patent_num
int64 3.93M
10.2M
| claim_num1
int64 1
519
| claim_num2
int64 2
520
| sentence1
stringlengths 40
15.9k
| sentence2
stringlengths 88
20k
| label
float64 0.5
0.99
|
|---|---|---|---|---|---|
9,690,468 | 1 | 9 | 1. A method of providing an interactive media presentation, the method comprising: by an electronic device associated with an embodier: receiving, from a user electronic device in communication with the electronic device, an indication that an interactive portion of a media presentation is about to begin; causing a graphical representation of a conversation atlas to be displayed to the embodier via a display device of the electronic device, wherein the conversation atlas comprises a plurality of selection elements that are each associated with an audio element for a character that is featured in the interactive portion, wherein the graphical representation of the conversation atlas is not displayed at the user electronic device; receiving, from the user electronic device, a user response comprising conversational information received from a user that is directed to a character of the media presentation; in response to receiving the user response, receiving, from the embodier, a selection of at least one of the plurality of selection elements of the conversation atlas; identifying the audio element that corresponds to the selected selection element; and causing the identified audio element to be presented at the user electronic device. | 1. A method of providing an interactive media presentation, the method comprising: by an electronic device associated with an embodier: receiving, from a user electronic device in communication with the electronic device, an indication that an interactive portion of a media presentation is about to begin; causing a graphical representation of a conversation atlas to be displayed to the embodier via a display device of the electronic device, wherein the conversation atlas comprises a plurality of selection elements that are each associated with an audio element for a character that is featured in the interactive portion, wherein the graphical representation of the conversation atlas is not displayed at the user electronic device; receiving, from the user electronic device, a user response comprising conversational information received from a user that is directed to a character of the media presentation; in response to receiving the user response, receiving, from the embodier, a selection of at least one of the plurality of selection elements of the conversation atlas; identifying the audio element that corresponds to the selected selection element; and causing the identified audio element to be presented at the user electronic device. 9. The method of claim 1 , further comprising repeating receiving a user response, receiving a selection of at least one of the plurality of selection elements of the conversation atlas, identifying the audio element that corresponds to the selected selection element, and causing the identified audio element to be played at the user electronic device until the interactive portion concludes. | 0.5 |
9,305,060 | 1 | 5 | 1. A method for performing a contextual search to locate content associated with a media file, comprising: at a media device, receiving a request from a user to perform a contextual search based on a media file; determining a type of the media file; presenting a contextual search menu to the user, wherein the contextual search menu includes one or more contextual search query types, and wherein the one or more contextual search query types are based on the type of the media file; receiving from the user a selected contextual search query type selected from the one or more contextual search query types; querying one or more search modules, each associated with one or more content sources, to determine which one or more search modules can perform the selected contextual search query type; requesting that one or more search modules that can perform the selected contextual search query type perform the selected contextual search query against one or more selected content sources associated with the one or more search module; receiving search results from the one or more search modules, wherein the search results include links to one or more files associated with the one or more selected content sources; and displaying the search results to the user. | 1. A method for performing a contextual search to locate content associated with a media file, comprising: at a media device, receiving a request from a user to perform a contextual search based on a media file; determining a type of the media file; presenting a contextual search menu to the user, wherein the contextual search menu includes one or more contextual search query types, and wherein the one or more contextual search query types are based on the type of the media file; receiving from the user a selected contextual search query type selected from the one or more contextual search query types; querying one or more search modules, each associated with one or more content sources, to determine which one or more search modules can perform the selected contextual search query type; requesting that one or more search modules that can perform the selected contextual search query type perform the selected contextual search query against one or more selected content sources associated with the one or more search module; receiving search results from the one or more search modules, wherein the search results include links to one or more files associated with the one or more selected content sources; and displaying the search results to the user. 5. The method of claim 1 , wherein the request to perform the contextual search based on the media file is received at a set-top box associated with a television set. | 0.90788 |
7,835,998 | 28 | 31 | 28. A user-interface method of selecting and presenting to a first user a collection of content items of a first content system in which the presentation is ordered at least in part based on content preferences of a second user learned from the second user selecting content of a second content system, the method comprising: receiving incremental input entered by the second user for incrementally identifying desired content items of the second content system, each content item having at least one associated descriptive term to describe the content item; in response to the incremental input entered by the second user, presenting a subset of content items of the second content system; receiving selection actions of content items of the subset from the second user; determining a user preference signature by analyzing the descriptive terms of the selected content items to learn the content preferences of the second user for the content of the second content system; determining a relationship between the content items of the first content system and the content items of the second content system, the relationship defining which learned user content preferences of the user preference signature are relevant to the content items of the first content system; and in response to receiving subsequent incremental input entered by the first user for incrementally identifying desired content items of the first content system, selecting and ordering a collection of content items of the first content system based on the learned content preferences of the second user determined to be relevant to the content items of the first content system. | 28. A user-interface method of selecting and presenting to a first user a collection of content items of a first content system in which the presentation is ordered at least in part based on content preferences of a second user learned from the second user selecting content of a second content system, the method comprising: receiving incremental input entered by the second user for incrementally identifying desired content items of the second content system, each content item having at least one associated descriptive term to describe the content item; in response to the incremental input entered by the second user, presenting a subset of content items of the second content system; receiving selection actions of content items of the subset from the second user; determining a user preference signature by analyzing the descriptive terms of the selected content items to learn the content preferences of the second user for the content of the second content system; determining a relationship between the content items of the first content system and the content items of the second content system, the relationship defining which learned user content preferences of the user preference signature are relevant to the content items of the first content system; and in response to receiving subsequent incremental input entered by the first user for incrementally identifying desired content items of the first content system, selecting and ordering a collection of content items of the first content system based on the learned content preferences of the second user determined to be relevant to the content items of the first content system. 31. The method of claim 28 , wherein the first content system and second content system are the same systems. | 0.921014 |
8,396,854 | 6 | 12 | 6. A system for managing digital documents, the system comprising: a host device configured to receive a first set of digital documents, the first set of digital documents including a first digital document and a second digital document; separate the first digital document and a second digital document; route the first digital document and the second digital document to a client pending queue associated with a first client account; extract a first set of structured information from the first digital document; store the first set of structured information in a first relational database; store the first digital document in a first directory; generate a web portal that includes the first set of structured information and the first digital document; receive, through the web portal, a first set of criteria related to a first document destination; and transmit the first digital document and the first set of structured information to the first document destination; extract a second set of structured information from the second digital document; store the second set of structured information in the first relational database; store the second digital document in the first directory; receive, through the web portal, a second set of criteria related to a second document destination; and transmit the second digital document and the second set of structured information to the second document destination; and one or more client devices connected to the host device over a wide area network, the one or more client devices configured to digitize a first document and a second document to generate the first set of digital documents, the first set of digital documents generated from a single digitizing process; transmit the first set of digital documents to the host device; display the first digital document and the first set of structured information in the web portal, wherein the first set of structured information is displayed in an editable interface of the web portal; and populate the web portal with the first set of criteria related to the first document destination for the first digital document and the first set of structured information. | 6. A system for managing digital documents, the system comprising: a host device configured to receive a first set of digital documents, the first set of digital documents including a first digital document and a second digital document; separate the first digital document and a second digital document; route the first digital document and the second digital document to a client pending queue associated with a first client account; extract a first set of structured information from the first digital document; store the first set of structured information in a first relational database; store the first digital document in a first directory; generate a web portal that includes the first set of structured information and the first digital document; receive, through the web portal, a first set of criteria related to a first document destination; and transmit the first digital document and the first set of structured information to the first document destination; extract a second set of structured information from the second digital document; store the second set of structured information in the first relational database; store the second digital document in the first directory; receive, through the web portal, a second set of criteria related to a second document destination; and transmit the second digital document and the second set of structured information to the second document destination; and one or more client devices connected to the host device over a wide area network, the one or more client devices configured to digitize a first document and a second document to generate the first set of digital documents, the first set of digital documents generated from a single digitizing process; transmit the first set of digital documents to the host device; display the first digital document and the first set of structured information in the web portal, wherein the first set of structured information is displayed in an editable interface of the web portal; and populate the web portal with the first set of criteria related to the first document destination for the first digital document and the first set of structured information. 12. The system of claim 6 , wherein the first digital document is an invoice. | 0.793011 |
9,495,331 | 27 | 28 | 27. A method according to claim 8 wherein said generating said directed graph employs a topic context tree having a root. | 27. A method according to claim 8 wherein said generating said directed graph employs a topic context tree having a root. 28. A method according to claim 27 wherein name references are translated into graph edges by searching for item names starting from bottom topic context and going up said topic context tree until said root is reached. | 0.5 |
7,647,528 | 10 | 15 | 10. An information processing system for software debugging, the system comprising: data storage for storing inputs, the inputs comprising: an interface configured for accessing at least a portion of a program exhibiting faulty behavior, a failing input sequence from the at least a portion of the program, a behavioral model of the at least a portion of the program, and mutation operators; a processor configured for performing steps of: executing the failing input sequence stepwise in parallel on both the behavioral model and the at least a portion of the program; validating, after each execution, an expected behavior of the at least a portion of the program by executing test sequences constructed from the behavioral model, wherein the test sequences comprise model states and transitions; performing model mutation using mutation operators, for creating a hypothesis of faulty program behaviors; verifying each faulty program behavior in the hypothesis using the model mutators; and assigning a score to each faulty behavior, wherein the score comprises a percentage of executions of confirming sequences that match the faulty program behavior; and ranking the scores for producing a ranked list of diagnoses, wherein the diagnoses correspond to the mutation operators; and an input/output subsystem for interacting with a user of the system. | 10. An information processing system for software debugging, the system comprising: data storage for storing inputs, the inputs comprising: an interface configured for accessing at least a portion of a program exhibiting faulty behavior, a failing input sequence from the at least a portion of the program, a behavioral model of the at least a portion of the program, and mutation operators; a processor configured for performing steps of: executing the failing input sequence stepwise in parallel on both the behavioral model and the at least a portion of the program; validating, after each execution, an expected behavior of the at least a portion of the program by executing test sequences constructed from the behavioral model, wherein the test sequences comprise model states and transitions; performing model mutation using mutation operators, for creating a hypothesis of faulty program behaviors; verifying each faulty program behavior in the hypothesis using the model mutators; and assigning a score to each faulty behavior, wherein the score comprises a percentage of executions of confirming sequences that match the faulty program behavior; and ranking the scores for producing a ranked list of diagnoses, wherein the diagnoses correspond to the mutation operators; and an input/output subsystem for interacting with a user of the system. 15. The system of claim 10 wherein the input/output subsystem comprises a network interface. | 0.676056 |
7,565,630 | 22 | 23 | 22. The method of claim 21 , wherein the degree of influence is received from an operator of the third party website. | 22. The method of claim 21 , wherein the degree of influence is received from an operator of the third party website. 23. The method of claim 22 , wherein the degree of influence is determined by the site operator using a slider type graphical control along a graphical axis, wherein the position of the slider is scaled to the weight. | 0.5 |
9,214,156 | 1 | 5 | 1. A method of automatically managing a dialogue with a user, comprising: transforming, at a dialogue manager, user-input data received from a client dialogue application into a generic semantic representation, the generic semantic representation being independent of a language and an input modality associated with the user-input data; determining, by the dialogue manager, whether the user-input data is indicative of a new request by the user or a refinement request by the user refining one or more previous requests by the user, based on the generic semantic representation and at least one of a maintained state of the dialogue, general knowledge data representing one or more concepts, and data representing history of the dialogue; sending multi-facet output data, indicative of one or more actions for the client dialogue application to perform, the one or more actions being determined based on a result of said determining whether the generic semantic representation is indicative of the new request by the user or the refinement by the user refining the one or more previous requests by the user; sending a query to a backend end system for retrieving data associated with the determined user-request, the query being generated based on the determined user-request and the maintained state of the dialogue; receiving a response to the query from the backend system; and updating a list of data items based on the response received from the backend system. | 1. A method of automatically managing a dialogue with a user, comprising: transforming, at a dialogue manager, user-input data received from a client dialogue application into a generic semantic representation, the generic semantic representation being independent of a language and an input modality associated with the user-input data; determining, by the dialogue manager, whether the user-input data is indicative of a new request by the user or a refinement request by the user refining one or more previous requests by the user, based on the generic semantic representation and at least one of a maintained state of the dialogue, general knowledge data representing one or more concepts, and data representing history of the dialogue; sending multi-facet output data, indicative of one or more actions for the client dialogue application to perform, the one or more actions being determined based on a result of said determining whether the generic semantic representation is indicative of the new request by the user or the refinement by the user refining the one or more previous requests by the user; sending a query to a backend end system for retrieving data associated with the determined user-request, the query being generated based on the determined user-request and the maintained state of the dialogue; receiving a response to the query from the backend system; and updating a list of data items based on the response received from the backend system. 5. The method according to claim 1 , wherein determining whether the user-input data is indicative of the new request by the user or the refinement request by the user includes employing a correlation measure, the correlation measure being evaluated based on linguistic features, natural language understanding (NLU) features, the data representing history of the dialogue, dialogue context, dialogue scope, or the general knowledge. | 0.604927 |
9,609,073 | 11 | 12 | 11. A method comprising: accessing a plurality of logged actions related to a subject user of a social networking system, the plurality of logged actions comprising logged actions of the viewing user or one or more other users connected to the viewing user in the social networking system; determining one or more story generators based on a view requested by a client device of a viewing user of a social networking system; selecting one or more of the logged actions of the plurality of logged actions based on a relevance of each of the logged actions to the subject user; generating a plurality of candidate stories from the selected logged actions using the one or more story generators, each of the plurality of candidate stories being associated with a story type of plurality of story types, two or more candidate stories of the plurality of candidate stories associated with a same logged action; generating an affinity for each of the plurality of candidate stories, wherein each affinity comprises a measure of the relevance of a candidate story of the plurality of candidate stories to the subject user; generating a ranking of the plurality of candidate stories based on the affinity generated for each the plurality of candidate stories; identifying the two or more candidate stories associated with the same logged action; responsive to the identifying, removing a subset of the two or more candidate stories from the plurality of stories; selecting one or more of the plurality of candidate stories as selected stories for the view requested by the client device of the viewing user based on the updated ranking; and sending the requested view comprising displayable representations of the selected one or more of the ranked stories to a client device for display to the viewing user. | 11. A method comprising: accessing a plurality of logged actions related to a subject user of a social networking system, the plurality of logged actions comprising logged actions of the viewing user or one or more other users connected to the viewing user in the social networking system; determining one or more story generators based on a view requested by a client device of a viewing user of a social networking system; selecting one or more of the logged actions of the plurality of logged actions based on a relevance of each of the logged actions to the subject user; generating a plurality of candidate stories from the selected logged actions using the one or more story generators, each of the plurality of candidate stories being associated with a story type of plurality of story types, two or more candidate stories of the plurality of candidate stories associated with a same logged action; generating an affinity for each of the plurality of candidate stories, wherein each affinity comprises a measure of the relevance of a candidate story of the plurality of candidate stories to the subject user; generating a ranking of the plurality of candidate stories based on the affinity generated for each the plurality of candidate stories; identifying the two or more candidate stories associated with the same logged action; responsive to the identifying, removing a subset of the two or more candidate stories from the plurality of stories; selecting one or more of the plurality of candidate stories as selected stories for the view requested by the client device of the viewing user based on the updated ranking; and sending the requested view comprising displayable representations of the selected one or more of the ranked stories to a client device for display to the viewing user. 12. The method of claim 11 , wherein selecting the one or more of the plurality of candidate stories based at least in part on the updated ranking comprises: removing one or more logged actions included in a candidate story from an candidate story. | 0.714943 |
8,259,910 | 19 | 20 | 19. A transcription system according to claim 18 , further including means for providing access to the audio file for the customer. | 19. A transcription system according to claim 18 , further including means for providing access to the audio file for the customer. 20. A transcription system according to claim 19 , further including means for determining whether the customer terminated the call in response to an agent transcriber becoming available. | 0.5 |
8,781,102 | 17 | 21 | 17. A method for analyzing an electronic communication between a customer and a contact center, the method comprising: receiving a single electronic communication from a communicant; generating a text file from the electronic communication; analyzing the text file of the electronic communication by mining the text file generated from the electronic communication and applying a predetermined linguistic-based psychological behavioral model to the text file generated from the electronic communication; and generating behavioral assessment data including a personality type corresponding to the analyzed text file of the electronic communication. | 17. A method for analyzing an electronic communication between a customer and a contact center, the method comprising: receiving a single electronic communication from a communicant; generating a text file from the electronic communication; analyzing the text file of the electronic communication by mining the text file generated from the electronic communication and applying a predetermined linguistic-based psychological behavioral model to the text file generated from the electronic communication; and generating behavioral assessment data including a personality type corresponding to the analyzed text file of the electronic communication. 21. The method of claim 17 , which further comprises selecting the electronic communication to include at least one of a digital recording of a telephone call, a facsimile transmission, an e-mail, a web interaction, voice over IP (“VoIP”), or a digital video. | 0.842266 |
