sentence1
stringlengths 40
15.9k
| sentence2
stringlengths 88
20k
| label
float64 0.5
0.99
|
|---|---|---|
4. The method according to claim 1 , wherein submitting the query to one or more knowledge bases comprises submitting the query to a knowledge search engine and automatically formatting the query to be compatible with the knowledge search engine. | 4. The method according to claim 1 , wherein submitting the query to one or more knowledge bases comprises submitting the query to a knowledge search engine and automatically formatting the query to be compatible with the knowledge search engine. 5. The method according to claim 4 , including formatting the query as either a natural language query or a keyword query. | 0.920813 |
16. A machine-readable storage device storing instructions which when executed cause one or more processors to perform: receiving a portion of content of an electronic document; analyzing the portion of the content to identify one or more predetermined words in the portion; associating the one or more predetermined words with contextually relevant information including a coupon information; augmenting the portion with the coupon information including linking the portion to one or more coupons associated with the coupon information by adding a script to the portion, wherein executing the script in a browser causes the browser to generate and display a user interface element in association with each of the one or more predetermined words of the content, receive user input selecting the user interface element with a particular one of the words, select a coupon offer for the one or more coupons that is contextually relevant to the selected particular one of the words, display the coupon offer for the one or more coupons in a window over the portion of content, and download the one or more coupons to the browser; wherein stored instructions are executed by one or more computers. | 16. A machine-readable storage device storing instructions which when executed cause one or more processors to perform: receiving a portion of content of an electronic document; analyzing the portion of the content to identify one or more predetermined words in the portion; associating the one or more predetermined words with contextually relevant information including a coupon information; augmenting the portion with the coupon information including linking the portion to one or more coupons associated with the coupon information by adding a script to the portion, wherein executing the script in a browser causes the browser to generate and display a user interface element in association with each of the one or more predetermined words of the content, receive user input selecting the user interface element with a particular one of the words, select a coupon offer for the one or more coupons that is contextually relevant to the selected particular one of the words, display the coupon offer for the one or more coupons in a window over the portion of content, and download the one or more coupons to the browser; wherein stored instructions are executed by one or more computers. 17. The machine-readable storage device of claim 16 , further comprising instructions which when executed cause receiving one or more keywords, the one or more keywords being associated with the coupon information, and associating the one or more predetermined words by comparing the one or more predetermined words with the one or more keywords; where linking the portion to one or more coupons includes linking the portion to a particular one of he coupons that is associated with a keyword matching the at least one predetermined word. | 0.5 |
8. A method for auditing a business document comprising: providing a slide; providing: a set of universal presentation rules comprising one or more parameters, said universal presentation rules configured to override all other presentation rules, wherein said universal presentation rules are enforced, irrespective of a user's wishes, across substantially every one of a user's one or more slides, wherein the universal presentation rules comprise: page word count rules which limit the total word count on each slide of the user; bullet point word count rules which limit the total word count on each bullet point included on each slide of the user; line count rules which limit the total number of lines on each slide of the user; color rules which limit the palette of colors used on each slide of the user; and density rules which limit the amount of white space on each slide of the user; and a set of customized presentation rules comprising one or more parameters, said customized presentation rules customized by the user, wherein said customized presentation rules allow the user to enforce the customized presentation rules on a single slide, wherein the customized presentation rules are included on a displayable audit panel, wherein the displayable audit panel is viewable adjacent to the slide; comparing a parameter of an element of the slide to a first parameter of the universal presentation rules; and conforming, when the parameter of the element of the slide is a non-compliant parameter, the element of the slide to the first parameter of the universal presentation rules; and comparing the universal presentation rules to the customized presentation rules, said comparing comprising: when the universal presentation rules are in conflict with the customized presentation rules, causing the universal presentation rules to override the customized presentation rules; and when the universal presentation rules are not in conflict with the customized presentation rules, comparing the parameter of the element of the slide to a second parameter of the customized presentation rules, wherein, when the parameter of the element of the slide is a non-compliant parameter, presenting to the user an option to conform the element of the slide to the second parameter of the customized presentation rules. | 8. A method for auditing a business document comprising: providing a