor, the note recognition module configured to process image data associated with the image to identify a predefined boundary of one of the plurality of physical notes from the visual representation; note extraction module configured to extract from the image data the recognizable content from within the predefined boundary of the one of the plurality of physical notes based on identifying the predefined boundary of the one of the plurality of physical notes, and based at least in part on a contrast between the recognizable content and a background of the one of the plurality of physical notes from the visual representation; and wherein the note extraction module associate the recognizable content with a digital representative of the one of the plurality of physical notes. 17. The note recognition device of claim 8 , wherein the note recognition device comprises a mobile computing device.
| 0.599315 |
8,862,602 | 1 | 8 |
1. A system comprising: a server computer configured to receive a search query from a client device, the search query comprising a text string; a parsing logic configured to parse the search query using parsing criteria based on one or more dictionaries to determine keywords associated with the search query, wherein the keywords associated with the search query include one or more portions of the text string; the server computer configured to search a database using the keywords associated with the search query and obtain a search result that includes a universal resource locator, wherein the server computer is configured to identify a plurality of the keywords in the universal resource locator, to modify the universal resource locator by inserting previously non-existing space between at least two of the plurality of identified keywords in the universal resource locator, to generate display data comprising the modified universal resource locator having the plurality of identified keywords and the inserted space therebetween, and to send the display data to the client device; wherein the server computer inserts the space in the universal resource locator by inserting a HTML tag between characters of the universal resource locator before sending the display data to the client device, wherein the HTML tag comprises at least one of a div tag, an italics tag, and a span tag.
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1. A system comprising: a server computer configured to receive a search query from a client device, the search query comprising a text string; a parsing logic configured to parse the search query using parsing criteria based on one or more dictionaries to determine keywords associated with the search query, wherein the keywords associated with the search query include one or more portions of the text string; the server computer configured to search a database using the keywords associated with the search query and obtain a search result that includes a universal resource locator, wherein the server computer is configured to identify a plurality of the keywords in the universal resource locator, to modify the universal resource locator by inserting previously non-existing space between at least two of the plurality of identified keywords in the universal resource locator, to generate display data comprising the modified universal resource locator having the plurality of identified keywords and the inserted space therebetween, and to send the display data to the client device; wherein the server computer inserts the space in the universal resource locator by inserting a HTML tag between characters of the universal resource locator before sending the display data to the client device, wherein the HTML tag comprises at least one of a div tag, an italics tag, and a span tag. 8. The system of claim 1 , wherein the display data is configured to display selectable characters which can be pasted into a web browser.
| 0.772277 |
10,120,903 | 8 | 9 |
8. A system comprising: a data store for storing data; and one or more processors configured to interact with the data store, the one or more processors being further configured to perform operations comprising: receiving a series of queries provided from a user device, the series of queries comprising two or more queries; determining a query pattern of the series of queries based on one or more entities and one or more aspects associated with the two or more queries, the determining comprising: for each query in the series of queries: determining a set of entities comprising one or more entities and described in the query, and determining a set of aspects comprising one or more aspects and described in the query; comparing sets of entities across queries in the series of queries; comparing sets of aspects across queries in the series of queries; determining that at least one of a set of aspects are consistent in each of the queries or a set of entities are consistent in each of the queries; and determining that the at least one of a set of aspects that are consistent in each of the queries or a set of entities that are consistent in each of the queries is a context of the queries, and that the context defines the query pattern; determining, at least partially based on the context defining the query pattern, that a teachable moment interface is to be displayed with search results on the user device; and transmitting content to be displayed in the teachable moment interface on a user device, the content including instructions to a user that instructs the user that the user need not include the content that defines the query pattern in queries that are subsequent to the series of queries.