8,626,789 | 1 | 6 | 1. A computer-implemented geocoding system, comprising: a processor; a triage component executing on the processor and configured to perform steps including: receiving a query comprising a string and user context information, wherein the user context information includes at least one of market data, culture data, map view data and user location data, wherein the market data indicates a preferred geographical region, the culture data indicates a language setting, the map view data provides a geographic context of a current user view, and the user location data is based on a physical location of user; parsing the string into a set of data-retrieval requests by extracting geographical information to generate a list of ranked geographical information using a weighting function and validating an address from the geographical information, wherein each data retrieval request comprises user context information and fields of a valid address, and wherein the set of data-retrieval requests comprises more than one data-retrieval request for execution by geocoders; selecting a plurality of geocoders to which to send the set of data retrieval requests for execution based on specific requirements of each geocoder, wherein the specific requirements of a first geocoder are different from the specific requirements of a second geocoder, and the data retrieval requests sent to the first geocoder are different from the data retrieval requests send to the second geocoder; and using at least one data retrieval request in the set of data retrieval requests to generate a geocoding command specifically for each of the selected plurality of geocoders to consume, wherein the geocoding command of the first geocoder is different from the geocoding command of the second geocoder; a geocoding component executing on the processor and configured to perform steps including: aggregating the set of data-retrieval requests; and federating the set of data-retrieval requests and generated geocoding commands to the selected plurality of geocoders for parallel execution, wherein the selected plurality of geocoders return responses, wherein the responses comprise a set of results; and a results component executing on the processor and configured to perform steps including: processing the responses from the selected geocoders by merging the responses into a ranked list of results using predetermined scoring and ranking rules, and a subset of the results is returned. | 1. A computer-implemented geocoding system, comprising: a processor; a triage component executing on the processor and configured to perform steps including: receiving a query comprising a string and user context information, wherein the user context information includes at least one of market data, culture data, map view data and user location data, wherein the market data indicates a preferred geographical region, the culture data indicates a language setting, the map view data provides a geographic context of a current user view, and the user location data is based on a physical location of user; parsing the string into a set of data-retrieval requests by extracting geographical information to generate a list of ranked geographical information using a weighting function and validating an address from the geographical information, wherein each data retrieval request comprises user context information and fields of a valid address, and wherein the set of data-retrieval requests comprises more than one data-retrieval request for execution by geocoders; selecting a plurality of geocoders to which to send the set of data retrieval requests for execution based on specific requirements of each geocoder, wherein the specific requirements of a first geocoder are different from the specific requirements of a second geocoder, and the data retrieval requests sent to the first geocoder are different from the data retrieval requests send to the second geocoder; and using at least one data retrieval request in the set of data retrieval requests to generate a geocoding command specifically for each of the selected plurality of geocoders to consume, wherein the geocoding command of the first geocoder is different from the geocoding command of the second geocoder; a geocoding component executing on the processor and configured to perform steps including: aggregating the set of data-retrieval requests; and federating the set of data-retrieval requests and generated geocoding commands to the selected plurality of geocoders for parallel execution, wherein the selected plurality of geocoders return responses, wherein the responses comprise a set of results; and a results component executing on the processor and configured to perform steps including: processing the responses from the selected geocoders by merging the responses into a ranked list of results using predetermined scoring and ranking rules, and a subset of the results is returned. 6. The system of claim 1 , wherein the results component executing on the processor and further configured to perform steps including generating scores for the results, and the results are ranked based on the scores. | 0.728643 |
10,008,196 | 1 | 5 | 1. A computer-implemented method of handling an audio dialog between a companion robot and a human user, the method comprising: during said audio dialog, receiving audio data and converting said audio data into text data; in response to verification of one or more dialog mode execution rules of said text data, selecting a modified dialog mode; wherein a dialog mode comprises one or more dialog contents and one or more dialog voice skins; wherein a dialog content comprises a collection of predefined sentences, said collection comprising question sentences and answer sentences; wherein a dialog voice skin comprises voice rendering parameters comprising frequency, tone, velocity and pitch; wherein said one or more dialog contents and/or voice skins are authored or edited online using a web platform; wherein one or more predefined dialog contents and/or voice skins are modified by multiple parties; wherein one or more dialog contents or a selection thereof are moderated by application of one or more filters, said filters comprising blacklists of one or more words, white lists of one or more words and/or dialog mode execution rules; and wherein a moderation of use of said one or more dialog content and/or voice skins to a final dialog expressed by the companion robot comprise the use of secured boot methods. | 1. A computer-implemented method of handling an audio dialog between a companion robot and a human user, the method comprising: during said audio dialog, receiving audio data and converting said audio data into text data; in response to verification of one or more dialog mode execution rules of said text data, selecting a modified dialog mode; wherein a dialog mode comprises one or more dialog contents and one or more dialog voice skins; wherein a dialog content comprises a collection of predefined sentences, said collection comprising question sentences and answer sentences; wherein a dialog voice skin comprises voice rendering parameters comprising frequency, tone, velocity and pitch; wherein said one or more dialog contents and/or voice skins are authored or edited online using a web platform; wherein one or more predefined dialog contents and/or voice skins are modified by multiple parties; wherein one or more dialog contents or a selection thereof are moderated by application of one or more filters, said filters comprising blacklists of one or more words, white lists of one or more words and/or dialog mode execution rules; and wherein a moderation of use of said one or more dialog content and/or voice skins to a final dialog expressed by the companion robot comprise the use of secured boot methods. 5. The method of claim 1 , further comprising regulating the use of one or more dialog contents, said regulating step comprising one or more steps comprising modulating, filtering attenuating, amplifying, increasing, encouraging, decreasing, inhibiting, limiting, avoiding or forbidding the use of one or more dialog contents and/or voice skins and/or associated execution rules. | 0.5 |
7,805,398 | 29 | 30 | 29. The system of claim 26 , wherein the processor further generates validation code based on the one or more registered rules. | 29. The system of claim 26 , wherein the processor further generates validation code based on the one or more registered rules. 30. The system of claim 29 , wherein the validation code when executed performs at least one of: infers the value of the second attribute, or validates the value of the first attribute or the value of the second attribute. | 0.5 |
8,255,217 | 16 | 20 | 16. The non-transitory computer-readable medium of claim 13 further comprising computer-executable instructions that, when executed by the processor, cause the processor to perform the additional steps of: receiving all the location; determining the geo-centric language model, corresponding to the region defined by the determined radius about the location of the mobile communications device, including selecting one of a plurality of language models stored in a language model database that covers said region; receiving an audio input provided by the user; and processing the audio input based at least in part upon the determined language model to determine a search request identified in the audio input. | 16. The non-transitory computer-readable medium of claim 13 further comprising computer-executable instructions that, when executed by the processor, cause the processor to perform the additional steps of: receiving all the location; determining the geo-centric language model, corresponding to the region defined by the determined radius about the location of the mobile communications device, including selecting one of a plurality of language models stored in a language model database that covers said region; receiving an audio input provided by the user; and processing the audio input based at least in part upon the determined language model to determine a search request identified in the audio input. 20. The non-transitory computer-readable medium of claim 16 further comprising computer-executable instructions that, when executed by the processor, cause the processor to perform an additional step of combining the geo-centric language model and a national language model using a count merging combination strategy or a language model union combination strategy. | 0.580645 |
7,644,052 | 2 | 3 | 2. The method of claim 1 , wherein the calculating comprises calculating information gain for an attribute A in relation to documents S and categories C by which the documents S are grouped, and the calculating the information gain comprises handling separately a subset of the documents S, for which the attribute A is absent, to improve performance with respect to populating sub-concepts in the second ontology. | 2. The method of claim 1 , wherein the calculating comprises calculating information gain for an attribute A in relation to documents S and categories C by which the documents S are grouped, and the calculating the information gain comprises handling separately a subset of the documents S, for which the attribute A is absent, to improve performance with respect to populating sub-concepts in the second ontology. 3. The method of claim 2 , wherein the handling separately comprises using a fraction of entropy associated with the document subset for which the attribute A is absent. | 0.825413 |
5,434,929 | 1 | 2 | 1. A method for indicating preferred character handwriting styles in a pen-based computer system that includes an input screen, a stylus for engaging the screen to input handwritten text to the computer system, and a recognizer for recognizing handwritten text, the method comprising the steps of: activating a character style preference editor; displaying a plurality of variant character styles for a selected character, each variant character style representing a distinct style of writing the selected character that is recognizable by the recognizer; and receiving inputs indicative of the likelihood that a handwritten character input with the stylus will have a form analogous to a selected variant character style and setting a use probability factor associated with the selected variant character style in accordance with the input. | 1. A method for indicating preferred character handwriting styles in a pen-based computer system that includes an input screen, a stylus for engaging the screen to input handwritten text to the computer system, and a recognizer for recognizing handwritten text, the method comprising the steps of: activating a character style preference editor; displaying a plurality of variant character styles for a selected character, each variant character style representing a distinct style of writing the selected character that is recognizable by the recognizer; and receiving inputs indicative of the likelihood that a handwritten character input with the stylus will have a form analogous to a selected variant character style and setting a use probability factor associated with the selected variant character style in accordance with the input. 2. A method as recited in claim 1 further including displaying a recognized character style area, a probability selection area, a character index area, and a control bar area. | 0.65415 |
9,235,846 | 1 | 14 | 1. A method in a host organization, the method comprising: receiving a tabular dataset from a user as input, the tabular dataset having data values organized as columns and rows; identifying a plurality of null values within the tabular dataset, the null values being dispersed across multiple rows and multiple columns of the tabular dataset; generating indices from the tabular dataset of columns and rows, the indices representing probabilistic relationships between the rows and the columns of the tabular dataset; displaying the tabular dataset as output to the user, the displayed output including the data values depicted as known values and the null values depicted as unknown values; receiving input from the user to populate at least a portion of the unknown values within the displayed tabular dataset with predicted values; querying the indices for the predicted values; receiving a confidence indicator for every one of the plurality of null values within the tabular dataset responsive to querying the indices for the predicted values, the confidence indicator based on a comparison of known results corresponding to known and non-null values within the dataset with the predicted values; and displaying the predicted values as updated output to the user, wherein displaying the predicted values as updated output to the user comprises displaying selected ones of the predicted values that correspond to the confidence indicator being in excess of a default minimum confidence threshold or a user specified minimum confidence threshold when present. | 1. A method in a host organization, the method comprising: receiving a tabular dataset from a user as input, the tabular dataset having data values organized as columns and rows; identifying a plurality of null values within the tabular dataset, the null values being dispersed across multiple rows and multiple columns of the tabular dataset; generating indices from the tabular dataset of columns and rows, the indices representing probabilistic relationships between the rows and the columns of the tabular dataset; displaying the tabular dataset as output to the user, the displayed output including the data values depicted as known values and the null values depicted as unknown values; receiving input from the user to populate at least a portion of the unknown values within the displayed tabular dataset with predicted values; querying the indices for the predicted values; receiving a confidence indicator for every one of the plurality of null values within the tabular dataset responsive to querying the indices for the predicted values, the confidence indicator based on a comparison of known results corresponding to known and non-null values within the dataset with the predicted values; and displaying the predicted values as updated output to the user, wherein displaying the predicted values as updated output to the user comprises displaying selected ones of the predicted values that correspond to the confidence indicator being in excess of a default minimum confidence threshold or a user specified minimum confidence threshold when present. 14. The method of claim 1 , further comprising: displaying a user controllable minimum confidence threshold at a Graphical User Interface (GUI) displaying the tabular dataset as output to the user within a spreadsheet or table; and displaying a maximum fill percentage for the GUI, wherein the maximum fill percentage corresponds to a sum of all known values and all null values returning a predicted value with the confidence indicator in excess of the user controllable minimum confidence threshold as a percentage of a sum of all null values and all known values. | 0.608575 |
9,122,932 | 1 | 9 | 1. A method for detecting multi-object anomalies in transportation related video footage, said method comprising: receiving in an offline training phase a first input video sequence at a first traffic location and identifying at least one normal event involving P moving objects, where P is greater than 1; assigning in said offline training phase said at least one normal event in said first input video sequence to at least one normal event class and building a training dictionary suitable for joint sparse reconstruction; receiving in an online detection phase a second input video sequence captured at a second traffic location similar to said first traffic location and identifying at least one event involving P moving objects; reconstructing in said online detection phase an approximation of said event within second input video sequence with respect to said training dictionary using a joint sparse reconstruction model; and determining in said online detection phase whether said event within second input video sequence is anomalous by evaluating an outlier rejection measure of said approximation and comparing said measure against a predetermined threshold, wherein said outlier rejection measure is given by JSCI ( S ′ ) = K · max i λ i ( S ′ ) row , 0 / S ′ row , 0 - 1 K - 1 , where S ′ = [ α 1 , 1 α 2 , 1 α 1 , 2 α 2 , 2 ] and α i,j are coefficient sub-vectors corresponding to coefficient vectors α i , where i=1, 2, . . . , P represents concatenation of sub-dictionaries from all classes belonging to an i-th trajectory and j represents a given class, K represents a number of normal event classes, λ i (S′) represents a characteristic function whose only non-zero entries are the rows in S′ that are associated with the i-th class, and row norm ∥ ∥ row,0 represents the number of non-zero rows of a matrix. | 1. A method for detecting multi-object anomalies in transportation related video footage, said method comprising: receiving in an offline training phase a first input video sequence at a first traffic location and identifying at least one normal event involving P moving objects, where P is greater than 1; assigning in said offline training phase said at least one normal event in said first input video sequence to at least one normal event class and building a training dictionary suitable for joint sparse reconstruction; receiving in an online detection phase a second input video sequence captured at a second traffic location similar to said first traffic location and identifying at least one event involving P moving objects; reconstructing in said online detection phase an approximation of said event within second input video sequence with respect to said training dictionary using a joint sparse reconstruction model; and determining in said online detection phase whether said event within second input video sequence is anomalous by evaluating an outlier rejection measure of said approximation and comparing said measure against a predetermined threshold, wherein said outlier rejection measure is given by JSCI ( S ′ ) = K · max i λ i ( S ′ ) row , 0 / S ′ row , 0 - 1 K - 1 , where S ′ = [ α 1 , 1 α 2 , 1 α 1 , 2 α 2 , 2 ] and α i,j are coefficient sub-vectors corresponding to coefficient vectors α i , where i=1, 2, . . . , P represents concatenation of sub-dictionaries from all classes belonging to an i-th trajectory and j represents a given class, K represents a number of normal event classes, λ i (S′) represents a characteristic function whose only non-zero entries are the rows in S′ that are associated with the i-th class, and row norm ∥ ∥ row,0 represents the number of non-zero rows of a matrix. 9. The method of claim 1 further comprising: identifying in said offline training phase at least one anomalous event in said first video sequence involving P moving objects, assigning said event to an anomalous event class, and adding said anomalous event class to said training dictionary; assigning in said online detection phase said event within second input video sequence to one of the event classes in said training dictionary by minimizing a reconstruction error; and determining in said online detection phase that said event within second input video sequence is anomalous if it is assigned to an anomalous event class. | 0.5 |
9,762,549 | 1 | 6 | 1. A method comprising: transmitting a first data stream, the transmitting comprising: parsing unstructured text content within a first message to identify, from the text content, a first set of sensitive data; encrypting the first set of sensitive data using an encryption type identified by a first name and value pair; transmitting the first message in the first data stream with a first set of private tags surrounding the first set of sensitive data, the first set of private tags identifying the first name and value pair; receiving a second data stream, the receiving comprising: parsing the second data stream to identify a second set of private tags surrounding a second set of sensitive data in a second message; and decrypting the second set of sensitive data using a decryption type identified by a second name and value pair identified by the second set of private tags. | 1. A method comprising: transmitting a first data stream, the transmitting comprising: parsing unstructured text content within a first message to identify, from the text content, a first set of sensitive data; encrypting the first set of sensitive data using an encryption type identified by a first name and value pair; transmitting the first message in the first data stream with a first set of private tags surrounding the first set of sensitive data, the first set of private tags identifying the first name and value pair; receiving a second data stream, the receiving comprising: parsing the second data stream to identify a second set of private tags surrounding a second set of sensitive data in a second message; and decrypting the second set of sensitive data using a decryption type identified by a second name and value pair identified by the second set of private tags. 6. The method of claim 1 , wherein the parsing the first message comprises: identifying the first set of sensitive data using identifiers entered by a user and forming part of the text content in the first message. | 0.5 |
9,317,676 | 16 | 18 | 16. A computer-implemented method, comprising: displaying, on a device interface, a set of candidate objects for object recognition and an image having a missing object, the set of candidate images including the missing object and a first candidate object, wherein the first candidate object does not correspond to the image; detecting a selection of a candidate object from the set of candidate objects; and determining whether the selection was made by a human based on whether the candidate object corresponds to the missing object. | 16. A computer-implemented method, comprising: displaying, on a device interface, a set of candidate objects for object recognition and an image having a missing object, the set of candidate images including the missing object and a first candidate object, wherein the first candidate object does not correspond to the image; detecting a selection of a candidate object from the set of candidate objects; and determining whether the selection was made by a human based on whether the candidate object corresponds to the missing object. 18. The computer-implemented method of claim 16 , further comprising: detecting an aligned position of the candidate object with respect to a position of the missing object in the image; and in response to determining that the aligned position of the candidate object does not match a position of the missing object in the image, denying access to an application; and in response to determining that the aligned position of the candidate object matches the position of the missing object in the image, granting access to the application. | 0.5 |
10,146,770 | 8 | 11 | 8. A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to implement a cognitive system for capturing referential information, wherein the computer readable program causes the computing device to: receive, by a message aggregator executing within the cognitive system, a first indication that a group text messaging conversation is in a muted state for a first user, detect, by a cognitive agent executing within the cognitive system, a first use of a referential phrase in the group text messaging conversation during a first time period when the group text messaging conversation is in the muted state wherein detecting the first use of the referential phrase comprises: parsing, by a parser executing within the cognitive agent, one or more conversation message within the group text messaging conversation to perform parsing and semantic analysis to annotate the one or more conversation messages; extracting, by a feature extraction component executing within the cognitive agent, a set of features from the one or more conversation message describing the one or more conversation message; and processing by a natural language classifier component executing within the cognitive agent, the set of features to identify that the one or more conversation messages contain the first use of the referential phrase using a machine learning model that determines a category for each term or phrase based on the set of features and calculates a confidence for each category; receive, by the message aggregator, a second indication that the group text messaging conversation is in a non-muted state for the first user; detect, by the cognitive agent, a second use of the referential phrase in the group text messaging conversation during a second time period when the group text messaging conversation is in the non-muted state, wherein the second time period is subsequent to the first time period; alter, by the cognitive agent, a message containing the second use of the referential phrase within the group text messaging conversation within a multi-user chat display; determine, by the cognitive system, a first probability that the first user understands the referential phrase; and provide, by the cognitive system, first information to the first user within the multi-user chat display when the first probability is below a threshold, wherein the first information pertains to the referential phrase. | 8. A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to implement a cognitive system for capturing referential information, wherein the computer readable program causes the computing device to: receive, by a message aggregator executing within the cognitive system, a first indication that a group text messaging conversation is in a muted state for a first user, detect, by a cognitive agent executing within the cognitive system, a first use of a referential phrase in the group text messaging conversation during a first time period when the group text messaging conversation is in the muted state wherein detecting the first use of the referential phrase comprises: parsing, by a parser executing within the cognitive agent, one or more conversation message within the group text messaging conversation to perform parsing and semantic analysis to annotate the one or more conversation messages; extracting, by a feature extraction component executing within the cognitive agent, a set of features from the one or more conversation message describing the one or more conversation message; and processing by a natural language classifier component executing within the cognitive agent, the set of features to identify that the one or more conversation messages contain the first use of the referential phrase using a machine learning model that determines a category for each term or phrase based on the set of features and calculates a confidence for each category; receive, by the message aggregator, a second indication that the group text messaging conversation is in a non-muted state for the first user; detect, by the cognitive agent, a second use of the referential phrase in the group text messaging conversation during a second time period when the group text messaging conversation is in the non-muted state, wherein the second time period is subsequent to the first time period; alter, by the cognitive agent, a message containing the second use of the referential phrase within the group text messaging conversation within a multi-user chat display; determine, by the cognitive system, a first probability that the first user understands the referential phrase; and provide, by the cognitive system, first information to the first user within the multi-user chat display when the first probability is below a threshold, wherein the first information pertains to the referential phrase. 11. The computer program product of claim 8 , wherein the providing first information to the first user further comprises replacing the referential phrase with the first information. | 0.775862 |