slide; providing: a set of universal presentation rules comprising one or more parameters, said universal presentation rules configured to override all other presentation rules, wherein said universal presentation rules are enforced, irrespective of a user's wishes, across substantially every one of a user's one or more slides, wherein the universal presentation rules comprise: page word count rules which limit the total word count on each slide of the user; bullet point word count rules which limit the total word count on each bullet point included on each slide of the user; line count rules which limit the total number of lines on each slide of the user; color rules which limit the palette of colors used on each slide of the user; and density rules which limit the amount of white space on each slide of the user; and a set of customized presentation rules comprising one or more parameters, said customized presentation rules customized by the user, wherein said customized presentation rules allow the user to enforce the customized presentation rules on a single slide, wherein the customized presentation rules are included on a displayable audit panel, wherein the displayable audit panel is viewable adjacent to the slide; comparing a parameter of an element of the slide to a first parameter of the universal presentation rules; and conforming, when the parameter of the element of the slide is a non-compliant parameter, the element of the slide to the first parameter of the universal presentation rules; and comparing the universal presentation rules to the customized presentation rules, said comparing comprising: when the universal presentation rules are in conflict with the customized presentation rules, causing the universal presentation rules to override the customized presentation rules; and when the universal presentation rules are not in conflict with the customized presentation rules, comparing the parameter of the element of the slide to a second parameter of the customized presentation rules, wherein, when the parameter of the element of the slide is a non-compliant parameter, presenting to the user an option to conform the element of the slide to the second parameter of the customized presentation rules. 11. The method of claim 8 , wherein, when the customized presentation rules are a first set of customized presentation rules, the method further comprises: receiving a second set of customized presentation rules; and conforming the element of the slide to the second set of customized presentation rules. | 0.621049 |
6. The speech recognition method according to claim 5 , in which distance value buffers which store distance values generated in the distance calculation step and acoustic lookahead value buffers which store acoustic lookahead values generated in the acoustic lookahead step are used, the method further comprising: to the distance value buffers, performing writing of the distance value in the distance calculation step, reading of the distance value in the acoustic lookahead step, and reading of the distance value in the word string matching step, in parallel; to the acoustic lookahead value buffers, performing writing of the acoustic lookahead value in the acoustic lookahead step and reading of the acoustic lookahead value in the word string matching step, in parallel; and causing, at any point in time, the distance value buffer in which the distance value is written in the distance calculation step, the distance value buffer from which the distance value is read out in the acoustic lookahead step, and the distance value buffer from which the distance value is read out in the word string matching step to be different from one another, and causing the acoustic lookahead value buffer in which the acoustic lookahead value is written in the acoustic lookahead step and the acoustic lookahead value buffer from which the acoustic lookahead value is read out in the word string matching step, to be different from each other. | 6. The speech recognition method according to claim 5 , in which distance value buffers which store distance values generated in the distance calculation step and acoustic lookahead value buffers which store acoustic lookahead values generated in the acoustic lookahead step are used, the method further comprising: to the distance value buffers, performing writing of the distance value in the distance calculation step, reading of the distance value in the acoustic lookahead step, and reading of the distance value in the word string matching step, in parallel; to the acoustic lookahead value buffers, performing writing of the acoustic lookahead value in the acoustic lookahead step and reading of the acoustic lookahead value in the word string matching step, in parallel; and causing, at any point in time, the distance value buffer in which the distance value is written in the distance calculation step, the distance value buffer from which the distance value is read out in the acoustic lookahead step, and the distance value buffer from which the distance value is read out in the word string matching step to be different from one another, and causing the acoustic lookahead value buffer in which the acoustic lookahead value is written in the acoustic lookahead step and the acoustic lookahead value buffer from which the acoustic lookahead value is read out in the word string matching step, to be different from each other. 8. The speech recognition method, according to claim 6 , further comprising a buffer management step to monitor operations in the distance calculation step, the acoustic lookahead step and the word string matching step, wherein in the buffer management step, on conditions that the distance values cannot be written in the distance value buffers any more in the distance calculation step, and all of the distance values are read out from the distance value buffers in the acoustic lookahead step and in the word string matching step, and the acoustic lookahead values cannot be written any more in the acoustic lookahead value buffers in the acoustic lookahead step, and all of the acoustic lookahead values are read out from the acoustic lookahead value buffers in the word string matching step, operating: the distance value buffer used for writing in the distance calculation step as a next one for reading in the acoustic lookahead step; the distance value buffer used for reading in the acoustic lookahead step as a next one for reading in the word string matching step; the distance value buffer used for reading in the word string matching step as a next one for writing in the distance calculation step; the acoustic lookahead value buffer used for writing in the acoustic lookahead step as a next one for reading in the word string matching step; and the acoustic lookahead value buffer used for reading in the word string matching step as a next one for writing in the acoustic lookahead step. | 0.5 |