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8. A system comprising: a data store for storing data; and one or more processors configured to interact with the data store, the one or more processors being further configured to perform operations comprising: receiving a series of queries provided from a user device, the series of queries comprising two or more queries; determining a query pattern of the series of queries based on one or more entities and one or more aspects associated with the two or more queries, the determining comprising: for each query in the series of queries: determining a set of entities comprising one or more entities and described in the query, and determining a set of aspects comprising one or more aspects and described in the query; comparing sets of entities across queries in the series of queries; comparing sets of aspects across queries in the series of queries; determining that at least one of a set of aspects are consistent in each of the queries or a set of entities are consistent in each of the queries; and determining that the at least one of a set of aspects that are consistent in each of the queries or a set of entities that are consistent in each of the queries is a context of the queries, and that the context defines the query pattern; determining, at least partially based on the context defining the query pattern, that a teachable moment interface is to be displayed with search results on the user device; and transmitting content to be displayed in the teachable moment interface on a user device, the content including instructions to a user that instructs the user that the user need not include the content that defines the query pattern in queries that are subsequent to the series of queries. 9. The system of claim 8 , wherein determining that a teachable moment interface is to be displayed with search results on the user device comprises determining that the query pattern is associated with an indication that a teachable moment interface can be displayed.
| 0.536332 |
8,244,222 | 8 | 26 |
8. A method in a data processing system for facilitating immediate real-time language translation and interpretation services, in the event of a critical or urgent situation or event, comprising the steps of: receiving an end user request for translation or interpretation services over an Internet-enabled global communications network or mobile and wireless network; defining a data set consisting of information from the group of: native language, dialect, culture, location, gender, age, education level, availability, rating range, experience, reviews, subject matter expertise, the desired need, including but not limited to medical, business, academic, legal, and personal reasons; with said data sets aggregated and implemented into an algorithm for the end users' personalized needs, for particular events, sessions, episodes, meetings, procedures, or interactions; determining which of the plurality of profiles matches the requests, in order of sequence, based on the priority and metric pre-assigned value level defined as information matching said end user request consisting of the group of: languages, location, subject matter expertise, culture, gender; and that is concomitant with the data sets and criteria; providing a list of matching profiles to the end user; receiving a selection of the matching profiles for the end user(s); receiving a selection from a plurality of matching provider profiles predefined by the end user(s); or using fewer data sets to initiate a quick match in the event of time restrictions or an urgent need or event requiring immediate attention or care; filtering, namely reducing the number of data sets, and curating, namely monitoring and reviewing, the plurality of provider profiles by selected end users' specific preferential and essential data sets within the plurality of provider profiles, especially if any providers update their profiles, including but not limited to locations and skill sets; notifying the matching providers, in order of compatibility based on the desired attributes and skills, and the requested needs and the degree of urgency required to provide translation or interpretation services for said end user(s); messaging to end user(s) alerting them of available translation and interpretation providers, based on predefined criteria, need, or situation; selecting the provider and submitting a time and date, including time zones from a drop-down menu, for a real-time session; confirmation by both parties, namely the end users and the providers, to participate in a real-time interaction and session; and transmission of an invite, with details and specifics, via email and/or short messaging service (SMS), for reminding the parties, namely end users and providers, of the immediate or scheduled real-time session or interaction, and; receiving confirmation by the providers or contacts that they are available to provide services in real-time, and subsequently, will receive corresponding profiles of the subject(s) and end user(s) in need of their services, the concomitant end user or subject event, and the location wherein said services will be rendered and delivered.