10,095,690 | 1 | 12 | 1. A method for automated ontology building, comprising: creating, from text, contextual tokens representing at least one of date and time; calculating a dependency graph across the contextual tokens using at least one parse tree obtained by parsing the text; generating concept instance candidates and parent-child relationships based on pattern matching and transformation of the at least one parse tree; grouping concept instance candidates into concept candidates using concept candidate keys, the concept candidate keys being a sequence of triplets; arranging the concept candidates into a tree having tree nodes and creating predicate-based relationships between the tree nodes based on patterns and predicates identified in the text; scoring and sorting the tree nodes; performing an analysis of the tree nodes and rebalancing the tree based on the analysis to provide an ontology based on the text and formed as an output graph comprising a plurality of nodes; and providing a user interface for editing the ontology by selectively choosing from a plurality of options including adding a new node to the output graph, removing an existing node from the output graph, moving one of the plurality of nodes or a sub-graph across a parent-child hierarchy in the output graph, creating a new relation across the plurality of nodes, and removing an existing relation edges from the output graph. | 1. A method for automated ontology building, comprising: creating, from text, contextual tokens representing at least one of date and time; calculating a dependency graph across the contextual tokens using at least one parse tree obtained by parsing the text; generating concept instance candidates and parent-child relationships based on pattern matching and transformation of the at least one parse tree; grouping concept instance candidates into concept candidates using concept candidate keys, the concept candidate keys being a sequence of triplets; arranging the concept candidates into a tree having tree nodes and creating predicate-based relationships between the tree nodes based on patterns and predicates identified in the text; scoring and sorting the tree nodes; performing an analysis of the tree nodes and rebalancing the tree based on the analysis to provide an ontology based on the text and formed as an output graph comprising a plurality of nodes; and providing a user interface for editing the ontology by selectively choosing from a plurality of options including adding a new node to the output graph, removing an existing node from the output graph, moving one of the plurality of nodes or a sub-graph across a parent-child hierarchy in the output graph, creating a new relation across the plurality of nodes, and removing an existing relation edges from the output graph. 12. The method of claim 1 , wherein the at least one parse tree detects applicable parts of speech of the text. | 0.877753 |
8,727,965 | 13 | 14 | 13. A method to improve penile erectile function in a subject comprising injecting into the corpus cavernosum of the penis of the subject a composition comprising a substantially pure population of freshly isolated stromal vascular fraction (SVF) cells present in a biocompatible liquid three-dimensional matrix. | 13. A method to improve penile erectile function in a subject comprising injecting into the corpus cavernosum of the penis of the subject a composition comprising a substantially pure population of freshly isolated stromal vascular fraction (SVF) cells present in a biocompatible liquid three-dimensional matrix. 14. The method of claim 13 , wherein the SVF cells are present on the surface, or embedded within the biocompatible liquid three-dimensional matrix. | 0.666667 |
9,538,252 | 16 | 17 | 16. A system for transcribing dialog associated with moving image content, the system comprising: a provider computer system including at least one electronic processor and at least one data storage device, the provider computer system programmed for: storing a master version of moving image content; providing access to a copy of the master version of the moving image content by multiple client devices associated with multiple transcribers, wherein each client device includes at least one electronic processor and at least one data storage device; receiving a request to transcribe the dialog associated with the master version of the moving image content; transmitting an interface to at least one of the multiple client devices, wherein the interface is programmed with instructions for: (i) requesting the copy of the master version of the moving image content from the provider computer system, (ii) interactively playing the moving image content, (iii) receiving input data representative of a transcription of the dialog associated with the master version of the moving image content, and (iv) receiving input data indicative of at least one starting time-stamp and at least one ending time-stamp for at least one segment of multiple segments of the transcription; receiving, in the provider computer system, the transcription of the at least one segment of the multiple segments, and the input data indicative of the at least one starting time-stamp and the at least one ending time-stamp for the at least one segment of the multiple segments of the transcription of the dialog associated with the master version of the moving image content; and storing, in the provider computer system, the transcription together with the starting and ending time-stamps for each segment of the multiple segments as a copy of the master version of the moving image content being reclassified as transcribed and time-stamped. | 16. A system for transcribing dialog associated with moving image content, the system comprising: a provider computer system including at least one electronic processor and at least one data storage device, the provider computer system programmed for: storing a master version of moving image content; providing access to a copy of the master version of the moving image content by multiple client devices associated with multiple transcribers, wherein each client device includes at least one electronic processor and at least one data storage device; receiving a request to transcribe the dialog associated with the master version of the moving image content; transmitting an interface to at least one of the multiple client devices, wherein the interface is programmed with instructions for: (i) requesting the copy of the master version of the moving image content from the provider computer system, (ii) interactively playing the moving image content, (iii) receiving input data representative of a transcription of the dialog associated with the master version of the moving image content, and (iv) receiving input data indicative of at least one starting time-stamp and at least one ending time-stamp for at least one segment of multiple segments of the transcription; receiving, in the provider computer system, the transcription of the at least one segment of the multiple segments, and the input data indicative of the at least one starting time-stamp and the at least one ending time-stamp for the at least one segment of the multiple segments of the transcription of the dialog associated with the master version of the moving image content; and storing, in the provider computer system, the transcription together with the starting and ending time-stamps for each segment of the multiple segments as a copy of the master version of the moving image content being reclassified as transcribed and time-stamped. 17. The system of claim 16 , wherein the provider computer system is further programmed to receive a request to time-stamp a transcription of the dialog associated with the master version of the moving image content. | 0.5 |
7,676,680 | 1 | 9 | 1. A computer assisted method of providing a private placement document to a potential investor in a private placement, the method comprising: generating the private placement document in an encrypted electronic format with a computer system comprising at least one processor and operatively associated memory, wherein generating the private placement document includes labeling the private placement document with a unique identifier and wherein the private placement document comprises a subscription document; providing the private placement document to the potential investor electronically with the computer system, wherein the subscription document is programmed to: (i) prompt the potential investor to enter information relating to the potential investor into the subscription document; (ii) conditioned upon the potential investor declining to enter the information relating to the potential investor into the subscription document, rendering the private placement document unreadable by the potential investor; (iii) conditioned upon the information relating to the potential investor failing to meet a pre-determined qualification standard, rendering the private placement document unreadable by the potential investor; and (iv) conditioned upon the potential investor failing to enter the information relating to the potential investor into the subscription document within a predetermined amount of time, rendering the private placement document unreadable by the potential investor; recording the unique identifier at a database in communication with the computer system, wherein the unique identifier is recorded at the database in association with the potential investor; electronically receiving the subscription document from the potential investor with the computer system, wherein the subscription document, when received from the potential investor, comprises the information relating to the potential investor; and verifying with the computer system that the information relating to the potential investor is correctly entered into the subscription document. | 1. A computer assisted method of providing a private placement document to a potential investor in a private placement, the method comprising: generating the private placement document in an encrypted electronic format with a computer system comprising at least one processor and operatively associated memory, wherein generating the private placement document includes labeling the private placement document with a unique identifier and wherein the private placement document comprises a subscription document; providing the private placement document to the potential investor electronically with the computer system, wherein the subscription document is programmed to: (i) prompt the potential investor to enter information relating to the potential investor into the subscription document; (ii) conditioned upon the potential investor declining to enter the information relating to the potential investor into the subscription document, rendering the private placement document unreadable by the potential investor; (iii) conditioned upon the information relating to the potential investor failing to meet a pre-determined qualification standard, rendering the private placement document unreadable by the potential investor; and (iv) conditioned upon the potential investor failing to enter the information relating to the potential investor into the subscription document within a predetermined amount of time, rendering the private placement document unreadable by the potential investor; recording the unique identifier at a database in communication with the computer system, wherein the unique identifier is recorded at the database in association with the potential investor; electronically receiving the subscription document from the potential investor with the computer system, wherein the subscription document, when received from the potential investor, comprises the information relating to the potential investor; and verifying with the computer system that the information relating to the potential investor is correctly entered into the subscription document. 9. The method of claim 1 , wherein the electronic format is the ADOBE PORTABLE DOCUMENT FORMAT (PDF). | 0.869171 |
9,002,772 | 14 | 16 | 14. The computer program product of claim 10 where any of the trigger rules are organized into a group of trigger rules, where the group serves as a workflow control block. | 14. The computer program product of claim 10 where any of the trigger rules are organized into a group of trigger rules, where the group serves as a workflow control block. 16. The computer program product of claim 14 where the computer-readable program code is configured to iteratively evaluate the trigger rules in the group. | 0.533133 |
7,539,635 | 6 | 8 | 6. A computerized method for generating a tax advice document from a tax return preparation program executing on a computer comprising: (a) entering in said computer a plurality of tax advice statements; (b) entering in said computer an assignment of at least two of said tax advice statements to a client-requested category; (c) entering in said computer an assignment of each of said plurality of tax advice statements except said at least two of said client-requested tax advice statements to one of a plurality of other categories; (d) entering in said computer an assignment of a category relevance value to each of said tax advice categories wherein said client-requested category is assigned a highest category relevance value; (e) entering in said computer an assignment of a statement relevance value to each of said plurality of tax advice statements in each of said tax advice categories; (f) entering in said computer an assignment of a trigger within said tax return preparation program to each of said plurality of tax advice statements; (g) specifying in said computer a category maximum number of tax advice statements from each of said tax advice categories to include on a tax advice document; (h) specifying in said computer a total number of tax advice statements to include on tax advice document; (i) determining at said computer whether one or more of said at least two of said client-requested tax advice statements are triggered after responses to interview questions are entered in said tax return preparation program; (j) determining at said computer which tax advice statements in said tax return preparation program are triggered when tax data for said taxpayer is entered in said tax return preparation system; and (k) generating at said computer for said taxpayer a tax advice document comprising a subset of triggered tax advice statements said tax advice document comprising: (1) triggered tax advice statements from said client-requested category up to said category maximum number of tax advice statements for said client-requested category; (2) triggered tax advice statements from said other tax advice categories up to said category maximum number of tax advice statements for each of said other tax advice categories; (3) a total number of said triggered tax advice statements from said client-requested category and said triggered tax advice statements from said other tax advice categories not exceeding said total number of tax advice statements; (4) a first triggered tax advice statement from said client-requested category; and (5) tax advice statements from said other tax advice categories ordered on said tax advice document according to said category relevance value and within each category, said statement relevance value. | 6. A computerized method for generating a tax advice document from a tax return preparation program executing on a computer comprising: (a) entering in said computer a plurality of tax advice statements; (b) entering in said computer an assignment of at least two of said tax advice statements to a client-requested category; (c) entering in said computer an assignment of each of said plurality of tax advice statements except said at least two of said client-requested tax advice statements to one of a plurality of other categories; (d) entering in said computer an assignment of a category relevance value to each of said tax advice categories wherein said client-requested category is assigned a highest category relevance value; (e) entering in said computer an assignment of a statement relevance value to each of said plurality of tax advice statements in each of said tax advice categories; (f) entering in said computer an assignment of a trigger within said tax return preparation program to each of said plurality of tax advice statements; (g) specifying in said computer a category maximum number of tax advice statements from each of said tax advice categories to include on a tax advice document; (h) specifying in said computer a total number of tax advice statements to include on tax advice document; (i) determining at said computer whether one or more of said at least two of said client-requested tax advice statements are triggered after responses to interview questions are entered in said tax return preparation program; (j) determining at said computer which tax advice statements in said tax return preparation program are triggered when tax data for said taxpayer is entered in said tax return preparation system; and (k) generating at said computer for said taxpayer a tax advice document comprising a subset of triggered tax advice statements said tax advice document comprising: (1) triggered tax advice statements from said client-requested category up to said category maximum number of tax advice statements for said client-requested category; (2) triggered tax advice statements from said other tax advice categories up to said category maximum number of tax advice statements for each of said other tax advice categories; (3) a total number of said triggered tax advice statements from said client-requested category and said triggered tax advice statements from said other tax advice categories not exceeding said total number of tax advice statements; (4) a first triggered tax advice statement from said client-requested category; and (5) tax advice statements from said other tax advice categories ordered on said tax advice document according to said category relevance value and within each category, said statement relevance value. 8. The computerized method of claim 6 wherein said subset of tax advice statements is selected from the group consisting of statements related to income, deductions, tax credits, qualified retirement, flexible spending accounts, IRA contributions, tax-exempt investments, and social security tax payments. | 0.5 |
8,370,313 | 12 | 17 | 12. A method of determining a multidimensional reputation score for each node in a directed graph, comprising: providing, by to a computer, the directed graph comprising a plurality of nodes, a plurality of positive links and a plurality of negative links, wherein each of the plurality of nodes represents an autonomous entity and each of the plurality of positive and negative links represents a positive or negative opinion which a source node of that link holds of a target node of that link, respectively, generating, by the computer, a reputation score for each node having at least a value of positive reputation, a value of negative reputation, and a value of gullibility wherein the gullibility value is a kind of negative reputation accrued by establishing positive links to nodes with negative reputation by performing one or more random traversals and counting visits to each node by incrementing a per-node set of counters, one for each of positive reputation, negative reputation, and gullibility, wherein the generating comprises: traversing by following links at random in outgoing direction, incrementing positive value counter for all visited nodes until the traversing terminates or traverses a negative link, in response to the traversing traverses the negative link, the traversing increments the negative value counter corresponding to a node which is the destination of the negative link, and proceeds to follow positive incoming links in a reverse direction at random from the destination of the negative link, incrementing a gullibility counter of every node the traversing visits until the traversal terminates; and producing a final reputation score for each node which includes a gullibility score computed in part from gullibility counter for each node, wherein positive links contribute to a source node of the positive links gullibility score in proportion to a negative reputation and gullibility scores of a target node of the positive links, wherein the final reputation score for a particular node comprises of positive value of counter of the particular node, negative value of the counter of the particular node, and gullibility counter of the particular node, each divided by a total sum of all hits of all types for all of the plurality of nodes. | 12. A method of determining a multidimensional reputation score for each node in a directed graph, comprising: providing, by to a computer, the directed graph comprising a plurality of nodes, a plurality of positive links and a plurality of negative links, wherein each of the plurality of nodes represents an autonomous entity and each of the plurality of positive and negative links represents a positive or negative opinion which a source node of that link holds of a target node of that link, respectively, generating, by the computer, a reputation score for each node having at least a value of positive reputation, a value of negative reputation, and a value of gullibility wherein the gullibility value is a kind of negative reputation accrued by establishing positive links to nodes with negative reputation by performing one or more random traversals and counting visits to each node by incrementing a per-node set of counters, one for each of positive reputation, negative reputation, and gullibility, wherein the generating comprises: traversing by following links at random in outgoing direction, incrementing positive value counter for all visited nodes until the traversing terminates or traverses a negative link, in response to the traversing traverses the negative link, the traversing increments the negative value counter corresponding to a node which is the destination of the negative link, and proceeds to follow positive incoming links in a reverse direction at random from the destination of the negative link, incrementing a gullibility counter of every node the traversing visits until the traversal terminates; and producing a final reputation score for each node which includes a gullibility score computed in part from gullibility counter for each node, wherein positive links contribute to a source node of the positive links gullibility score in proportion to a negative reputation and gullibility scores of a target node of the positive links, wherein the final reputation score for a particular node comprises of positive value of counter of the particular node, negative value of the counter of the particular node, and gullibility counter of the particular node, each divided by a total sum of all hits of all types for all of the plurality of nodes. 17. The method of claim 12 , where the directed graph represents relationships between users of an online forum, wherein the nodes represent the users, and the links represent the relationships. | 0.570796 |
9,817,991 | 10 | 11 | 10. A multi-tenant computing platform, comprising: a records application configured to store records in a tenant data store; each record having record data stored therein; a graphical user interface configured to display a graphical representation of data associated with one or more records such that the display is restricted to a first set of one or more credentialed users; and a notes application configured to facilitate generation of a note associated with one or more records in the tenant data store, each note having restricted access such that only a second set of one or more credentialed users may access the note, the second set different from the first set; wherein the graphical user interface is configured to display a note over a record if and only if a credentialed user of the graphical user interface is in the first set of one or more credentialed users and in the second set of one or more credentialed users. | 10. A multi-tenant computing platform, comprising: a records application configured to store records in a tenant data store; each record having record data stored therein; a graphical user interface configured to display a graphical representation of data associated with one or more records such that the display is restricted to a first set of one or more credentialed users; and a notes application configured to facilitate generation of a note associated with one or more records in the tenant data store, each note having restricted access such that only a second set of one or more credentialed users may access the note, the second set different from the first set; wherein the graphical user interface is configured to display a note over a record if and only if a credentialed user of the graphical user interface is in the first set of one or more credentialed users and in the second set of one or more credentialed users. 11. The multi-tenant computing platform of claim 10 , further comprising a credentials application configured to store credentials data for users wherein users may have credentials associated with access to notes. | 0.5 |
8,965,753 | 7 | 8 | 7. A method, comprising: using at least one computer to perform: assigning word-class information to words in a medical report, the assigning comprising: assigning first word-class information to a first word or word sequence in the medical report, wherein the first word or word sequence names a particular medicament; and assigning second word-class information to a second word or word sequence in the medical report; performing at least one action related to the particular medicament and associated with a combination of the first word-class information and the second word-class information, wherein assigning the word class information comprises: identifying a medicament word class as a word class of the first word or word sequence in the medical report; and identifying an allergy word class as a word class of the second word or word sequence in the medical report, wherein the second word or word sequence names an allergy of a patient toward an active agent. | 7. A method, comprising: using at least one computer to perform: assigning word-class information to words in a medical report, the assigning comprising: assigning first word-class information to a first word or word sequence in the medical report, wherein the first word or word sequence names a particular medicament; and assigning second word-class information to a second word or word sequence in the medical report; performing at least one action related to the particular medicament and associated with a combination of the first word-class information and the second word-class information, wherein assigning the word class information comprises: identifying a medicament word class as a word class of the first word or word sequence in the medical report; and identifying an allergy word class as a word class of the second word or word sequence in the medical report, wherein the second word or word sequence names an allergy of a patient toward an active agent. 8. The method of claim 7 , wherein performing the at least one action comprises providing a warning that a patient is allergic to the particular medicament. | 0.5 |
8,447,702 | 5 | 6 | 5. The system of claim 1 , wherein said domain name appraisal module is further configured to calculate said popularity value for said domain name by: i) receiving, from one or more search engines, one or more search result metrics measured by said one or more search engines; ii) generating a search engine metrics value comprising said one or more search result metrics; iii) receiving, from one or more search engine optimization monitoring services or software, one or more search tracking metrics tracking, at regular intervals, an estimated number of searches of a plurality of words via said one or more search engine optimization monitoring services or software; and iv) generating a search tracking metrics value comprising said one or more search tracking metrics. | 5. The system of claim 1 , wherein said domain name appraisal module is further configured to calculate said popularity value for said domain name by: i) receiving, from one or more search engines, one or more search result metrics measured by said one or more search engines; ii) generating a search engine metrics value comprising said one or more search result metrics; iii) receiving, from one or more search engine optimization monitoring services or software, one or more search tracking metrics tracking, at regular intervals, an estimated number of searches of a plurality of words via said one or more search engine optimization monitoring services or software; and iv) generating a search tracking metrics value comprising said one or more search tracking metrics. 6. The system of claim 5 , wherein said domain name appraisal module is further configured to calculate said popularity value for said domain name by: i) initializing said popularity value to 0; ii) determining whether: a) said one or more search engine metrics comprise one or more positive or one or more negative search engines metrics; and b) said one or more search tracking metrics comprise one or more high or one or more low estimated searches per month; iii) responsive to a determination that said one or more search engine metrics comprise said one or more positive search engines metrics or said one or more search tracking metrics comprise said one or more high estimated searches per month, increasing said popularity value; and iv) responsive to a determination that said one or more search engine metrics comprise said one or more negative search engines metrics or said one or more search tracking metrics comprise said one or more low estimated searches per month, decreasing said popularity value. | 0.5 |