13. A computer readable storage medium for tracking a user, the computer readable storage medium having stored thereon computer executable instructions that, when executed on a computer, cause the computer to perform operations comprising: receiving a depth image; identifying an estimated location or position of a part of the user in the depth image; adjusting a model of the user based on the estimated location or position of the part of the user; and in response to a failure to identify a location or position of the part of the user in a second depth image, adjusting the model to move the location or position of the part of the user in the model to a default position of the part of the user. | 13. A computer readable storage medium for tracking a user, the computer readable storage medium having stored thereon computer executable instructions that, when executed on a computer, cause the computer to perform operations comprising: receiving a depth image; identifying an estimated location or position of a part of the user in the depth image; adjusting a model of the user based on the estimated location or position of the part of the user; and in response to a failure to identify a location or position of the part of the user in a second depth image, adjusting the model to move the location or position of the part of the user in the model to a default position of the part of the user. 16. The computer readable storage medium of claim 13 , wherein the model comprises: a skeletal model having joints and bones. | 0.565398 |
7. An apparatus, comprising: a storage device configured to store a plurality of portable voice profiles; and a computer-based system coupled to the storage device, the computer-based system including (1) a speech recognition engine configured to convert a voice signal to text, the speech recognition engine including speaker identification logic configured to analyze the voice signal to identify the speaker and then dynamically select a particular portable voice profile associated with the identified speaker, in real-time, from the plurality of portable voice profiles; (2) a local group manager configured to manage access privileges to a local user's portable voice profile according to connections between a local user of the apparatus and other members of a group to which the local user belongs and instructions of the local user; and (3) a local voice profile manager configured to receive the plurality of portable voice profiles, each portable voice profile associated with a speaker and including speaker-dependent data accessible to a plurality of speech recognition engines through an interface, the speaker-dependent data to enhance an accuracy with which each speech recognition engine in the plurality of speech recognition engines recognizes spoken words in a voice signal from a speaker associated with a portable voice profile, wherein at least one of the plurality of portable voice profiles includes data derived from use with a variety of speech recognition engines. | 7. An apparatus, comprising: a storage device configured to store a plurality of portable voice profiles; and a computer-based system coupled to the storage device, the computer-based system including (1) a speech recognition engine configured to convert a voice signal to text, the speech recognition engine including speaker identification logic configured to analyze the voice signal to identify the speaker and then dynamically select a particular portable voice profile associated with the identified speaker, in real-time, from the plurality of portable voice profiles; (2) a local group manager configured to manage access privileges to a local user's portable voice profile according to connections between a local user of the apparatus and other members of a group to which the local user belongs and instructions of the local user; and (3) a local voice profile manager configured to receive the plurality of portable voice profiles, each portable voice profile associated with a speaker and including speaker-dependent data accessible to a plurality of speech recognition engines through an interface, the speaker-dependent data to enhance an accuracy with which each speech recognition engine in the plurality of speech recognition engines recognizes spoken words in a voice signal from a speaker associated with a portable voice profile, wherein at least one of the plurality of portable voice profiles includes data derived from use with a variety of speech recognition engines. 10. The apparatus of claim 7 , wherein the interface is an application programming interface (API), and the speech recognition engine accesses speaker-dependent data from one or more of the plurality of portable voice profiles by making an API function call, and utilizes the accessed speaker-dependent data to enhance the accuracy with which the speech recognition engine converts the voice signal to text. | 0.52705 |