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8. A method in a data processing system for facilitating immediate real-time language translation and interpretation services, in the event of a critical or urgent situation or event, comprising the steps of: receiving an end user request for translation or interpretation services over an Internet-enabled global communications network or mobile and wireless network; defining a data set consisting of information from the group of: native language, dialect, culture, location, gender, age, education level, availability, rating range, experience, reviews, subject matter expertise, the desired need, including but not limited to medical, business, academic, legal, and personal reasons; with said data sets aggregated and implemented into an algorithm for the end users' personalized needs, for particular events, sessions, episodes, meetings, procedures, or interactions; determining which of the plurality of profiles matches the requests, in order of sequence, based on the priority and metric pre-assigned value level defined as information matching said end user request consisting of the group of: languages, location, subject matter expertise, culture, gender; and that is concomitant with the data sets and criteria; providing a list of matching profiles to the end user; receiving a selection of the matching profiles for the end user(s); receiving a selection from a plurality of matching provider profiles predefined by the end user(s); or using fewer data sets to initiate a quick match in the event of time restrictions or an urgent need or event requiring immediate attention or care; filtering, namely reducing the number of data sets, and curating, namely monitoring and reviewing, the plurality of provider profiles by selected end users' specific preferential and essential data sets within the plurality of provider profiles, especially if any providers update their profiles, including but not limited to locations and skill sets; notifying the matching providers, in order of compatibility based on the desired attributes and skills, and the requested needs and the degree of urgency required to provide translation or interpretation services for said end user(s); messaging to end user(s) alerting them of available translation and interpretation providers, based on predefined criteria, need, or situation; selecting the provider and submitting a time and date, including time zones from a drop-down menu, for a real-time session; confirmation by both parties, namely the end users and the providers, to participate in a real-time interaction and session; and transmission of an invite, with details and specifics, via email and/or short messaging service (SMS), for reminding the parties, namely end users and providers, of the immediate or scheduled real-time session or interaction, and; receiving confirmation by the providers or contacts that they are available to provide services in real-time, and subsequently, will receive corresponding profiles of the subject(s) and end user(s) in need of their services, the concomitant end user or subject event, and the location wherein said services will be rendered and delivered. 26. The method of claim 8 , further comprising the steps of: selecting a list of providers based on one or more of language, age, gender, education, location, price, and cultural background; and searching for providers having specific expertise or ratings with keywords.
| 0.529617 |
5,504,891 | 6 | 7 |
6. An apparatus for changing a format of a file, comprising: means for inputting a first element having a first representation; means for converting said first element of the first representation to a second representation; means for inputting a subsequent element of the first representation; means for determining if a second representation of the subsequent element includes a portion which is found in the second representation of said first element; means for converting said subsequent element to the second representation including said portion when said means for determining determines that said portion is not found in the second representation of said first element; and means for converting said subsequent element to the second representation without said portion when said means for determining determines that said portion is found in the second representation of said first element.
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6. An apparatus for changing a format of a file, comprising: means for inputting a first element having a first representation; means for converting said first element of the first representation to a second representation; means for inputting a subsequent element of the first representation; means for determining if a second representation of the subsequent element includes a portion which is found in the second representation of said first element; means for converting said subsequent element to the second representation including said portion when said means for determining determines that said portion is not found in the second representation of said first element; and means for converting said subsequent element to the second representation without said portion when said means for determining determines that said portion is found in the second representation of said first element. 7. An apparatus according to claim 6, wherein: said determining means examines a flag which indicates if said second representation of said first element includes said portion.
| 0.860979 |
8,190,618 | 22 | 23 |
22. A computer system comprising one or more computers programmed with instructions that are effective when executed to perform operations comprising: assigning a unique identifier to each segment of a plurality of segments of the electronic document at a time of creation of the electronic document; determining usage indication data associated with each segment of the plurality of segments of the electronic document, wherein the usage indication data comprise a measure of time spent using the electronic document; aggregating the usage indication data for a plurality of users of the electronic document on a segment by segment basis; and communicating to a user of the electronic document the aggregate usage indication data on a segment by segment basis.