7,899,666 | 28 | 29 | 28. A method for automatically extracting relations between concepts included in electronic text, comprising: accessing, by a program executing on a computer, a semantic network, wherein the semantic network comprises a plurality of lemmas that are grouped into synsets representing concepts, each of the synsets having a corresponding sense, and a plurality of links connected between the synsets that represent semantic relations between the synsets; wherein the semantic network further includes semantic information comprising at least one of: an expanded set of semantic relation links representing: hierarchical semantic relations, synset/corpus semantic relations verb/subject semantic relations, verb/direct object semantic relations, and fine grain/coarse grain semantic relationship; a hierarchical category tree having a plurality of categories, wherein each of the categories contains a group of one or more synsets and a set of attributes, wherein the set of attributes of each of the categories are associated with each of the synsets in the respective category; and a plurality of domains, wherein one or more of the domains is associated with at least a portion of the synsets, wherein each domain adds information regarding a linguistic context in which the corresponding synset is used in a language; and performing, by the program, semantic disambiguation on the electronic text using the semantic network and the at least one of the expanded set of semantic relation links, the hierarchical category tree, and the plurality of domains to assign a respective one of the senses to elements in the electronic text independently from contextual reference of the electronic text. | 28. A method for automatically extracting relations between concepts included in electronic text, comprising: accessing, by a program executing on a computer, a semantic network, wherein the semantic network comprises a plurality of lemmas that are grouped into synsets representing concepts, each of the synsets having a corresponding sense, and a plurality of links connected between the synsets that represent semantic relations between the synsets; wherein the semantic network further includes semantic information comprising at least one of: an expanded set of semantic relation links representing: hierarchical semantic relations, synset/corpus semantic relations verb/subject semantic relations, verb/direct object semantic relations, and fine grain/coarse grain semantic relationship; a hierarchical category tree having a plurality of categories, wherein each of the categories contains a group of one or more synsets and a set of attributes, wherein the set of attributes of each of the categories are associated with each of the synsets in the respective category; and a plurality of domains, wherein one or more of the domains is associated with at least a portion of the synsets, wherein each domain adds information regarding a linguistic context in which the corresponding synset is used in a language; and performing, by the program, semantic disambiguation on the electronic text using the semantic network and the at least one of the expanded set of semantic relation links, the hierarchical category tree, and the plurality of domains to assign a respective one of the senses to elements in the electronic text independently from contextual reference of the electronic text. 29. The method of claim 28 further comprising performing the semantic disambiguation by: identifying structural elements in the text; for each structural element, searching the semantic network to find a matching lemma; using the expanded set of semantic relation links of the synsets containing the matching lemma to retrieve the senses of related synsets; using the categories and domains for disambiguating the senses of synsets having common lemmas. | 0.5 |
9,357,071 | 17 | 25 | 17. A method for analyzing an electronic communication for behavioral assessment data, the method comprising: receiving, by a control processor, a single electronic communication in text form from a communicant; analyzing the text of the electronic communication by mining the text of the electronic communication and applying a predetermined linguistic-based psychological behavioral model to the text of the electronic communication; and generating behavioral assessment data including a personality type corresponding to the analyzed text of the electronic communication. | 17. A method for analyzing an electronic communication for behavioral assessment data, the method comprising: receiving, by a control processor, a single electronic communication in text form from a communicant; analyzing the text of the electronic communication by mining the text of the electronic communication and applying a predetermined linguistic-based psychological behavioral model to the text of the electronic communication; and generating behavioral assessment data including a personality type corresponding to the analyzed text of the electronic communication. 25. The method of claim 17 , which further comprises: separating the electronic communication into at least first and second constituent text data, the first constituent text data being generated by the communicant; and analyzing one of the separated first and second constituent text data by mining the separated one of the first and second constituent text data and applying a predetermined linguistic-based psychological behavioral model to the one of the separated first and second constituent text data, wherein the generating behavioral assessment data including a personality type corresponding to the analyzed text is additionally corresponding to one of the separated first and second constituent text data based on the step of analyzing one of the first and second constituent text data. | 0.5 |
7,496,854 | 60 | 61 | 60. The method of claim 57 , wherein the operation performed is entering additional data into a database. | 60. The method of claim 57 , wherein the operation performed is entering additional data into a database. 61. The method of claim 60 , wherein the additional data is entered by a user. | 0.556818 |
7,664,759 | 9 | 11 | 9. An article comprising a computer-readable medium storing instructions for storing and retrieving self-descriptive tabular data with alphanumeric and binary values, the instructions operable to cause one or more data apparatus to perform operations comprising: storing binary values in a plurality of individual binary files; generating, in an open tabular format, a file comprising: a plurality of alphanumeric data values; meta-information associated with the alphanumeric data values and describing a predetermined set of characteristics of said open tabular format and the alphanumeric data values, the open tabular format includes series data values and associated series meta-information for describing alphanumeric and binary reference data values appearing repetitively in the open tabular format, the open tabular format is formed with gap data values and associated gap meta-information for describing gaps in alphanumeric and binary reference data values appearing in the open tabular format; and a plurality of references, with each reference corresponding to one of the individual binary files; and parsing the generated file to identify locations of the alphanumeric data values and the plurality of references and to allow subsequent selective retrieval of each alphanumeric data value and each reference to an individual binary file. | 9. An article comprising a computer-readable medium storing instructions for storing and retrieving self-descriptive tabular data with alphanumeric and binary values, the instructions operable to cause one or more data apparatus to perform operations comprising: storing binary values in a plurality of individual binary files; generating, in an open tabular format, a file comprising: a plurality of alphanumeric data values; meta-information associated with the alphanumeric data values and describing a predetermined set of characteristics of said open tabular format and the alphanumeric data values, the open tabular format includes series data values and associated series meta-information for describing alphanumeric and binary reference data values appearing repetitively in the open tabular format, the open tabular format is formed with gap data values and associated gap meta-information for describing gaps in alphanumeric and binary reference data values appearing in the open tabular format; and a plurality of references, with each reference corresponding to one of the individual binary files; and parsing the generated file to identify locations of the alphanumeric data values and the plurality of references and to allow subsequent selective retrieval of each alphanumeric data value and each reference to an individual binary file. 11. The article medium of claim 9 , further comprising instructions for associating with said alphanumeric data values and said binary reference values a set of row values and associated row meta-information. | 0.588933 |
8,756,050 | 18 | 23 | 18. A computer-implemented method for providing translated content, comprising: providing, by one or more processors, to a first user a content instance in connection with an offer for consumption of an item in an electronic marketplace; receiving a translation of a content instance from the first user, the first user having a translator score, the translator score being based at least in part on particular translation scores for particular translations submitted by the first user and reviewer scores associated with one or more reviewers of the received translation; providing the received translation to the one or more reviewers; receiving one or more votes from a set of the one or more reviewers; calculating, based at least in part on the received one or more votes and the translator score, a translation score for the translation; and when the translation score satisfies one or more criteria, providing to a second user the translation of the content instance in connection with another offer for consumption of the item. | 18. A computer-implemented method for providing translated content, comprising: providing, by one or more processors, to a first user a content instance in connection with an offer for consumption of an item in an electronic marketplace; receiving a translation of a content instance from the first user, the first user having a translator score, the translator score being based at least in part on particular translation scores for particular translations submitted by the first user and reviewer scores associated with one or more reviewers of the received translation; providing the received translation to the one or more reviewers; receiving one or more votes from a set of the one or more reviewers; calculating, based at least in part on the received one or more votes and the translator score, a translation score for the translation; and when the translation score satisfies one or more criteria, providing to a second user the translation of the content instance in connection with another offer for consumption of the item. 23. The computer implemented method of claim 18 , further comprising calculating a reviewer score for a particular reviewer of the one or more reviewers based at least in part on agreement of the particular reviewer with one or more other reviewers, and wherein calculating the translation score for a translation reviewed by the particular reviewer is based at least in part on the particular reviewer score. | 0.5 |
9,215,194 | 3 | 4 | 3. A method comprising: at a recipient system, presenting a keyword user interface to a recipient user; receiving a new keyword from the recipient user via the keyword user interface; receiving a new action associated with the new keyword, from the recipient user; storing the new keyword and the new action in a profile associated with the recipient user; receiving an Instant Message from a message communicator, the Instant Message including instant message content; automatically analyzing the instant message content of the Instant Message to identify at least one keyword of a set of keywords that includes the new keyword, wherein the at least one keyword is included in the instant message content; and identifying a predefined action associated with the at least one keyword, the predefined action defining processing of the Instant Message by the recipient system Instant Message client. | 3. A method comprising: at a recipient system, presenting a keyword user interface to a recipient user; receiving a new keyword from the recipient user via the keyword user interface; receiving a new action associated with the new keyword, from the recipient user; storing the new keyword and the new action in a profile associated with the recipient user; receiving an Instant Message from a message communicator, the Instant Message including instant message content; automatically analyzing the instant message content of the Instant Message to identify at least one keyword of a set of keywords that includes the new keyword, wherein the at least one keyword is included in the instant message content; and identifying a predefined action associated with the at least one keyword, the predefined action defining processing of the Instant Message by the recipient system Instant Message client. 4. The method of claim 3 , in which wherein the predefined action comprises displaying the incoming Instant Message to the recipient user of the recipient system based on the at least one keyword even though the recipient system user's Instant Messaging client indicates to a sender of the Instant Message that the recipient user is unavailable. | 0.5 |
9,196,240 | 21 | 22 | 21. A system comprising: one or more processors; a computer-readable memory; and a module comprising executable instructions stored in the computer-readable memory, the module, when executed by the one or more processors, configured to: generate an audio representation of a text, wherein the audio representation comprises a sequence of speech segments of a plurality of speech segments, and wherein the sequence is based at least in part on a plurality of conversion rules; transmit the audio representation to a first client device and a second client device of a plurality of client devices; receive first feedback data from the first client device, wherein the first feedback data relates to the audio representation; receive second feedback data from the second client device, wherein the second feedback data relates to the audio representation; and determine whether to modify at least one of (i) the plurality of conversion rules or (ii) the plurality of speech segments based at least in part on at least one of the first feedback data and the second feedback data. | 21. A system comprising: one or more processors; a computer-readable memory; and a module comprising executable instructions stored in the computer-readable memory, the module, when executed by the one or more processors, configured to: generate an audio representation of a text, wherein the audio representation comprises a sequence of speech segments of a plurality of speech segments, and wherein the sequence is based at least in part on a plurality of conversion rules; transmit the audio representation to a first client device and a second client device of a plurality of client devices; receive first feedback data from the first client device, wherein the first feedback data relates to the audio representation; receive second feedback data from the second client device, wherein the second feedback data relates to the audio representation; and determine whether to modify at least one of (i) the plurality of conversion rules or (ii) the plurality of speech segments based at least in part on at least one of the first feedback data and the second feedback data. 22. The system of claim 21 , wherein the plurality of conversion rules comprises rules for determining pronunciation, accentuation, or prosody. | 0.907979 |
9,058,811 | 7 | 8 | 7. The method according to claim 6 , wherein the step of determining the fuzzy data further comprises: estimating the speech unit; determining a degree to which candidate context labels of the speech unit fall into a category; and determining the speech unit as the fuzzy data if the degree satisfies a predetermined threshold. | 7. The method according to claim 6 , wherein the step of determining the fuzzy data further comprises: estimating the speech unit; determining a degree to which candidate context labels of the speech unit fall into a category; and determining the speech unit as the fuzzy data if the degree satisfies a predetermined threshold. 8. The method according to claim 7 , wherein the step of estimating the speech unit further comprises: estimating scores of the context feature labels of candidate pronunciations of the speech unit by model posterior probability or distance between model generating parameters and speech unit parameters. | 0.5 |
9,882,860 | 2 | 3 | 2. The method as recited in claim 1 further comprising: receiving feedback regarding approval or disapproval of order of said message segments in a group of message segments. | 2. The method as recited in claim 1 further comprising: receiving feedback regarding approval or disapproval of order of said message segments in a group of message segments. 3. The method as recited in claim 2 further comprising: storing said feedback to be used in analysis as to whether to reorder said message segments in said group of message segments. | 0.5 |
8,301,544 | 40 | 42 | 40. The method of claim 11 wherein when a buyer request is received, an expected contribution to the seller utility from that request is calculated such that the optimal price computed by the seller engine will be the price that maximizes such contribution. | 40. The method of claim 11 wherein when a buyer request is received, an expected contribution to the seller utility from that request is calculated such that the optimal price computed by the seller engine will be the price that maximizes such contribution. 42. The method of claim 40 wherein the utility function is linear as given substantially by u = w R R t + Q ρ ( p ) p R T + w M M t + Q ρ ( p ) ( p - C ) M T + w S S t + Q ρ ( p ) S T where w R is revenue utility weights, w M is profit utility weights, w S is sales volume utility weights, Q is number of units, p is unit quote price by seller and the variable being optimized, ρ(p) is historic bid-response function, C is unit cost for seller, R t is revenue achieved so far, R T is revenue target as configured by the seller where T is the total time to achieve the target, M t is achieved profit so far, M T is profit target as configured by the seller where T is the total time to achieve the target, S t is achieved sales volume so far, and S T is sales volume target as configured by the seller where T is the total time to achieve the target. | 0.5 |
9,251,791 | 11 | 19 | 11. A system comprising: a data processing apparatus; and a data store coupled to the data processing apparatus, in which is stored: an application-independent input method editor configured to receive input for a plurality of applications executable by an electronic device, the application-independent input method editor operable to: receive spoken input from a user of the electronic device in an application of the plurality of applications; determine a category for the application; provide the spoken input and data that indicates the application category to a server that includes a speech recognition system configured to select one or more language models to generate text based on the spoken input, wherein the one or more language models are selected based on the data that indicates the application category; receive text from the server, wherein the text represents a transcription of the spoken input; and provide the text as input to the application. | 11. A system comprising: a data processing apparatus; and a data store coupled to the data processing apparatus, in which is stored: an application-independent input method editor configured to receive input for a plurality of applications executable by an electronic device, the application-independent input method editor operable to: receive spoken input from a user of the electronic device in an application of the plurality of applications; determine a category for the application; provide the spoken input and data that indicates the application category to a server that includes a speech recognition system configured to select one or more language models to generate text based on the spoken input, wherein the one or more language models are selected based on the data that indicates the application category; receive text from the server, wherein the text represents a transcription of the spoken input; and provide the text as input to the application. 19. The system of claim 11 , wherein the application-independent input method editor is further operable to: generate a correlation between the spoken input and the application category, comprising matching one or more utterances in the spoken input to one or more stored items that are associated with the application category, wherein the one or more language models are further selected based on the correlation. | 0.5 |
7,499,858 | 12 | 13 | 12. The method of claim 10 wherein a step between the receiving and retrieving steps comprises using a speech recognizer to process the spoken query. | 12. The method of claim 10 wherein a step between the receiving and retrieving steps comprises using a speech recognizer to process the spoken query. 13. The method of claim 12 wherein the retrieving step comprises using the processed spoken query, the language model, and the index to retrieve the one or more retrieved documents. | 0.5 |
10,162,900 | 1 | 5 | 1. A computer-implemented method for conducting an opinion search and generating an opinion visualization result for display on a computer screen, comprising: receiving a query to an opinion search engine; extracting entity information and attributes from each structured electronic social media message in the plurality of structured electronic social media messages and extracting entity information and attributes from each normalized unstructured electronic social media message in the plurality of unstructured electronic social media messages; scoring a composite sentiment value and attributes for the text in each structured electronic social media message or each normalized unstructured electronic social media message, storing the scored structured electronic social media messages and the scored normalized unstructured electronic social media message in a database; and aggregating the results of the scored structured electronic social media messages and the scored normalized unstructured electronic social media messages for one or more entities organized for display as a transformed visual representation based on the extracted one or more entities from the search query; wherein the transformed visual representation comprises a timeline entity graphical view with social media inflections over a period of time and a heat map of social media sentiments for the industry associated with the entity, the size and color coding for each company in the industry dependent on the amount of social medial posts and the type of sentiment. | 1. A computer-implemented method for conducting an opinion search and generating an opinion visualization result for display on a computer screen, comprising: receiving a query to an opinion search engine; extracting entity information and attributes from each structured electronic social media message in the plurality of structured electronic social media messages and extracting entity information and attributes from each normalized unstructured electronic social media message in the plurality of unstructured electronic social media messages; scoring a composite sentiment value and attributes for the text in each structured electronic social media message or each normalized unstructured electronic social media message, storing the scored structured electronic social media messages and the scored normalized unstructured electronic social media message in a database; and aggregating the results of the scored structured electronic social media messages and the scored normalized unstructured electronic social media messages for one or more entities organized for display as a transformed visual representation based on the extracted one or more entities from the search query; wherein the transformed visual representation comprises a timeline entity graphical view with social media inflections over a period of time and a heat map of social media sentiments for the industry associated with the entity, the size and color coding for each company in the industry dependent on the amount of social medial posts and the type of sentiment. 5. The system of claim 1 , wherein the transformed visual representation comprises a timeline entity graphical view with social media inflections over a period of time. | 0.573604 |
8,762,191 | 10 | 11 | 10. The method of claim 8 , wherein the individual includes an individual that is a party involved in a transaction. | 10. The method of claim 8 , wherein the individual includes an individual that is a party involved in a transaction. 11. The method of claim 10 , wherein the transaction includes a financial transaction. | 0.5 |
8,738,558 | 1 | 9 | 1. A method of operating a computer to provide a response to a received user input, the method comprising: automatically with the computer: (a) in response to receiving, from a user's device, a partial user input signifying a portion of an answerable statement, before receiving a full user input representing the entire answerable statement, calculating for each of a plurality of predefined answerable statements, a metric that is, at least in part, based on a frequency with which the predefined answerable statement had been selected by previous users; and (b) (1) if the metric for one of the predefined answerable statements exceeds a threshold, sending, to the user's device, information representing a response associated with said one of the predefined answerable statements, and (2) if part (b)(1) does not apply, sending, to the user's device, information representing at least one of the predefined answerable statements, which predefined answerable statements are selected based on the respective associated metrics. | 1. A method of operating a computer to provide a response to a received user input, the method comprising: automatically with the computer: (a) in response to receiving, from a user's device, a partial user input signifying a portion of an answerable statement, before receiving a full user input representing the entire answerable statement, calculating for each of a plurality of predefined answerable statements, a metric that is, at least in part, based on a frequency with which the predefined answerable statement had been selected by previous users; and (b) (1) if the metric for one of the predefined answerable statements exceeds a threshold, sending, to the user's device, information representing a response associated with said one of the predefined answerable statements, and (2) if part (b)(1) does not apply, sending, to the user's device, information representing at least one of the predefined answerable statements, which predefined answerable statements are selected based on the respective associated metrics. 9. The method of claim 1 wherein “selected based on” in part (b)(2) includes, automatically with the computer accessing frequencies with which a plurality of previous users have presented the predefined answerable statements. | 0.745475 |