1. A prosody editing apparatus comprising: a storage configured to store attribute information items of phrases and one or more first prosodic patterns corresponding to each of the attribute information items of the phrases; a search unit configured to search the storage for one or more second prosodic patterns corresponding to an attribute information item that matches an attribute information item of a predetermined phrase, the second prosodic patterns being included in the first prosodic patterns; a mapping unit configured to map each of the second prosodic patterns on a low dimensional space to generate mapping coordinates, the mapping coordinates being used to suppress a first prosodic pattern which is not assumed normally, wherein a first distance between coordinates of the first prosodic pattern and coordinates of a target prosodic pattern is not within a first threshold; a selection unit configured to obtain coordinates selected from the mapping coordinates as selected coordinates; a restoring unit configured to restore a second prosodic pattern according to the selected coordinates to obtain a restored prosodic pattern; and a replacing unit configured to replace prosody of synthetic speech generated based on the predetermined phrase by the restored prosodic pattern. | 1. A prosody editing apparatus comprising: a storage configured to store attribute information items of phrases and one or more first prosodic patterns corresponding to each of the attribute information items of the phrases; a search unit configured to search the storage for one or more second prosodic patterns corresponding to an attribute information item that matches an attribute information item of a predetermined phrase, the second prosodic patterns being included in the first prosodic patterns; a mapping unit configured to map each of the second prosodic patterns on a low dimensional space to generate mapping coordinates, the mapping coordinates being used to suppress a first prosodic pattern which is not assumed normally, wherein a first distance between coordinates of the first prosodic pattern and coordinates of a target prosodic pattern is not within a first threshold; a selection unit configured to obtain coordinates selected from the mapping coordinates as selected coordinates; a restoring unit configured to restore a second prosodic pattern according to the selected coordinates to obtain a restored prosodic pattern; and a replacing unit configured to replace prosody of synthetic speech generated based on the predetermined phrase by the restored prosodic pattern. 9. The apparatus of claim 1 , wherein if a second distance between the selected coordinates and the mapping coordinates is not more than a second threshold, the restoring unit obtains a fourth prosodic pattern before mapping the mapping coordinates as the restored prosodic pattern. | 0.521145 |
1. An online legal research system for researching expert witnesses, the system comprising: one or more databases containing expert witness data, including area of expertise information; a server operatively coupled to the one or more databases and configured to provide one or more client access devices a graphical user interface, the graphical user interface including: hierarchical means for receiving a query regarding an area of expertise; means for listing two or more experts found in the one or more databases in response to the received query; means for selecting two or more of the listed expert witnesses; and means, responsive to the selection of the two or more listed experts, for automatically retrieving from the one or more databases, tabulating, and displaying simultaneously side-by-side data regarding the cumulative litigation history of a plurality of the two or more selected experts. | 1. An online legal research system for researching expert witnesses, the system comprising: one or more databases containing expert witness data, including area of expertise information; a server operatively coupled to the one or more databases and configured to provide one or more client access devices a graphical user interface, the graphical user interface including: hierarchical means for receiving a query regarding an area of expertise; means for listing two or more experts found in the one or more databases in response to the received query; means for selecting two or more of the listed expert witnesses; and means, responsive to the selection of the two or more listed experts, for automatically retrieving from the one or more databases, tabulating, and displaying simultaneously side-by-side data regarding the cumulative litigation history of a plurality of the two or more selected experts. 4. The system of claim 1 , wherein the data regarding cumulative litigation history comprises legal roles, trial documents, testimony, attorneys, parties, courts, judges, case types, and awards. | 0.60041 |
1. A method for sequential equivalence checking of two netlists, the method comprising: a processor executing program instructions to perform the functions of: (a) receiving a first netlist and a second netlist of an original model, wherein each netlist of the first netlist and the second netlists includes at least one representation of one or more hardware registers coupling a plurality of gates; (b) determining, from netlists of the original model, logic to be abstracted; (c) determining a condition for functional consistency; (d) creating an abstract model by replacing the logic with abstracted logic using one or more uninterpreted functions; (e) performing one or more functions on the abstract model; (f) determining whether or not a counterexample is obtained from said performing the one or more functions on the abstract model; (g) if a counter example is not obtained, determining whether or not a proof is complete; (h) if the proof is complete, reporting that the proof is complete; (i) if the proof is not complete, repeating the method from step (e) using another function on the abstract model, wherein said another function is different from any one or more functions previously used in performing step (e); (j) if a counter example is obtained, determining whether or not the counter example can be transferred onto the original model; (k) if the counter