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22. A computer system comprising one or more computers programmed with instructions that are effective when executed to perform operations comprising: assigning a unique identifier to each segment of a plurality of segments of the electronic document at a time of creation of the electronic document; determining usage indication data associated with each segment of the plurality of segments of the electronic document, wherein the usage indication data comprise a measure of time spent using the electronic document; aggregating the usage indication data for a plurality of users of the electronic document on a segment by segment basis; and communicating to a user of the electronic document the aggregate usage indication data on a segment by segment basis. 23. The computer system of claim 22 , the operations further comprising: aggregating usage indication data for corresponding segments of a plurality of versions of the electronic document on a segment by segment basis.
| 0.748268 |
9,812,127 | 1 | 11 |
1. A method for generating dialogs and learning a dialog policy for a dialog system, comprising: for each of at least one scenario, in which annotators in a pool of annotators serve as virtual agents and users, generating a respective dialog tree in which each path through the tree corresponds to a dialog and nodes of the tree correspond to turn of a dialog, the generation comprising, with a processor: a) computing a measure of uncertainty for nodes in the dialog tree, comprising: for each of a plurality of nodes, computing a conflict coefficient C i which quantifies the diversity of its child-node set, as a function of: max j ( n ) - 1 k ∑ i = 1 k ( n i - n ^ ) 2 where max j ( n ) is the maximum size of any subtree from the node j, k is the number of child nodes of node j, n i is the number of child nodes of one of these child nodes, {circumflex over (n)} is the mean number of child nodes of the subtrees that have as root the child nodes of the considered node j; b) identifying a next node to be annotated, based on the measure of uncertainty, c) selecting an annotator from the pool to provide an annotation for the next node, d) receiving an annotation from the selected annotator for the next node, and e) generating a new node of the dialog tree based on the received annotation; generating a corpus of dialogs from the dialog tree; learning a dialog policy based on the corpus of dialogs; and incorporating the learned dialog policy into a dialog system for conducting a dialog between a virtual agent and a user, in which the learned dialog policy predicts, based on a state of the dialog, a next action to perform, the action being converted, by the dialog system, to a next utterance of the virtual agent.
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1. A method for generating dialogs and learning a dialog policy for a dialog system, comprising: for each of at least one scenario, in which annotators in a pool of annotators serve as virtual agents and users, generating a respective dialog tree in which each path through the tree corresponds to a dialog and nodes of the tree correspond to turn of a dialog, the generation comprising, with a processor: a) computing a measure of uncertainty for nodes in the dialog tree, comprising: for each of a plurality of nodes, computing a conflict coefficient C i which quantifies the diversity of its child-node set, as a function of: max j ( n ) - 1 k ∑ i = 1 k ( n i - n ^ ) 2 where max j ( n ) is the maximum size of any subtree from the node j, k is the number of child nodes of node j, n i is the number of child nodes of one of these child nodes, {circumflex over (n)} is the mean number of child nodes of the subtrees that have as root the child nodes of the considered node j; b) identifying a next node to be annotated, based on the measure of uncertainty, c) selecting an annotator from the pool to provide an annotation for the next node, d) receiving an annotation from the selected annotator for the next node, and e) generating a new node of the dialog tree based on the received annotation; generating a corpus of dialogs from the dialog tree; learning a dialog policy based on the corpus of dialogs; and incorporating the learned dialog policy into a dialog system for conducting a dialog between a virtual agent and a user, in which the learned dialog policy predicts, based on a state of the dialog, a next action to perform, the action being converted, by the dialog system, to a next utterance of the virtual agent. 11. The method of claim 1 , further comprising outputting at least one of: the corpus of dialogs; and a dialog policy generated based on the corpus of dialogs.
| 0.776056 |
9,430,531 | 68 | 83 |
68. A method comprising: receiving first activity information for a sender of a first link to at least one recipient collected by a collection resource at a Web site, wherein no personally identifiable information of the sender is collected in collecting the first activity information; storing the first activity information at a storage server; receiving second activity information when a recipient accesses the first link sent by the sender corresponding to the first activity information stored at the storage server, wherein no personally identifiable information of the recipient collected in collecting the second activity information, and the no personally identifiable information of the recipient is collected comprises not collecting an e-mail address of the recipient; using at least one processor, attempting to identify a first node representative of the sender in a social graph; when a first node representative of the sender in a social graph is not identified and after receiving the second activity information, creating a second node to represent the sender in the social graph; and based on at least information associated with the second node in the social graph, selecting a personalized digital content for delivery to the sender.