8,947,685 | 16 | 19 | 16. The computer program product of claim 15 , wherein the characteristics analyzed in step (a) include a PDF version of the document and presence of specified types of data in the PDF document. | 16. The computer program product of claim 15 , wherein the characteristics analyzed in step (a) include a PDF version of the document and presence of specified types of data in the PDF document. 19. The computer program product of claim 16 , wherein the first interpreter is Adobe PDF Print Engine (APPE) and the second interpreter is a Postscript interpreter, wherein step (a) includes detecting a version of the PDF by checking the document header without analyzing a content of the document; and wherein step (b) includes: (b1) if the version of the PDF document determined in step (a) is one of a pre-defined plurality of PDF versions, selecting only the APPE interpreter to be used to interpret the PDF document without analyzing the content of the document; and (b2) if the version of the PDF document determined in step (a) is not one of the pre-defined plurality of PDF versions, performing steps (b3) to (b5) for each page of the PDF document: (b3) determining whether the page contains transparency data; (b4) if the page contains transparency data, selecting the APPE interpreter to be used to interpret the page; and (b5) if the page contains no transparency data, selecting either the APPE interpreter or the second interpreter to be used to interpret the page. | 0.5 |
8,819,051 | 1 | 3 | 1. One or more non-transitory computer-readable media storing instructions which, when processed by one or more processors, cause: an information service receiving, over one or more communications networks from a client device, one or more alphanumeric keywords, wherein both the one or more alphanumeric keywords and an information service indicator that uniquely identifies the information service are displayed to a user of the client device in visual content on a non-Web page medium; the information service identifying, at a first time, one or more first search-limiting criteria that both correspond to the one or more alphanumeric keywords and provide a first context for the one or more alphanumeric keywords, wherein the one or more first search-limiting criteria are not included in the visual content on the non-Web page medium; the information service providing both the one or more alphanumeric keywords and the one or more first search-limiting criteria to a search engine, wherein the search engine performs a first search using both the one or more alphanumeric keywords and the one or more first search-limiting criteria and generates first search results; the information service receiving the first search results from the search engine; the information service transmitting the first search results over the one or more communications networks to the client device; the information service identifying, at a second time that is different than the first time, one or more second search-limiting criteria that both correspond to the one or more alphanumeric keywords and provide, for the one or more alphanumeric keywords, a second context that is different than the first context; and the information service providing both the one or more alphanumeric keywords and the one or more second search-limiting criteria to the search engine, wherein the search engine performs a second search using both the one or more alphanumeric keywords and the one or more second search-limiting criteria and generates second search results. | 1. One or more non-transitory computer-readable media storing instructions which, when processed by one or more processors, cause: an information service receiving, over one or more communications networks from a client device, one or more alphanumeric keywords, wherein both the one or more alphanumeric keywords and an information service indicator that uniquely identifies the information service are displayed to a user of the client device in visual content on a non-Web page medium; the information service identifying, at a first time, one or more first search-limiting criteria that both correspond to the one or more alphanumeric keywords and provide a first context for the one or more alphanumeric keywords, wherein the one or more first search-limiting criteria are not included in the visual content on the non-Web page medium; the information service providing both the one or more alphanumeric keywords and the one or more first search-limiting criteria to a search engine, wherein the search engine performs a first search using both the one or more alphanumeric keywords and the one or more first search-limiting criteria and generates first search results; the information service receiving the first search results from the search engine; the information service transmitting the first search results over the one or more communications networks to the client device; the information service identifying, at a second time that is different than the first time, one or more second search-limiting criteria that both correspond to the one or more alphanumeric keywords and provide, for the one or more alphanumeric keywords, a second context that is different than the first context; and the information service providing both the one or more alphanumeric keywords and the one or more second search-limiting criteria to the search engine, wherein the search engine performs a second search using both the one or more alphanumeric keywords and the one or more second search-limiting criteria and generates second search results. 3. The one or more non-transitory computer-readable media of claim 1 , wherein the one or more first search-limiting criteria include one or more additional alphanumeric keywords. | 0.878726 |
4,516,260 | 13 | 16 | 13. A talking electronic apparatus as set forth in claim 1, wherein said means responsive to said digital control data and said operator response to said selected audible request is effective to cause said speech synthesizer means to repeat said selected audible request if said operator response is inappropriate. | 13. A talking electronic apparatus as set forth in claim 1, wherein said means responsive to said digital control data and said operator response to said selected audible request is effective to cause said speech synthesizer means to repeat said selected audible request if said operator response is inappropriate. 16. A talking electronic apparatus according to claim 13, wherein said memory means comprises non-volatile digital semiconductor memory means. | 0.733083 |
9,294,817 | 1 | 3 | 1. A method for providing media asset information using media asset categories, the method comprising: storing, with processing circuitry, a plurality of simple categories; determining a frequency of use for each of a plurality of combination categories of at least two simple categories from the plurality of simple categories; identifying, based on the determined frequency of use, a first combination category of the plurality of combination categories that is determined to have a frequency of use that is higher than a second combination category of the plurality of combination categories and; generating a display of the identified first combination category based on the determined frequency of use of the identified first combination category. | 1. A method for providing media asset information using media asset categories, the method comprising: storing, with processing circuitry, a plurality of simple categories; determining a frequency of use for each of a plurality of combination categories of at least two simple categories from the plurality of simple categories; identifying, based on the determined frequency of use, a first combination category of the plurality of combination categories that is determined to have a frequency of use that is higher than a second combination category of the plurality of combination categories and; generating a display of the identified first combination category based on the determined frequency of use of the identified first combination category. 3. The method of claim 1 , wherein the generated display of the first combination category further comprises at least one simple category selected from the plurality of simple categories. | 0.675347 |
7,711,573 | 236 | 237 | 236. The system of claim 203 , wherein the job description further includes a required level of education or a required field of specialization, the system further comprising: means for storing the job description in the resume database; and means for sending a portion of the result set, wherein the result set includes at least one matching resume from the resume database, each said at least one matching resume satisfying the job description. | 236. The system of claim 203 , wherein the job description further includes a required level of education or a required field of specialization, the system further comprising: means for storing the job description in the resume database; and means for sending a portion of the result set, wherein the result set includes at least one matching resume from the resume database, each said at least one matching resume satisfying the job description. 237. The system of claim 236 , wherein each said at least one matching resume satisfies the job description when the parsed resume includes the required level of education, the required field of specialization, or a phrase implying the required level of education or the required field of specialization. | 0.5 |
8,732,204 | 9 | 12 | 9. A non-transitory computer-readable medium storing executable computer program code for generating Frequently Asked Questions (FAQ) data from Community-based Question Answering (CQA) data, the computer program code comprising code for: receiving a plurality of data sources, a data source having data associated with one or more topics, and a topic having one or more themes; generating a thematic hierarchy of the plurality of data sources; classifying a plurality of CQA data into one or more themes based on the thematic hierarchy, where the CQA data containing a plurality of question-answer pairs; selecting a plurality of question-answer pairs from the CQA data based on the classification, the selecting comprising: for each theme of the CQA data, grouping a plurality of CQA data into a plurality of clusters, wherein the CQA data in a cluster share one or more features associated with the theme, and a cluster of CQA data has a centroid representing the theme of the cluster; and generating FAQ data using the selected question-answer pairs of the CQA data. | 9. A non-transitory computer-readable medium storing executable computer program code for generating Frequently Asked Questions (FAQ) data from Community-based Question Answering (CQA) data, the computer program code comprising code for: receiving a plurality of data sources, a data source having data associated with one or more topics, and a topic having one or more themes; generating a thematic hierarchy of the plurality of data sources; classifying a plurality of CQA data into one or more themes based on the thematic hierarchy, where the CQA data containing a plurality of question-answer pairs; selecting a plurality of question-answer pairs from the CQA data based on the classification, the selecting comprising: for each theme of the CQA data, grouping a plurality of CQA data into a plurality of clusters, wherein the CQA data in a cluster share one or more features associated with the theme, and a cluster of CQA data has a centroid representing the theme of the cluster; and generating FAQ data using the selected question-answer pairs of the CQA data. 12. The computer-readable medium of claim 9 , wherein the computer program code for selecting the plurality of question-answer pairs from the CQA data further comprises computer program code for, for each cluster of CQA data: selecting a plurality of representative data from the cluster; measuring quality of the representative data; and generating a representative score for each question-answer pairs of the representative data. | 0.5 |
8,375,293 | 8 | 26 | 8. A method of storing a document, the method comprising: creating an object representation of a document using a set of template pages; storing the document by converting the document's object representation to a storage representation that is stored in a single data storage structure comprising a first section for storing information describing the set of template pages, the first section comprising a sub-section for each template page in the set, each of said sub-sections for storing information about its particular template page, wherein said information comprises a thumbnail used to generate a visual representation of the template page; and parsing the single data storage structure to convert the storage representation to the object representation when the document is opened, each particular template page corresponding to a different object that contains additional objects corresponding to the information for the particular template page. | 8. A method of storing a document, the method comprising: creating an object representation of a document using a set of template pages; storing the document by converting the document's object representation to a storage representation that is stored in a single data storage structure comprising a first section for storing information describing the set of template pages, the first section comprising a sub-section for each template page in the set, each of said sub-sections for storing information about its particular template page, wherein said information comprises a thumbnail used to generate a visual representation of the template page; and parsing the single data storage structure to convert the storage representation to the object representation when the document is opened, each particular template page corresponding to a different object that contains additional objects corresponding to the information for the particular template page. 26. The method of claim 8 , wherein the single data storage structure comprises a second section for storing a set of user-defined content, the method further comprising: receiving a set of modifications to a set of pages of the document that modifies the set of user-defined content; modifying the document's object representation in response to receiving the set of modifications; and saving the document by converting the modified object representation to the document's storage representation and storing the storage representation in the single data storage structure. | 0.5 |
8,875,038 | 54 | 55 | 54. Apparatus for use with a network, the apparatus comprising: an interface; and a processor, which is configured to cause a web browser to display (a) a first content item in a first content area displayed on a webpage, and (b) a second content item in a second content area displayed separately from the first content area on the webpage; cause the web browser to display, in the second content area, at least a portion of a set of one or more third content items related to the first content item, if the processor receives, from the web browser via the interface over the network, an indication of dragging, by a user of the web browser, of a first element displayed in the first content area, and dropping, by the user, of the first element into the second content area; and cause the web browser to display, in the first content area, at least a portion of a set of one or more fourth content items related to the second content item, if the processor receives, from the web browser via the interface over the network, an indication of dragging, by the user, of a second element displayed in the second content area, and dropping, by the user, of the second element into the first content area, wherein the first element is selected from the group consisting of: the first content item, and a first graphical element displayed in association with the first content item, and wherein the second element is selected from the group consisting of: the second content item, and a second graphical element displayed in association with the second content item. | 54. Apparatus for use with a network, the apparatus comprising: an interface; and a processor, which is configured to cause a web browser to display (a) a first content item in a first content area displayed on a webpage, and (b) a second content item in a second content area displayed separately from the first content area on the webpage; cause the web browser to display, in the second content area, at least a portion of a set of one or more third content items related to the first content item, if the processor receives, from the web browser via the interface over the network, an indication of dragging, by a user of the web browser, of a first element displayed in the first content area, and dropping, by the user, of the first element into the second content area; and cause the web browser to display, in the first content area, at least a portion of a set of one or more fourth content items related to the second content item, if the processor receives, from the web browser via the interface over the network, an indication of dragging, by the user, of a second element displayed in the second content area, and dropping, by the user, of the second element into the first content area, wherein the first element is selected from the group consisting of: the first content item, and a first graphical element displayed in association with the first content item, and wherein the second element is selected from the group consisting of: the second content item, and a second graphical element displayed in association with the second content item. 55. The apparatus according to claim 54 , wherein the first content item is of a first content category, and wherein the second content item is of a second content category different from the first content category. | 0.793269 |
8,099,283 | 16 | 18 | 16. The medium of claim 15 , wherein the determining step further includes: accessing a first XML document configured for storing user-specific application state and attribute information; and determining whether the first XML document for the corresponding user specifies the presence of the user-specific XML document for the requested prescribed voice application operation. | 16. The medium of claim 15 , wherein the determining step further includes: accessing a first XML document configured for storing user-specific application state and attribute information; and determining whether the first XML document for the corresponding user specifies the presence of the user-specific XML document for the requested prescribed voice application operation. 18. The medium of claim 16 , wherein the step of accessing an external database includes accessing a Lightweight Directory Access Protocol (LDAP) database for retrieval of the user-specific XML document. | 0.5 |
8,280,101 | 1 | 5 | 1. An apparatus to authenticate an identification document, the identification document comprising: an image including steganographically encoded first machine-readable information including a first plural-bit message, a background including steganographically encoded second machine-readable information including a second plural-bit message, and semantic information including authentication information carried on or in the identification document, the apparatus comprising: a first reader configured to determine the first plural-bit message based on the first machine-readable information and determine the second plural-bit message based on the second machine-readable information; a second reader configured to determine authentication information from the semantic information carried on or in the identification document; and an electronic processor configured to: decrypt the first plural-bit message or the authentication information; and determine whether the identification document is authentic based at least in part on the first plural-bit message, the second plural-bit message, and the authentication information, wherein the second plural-bit message comprises information from the first plural-bit message and the authentication information. | 1. An apparatus to authenticate an identification document, the identification document comprising: an image including steganographically encoded first machine-readable information including a first plural-bit message, a background including steganographically encoded second machine-readable information including a second plural-bit message, and semantic information including authentication information carried on or in the identification document, the apparatus comprising: a first reader configured to determine the first plural-bit message based on the first machine-readable information and determine the second plural-bit message based on the second machine-readable information; a second reader configured to determine authentication information from the semantic information carried on or in the identification document; and an electronic processor configured to: decrypt the first plural-bit message or the authentication information; and determine whether the identification document is authentic based at least in part on the first plural-bit message, the second plural-bit message, and the authentication information, wherein the second plural-bit message comprises information from the first plural-bit message and the authentication information. 5. The apparatus of claim 1 , wherein the first machine-readable information comprises a digital watermark. | 0.790196 |
8,825,614 | 6 | 16 | 6. A method of performing XBRL taxonomy migration comprising: receiving an XBRL document having XBRL tags of a first version of an XBRL taxonomy; migrating, by a processor, the received XBRL document to a second version of the XBRL taxonomy by gathering metadata that corresponds to the first version of the XBRL taxonomy and replacing XBRL concepts of the first version of the XBRL taxonomy in the received XBRL document with XBRL concepts of the second version of the XBRL taxonomy; detecting dependencies in calculations in the received XBRL document using the XBRL concepts in the received XBRL document; when dependencies are detected, determining whether a balance type of the first version XBRL taxonomy concept matches a balance type of a related second version XBRL taxonomy concept; when the balance type of the first version XBRL taxonomy concept matches the balance type of the related second version XBRL taxonomy concept, replacing the first version XBRL taxonomy concept in the received XBRL document with the related second version XBRL taxonomy concept of the matched balance type; and when the balance type of the first version XBRL taxonomy concept does not match the balance type of the related second version XBRL taxonomy concept, adjusting a weight of an arc using the related second version XBRL taxonomy concept in a calculation assertion when replacing the first version XBRL taxonomy concept in the received XBRL document with the related second version XBRL taxonomy concept, wherein after completion of the method of performing XBRL taxonomy migration, the migrated XBRL document no longer uses the first version of the XBRL taxonomy. | 6. A method of performing XBRL taxonomy migration comprising: receiving an XBRL document having XBRL tags of a first version of an XBRL taxonomy; migrating, by a processor, the received XBRL document to a second version of the XBRL taxonomy by gathering metadata that corresponds to the first version of the XBRL taxonomy and replacing XBRL concepts of the first version of the XBRL taxonomy in the received XBRL document with XBRL concepts of the second version of the XBRL taxonomy; detecting dependencies in calculations in the received XBRL document using the XBRL concepts in the received XBRL document; when dependencies are detected, determining whether a balance type of the first version XBRL taxonomy concept matches a balance type of a related second version XBRL taxonomy concept; when the balance type of the first version XBRL taxonomy concept matches the balance type of the related second version XBRL taxonomy concept, replacing the first version XBRL taxonomy concept in the received XBRL document with the related second version XBRL taxonomy concept of the matched balance type; and when the balance type of the first version XBRL taxonomy concept does not match the balance type of the related second version XBRL taxonomy concept, adjusting a weight of an arc using the related second version XBRL taxonomy concept in a calculation assertion when replacing the first version XBRL taxonomy concept in the received XBRL document with the related second version XBRL taxonomy concept, wherein after completion of the method of performing XBRL taxonomy migration, the migrated XBRL document no longer uses the first version of the XBRL taxonomy. 16. The method of claim 6 , wherein the migrating comprises a many-to-one mapping in which a plurality of deprecated XBRL concepts of the first version of the XBRL taxonomy are mapped to a single XBRL concept of the second version of the XBRL taxonomy, and the single XBRL concept of the second version is segmented by an axis and a member at fact usages of the single XBRL concept of the second version. | 0.557018 |