example can be transferred onto the original model, reporting that the counter example can be transferred onto the original model and reporting the counter example; and (l) if the counter example can not be transferred onto the original model: refining the abstraction; and repeating the method from step (e) with the abstract model including the refined abstraction. | 1. A method for sequential equivalence checking of two netlists, the method comprising: a processor executing program instructions to perform the functions of: (a) receiving a first netlist and a second netlist of an original model, wherein each netlist of the first netlist and the second netlists includes at least one representation of one or more hardware registers coupling a plurality of gates; (b) determining, from netlists of the original model, logic to be abstracted; (c) determining a condition for functional consistency; (d) creating an abstract model by replacing the logic with abstracted logic using one or more uninterpreted functions; (e) performing one or more functions on the abstract model; (f) determining whether or not a counterexample is obtained from said performing the one or more functions on the abstract model; (g) if a counter example is not obtained, determining whether or not a proof is complete; (h) if the proof is complete, reporting that the proof is complete; (i) if the proof is not complete, repeating the method from step (e) using another function on the abstract model, wherein said another function is different from any one or more functions previously used in performing step (e); (j) if a counter example is obtained, determining whether or not the counter example can be transferred onto the original model; (k) if the counter example can be transferred onto the original model, reporting that the counter example can be transferred onto the original model and reporting the counter example; and (l) if the counter example can not be transferred onto the original model: refining the abstraction; and repeating the method from step (e) with the abstract model including the refined abstraction. 9. The method of claim 1 , wherein said refining the abstraction includes: replacing a portion of logic of the first netlist with first abstract logic; replacing a portion of logic of the second netlist with second abstract logic; wherein at least one of the first abstract logic and the second abstract logic includes a multiplexor-based uninterpreted function. | 0.648218 |
1. A computer-implemented method comprising: receiving a request to identify trending search queries in a search system; grouping a plurality of search queries into a plurality of clusters of search queries; associating each cluster of search queries with a respective representative category; determining, by one or more computers and for each cluster of search queries, a cluster score based on a cluster performance score or a category popularity score, wherein the category popularity score of a particular category is a score whose value correlates with the number of clusters associated with the particular category, and wherein the cluster performance score of a particular cluster is a score whose value correlates with a respective rank of one or more pages that are identified for one or more of search queries that are grouped into the particular cluster; generating a ranking of the clusters of search queries based on the cluster scores; and presenting, as a representation of the trending search queries in the search system, information identifying a subset of the clusters of search queries as ranked according to the ranking. | 1. A computer-implemented method comprising: receiving a request to identify trending search queries in a search system; grouping a plurality of search queries into a plurality of clusters of search queries; associating each cluster of search queries with a respective representative category; determining, by one or more computers and for each cluster of search queries, a cluster score based on a cluster performance score or a category popularity score, wherein the category popularity score of a particular category is a score whose value correlates with the number of clusters associated with the particular category, and wherein the cluster performance score of a particular cluster is a score whose value correlates with a respective rank of one or more pages that are identified for one or more of search queries that are grouped into the particular cluster; generating a ranking of the clusters of search queries based on the cluster scores; and presenting, as a representation of the trending search queries in the search system, information identifying a subset of the clusters of search queries as ranked according to the ranking. 3. The method of claim 1 , wherein grouping the plurality of search queries further comprises: identifying a set of result documents that a search engine has identified for each search query; and grouping the plurality of search queries into a plurality of clusters of search queries based on each search query's respective set of result documents. | 0.600281 |
1. A method comprising: receiving, by a server computer from a client device operated by a user, a travel request; identifying, by the server computer, travel options according to the travel request; ranking, by the server computer, each travel option in the identified travel options, the ranking based on travel attributes of each travel option and user preferences; training, by the server computer, a bucket configuration module to determine a bucket algorithm and a bucket context for grouping travel options, one or more of the bucket algorithm and the bucket context based on input from domain experts, input from semantic analysts, analytics data, user preferences, company policies, and past transaction analysis; classifying, by a bucket orchestration module of the server computer from the bucket algorithm and the bucket context, the ranked travel options into predefined buckets, the classifying further comprising