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68. A method comprising: receiving first activity information for a sender of a first link to at least one recipient collected by a collection resource at a Web site, wherein no personally identifiable information of the sender is collected in collecting the first activity information; storing the first activity information at a storage server; receiving second activity information when a recipient accesses the first link sent by the sender corresponding to the first activity information stored at the storage server, wherein no personally identifiable information of the recipient collected in collecting the second activity information, and the no personally identifiable information of the recipient is collected comprises not collecting an e-mail address of the recipient; using at least one processor, attempting to identify a first node representative of the sender in a social graph; when a first node representative of the sender in a social graph is not identified and after receiving the second activity information, creating a second node to represent the sender in the social graph; and based on at least information associated with the second node in the social graph, selecting a personalized digital content for delivery to the sender. 83. The method of claim 68 wherein the receiving first activity information for the sender of the first link comprises: sending of an e-mail including the first link by the sender via a mobile device.
| 0.800797 |
8,812,452 | 9 | 10 |
9. The method of claim 7 wherein the meta-model is configured such that a predicate of a given RDF triple is subject to different interpretations for different contextual indicators.
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9. The method of claim 7 wherein the meta-model is configured such that a predicate of a given RDF triple is subject to different interpretations for different contextual indicators. 10. The method of claim 9 wherein the different contextual indicators are derived from independent graphs having respective differentiated namespaces.
| 0.642857 |
8,509,524 | 7 | 8 |
7. The image processing method according to claim 6 , further comprising: detecting another object with the specific feature from another input image based on the calculated parameter and the dictionary data.
|
7. The image processing method according to claim 6 , further comprising: detecting another object with the specific feature from another input image based on the calculated parameter and the dictionary data. 8. The image processing method according to claim 7 , further comprising: determining a method to use the dictionary data in detecting the other object with the specific feature based on the calculated parameter; and detecting the other object using the method.
| 0.832478 |
7,634,409 | 12 | 13 |
12. The system of claim 11 , the acoustic elements including at least an unstressed central vowel and a plurality of phonemic elements associated with the acoustic speech model, wherein the acoustic grammar uses the unstressed central vowel as a linking element between sequential phonemic elements.
|
12. The system of claim 11 , the acoustic elements including at least an unstressed central vowel and a plurality of phonemic elements associated with the acoustic speech model, wherein the acoustic grammar uses the unstressed central vowel as a linking element between sequential phonemic elements. 13. The system of claim 12 , the unstressed central vowel including a schwa acoustic element.
| 0.5 |
4,799,190 | 1 | 2 |
1. A radiation pyrometer system for measuring a radiance of at least a portion of an object moving repetitively through a field of view comprising: means for detecting the radiance from at least a portion of said object as said object repetitively moves through said field of view; means for converting said detected radiance to a series of data words, each of said data words representing the value of said detected radiance at a given time; a temporary data store communicating with said converting means for storing said data words and being resettable to an initial state representing an initially stored data word of predetermined value, said temporary data store having an input and an output; a controlling means communicating with the input and output of said temporary data store for controlling entry of input data words into said temporary data store from said converting means, said controlling means receiving from the output of said temporary data store the data word stored thereby, receiving from the input of said temporary data store each of said input data words and comparing, according to a predetermined criterion, the value of each input word with the value of the data words stored in said temporary data store and if the value of an input data word meets said criterion, said controlling means outputting a control signal to said temporary data store to cause said temporary data store to discard said stored data word and replace same with said input data word, said criterion being that said input data word is different from the stored word and that its difference be in a predetermined direction; an AND gate for gating the data word stored in said temporary data store to said output; a flip-flop connected to said AND gate for enabling said AND gate to pass said data word from said temporary data store to said output; and a resettable counter means for counting the number of input data words generated by said converting means and for enabling said gate means to control said flip-flop to output said stored data word, said resettable counter means being reset by said output control signal from said controlling means wherein said resettable counter means is reset to zero by said output control signal from said controlling means each time said control signal is outputted, said resettable counter means controlling said flip-flop to enable said AND gate whenever said resettable counter means counts the generation of a predetermined number of said input data words without being reset.