5,440,481 | 1 | 2 | 1. A system for full-text database searching, for identification of often repeated phrases which by virtue of their repeated occurrence, frequency of occurrence above a user-set threshold, or user input constitute phrases having a high user-interest designated as pervasive them areas (PTAs), said phrases consisting of one to n words (n*words), where n is an integer, in one or more documents defined as the database, relationships defined as connectivity among said PTAs, and phrases in close physical proximity to and which are supportive of said PTAs, comprising: means for introducing document information content into a full-text database in digital form; means for digitally storing said database; means for processing said digitally stored database; means operatively associated with said processing means and said storing means for identifying pervasive theme areas (PTAs) defined as often-repeating word phrases consisting of one or more adjacent words such that said phrases are one word phrases, adjacent 2 word phrases, adjacent 3 word phrases . . . and adjacent n* word phrases, and for entering said phrases in said storing means; means for identifying phrases in said database related to said PTAs, said phrases being defined as m words, where m=1,2,3, . . . n and where each word phrase for m=2,3, . . . n is composed of adjacent words, said word phrase for m=1 being a single word phrase, for m=2 a double word phrase, for m=3 a triple word phrase . . . and for n=m an nth word phrase, by applying a user specified range of interest R expressed as a number of single words appearing both before and after said PTAs, and for storing said identified phrases in said storing means; means for counting for each PTA the extracted phrases within said range of said PTA stored in said storage means, sorting all phases found for each PTA by frequency of occurrence, listing each PTA and its related sorted list of extracted phrases, and storing said counts and said lists of PTA's and their related sorted list of extracted phrases in said storing means; means for quantifying the strength of relationship between extracted phrases and each pervasive theme area (PTA) applying user-predefined numerical indices and figures of merit, and providing the results of said quantifying means to said storing means; means for obtaining the results of said quantification from said quantifying means and said storing means and presenting said results to said user for user-selection of phrases having a relationship to each PTA predicated on the relationship strengths obtained by said quantifying means; means for identifying PTAs which are closely related, said means employing user-input figure of merit threshold values above a user-predetermined number for selecting phrases of high-user interest, said means storing identified closely related PTAs in said storing means; means for identifying phrases in common among PTA and storing those identified in said storing means; means for identifying and grouping related PTA based upon the number of phrases in common among the PTA, said number being above a user-input predetermined threshold, each group having at least one PTA having extracted phrases in common with one or more other PTA in said group, said groupings of PTA's stored in said storing means; and means for displaying relationships among related PTA and between PTA and related phrases said display means connected to said processing means. | 1. A system for full-text database searching, for identification of often repeated phrases which by virtue of their repeated occurrence, frequency of occurrence above a user-set threshold, or user input constitute phrases having a high user-interest designated as pervasive them areas (PTAs), said phrases consisting of one to n words (n*words), where n is an integer, in one or more documents defined as the database, relationships defined as connectivity among said PTAs, and phrases in close physical proximity to and which are supportive of said PTAs, comprising: means for introducing document information content into a full-text database in digital form; means for digitally storing said database; means for processing said digitally stored database; means operatively associated with said processing means and said storing means for identifying pervasive theme areas (PTAs) defined as often-repeating word phrases consisting of one or more adjacent words such that said phrases are one word phrases, adjacent 2 word phrases, adjacent 3 word phrases . . . and adjacent n* word phrases, and for entering said phrases in said storing means; means for identifying phrases in said database related to said PTAs, said phrases being defined as m words, where m=1,2,3, . . . n and where each word phrase for m=2,3, . . . n is composed of adjacent words, said word phrase for m=1 being a single word phrase, for m=2 a double word phrase, for m=3 a triple word phrase . . . and for n=m an nth word phrase, by applying a user specified range of interest R expressed as a number of single words appearing both before and after said PTAs, and for storing said identified phrases in said storing means; means for counting for each PTA the extracted phrases within said range of said PTA stored in said storage means, sorting all phases found for each PTA by frequency of occurrence, listing each PTA and its related sorted list of extracted phrases, and storing said counts and said lists of PTA's and their related sorted list of extracted phrases in said storing means; means for quantifying the strength of relationship between extracted phrases and each pervasive theme area (PTA) applying user-predefined numerical indices and figures of merit, and providing the results of said quantifying means to said storing means; means for obtaining the results of said quantification from said quantifying means and said storing means and presenting said results to said user for user-selection of phrases having a relationship to each PTA predicated on the relationship strengths obtained by said quantifying means; means for identifying PTAs which are closely related, said means employing user-input figure of merit threshold values above a user-predetermined number for selecting phrases of high-user interest, said means storing identified closely related PTAs in said storing means; means for identifying phrases in common among PTA and storing those identified in said storing means; means for identifying and grouping related PTA based upon the number of phrases in common among the PTA, said number being above a user-input predetermined threshold, each group having at least one PTA having extracted phrases in common with one or more other PTA in said group, said groupings of PTA's stored in said storing means; and means for displaying relationships among related PTA and between PTA and related phrases said display means connected to said processing means. 2. The system of claim 1 wherein said means for identifying pervasive theme areas in said database, comprises: a means for counting frequency of occurrence of said n* word phrases; a means for creating a list of all n* word phrases and the frequency of occurrence for each of said n* word phrases; a means for sorting said list of n* word phrases by frequency of occurrence: a means for defining pervasive theme areas from said list of sorted phrases; and a means for selecting the number of said n* word phrases to be used as pervasive theme areas. | 0.5 |
9,836,994 | 3 | 4 | 3. The system of claim 2 , wherein the simulated welding console comprises a second display device. | 3. The system of claim 2 , wherein the simulated welding console comprises a second display device. 4. The system of claim 3 , wherein the processor based subsystem further executes instructions to display at least one of the simulated welding environment, the simulated weld puddle, the simulated weld bead, or the simulated welding surface on the second display device. | 0.5 |
8,549,030 | 1 | 3 | 1. A computer-implemented method of providing a query service, comprising: enabling a user to select template identifiers from a plurality of available template identifiers via a user interface for inclusion in a query, the respective template identifiers indicative of conformance with a standard related to content of a document; when the user selects two or more of the available template identifiers for the query, generating, using a processor, an expression including the two or more template identifiers to define a search of a data collection system; selecting a query module assigned to a first one of a plurality of standards to handle the query based on at least one of the two or more template identifiers being associated with the first standard; and performing, using a processor, the search of the data collection according to the expression, wherein the search is to identify documents having metadata associated with received template identifiers. | 1. A computer-implemented method of providing a query service, comprising: enabling a user to select template identifiers from a plurality of available template identifiers via a user interface for inclusion in a query, the respective template identifiers indicative of conformance with a standard related to content of a document; when the user selects two or more of the available template identifiers for the query, generating, using a processor, an expression including the two or more template identifiers to define a search of a data collection system; selecting a query module assigned to a first one of a plurality of standards to handle the query based on at least one of the two or more template identifiers being associated with the first standard; and performing, using a processor, the search of the data collection according to the expression, wherein the search is to identify documents having metadata associated with received template identifiers. 3. A method as defined in claim 1 , further comprising selecting one of a plurality of parameter mechanisms to generate the expression based on logical instructions received in connection with the two or more template identifiers. | 0.604811 |
8,423,576 | 1 | 11 | 1. A method to process data, comprising: parsing input from a requestor, the input comprising at least one of a query or a command, expressed in a natural human language, that is parsed using action extractors and named entity extractors into a structured stream query language query comprising an indication of at least one data stream or set of data streams and at least one action to be performed on the, at least one, data stream or set of data streams; mapping the structured stream query language query into a graph of processing elements that are selected and interconnected so as to execute the structured stream query language query comprising at least one action and at least one named entity; instantiating the graph of processing elements and connecting and initializing the instantiated graph of processing elements with an identified, at least one, data stream or set of data streams to receive data from the identified at least one data stream or set of data streams; and outputting a result of the structured stream query language query to the requestor. | 1. A method to process data, comprising: parsing input from a requestor, the input comprising at least one of a query or a command, expressed in a natural human language, that is parsed using action extractors and named entity extractors into a structured stream query language query comprising an indication of at least one data stream or set of data streams and at least one action to be performed on the, at least one, data stream or set of data streams; mapping the structured stream query language query into a graph of processing elements that are selected and interconnected so as to execute the structured stream query language query comprising at least one action and at least one named entity; instantiating the graph of processing elements and connecting and initializing the instantiated graph of processing elements with an identified, at least one, data stream or set of data streams to receive data from the identified at least one data stream or set of data streams; and outputting a result of the structured stream query language query to the requestor. 11. The method of claim 1 , where a particular stream is one of data output from a sensor, a computed stream, or a re-streaming of historical data, and where an output of the instantiated graph of processing elements is a sink that comprises a storage medium and a file system. | 0.769551 |
5,530,645 | 15 | 16 | 15. A composite dictionary data compression process of claim 14 wherein the step of updating the composite dictionary in response to detecting the literal data string further comprises: detecting whether the adaptive dictionary is full; wherein if the adaptive dictionary is not full, then storing the literal data string in the adaptive dictionary. | 15. A composite dictionary data compression process of claim 14 wherein the step of updating the composite dictionary in response to detecting the literal data string further comprises: detecting whether the adaptive dictionary is full; wherein if the adaptive dictionary is not full, then storing the literal data string in the adaptive dictionary. 16. A composite dictionary data compression of claim 15 wherein if the adaptive dictionary is full, then replacing an old dictionary entry from the adaptive dictionary with the literal data string. | 0.5 |
7,689,426 | 8 | 9 | 8. An interactive voice response (IVR) system for assigning a telephone call to an automated agent of a call center, said automated agent executing on an agent terminal employed by a corresponding human agent, said IVR system comprising: a memory; and at least one processor, coupled to the memory, operative to: receive a telephone call from a caller; assign said telephone call to an IVR queue monitor an availability status of said automated agent, wherein: i. said automated agent is executing on an agent terminal, and ii. said agent terminal is employed by a corresponding human agent; and route said telephone call from said IVR queue to an available automated agent based on CPU utilization of said agent terminal, on which said automated agent is running. | 8. An interactive voice response (IVR) system for assigning a telephone call to an automated agent of a call center, said automated agent executing on an agent terminal employed by a corresponding human agent, said IVR system comprising: a memory; and at least one processor, coupled to the memory, operative to: receive a telephone call from a caller; assign said telephone call to an IVR queue monitor an availability status of said automated agent, wherein: i. said automated agent is executing on an agent terminal, and ii. said agent terminal is employed by a corresponding human agent; and route said telephone call from said IVR queue to an available automated agent based on CPU utilization of said agent terminal, on which said automated agent is running. 9. The IVR system of claim 8 , wherein said availability status is based on a number of predicted available CPU cycles. | 0.608553 |
9,558,276 | 1 | 9 | 1. A computer-implemented method, comprising: determining, by the agent device, availability as an agent for topic data stored in a system, wherein agents are active or inactive and are relevant or irrelevant to the topic data, and wherein agents are relevant when a topic included in one or more profiles of one or more relevant agents matches the topic data; transmitting availability as an active relevant agent for the topic data, wherein one or more real-time interaction options are associated with active relevant agents, wherein status data is generated and remotely stored according to the availability of active relevant agents associated with real-time interaction options, wherein the status data is transmitted by an agent search server to a search engine server operating remotely from the agent search server and in communication with the agent search server over a network, wherein an agent search request is generated using the topic data and includes the topic data, and wherein the agent search request is used to determine relevant agents associated with the topic data; detecting data corresponding to a selection of an interactive element associated with search results generated by the search engine server, wherein the search results are generated in response to a search request including the topic data, wherein the search request does not include a request for an agent, wherein the search request is separate from the agent search request, wherein interactive elements are associated with the search results according to remotely received status data, wherein the interactive elements are separate from the search results, wherein an interactive element is displayed concurrently with a search result, and wherein the selection of an interactive element facilitates a real-time interaction option among two or more devices; and participating in a real-time interaction option as an active relevant agent associated with the topic data. | 1. A computer-implemented method, comprising: determining, by the agent device, availability as an agent for topic data stored in a system, wherein agents are active or inactive and are relevant or irrelevant to the topic data, and wherein agents are relevant when a topic included in one or more profiles of one or more relevant agents matches the topic data; transmitting availability as an active relevant agent for the topic data, wherein one or more real-time interaction options are associated with active relevant agents, wherein status data is generated and remotely stored according to the availability of active relevant agents associated with real-time interaction options, wherein the status data is transmitted by an agent search server to a search engine server operating remotely from the agent search server and in communication with the agent search server over a network, wherein an agent search request is generated using the topic data and includes the topic data, and wherein the agent search request is used to determine relevant agents associated with the topic data; detecting data corresponding to a selection of an interactive element associated with search results generated by the search engine server, wherein the search results are generated in response to a search request including the topic data, wherein the search request does not include a request for an agent, wherein the search request is separate from the agent search request, wherein interactive elements are associated with the search results according to remotely received status data, wherein the interactive elements are separate from the search results, wherein an interactive element is displayed concurrently with a search result, and wherein the selection of an interactive element facilitates a real-time interaction option among two or more devices; and participating in a real-time interaction option as an active relevant agent associated with the topic data. 9. The method of claim 1 , wherein status data is generated in response to the search request or one or more keywords. | 0.768627 |
8,024,652 | 8 | 14 | 8. An article comprising a computer-readable storage medium containing instructions that if executed enable a system to: create a note with a first application program; generate a context reference for a target document for a second application program, the context reference comprising context information useable to recreate a user context for the document; and insert the context reference within the note. | 8. An article comprising a computer-readable storage medium containing instructions that if executed enable a system to: create a note with a first application program; generate a context reference for a target document for a second application program, the context reference comprising context information useable to recreate a user context for the document; and insert the context reference within the note. 14. The article of claim 8 , further comprising instructions that if executed enable the system to generate a context reference view to display the context reference while maintaining a note view for the note. | 0.5 |
9,619,571 | 13 | 15 | 13. A non-transitory computer readable medium having stored thereon computer executable instructions comprising: receiving, by an entity extraction computer, user input of search query parameters; extracting, by the entity extraction computer, a plurality of entities from the search query parameters by comparing each entity in the plurality of extracted entities with an entity co-occurrence in-memory database that includes a confidence score indicative of a degree of certainty of co-occurrence of an extracted entity with one or more related entities in an electronic data corpus, wherein the entity co-occurrence database further comprises one or more entries for the plurality of entities, and wherein each entry of the one or more entries for a given entity of the plurality of entities contains its semantically related entities, and wherein the co-occurrence is an instance of an entity of plurality of entities identified by an entry of the one or more entries in the entity co-occurrence database, and wherein the semantically-related entity corresponds to a model indicating distinct entities, assigning, by the entity extraction computer, an index identifier (index ID) to each of the entities in the plurality of extracted entities; disambiguating, by the entity extraction computer, each of the entities in the plurality of extracted entities from one another based on relatedness of index IDs; identifying, by the entity extraction computer, a subset of entities associated with each of the entities in the plurality of extracted entities based on relatedness of index IDs; linking, by the entity extraction computer, each entity to the associated subset of entities based at least on confidence scores; saving, by the entity extraction computer, the index ID for each of the plurality of extracted entities in the electronic data corpus, the electronic data corpus being indexed by an index ID corresponding to each of the one or more related entities; searching, by a search server computer, the entity indexed electronic data corpus to locate the plurality of extracted entities and identify index IDs of data records in which at least two of the plurality of extracted entities co-occur; and building, by the search server computer, a search result list having data records corresponding to the identified index IDs. | 13. A non-transitory computer readable medium having stored thereon computer executable instructions comprising: receiving, by an entity extraction computer, user input of search query parameters; extracting, by the entity extraction computer, a plurality of entities from the search query parameters by comparing each entity in the plurality of extracted entities with an entity co-occurrence in-memory database that includes a confidence score indicative of a degree of certainty of co-occurrence of an extracted entity with one or more related entities in an electronic data corpus, wherein the entity co-occurrence database further comprises one or more entries for the plurality of entities, and wherein each entry of the one or more entries for a given entity of the plurality of entities contains its semantically related entities, and wherein the co-occurrence is an instance of an entity of plurality of entities identified by an entry of the one or more entries in the entity co-occurrence database, and wherein the semantically-related entity corresponds to a model indicating distinct entities, assigning, by the entity extraction computer, an index identifier (index ID) to each of the entities in the plurality of extracted entities; disambiguating, by the entity extraction computer, each of the entities in the plurality of extracted entities from one another based on relatedness of index IDs; identifying, by the entity extraction computer, a subset of entities associated with each of the entities in the plurality of extracted entities based on relatedness of index IDs; linking, by the entity extraction computer, each entity to the associated subset of entities based at least on confidence scores; saving, by the entity extraction computer, the index ID for each of the plurality of extracted entities in the electronic data corpus, the electronic data corpus being indexed by an index ID corresponding to each of the one or more related entities; searching, by a search server computer, the entity indexed electronic data corpus to locate the plurality of extracted entities and identify index IDs of data records in which at least two of the plurality of extracted entities co-occur; and building, by the search server computer, a search result list having data records corresponding to the identified index IDs. 15. The computer readable medium of claim 13 wherein the plurality of extracted entities is ranked based on the confidence score. | 0.775261 |
10,146,742 | 1 | 2 | 1. A computer-implemented method comprising: in a computational device: configuring a document creation application with an add-in comprising instructions for linking said document creation application with a business management application that runs on a platform of a structured database management system and stores datum in said structured database management system; configuring said add-in with one or more tools that, when executed, accesses utilities of said business management application via said document creation application without leaving an interface of said document creation application, wherein said one or more tools at least comprises data linking tools comprising an executable data-pull utility that pulls datum from the structured database management system and inserts said datum into a structured field in a document in said document creation application; and configuring said add-in with a social network integration tool for integrating one or more social media interfaces of one or more social networks into an interface of the document creation application, wherein said integrated one or more social media interfaces comprises media feeds including a first media feed comprising information relating to a selected portion of text in a document open in said document creation application. | 1. A computer-implemented method comprising: in a computational device: configuring a document creation application with an add-in comprising instructions for linking said document creation application with a business management application that runs on a platform of a structured database management system and stores datum in said structured database management system; configuring said add-in with one or more tools that, when executed, accesses utilities of said business management application via said document creation application without leaving an interface of said document creation application, wherein said one or more tools at least comprises data linking tools comprising an executable data-pull utility that pulls datum from the structured database management system and inserts said datum into a structured field in a document in said document creation application; and configuring said add-in with a social network integration tool for integrating one or more social media interfaces of one or more social networks into an interface of the document creation application, wherein said integrated one or more social media interfaces comprises media feeds including a first media feed comprising information relating to a selected portion of text in a document open in said document creation application. 2. The computer-implemented method of claim 1 , wherein said document creation application comprises a word processing application, and wherein said add-in comprises a plugin for said word processing application. | 0.747017 |
6,151,021 | 15 | 20 | 15. A computer readable storage medium having program code stored thereon, wherein the program code is arranged so that, when the program code is executed by a computer, a note is displayed, the note has contents and a location, and at least a representation of the contents of the note and the location of the note are added to an index, wherein the location of the note added to the index is with respect to an object within a window. | 15. A computer readable storage medium having program code stored thereon, wherein the program code is arranged so that, when the program code is executed by a computer, a note is displayed, the note has contents and a location, and at least a representation of the contents of the note and the location of the note are added to an index, wherein the location of the note added to the index is with respect to an object within a window. 20. The computer readable storage medium of claim 15 wherein the object is a page. | 0.878698 |
9,779,131 | 6 | 9 | 6. The method of claim 1 , wherein the input data comprises a plurality of data streams. | 6. The method of claim 1 , wherein the input data comprises a plurality of data streams. 9. The method of claim 6 , comprising performing semantic aggregation including collecting relevant data events resulting from preceding operations. | 0.602151 |
7,636,945 | 4 | 5 | 4. The method of claim 1 , wherein the language description data correspond to language definition rules and check rules, wherein the language definition rules include descriptions of constructs of the target script language and relationships between the constructs. | 4. The method of claim 1 , wherein the language description data correspond to language definition rules and check rules, wherein the language definition rules include descriptions of constructs of the target script language and relationships between the constructs. 5. The method of claim 4 , wherein the lexical analysis includes one or more pattern matches based on the language definition rules. | 0.547945 |
9,990,916 | 7 | 10 | 7. A computer-implemented method comprising: receiving an audio input, the audio input including speech; identifying a regional noun in the speech; generating a phonetic transcription of the regional noun using the audio input; determining a user accent classification based on a context of the audio input; executing a search of a phonetic inventory stored in a database, using the generated phonetic transcription and the user accent classification as search parameters, wherein the phonetic inventory stores multiple phonetic transcriptions for each of a plurality of regional nouns, including the regional noun; returning the stored phonetic transcription of the regional noun as a search result of executing the search of the phonetic inventory stored in the database; translating the regional noun into textual data using the stored phonetic transcription; and outputting the textual data. | 7. A computer-implemented method comprising: receiving an audio input, the audio input including speech; identifying a regional noun in the speech; generating a phonetic transcription of the regional noun using the audio input; determining a user accent classification based on a context of the audio input; executing a search of a phonetic inventory stored in a database, using the generated phonetic transcription and the user accent classification as search parameters, wherein the phonetic inventory stores multiple phonetic transcriptions for each of a plurality of regional nouns, including the regional noun; returning the stored phonetic transcription of the regional noun as a search result of executing the search of the phonetic inventory stored in the database; translating the regional noun into textual data using the stored phonetic transcription; and outputting the textual data. 10. The computer-implemented method of claim 7 , wherein the textual data includes orthographic text. | 0.829392 |