defining, by the server computer, the buckets, the defining of the buckets personalized for the user; filtering, by the server computer, the ranked travel options in the predefined buckets; communicating, by the server computer to the client device, the filtered classified ranked travel options in the corresponding predefined buckets for display, the filtered classified ranked travel options in the corresponding predefined buckets displayed at the client device in an order associated with the user, the order enabling the user to determine a tradeoff between one bucket and another bucket; receiving, by the server computer from the client device, interactions from the user with one or more of the displayed predefined buckets, the interactions comprising a voting of the travel options in the bucket; and in response to receiving the interactions from the user with the one or more of the displayed predefined buckets, using, by the bucket configuration module, the interactions in the bucket algorithm for future classifying. | 1. A method comprising: receiving, by a server computer from a client device operated by a user, a travel request; identifying, by the server computer, travel options according to the travel request; ranking, by the server computer, each travel option in the identified travel options, the ranking based on travel attributes of each travel option and user preferences; training, by the server computer, a bucket configuration module to determine a bucket algorithm and a bucket context for grouping travel options, one or more of the bucket algorithm and the bucket context based on input from domain experts, input from semantic analysts, analytics data, user preferences, company policies, and past transaction analysis; classifying, by a bucket orchestration module of the server computer from the bucket algorithm and the bucket context, the ranked travel options into predefined buckets, the classifying further comprising defining, by the server computer, the buckets, the defining of the buckets personalized for the user; filtering, by the server computer, the ranked travel options in the predefined buckets; communicating, by the server computer to the client device, the filtered classified ranked travel options in the corresponding predefined buckets for display, the filtered classified ranked travel options in the corresponding predefined buckets displayed at the client device in an order associated with the user, the order enabling the user to determine a tradeoff between one bucket and another bucket; receiving, by the server computer from the client device, interactions from the user with one or more of the displayed predefined buckets, the interactions comprising a voting of the travel options in the bucket; and in response to receiving the interactions from the user with the one or more of the displayed predefined buckets, using, by the bucket configuration module, the interactions in the bucket algorithm for future classifying. 3. The method of claim 1 , wherein the classifying of the travel options into the predefined buckets further comprises defining, by the user, a function to classify an option. | 0.5 |
3. The baggage system of claim 2 , where the server is further configured to: receive plurality of input attributes of the present event; performing pre-processing on the plurality of input attributes to generate an input data set; generating the output value from the trained model based upon the input data set; and predict an outcome associated with the present event based upon the output value. | 3. The baggage system of claim 2 , where the server is further configured to: receive plurality of input attributes of the present event; performing pre-processing on the plurality of input attributes to generate an input data set; generating the output value from the trained model based upon the input data set; and predict an outcome associated with the present event based upon the output value. 13. The baggage system of claim 3 , wherein the input attributes include: a location and identification of a baggage item; and a baggage handler identification associated with the baggage item. | 0.928373 |
33. A computer system, comprising: a target processor; a shared code cache facility; and translator code for translating a subject program code into target code executable on said target processor, said translator code comprising code executable by said target processor to: provide a first translator instance which translates the subject code of a first program into the target code including translating a first portion of the subject code into a portion of the target code; cache said portion of the target code in the shared code cache facility; and provide a second translator instance which translates the subject code of a second program into the target code, wherein the second translator instance is different from the first translator instance and operates simultaneously with the first translator instance, and wherein the second translator instance retrieves the cached portion of the target code from the shared code cache facility upon a compatibility detection between said cached portion of the target code and a second portion of the subject code in the second program, including loading the portion of the target code in the shared code cache facility into a portion of memory which is shared amongst at least the first and second translator instances; and, copying at least one part of the shared code cache facility to a private portion of memory associated with the second translator instance upon modification of the at least one part of the shared code cache facility by the second translator instance. | 33. A computer system, comprising: a target processor; a shared code cache facility; and translator code for translating a subject program code into target code executable on said target processor, said translator code comprising code executable by said target processor to: provide a first translator instance which translates the subject code