|
1. A radiation pyrometer system for measuring a radiance of at least a portion of an object moving repetitively through a field of view comprising: means for detecting the radiance from at least a portion of said object as said object repetitively moves through said field of view; means for converting said detected radiance to a series of data words, each of said data words representing the value of said detected radiance at a given time; a temporary data store communicating with said converting means for storing said data words and being resettable to an initial state representing an initially stored data word of predetermined value, said temporary data store having an input and an output; a controlling means communicating with the input and output of said temporary data store for controlling entry of input data words into said temporary data store from said converting means, said controlling means receiving from the output of said temporary data store the data word stored thereby, receiving from the input of said temporary data store each of said input data words and comparing, according to a predetermined criterion, the value of each input word with the value of the data words stored in said temporary data store and if the value of an input data word meets said criterion, said controlling means outputting a control signal to said temporary data store to cause said temporary data store to discard said stored data word and replace same with said input data word, said criterion being that said input data word is different from the stored word and that its difference be in a predetermined direction; an AND gate for gating the data word stored in said temporary data store to said output; a flip-flop connected to said AND gate for enabling said AND gate to pass said data word from said temporary data store to said output; and a resettable counter means for counting the number of input data words generated by said converting means and for enabling said gate means to control said flip-flop to output said stored data word, said resettable counter means being reset by said output control signal from said controlling means wherein said resettable counter means is reset to zero by said output control signal from said controlling means each time said control signal is outputted, said resettable counter means controlling said flip-flop to enable said AND gate whenever said resettable counter means counts the generation of a predetermined number of said input data words without being reset. 2. The system of claim 1 wherein the controlling means compares in accordance with said criterion which states that the input word must be of less value than the stored data word.
| 0.52139 |
8,370,158 | 27 | 28 |
27. The server apparatus of claim 16 , where the at least one computer program is further configured to: generate a listing of a plurality of possible matches to at least one of said first and/or second inputs; and receive further input regarding at least one of the listed plurality of possible matches.
|
27. The server apparatus of claim 16 , where the at least one computer program is further configured to: generate a listing of a plurality of possible matches to at least one of said first and/or second inputs; and receive further input regarding at least one of the listed plurality of possible matches. 28. The server apparatus of claim 27 , where the received further input is generated via a remotely disposed touch-screen input and display device in data communication with the server apparatus via a communications network.
| 0.5 |
9,436,438 | 2 | 3 |
2. The non-transitory computer-accessible memory medium of claim 1 , wherein the program instructions are further executable to perform: analyzing the graphical program, including analyzing the specifications or constraints, thereby producing analysis results; wherein said automatically generating the output program is performed based on the analysis results.
|
2. The non-transitory computer-accessible memory medium of claim 1 , wherein the program instructions are further executable to perform: analyzing the graphical program, including analyzing the specifications or constraints, thereby producing analysis results; wherein said automatically generating the output program is performed based on the analysis results. 3. The non-transitory computer-accessible memory medium of claim 2 , wherein the specification or constraints comprise one or more user-provided specifications, and wherein the program instructions are further executable to automatically convert the one or more user-provided specifications into a corresponding one or more constraints before said analyzing and said automatically generating the output program.