8,442,813 | 6 | 7 | 6. The method of claim 1 , wherein determining, for the plurality of characters generated from the image of the document, language-conditional character probabilities based on a set of language models and an ordering of characters further comprises: determining a language-conditional character probability for a character based on a specified number of characters which precede the character in the order. | 6. The method of claim 1 , wherein determining, for the plurality of characters generated from the image of the document, language-conditional character probabilities based on a set of language models and an ordering of characters further comprises: determining a language-conditional character probability for a character based on a specified number of characters which precede the character in the order. 7. The method of claim 6 , wherein the language-conditional character probability is determined based on a conditional probability defined by the given language model, the conditional probability representing a likelihood of observing the character given the specified number of characters, and their order, which precede the character in the writing system represented by the given language model and wherein a high likelihood of observing the character indicates that the character concords with the language model. | 0.5 |
9,747,499 | 3 | 4 | 3. The computer-implemented method of claim 1 , wherein in a. the first plurality of images is acquired by moving the camera to a plurality of fixed positions along a predetermined path, wherein each image of the first plurality of images corresponds to a fixed position of the plurality of fixed positions, where the image was acquired. | 3. The computer-implemented method of claim 1 , wherein in a. the first plurality of images is acquired by moving the camera to a plurality of fixed positions along a predetermined path, wherein each image of the first plurality of images corresponds to a fixed position of the plurality of fixed positions, where the image was acquired. 4. The computer-implemented method of claim 3 , wherein a. further comprises, for each image in the first plurality of images of the surface, computing feature points and a text response map. | 0.5 |
9,678,619 | 15 | 17 | 15. At least one hardware computer-readable storage medium comprising: hardware memory that comprises instructions that, based on execution by a computing device, configure the computing device to perform actions comprising: generating, by the computing device, a graph representing switches between various windows, the graph comprising nodes, edges, and weights, where each node in the graph represents one of the various windows, and where each edge of the graph represents a switch between two of the various windows, and where the each edge is weighted based on a number of switches between the two of the various windows; discarding directionality of the edges of the generated graph; eliminating the edges of the generated graph that are weighted less than a particular threshold; grouping, based on the generated graph subsequent to the discarding and the eliminating, some of the various windows. | 15. At least one hardware computer-readable storage medium comprising: hardware memory that comprises instructions that, based on execution by a computing device, configure the computing device to perform actions comprising: generating, by the computing device, a graph representing switches between various windows, the graph comprising nodes, edges, and weights, where each node in the graph represents one of the various windows, and where each edge of the graph represents a switch between two of the various windows, and where the each edge is weighted based on a number of switches between the two of the various windows; discarding directionality of the edges of the generated graph; eliminating the edges of the generated graph that are weighted less than a particular threshold; grouping, based on the generated graph subsequent to the discarding and the eliminating, some of the various windows. 17. The at least one hardware computer-readable storage medium of claim 15 where the grouping results in a group that contains the some of the various windows. | 0.596447 |
6,108,675 | 7 | 8 | 7. The communications network of claim 6 wherein said receiving display station further includes: means for calculating a distance by which the width of a screen page exceeds the width of the window within which the page is to be displayed, and means for setting said sequence of horizontal sampling positions at increments of said distance. | 7. The communications network of claim 6 wherein said receiving display station further includes: means for calculating a distance by which the width of a screen page exceeds the width of the window within which the page is to be displayed, and means for setting said sequence of horizontal sampling positions at increments of said distance. 8. The communications network of claim 7 wherein: said means for sampling said information density sequentially samples the information along a plurality of horizontal lines, and said means for comparing compares each sequential sample on each of said plurality of lines to said selected density level, and further including means for calculating a mean horizontal position at which said selected density level is attained in said plurality of lines, said mean horizontal position being said reference margin. | 0.5 |
8,681,098 | 6 | 9 | 6. The system of claim 1 , wherein the input data comprises a plurality of data streams. | 6. The system of claim 1 , wherein the input data comprises a plurality of data streams. 9. The system of claim 6 , wherein the data funnel performs semantic aggregation including collecting relevant events resulting from preceding operations of the data funnel. | 0.593897 |
9,189,501 | 4 | 10 | 4. The system platform of claim 3 , wherein the host device and the client devices connected by the network are configured to perform the steps of: receiving, at the host device, a request for access to the host device, the request received from the client devices; authenticating the identity of the client devices; granting the client devices access to the host device, for the client devices that are authenticated; initiating, in the client devices, a client request for registration of an owned event; submitting the owned event to the host device; and storing the owned event at the host device in a host non-transitory computer-readable storage medium. | 4. The system platform of claim 3 , wherein the host device and the client devices connected by the network are configured to perform the steps of: receiving, at the host device, a request for access to the host device, the request received from the client devices; authenticating the identity of the client devices; granting the client devices access to the host device, for the client devices that are authenticated; initiating, in the client devices, a client request for registration of an owned event; submitting the owned event to the host device; and storing the owned event at the host device in a host non-transitory computer-readable storage medium. 10. The system platform of claim 4 , wherein the host device and the client devices connected by the network are configured to perform the steps of: identifying the owned event using the UR-URL Identifier Method; storing the owned event in the database; and labeling a representation of the owned event in the database with at least one label of a set of labels according to an RDF Array to create a labeled owned event, the RDF Array comprising a resource description framework that unique identifies a description of the owned event from any other description of any other event, wherein labeling the representation of the owned event associates the representation of the owned event with at least one of the labels; wherein labeling the owned event comprises associating terms in the RDF Array with Unitary Ontology terms, and wherein the labeled owned event is submitted to and stored at the host device. | 0.5 |
8,327,274 | 1 | 6 | 1. A method of customizing a model entity presentation based on a presentation policy comprising: creating a plurality of first models according to a second model including elements with information defining a structure and hierarchical arrangement of elements for the first models; associating elements of the second model with a presentation policy model including presentation policies, wherein the presentation policies indicate display characteristics controlling a visual appearance for presented elements; applying the presentation policies to the elements of the first models to generate a corresponding presentation model for each of the first models based on the association between the elements of the second model and the presentation policy model, wherein the corresponding presentation models provide different customized display characteristics for the first models and corresponding model entities created from those first models, and wherein each presentation model associates each of one or more individual elements of a corresponding first model with one or more corresponding display characteristics controlling a visual appearance of a presented element; creating a model entity according to a first model, wherein the model entity includes actual data pertaining to an entity and the first model includes elements with information defining a structure and hierarchical arrangement of elements for the model entity; reading model content from said first model and reading presentation data from said corresponding presentation model; and applying the read model content and presentation data to said model entity to present elements of the model entity including the actual data with a visual appearance in accordance with the associated display characteristics of corresponding individual elements of the first model, wherein at least two elements of the model entity are presented with different display characteristics within a presentation. | 1. A method of customizing a model entity presentation based on a presentation policy comprising: creating a plurality of first models according to a second model including elements with information defining a structure and hierarchical arrangement of elements for the first models; associating elements of the second model with a presentation policy model including presentation policies, wherein the presentation policies indicate display characteristics controlling a visual appearance for presented elements; applying the presentation policies to the elements of the first models to generate a corresponding presentation model for each of the first models based on the association between the elements of the second model and the presentation policy model, wherein the corresponding presentation models provide different customized display characteristics for the first models and corresponding model entities created from those first models, and wherein each presentation model associates each of one or more individual elements of a corresponding first model with one or more corresponding display characteristics controlling a visual appearance of a presented element; creating a model entity according to a first model, wherein the model entity includes actual data pertaining to an entity and the first model includes elements with information defining a structure and hierarchical arrangement of elements for the model entity; reading model content from said first model and reading presentation data from said corresponding presentation model; and applying the read model content and presentation data to said model entity to present elements of the model entity including the actual data with a visual appearance in accordance with the associated display characteristics of corresponding individual elements of the first model, wherein at least two elements of the model entity are presented with different display characteristics within a presentation. 6. The method of claim 1 , wherein a presentation policy includes a model property expression and presentation form thereof indicating said display characteristics, said presentation form including at least one of a node icon, word background, word format, size, color, and the color, thickness and form of lines that connect nodes. | 0.5 |
7,650,286 | 85 | 89 | 85. A computer program product, to be used on a computer, for identifying a matching resume for a job description, comprising: a computer readable medium storing: program code for receiving the job description that includes at least one job requirement, each said at least one job requirement comprising a required skill or experience-related phrase and a required term of experience for the required skill or experience-related phrase; program code for storing the job description; program code for receiving at least one resume; program code for parsing each said at least one resume to: locate at least one skill or experience-related phrase in the resume; determine an experience range for each said at least one skill or experience-related phrase by examining a use of each said at least one skill or experience-related phrase in the resume; and compute a term of experience for each said at least one skill or experience-related phrase based on the experience range, wherein the term of experience for each said at least one skill or experience-related phrase is a summation of the term of experience for each occurrence of the phrase associated with a different experience range, wherein said at least one skill or experience-related phrase includes the required skill or experience-related phrase for at least one of said at least one job requirement, wherein each resume summarizes a candidate's career and qualifications, and wherein each resume conveys personal and business-related characteristics that the candidate believes to be relevant to a prospective employer; program code for storing each said at least one resume; program code for computing, for each said at least one resume, a term of experience for the required skill or experience-related phrase for each said at least one job requirement; and program code for determining whether each said at least one resume is the matching resume that satisfies the job description. | 85. A computer program product, to be used on a computer, for identifying a matching resume for a job description, comprising: a computer readable medium storing: program code for receiving the job description that includes at least one job requirement, each said at least one job requirement comprising a required skill or experience-related phrase and a required term of experience for the required skill or experience-related phrase; program code for storing the job description; program code for receiving at least one resume; program code for parsing each said at least one resume to: locate at least one skill or experience-related phrase in the resume; determine an experience range for each said at least one skill or experience-related phrase by examining a use of each said at least one skill or experience-related phrase in the resume; and compute a term of experience for each said at least one skill or experience-related phrase based on the experience range, wherein the term of experience for each said at least one skill or experience-related phrase is a summation of the term of experience for each occurrence of the phrase associated with a different experience range, wherein said at least one skill or experience-related phrase includes the required skill or experience-related phrase for at least one of said at least one job requirement, wherein each resume summarizes a candidate's career and qualifications, and wherein each resume conveys personal and business-related characteristics that the candidate believes to be relevant to a prospective employer; program code for storing each said at least one resume; program code for computing, for each said at least one resume, a term of experience for the required skill or experience-related phrase for each said at least one job requirement; and program code for determining whether each said at least one resume is the matching resume that satisfies the job description. 89. The computer program product of claim 85 , wherein each said at least one resume includes at least one word, and wherein said at least one skill or experience-related phrase comprises said at least one word. | 0.717914 |
9,165,075 | 15 | 18 | 15. A computer program product comprising a tangible computer usable storage memory or device having a computer readable program embodied in the tangible computer usable storage memory or device, wherein the computer readable program when executed on a computing device is operable such that the computing device can: store information associated with a web service in a first database of a Universal Description, Discovery, and Integration (UDDI) registry, wherein the first database of the UDDI registry comprises data entries from at least one of white pages, yellow pages, and green pages of the UDDI registry that include the information associated with the web service; store a comment from a user concerning the web service in a second database, wherein the second database is linked to the first database; determine a rating associated with the user, wherein the rating is determined by a user score manager (USM) that interacts with the second database and is stored and executed on the UDDI registry; store the rating of the user in the second database; receive a search query from a different user for the information about the web service; retrieve the information, the comment, and the rating associated with the web service; receive feedback from the different user regarding the comment from the user; and determine an updated rating based upon the feedback and the rating. | 15. A computer program product comprising a tangible computer usable storage memory or device having a computer readable program embodied in the tangible computer usable storage memory or device, wherein the computer readable program when executed on a computing device is operable such that the computing device can: store information associated with a web service in a first database of a Universal Description, Discovery, and Integration (UDDI) registry, wherein the first database of the UDDI registry comprises data entries from at least one of white pages, yellow pages, and green pages of the UDDI registry that include the information associated with the web service; store a comment from a user concerning the web service in a second database, wherein the second database is linked to the first database; determine a rating associated with the user, wherein the rating is determined by a user score manager (USM) that interacts with the second database and is stored and executed on the UDDI registry; store the rating of the user in the second database; receive a search query from a different user for the information about the web service; retrieve the information, the comment, and the rating associated with the web service; receive feedback from the different user regarding the comment from the user; and determine an updated rating based upon the feedback and the rating. 18. The computer program product of claim 15 , wherein the computer readable program is operable such that the computing device can retrieve the information, the comment, and the rating based upon the search query. | 0.810284 |
9,037,464 | 9 | 10 | 9. A method for assigning a respective point in a high-dimensional space to each word in a vocabulary of words, the method comprising: obtaining a set of training data, wherein the set of training data comprises sequences of words; training a plurality of classifiers and an embedding function on the set of training data, wherein the embedding function receives an input word and maps the input word to a numeric representation in the high-dimensional space in accordance with a set of embedding function parameters, wherein each of the classifiers corresponds to a respective position surrounding the input word in a sequence of words, and wherein each of the classifiers processes the numeric representation of the input word to generate a respective word score for each word in a pre-determined set of words, wherein each of the respective word scores represents a predicted likelihood that the corresponding word will be found in the corresponding position relative to the input word, and wherein training the embedding function comprises obtaining trained values of the embedding function parameters; processing each word in the vocabulary using the embedding function in accordance with the trained values of the embedding function parameters to generate a respective numerical representation of each word in the vocabulary; and associating each word in the vocabulary with the respective numeric representation of the word in the high-dimensional space. | 9. A method for assigning a respective point in a high-dimensional space to each word in a vocabulary of words, the method comprising: obtaining a set of training data, wherein the set of training data comprises sequences of words; training a plurality of classifiers and an embedding function on the set of training data, wherein the embedding function receives an input word and maps the input word to a numeric representation in the high-dimensional space in accordance with a set of embedding function parameters, wherein each of the classifiers corresponds to a respective position surrounding the input word in a sequence of words, and wherein each of the classifiers processes the numeric representation of the input word to generate a respective word score for each word in a pre-determined set of words, wherein each of the respective word scores represents a predicted likelihood that the corresponding word will be found in the corresponding position relative to the input word, and wherein training the embedding function comprises obtaining trained values of the embedding function parameters; processing each word in the vocabulary using the embedding function in accordance with the trained values of the embedding function parameters to generate a respective numerical representation of each word in the vocabulary; and associating each word in the vocabulary with the respective numeric representation of the word in the high-dimensional space. 10. The method of claim 9 , wherein the numeric representations are continuous representations represented using floating-point numbers. | 0.697778 |
7,805,673 | 37 | 38 | 37. The method of claim 1 , further comprising: utilizing redundant verification to verifying that text content was successfully redacted. | 37. The method of claim 1 , further comprising: utilizing redundant verification to verifying that text content was successfully redacted. 38. The method of claim 37 , wherein the redundant verification comprises performing optical character recognition (OCR) on the redacted text content; and verifying that none of the redacted text is present. | 0.5 |
8,850,414 | 8 | 10 | 8. An apparatus comprising: a processor; and a compiler comprising: a parse engine accessible to the processor to receive input script; a standard language grammar module accessible to the processor to define features of a programming language of the input script; an active profile module accessible to the processor to define a sequence of namespaces; and a language metadata provider accessible to the processor to: modify language metadata of the programming language, the language metadata specifying runtime characteristics of the programming language; and modify one or more of the namespaces based on respective modifications to the language metadata, each of the respective modifications being associated with a particular scope indicating one or more sessions for which to apply a respective modification. | 8. An apparatus comprising: a processor; and a compiler comprising: a parse engine accessible to the processor to receive input script; a standard language grammar module accessible to the processor to define features of a programming language of the input script; an active profile module accessible to the processor to define a sequence of namespaces; and a language metadata provider accessible to the processor to: modify language metadata of the programming language, the language metadata specifying runtime characteristics of the programming language; and modify one or more of the namespaces based on respective modifications to the language metadata, each of the respective modifications being associated with a particular scope indicating one or more sessions for which to apply a respective modification. 10. The apparatus of claim 8 , wherein the active profile module defines the sequence of namespaces, wherein the namespaces are implemented in an array and each of the namespaces define particular elements. | 0.516432 |
9,756,161 | 1 | 4 | 1. A voice recognition apparatus comprising: a context model created by modeling recognition target context; a model creator configured to classify the recognition target context according to a length of syllables, and to create a context model for each length of syllables; and a voice recognizer configured to create a candidate group corresponding to a received voice signal based on an acoustic model and the context model for each length of syllables, calculate a length of a user's speech based on a Begin of Speech (BoS) and an End of Speech (EoS), and apply a high reliability weight value to a candidate having a length of syllables corresponding to the length of speech. | 1. A voice recognition apparatus comprising: a context model created by modeling recognition target context; a model creator configured to classify the recognition target context according to a length of syllables, and to create a context model for each length of syllables; and a voice recognizer configured to create a candidate group corresponding to a received voice signal based on an acoustic model and the context model for each length of syllables, calculate a length of a user's speech based on a Begin of Speech (BoS) and an End of Speech (EoS), and apply a high reliability weight value to a candidate having a length of syllables corresponding to the length of speech. 4. The voice recognition apparatus according to claim 1 , wherein the voice recognizer creates candidate groups for context models created for lengths of syllables, and applies different weight values to the candidate groups, respectively, according to the length of syllables corresponding to the length of speech. | 0.504717 |
8,738,355 | 24 | 30 | 24. An apparatus comprising: a network interface; memory; and one or more processing units to: access a request for translation information obtained from a mobile station via said network interface, said translation information being associated with one or more written and/or spoken languages; associate a location with said request for translation information; access predicted information stored in said memory, said predicted information being associated with at least one other request for translation information associated with said location, and at least one other request for translation information associated with at least one other location, and previously obtained from at least one other mobile station; and generate requested translation information based, at least in part, on said request for translation information and said predicted information. | 24. An apparatus comprising: a network interface; memory; and one or more processing units to: access a request for translation information obtained from a mobile station via said network interface, said translation information being associated with one or more written and/or spoken languages; associate a location with said request for translation information; access predicted information stored in said memory, said predicted information being associated with at least one other request for translation information associated with said location, and at least one other request for translation information associated with at least one other location, and previously obtained from at least one other mobile station; and generate requested translation information based, at least in part, on said request for translation information and said predicted information. 30. The apparatus as recited in claim 24 , said one or more processing units to: identify metadata information associated with said mobile station based, at least in part, on said request for translation information. | 0.687861 |
8,887,140 | 18 | 19 | 18. The non-transitory computer-readable storage medium of claim 17 , the steps further comprising creating inline control flow structures in the program listing for annotated functions called a greatest number of times until the program listing meets a threshold. | 18. The non-transitory computer-readable storage medium of claim 17 , the steps further comprising creating inline control flow structures in the program listing for annotated functions called a greatest number of times until the program listing meets a threshold. 19. The non-transitory computer-readable storage medium of claim 18 , wherein the threshold is one of size, runtime performance, compile time, and complexity. | 0.5 |