| 0.5 |
9,224,112 | 1 | 3 |
1. A method comprising: receiving, by an enterprise content management (ECM) computing system comprising a computer processor, data associated with a subscriber; registering, by said computer processor based on said data, said subscriber with said ECM computing system; connecting, by said computer processor, devices belonging to said subscriber to said ECM computing system via an Intranet, wherein said devices comprise computing devices and storage devices; connecting, by said computer processor, end user systems associated with said subscriber to said ECM computing system via said Intranet, wherein said end user systems comprise service tools, documentation systems, and storage systems; connecting, by said computer processor, database and repository systems associated with said subscriber to said ECM computing system via said Intranet, wherein said database and repository systems comprise a database, an enterprise content management metadata system, and past search results; retrieving, by said computer processor from said devices, said end user systems, and said database and repository systems, metadata associated with content retrieved by said subscriber via said devices and said end user systems; analyzing, by said computer processor, said metadata, wherein said analyzing comprises: executing a text analytics process with respect to said metadata; executing a Web analytics process with respect to said metadata; and performing an analysis of said metadata with respect to dates of creation and modification of said content, a frequency of said modification being performed with respect to time periods, and numbers of shares of said content via emails; classifying, by said computer processor based on said analyzing said metadata, said content into formal content and informal content, wherein said formal content comprises content that has been uploaded to a primary repository of said ECM computing system, and wherein said informal content comprises content that has not been uploaded to said primary repository of said ECM computing system; monitoring, by said computer processor, multiple searches for additional content initiated by said subscriber; generating, by said computer processor for said subscriber based on said monitoring and results of said classifying, multifaceted search results associated with said formal content and said informal content; and presenting, by said computer processor to said subscriber, said multifaceted search results.
|
1. A method comprising: receiving, by an enterprise content management (ECM) computing system comprising a computer processor, data associated with a subscriber; registering, by said computer processor based on said data, said subscriber with said ECM computing system; connecting, by said computer processor, devices belonging to said subscriber to said ECM computing system via an Intranet, wherein said devices comprise computing devices and storage devices; connecting, by said computer processor, end user systems associated with said subscriber to said ECM computing system via said Intranet, wherein said end user systems comprise service tools, documentation systems, and storage systems; connecting, by said computer processor, database and repository systems associated with said subscriber to said ECM computing system via said Intranet, wherein said database and repository systems comprise a database, an enterprise content management metadata system, and past search results; retrieving, by said computer processor from said devices, said end user systems, and said database and repository systems, metadata associated with content retrieved by said subscriber via said devices and said end user systems; analyzing, by said computer processor, said metadata, wherein said analyzing comprises: executing a text analytics process with respect to said metadata; executing a Web analytics process with respect to said metadata; and performing an analysis of said metadata with respect to dates of creation and modification of said content, a frequency of said modification being performed with respect to time periods, and numbers of shares of said content via emails; classifying, by said computer processor based on said analyzing said metadata, said content into formal content and informal content, wherein said formal content comprises content that has been uploaded to a primary repository of said ECM computing system, and wherein said informal content comprises content that has not been uploaded to said primary repository of said ECM computing system; monitoring, by said computer processor, multiple searches for additional content initiated by said subscriber; generating, by said computer processor for said subscriber based on said monitoring and results of said classifying, multifaceted search results associated with said formal content and said informal content; and presenting, by said computer processor to said subscriber, said multifaceted search results. 3. The method of claim 1 , further comprising: downloading, by said computer processor in accordance with said multifaceted search results, relevant informal content from contact devices belonging to users associated with said subscriber.
| 0.78125 |
8,301,619 | 10 | 14 |
10. A system for generating a Boolean query comprising: a. a processor; b. a memory coupled to the processor: c. a data manager configured to get training data and production data, wherein the training data comprises a plurality of training documents and each of the plurality of training documents comprises at least one training token, wherein the production data comprises a plurality of production documents and each of the plurality of production documents comprises at least one production token, and wherein data manager is configured to clean the training data, and identify at least one salient token from the at least one training token in each of the plurality of training documents; d. a clustering manager configured to cluster the plurality of training documents into a plurality of clusters based on the at least one training token in the plurality of training documents or the at least one salient token, wherein each cluster comprises at least one training document; and e. a query manager configured to generate the Boolean query for a cluster of the plurality of clusters based on an occurrence of the at least one salient token in the at least one training document of the plurality of training documents, and to execute the Boolean query on the plurality of production documents in the production data.
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10. A system for generating a Boolean query comprising: a. a processor; b. a memory coupled to the processor: c. a data manager configured to get training data and production data, wherein the training data comprises a plurality of training documents and each of the plurality of training documents comprises at least one training token, wherein the production data comprises a plurality of production documents and each of the plurality of production documents comprises at least one production token, and wherein data manager is configured to clean the training data, and identify at least one salient token from the at least one training token in each of the plurality of training documents; d. a clustering manager configured to cluster the plurality of training documents into a plurality of clusters based on the at least one training token in the plurality of training documents or the at least one salient token, wherein each cluster comprises at least one training document; and e. a query manager configured to generate the Boolean query for a cluster of the plurality of clusters based on an occurrence of the at least one salient token in the at least one training document of the plurality of training documents, and to execute the Boolean query on the plurality of production documents in the production data. 14. The system of claim 10 , wherein the data manager is configured to: analyze results from the Boolean query, determine whether there is new training data, get new training data responsive to there being new training data, determine whether the Boolean query has been modified responsive to there not being new training data, execute the Boolean query on the plurality of production documents in the production data responsive to the Boolean query being modified, determine whether there is a change in the number of clusters responsive to the Boolean query not being modified, cluster the plurality of training documents into a plurality of clusters based on at least one training token in the plurality of training documents responsive to there being a change in the number of clusters, respond to there not being a change in the number of clusters by determining if there is new production data, get production data responsive to either (a) there being new production data or (b) there being new production data and the Boolean query having been modified, being done responsive to either (c) there not being new production data or (d) there not being new production data and the Boolean query having not been modified.
| 0.5 |
9,905,223 | 8 | 11 |
8. A system comprising: a processor; and a computer-readable storage medium having instructions stored which, when executed by the processor, cause the processor to perform operations comprising: receiving, during a natural language dialog between a user and a computing device, an utterance; generating a semantic and syntactic graph using the utterance, the semantic and syntactic graph comprising an initial state and a final state associated with the utterance and being generated by adding transitions that encode semantic and syntactic categories of words or word sequences within the utterance; extracting possible combinations of features from the semantic and syntactic graph, to yield extracted n-grams; comparing the extracted n-grams to previously classified n-grams, to yield a comparison; classifying the utterance as being associated with a call type based on the extracted n-grams and the comparison, wherein the call type is chosen from a predefined set of call types and wherein the call type characterizes a communication, to yield a classified utterance; and responding, with a computer-generated response, to the user in the natural language dialog based on the classified utterance.
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8. A system comprising: a processor; and a computer-readable storage medium having instructions stored which, when executed by the processor, cause the processor to perform operations comprising: receiving, during a natural language dialog between a user and a computing device, an utterance; generating a semantic and syntactic graph using the utterance, the semantic and syntactic graph comprising an initial state and a final state associated with the utterance and being generated by adding transitions that encode semantic and syntactic categories of words or word sequences within the utterance; extracting possible combinations of features from the semantic and syntactic graph, to yield extracted n-grams; comparing the extracted n-grams to previously classified n-grams, to yield a comparison; classifying the utterance as being associated with a call type based on the extracted n-grams and the comparison, wherein the call type is chosen from a predefined set of call types and wherein the call type characterizes a communication, to yield a classified utterance; and responding, with a computer-generated response, to the user in the natural language dialog based on the classified utterance. 11. The system of claim 8 , wherein the semantic and syntactic graph further comprises one of speech tags and a syntactic parse of the utterance.
| 0.691489 |
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