8,676,695 | 10 | 14 | 10. The memory device of claim 6 , wherein the entity of the selected subset comprises one of the first and second entities and a third entity, and wherein the selecting comprises taking a mutual dependency associated with the combination of entity selections into consideration. | 10. The memory device of claim 6 , wherein the entity of the selected subset comprises one of the first and second entities and a third entity, and wherein the selecting comprises taking a mutual dependency associated with the combination of entity selections into consideration. 14. The memory device of claim 10 , wherein said mutual dependency comprises compatibility information. | 0.697059 |
9,286,035 | 1 | 9 | 1. A method implemented at least in part by a computing device, the method comprising: (a) receiving source code to be remediated in light of a regulation set affecting logic of the source code, wherein the source code is of a programming language; (b) generating a plurality of language-independent annotations for the source code to be remediated, wherein the language-independent annotations are of a format having a grammar that forms an executable language and wherein the executable language comprises at least one change function and one or more parameters for the change function, wherein the annotations comprise: an indication of a token representing a constant or a variable name appearing in the source code; parameters comprising an indication of a change type associated with the token, an indication of a statement type associated with the token, and an indication of an impact location associated with the token; and an indication of a new value associated with the token; (c) based on the language-independent annotations, outputting a language-independent analysis tree comprising breaking down the annotations into a set of patterns and sequentially arranging the patterns for translation, wherein the analysis tree comprises nodes specifying the indication of the token and the parameters; and (d) generating a remediated version of the source code, wherein generating the remediated version comprises translating the annotations, wherein the translating comprises consuming the parameters of the analysis tree, and applying the parameters from the annotations to the change function indicated in the annotations, wherein the change function generates source code comprising the new value associated with the token complying with the regulation set in the programming language of the source code and outputting lines of remediated source code in the programming language of the source code according to the plurality of language-independent annotations, wherein the remediated version complies with the regulation set. | 1. A method implemented at least in part by a computing device, the method comprising: (a) receiving source code to be remediated in light of a regulation set affecting logic of the source code, wherein the source code is of a programming language; (b) generating a plurality of language-independent annotations for the source code to be remediated, wherein the language-independent annotations are of a format having a grammar that forms an executable language and wherein the executable language comprises at least one change function and one or more parameters for the change function, wherein the annotations comprise: an indication of a token representing a constant or a variable name appearing in the source code; parameters comprising an indication of a change type associated with the token, an indication of a statement type associated with the token, and an indication of an impact location associated with the token; and an indication of a new value associated with the token; (c) based on the language-independent annotations, outputting a language-independent analysis tree comprising breaking down the annotations into a set of patterns and sequentially arranging the patterns for translation, wherein the analysis tree comprises nodes specifying the indication of the token and the parameters; and (d) generating a remediated version of the source code, wherein generating the remediated version comprises translating the annotations, wherein the translating comprises consuming the parameters of the analysis tree, and applying the parameters from the annotations to the change function indicated in the annotations, wherein the change function generates source code comprising the new value associated with the token complying with the regulation set in the programming language of the source code and outputting lines of remediated source code in the programming language of the source code according to the plurality of language-independent annotations, wherein the remediated version complies with the regulation set. 9. The method of claim 1 further comprising: identifying, via a token search pattern, an impact point at a location in the source code at which a program variable or constant affected by the regulation set appears in the source code; and wherein generating the plurality of language-independent annotations comprises generating a language-independent annotation for the impact point. | 0.539663 |
5,526,463 | 11 | 12 | 11. A system according to claim 10 wherein said data reduction means is adapted whereby s=3. | 11. A system according to claim 10 wherein said data reduction means is adapted whereby s=3. 12. A system according to claim 11 wherein said data reduction means is adapted whereby b=4. | 0.5 |
8,565,537 | 10 | 12 | 10. An apparatus comprising: a non-transitory machine-accessible medium; and instructions in the machine-accessible medium which, when executed by a processing system, enable the processing system to perform operations comprising: receiving an example image for use in querying a collection of digital images; using a local feature descriptor representing a portion of the contents of a digital image and a global feature descriptor representing substantially all of the contents of the digital image to perform a content-based image comparison of the collection of digital images with the example image, to automatically rank the collection of digital images with respect to similarity to the example image, wherein the global feature descriptor is content based; using a final classifier and multiple different intermediate classifiers to perform the automatic ranking, comprising: generating, by the different intermediate classifiers, intermediate relevance metrics with respect to different modalities; and generating, by the final classifier, results from the intermediate classifiers into a final relevance metric to display images in ranked order; after generating the final relevance metric, receiving input identifying a second example image for use in querying the collection of digital images; automatically determining at least one new intermediate classifier, based at least in part on the example images; automatically determining a new final classifier, based at least in part on the example images; and using the new intermediate classifier and the new final classifier to automatically re-rank the digital images in the collection with respect to similarity to the example images. | 10. An apparatus comprising: a non-transitory machine-accessible medium; and instructions in the machine-accessible medium which, when executed by a processing system, enable the processing system to perform operations comprising: receiving an example image for use in querying a collection of digital images; using a local feature descriptor representing a portion of the contents of a digital image and a global feature descriptor representing substantially all of the contents of the digital image to perform a content-based image comparison of the collection of digital images with the example image, to automatically rank the collection of digital images with respect to similarity to the example image, wherein the global feature descriptor is content based; using a final classifier and multiple different intermediate classifiers to perform the automatic ranking, comprising: generating, by the different intermediate classifiers, intermediate relevance metrics with respect to different modalities; and generating, by the final classifier, results from the intermediate classifiers into a final relevance metric to display images in ranked order; after generating the final relevance metric, receiving input identifying a second example image for use in querying the collection of digital images; automatically determining at least one new intermediate classifier, based at least in part on the example images; automatically determining a new final classifier, based at least in part on the example images; and using the new intermediate classifier and the new final classifier to automatically re-rank the digital images in the collection with respect to similarity to the example images. 12. An apparatus according to claim 10 , wherein: each digital image comprises a set of pixel values; and the operation of using local and global feature descriptors to perform the content-based image comparison of the collection of digital images with the example image comprises: using a first local feature descriptor and a second local feature descriptor to perform the content-based image comparison, wherein: the first local feature descriptor represents pixel values from a first portion of the digital image; and the second local feature descriptor represents pixel values from a second portion of the digital image. | 0.5 |
8,219,399 | 6 | 9 | 6. A position-determining device comprising: a processor; and memory configured to maintain: automated speech recognition (ASR) data divided according to partitions into a plurality of tiles, at least one of the tiles being configured as a common tile and a plurality of the tiles being geographically partitioned, wherein the common tile includes automated speech recognition (ASR) data common to at least two of the plurality of geographically-partitioned tiles, wherein the geographically-partitioned tiles are sized based on a minimum geographic search area; and one or more modules that are executable on the processor to: select one or more of the plurality of tiles based on information related to a user-initiated search and a geographic position, select the common tile based on the search information but not the geographic position; and translate an audio input using the selected tiles. | 6. A position-determining device comprising: a processor; and memory configured to maintain: automated speech recognition (ASR) data divided according to partitions into a plurality of tiles, at least one of the tiles being configured as a common tile and a plurality of the tiles being geographically partitioned, wherein the common tile includes automated speech recognition (ASR) data common to at least two of the plurality of geographically-partitioned tiles, wherein the geographically-partitioned tiles are sized based on a minimum geographic search area; and one or more modules that are executable on the processor to: select one or more of the plurality of tiles based on information related to a user-initiated search and a geographic position, select the common tile based on the search information but not the geographic position; and translate an audio input using the selected tiles. 9. A position-determining device as described in claim 6 , wherein at least one of the common tiles includes a portion of said automated speech recognition (ASR) data that has been identified of having an increased likelihood of being requested by a user and is relevant to the geographic position. | 0.5 |
8,904,276 | 8 | 13 | 8. A system, comprising: a processor; and a memory storing instructions which when executed cause the processor to perform operations, the operations comprising: scanning a hybrid markup language document; detecting opening tags in the hybrid markup language document; recording the opening tags in a tag stack; detecting a closing tag that corresponds to one of the opening tags; deleting the one of the opening tags from the tag stack responsive to detecting the closing tag; detecting a first partition boundary in the hybrid markup language document; copying, to an output markup language document, content in the hybrid markup language document that precedes the first partition boundary; splitting the hybrid markup language document at the first partition boundary, wherein the splitting comprises discarding the content in the hybrid markup language document that precedes the first partition boundary and replacing the content copied to the output markup language document with remaining content occurring after the first partition boundary; and generating closing tags corresponding to the opening tags that remain in the remaining content in the output markup language document. | 8. A system, comprising: a processor; and a memory storing instructions which when executed cause the processor to perform operations, the operations comprising: scanning a hybrid markup language document; detecting opening tags in the hybrid markup language document; recording the opening tags in a tag stack; detecting a closing tag that corresponds to one of the opening tags; deleting the one of the opening tags from the tag stack responsive to detecting the closing tag; detecting a first partition boundary in the hybrid markup language document; copying, to an output markup language document, content in the hybrid markup language document that precedes the first partition boundary; splitting the hybrid markup language document at the first partition boundary, wherein the splitting comprises discarding the content in the hybrid markup language document that precedes the first partition boundary and replacing the content copied to the output markup language document with remaining content occurring after the first partition boundary; and generating closing tags corresponding to the opening tags that remain in the remaining content in the output markup language document. 13. The system according to claim 8 , wherein the operations further comprise performing a text-to-speech conversion of the remaining content in the output markup language document. | 0.510811 |
8,127,220 | 66 | 74 | 66. A computer-readable memory device storing instructions executable by at least one processor, the computer-readable memory device comprising: one or more instructions to identify a document that is stored on a server in a network and that includes links to linked documents; one or more instructions to determine scores for a plurality of the links in the identified document; one or more instructions to compare the determined scores to a threshold; one or more instructions to delete one of the plurality of links from the identified document when the determined score for the one of the links falls below the threshold; and one or more instructions to provide, to a user, the identified document without the deleted link. | 66. A computer-readable memory device storing instructions executable by at least one processor, the computer-readable memory device comprising: one or more instructions to identify a document that is stored on a server in a network and that includes links to linked documents; one or more instructions to determine scores for a plurality of the links in the identified document; one or more instructions to compare the determined scores to a threshold; one or more instructions to delete one of the plurality of links from the identified document when the determined score for the one of the links falls below the threshold; and one or more instructions to provide, to a user, the identified document without the deleted link. 74. The computer-readable memory device of claim 66 , further comprising: one or more instructions to sort at least two of the plurality of links within the identified document based on the determined scores for the at least two of the plurality of links. | 0.906662 |
7,490,034 | 1 | 5 | 1. A computer readable storage medium having a lexicon for storing word information and adapted for use with a text analyzer in a language processing system, wherein the lexicon is adapted to be used in a plurality of language processing tasks, the lexicon comprising: a word list section for storing a list of words; a set of data sections corresponding with each word on the word list, wherein the data sections store substantially different selected information about the corresponding word in the word list; and for each word on the word list, a plurality of pointers stored in an indices table apart from the sets of data sections, each of the pointers pointing to a different data section related to different information about the corresponding word, wherein the plurality of pointers comprises a first set and a second set of the pointers, the first set used to access information related to a first natural language processing task and the second set used to access information related to a second natural language processing task, wherein the first set of the pointers is not the same as the second set of the pointers. | 1. A computer readable storage medium having a lexicon for storing word information and adapted for use with a text analyzer in a language processing system, wherein the lexicon is adapted to be used in a plurality of language processing tasks, the lexicon comprising: a word list section for storing a list of words; a set of data sections corresponding with each word on the word list, wherein the data sections store substantially different selected information about the corresponding word in the word list; and for each word on the word list, a plurality of pointers stored in an indices table apart from the sets of data sections, each of the pointers pointing to a different data section related to different information about the corresponding word, wherein the plurality of pointers comprises a first set and a second set of the pointers, the first set used to access information related to a first natural language processing task and the second set used to access information related to a second natural language processing task, wherein the first set of the pointers is not the same as the second set of the pointers. 5. The computer readable storage medium of claim 1 wherein each pointer includes an identification comprising an offset value stored with the word list section. | 0.746835 |
8,924,844 | 1 | 4 | 1. A method, comprising: selecting, by a computing device, an object rendered on a display, wherein the object is associated with an application that includes a first call to a program, wherein the first call is configured to cause the program to render the object on the display; receiving, by the computing device, a plurality of annotations associated with the selected object; determining, by the computing device, the verbosity level of each annotation of the plurality of annotations, wherein each of the plurality of annotations includes a different verbosity level, and wherein for any two of the annotations associated with the selected object and having different verbosity levels, a first annotation associated with a first verbosity level represents information in a textually-shortened form from information represented in a second of the two annotations with a second verbosity level higher than the first verbosity level, and wherein the first annotation associated with the lower verbosity level comprises an icon; and during execution of the application, and prior to execution of the first call, replacing, by the computing device, the first call with a second call to be executed in place of the first call, wherein the second call is different from the first call and is itself configured to both render the plurality of annotations on the display and to cause the program to render the object without requiring execution of the first call, wherein the second call is configured to render the annotations individually based on a requested verbosity level. | 1. A method, comprising: selecting, by a computing device, an object rendered on a display, wherein the object is associated with an application that includes a first call to a program, wherein the first call is configured to cause the program to render the object on the display; receiving, by the computing device, a plurality of annotations associated with the selected object; determining, by the computing device, the verbosity level of each annotation of the plurality of annotations, wherein each of the plurality of annotations includes a different verbosity level, and wherein for any two of the annotations associated with the selected object and having different verbosity levels, a first annotation associated with a first verbosity level represents information in a textually-shortened form from information represented in a second of the two annotations with a second verbosity level higher than the first verbosity level, and wherein the first annotation associated with the lower verbosity level comprises an icon; and during execution of the application, and prior to execution of the first call, replacing, by the computing device, the first call with a second call to be executed in place of the first call, wherein the second call is different from the first call and is itself configured to both render the plurality of annotations on the display and to cause the program to render the object without requiring execution of the first call, wherein the second call is configured to render the annotations individually based on a requested verbosity level. 4. The method of claim 1 , wherein receiving comprises receiving, by the computing device, an annotation that is user non-interactive. | 0.819892 |
8,650,509 | 6 | 7 | 6. The computer implemented method of claim 5 , wherein the second predefined gesture comprises: a swipe leading to the removal of the first finger from the touchscreen. | 6. The computer implemented method of claim 5 , wherein the second predefined gesture comprises: a swipe leading to the removal of the first finger from the touchscreen. 7. The computer implemented method of claim 6 , wherein the second predefined gesture further comprises: the swipe extending to outside a display area to which the second selected page is confined. | 0.5 |
8,117,023 | 1 | 2 | 1. A language understanding apparatus comprising: a storage section that stores: concept structure data indicating a correlation between a tree structure of a concept to be used in a domain and a concept representation specifying the concept to be used in the domain, frame definition data corresponding to the concept to be used in the domain and indicating a correlation between one or more semantic frames for representing the concept to be used in the domain by another concept, one or more variables to be used in the one or more semantic frames, and variable definition data indicating a concept to be used as a variable; a phrase interpreter that receives input utterance data detects a concept representation included in an utterance content indicated by the input utterance data by referring to the storage section, and reads information of a concept corresponding to the detected concept representation from the storage section; and a bidding section that; reads information on a variable of a semantic frame corresponding to the concept indicated by the information read by the phrase interpreter and information on a concept corresponding to the variable by referring to the storage section, and allocates by writing to a variable allocation table from the concept representation detected by the phrase interpreter, for each semantic frame read: a concept representation associated with a concept corresponding to each variable of the semantic frame or a subordinate concept of that concept, and a concept representation whose variable of another semantic frame is not written, and generates variable allocation table data indicating a bidding result. | 1. A language understanding apparatus comprising: a storage section that stores: concept structure data indicating a correlation between a tree structure of a concept to be used in a domain and a concept representation specifying the concept to be used in the domain, frame definition data corresponding to the concept to be used in the domain and indicating a correlation between one or more semantic frames for representing the concept to be used in the domain by another concept, one or more variables to be used in the one or more semantic frames, and variable definition data indicating a concept to be used as a variable; a phrase interpreter that receives input utterance data detects a concept representation included in an utterance content indicated by the input utterance data by referring to the storage section, and reads information of a concept corresponding to the detected concept representation from the storage section; and a bidding section that; reads information on a variable of a semantic frame corresponding to the concept indicated by the information read by the phrase interpreter and information on a concept corresponding to the variable by referring to the storage section, and allocates by writing to a variable allocation table from the concept representation detected by the phrase interpreter, for each semantic frame read: a concept representation associated with a concept corresponding to each variable of the semantic frame or a subordinate concept of that concept, and a concept representation whose variable of another semantic frame is not written, and generates variable allocation table data indicating a bidding result. 2. The language understanding apparatus according to claim 1 , further comprising an access table generator that receives the input utterance data, performs parsing of the utterance content indicated by the received input utterance data to acquire a dependency relationship among morpheme sequences constituting the utterance content, and generates access table data indicating an accessibility among the morpheme sequences based on the acquired dependency relationship, wherein when making a bid for each semantic frame, the bidding section specifies, from the concept representation detected by the phrase interpreter, a concept representation corresponding to a concept corresponding to each variable of the semantic frame or a subordinate concept of that concept, and a concept representation whose variable of another semantic frame is not bidden, and bids the variable for the specified concept representation when the access table data generated by the access table generator indicates that the concept representation corresponding to the semantic frame is accessible to the specified concept representation. | 0.534641 |
8,086,623 | 16 | 17 | 16. The system of claim 13 , wherein the at least one expanded term repository comprises a single repository containing different sets of expanded search terms associated with the same base term. | 16. The system of claim 13 , wherein the at least one expanded term repository comprises a single repository containing different sets of expanded search terms associated with the same base term. 17. The system of claim 16 , wherein the at least one parameter indicative of a context of the query comprises one of a plurality of available expansion levels, each associated with one of the different sets of expanded search terms associated with the same base term. | 0.5 |
7,634,406 | 28 | 29 | 28. The system of claim 25 wherein the clustering component is configured to merge similar clusters by computing a distance between a pair of clusters, merging the pair of clusters into one cluster if the computed distance meets a threshold distance, and performing the steps of computing and merging for a plurality of pairs of clusters to obtain a refined set of clusters. | 28. The system of claim 25 wherein the clustering component is configured to merge similar clusters by computing a distance between a pair of clusters, merging the pair of clusters into one cluster if the computed distance meets a threshold distance, and performing the steps of computing and merging for a plurality of pairs of clusters to obtain a refined set of clusters. 29. The system of claim 28 wherein the clustering component is configured to re-assign the speech recognition results to the refined set of clusters to obtain a set of refined clusters. | 0.853175 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.