of a first program into the target code including translating a first portion of the subject code into a portion of the target code; cache said portion of the target code in the shared code cache facility; and provide a second translator instance which translates the subject code of a second program into the target code, wherein the second translator instance is different from the first translator instance and operates simultaneously with the first translator instance, and wherein the second translator instance retrieves the cached portion of the target code from the shared code cache facility upon a compatibility detection between said cached portion of the target code and a second portion of the subject code in the second program, including loading the portion of the target code in the shared code cache facility into a portion of memory which is shared amongst at least the first and second translator instances; and, copying at least one part of the shared code cache facility to a private portion of memory associated with the second translator instance upon modification of the at least one part of the shared code cache facility by the second translator instance. 44. The computer system combination of claim 33 wherein the portion of target code is converted into a single cache unit comprising a subject program and all its associated libraries. | 0.535629 |
4. The method according to claim 3 , further comprising: wherein the information retrieval system further comprises an identification model; and wherein the identifying the stop word in the first query according to the statistical feature of said each word in the first query and the change-based feature of said each word in the first query relative to the second query comprises: inputting the change-based feature of said each word in the first query relative to the second query and the statistical feature of said each word in the first query to the identification model; and obtaining the stop word identified by the identification model, in the first query. | 4. The method according to claim 3 , further comprising: wherein the information retrieval system further comprises an identification model; and wherein the identifying the stop word in the first query according to the statistical feature of said each word in the first query and the change-based feature of said each word in the first query relative to the second query comprises: inputting the change-based feature of said each word in the first query relative to the second query and the statistical feature of said each word in the first query to the identification model; and obtaining the stop word identified by the identification model, in the first query. 5. The method according to claim 4 , wherein the method further comprises: using a statistical feature of the stop word in the first query and a change-based feature of the stop word in the first query relative to the second query as a positive sample; using a statistical feature of a word except the stop word in the first query and a change-based feature of the word in the first query relative to the second query as a negative sample; and training the identification model according to the positive sample and the negative sample. | 0.794955 |
15. An electronic device comprising: one or more processors; and memory storing instructions which, when executed by the one or more processors, cause the device to: while the device is in a locked state at a first time, receive non-voice authentication information from a user; responsive to receiving valid authentication information, transition the device to an unlocked state; while in the unlocked state, receive a first speech input from the user; generate a voiceprint based on a voice sample of the first speech input; while the device is in the locked state at a second time, receiving a second speech input, the second speech input including a command associated with a restricted feature of the device; determine a degree of match between the generated voiceprint and the second speech input; and upon determining that the degree of match is above a predetermined threshold, execute the command including invoking the restricted feature of the device, wherein the command is executed while the device is in the locked state, and wherein the command is a request to perform an action other than unlocking the device. | 15. An electronic device comprising: one or more processors; and memory storing instructions which, when executed by the one or more processors, cause the device to: while the device is in a locked state at a first time, receive non-voice authentication information from a user; responsive to receiving valid authentication information, transition the device to an unlocked state; while in the unlocked state, receive a first speech input from the user; generate a voiceprint based on a voice sample of the first speech input; while the device is in the locked state at a second time, receiving a second speech input, the second speech input including a command associated with a restricted feature of the device; determine a degree of match between the generated voiceprint and the second speech input; and upon determining that the degree of match is above a predetermined threshold, execute the command including invoking the restricted feature of the device, wherein the command is executed while the device is in the locked state, and wherein the command is a request to perform an action other than unlocking the device. 17. The device of claim 15 , wherein the first speech input includes a command, and wherein the instructions further cause the device to execute the command of the first speech input. | 0.608383 |
1. A method for use in a computer system of generating a composite character configured for presentation by an output device, the composite character including a first component glyph and a second component glyph, each component glyph being defined by an outline, the method comprising: identifying, by the computer system, a first control point associated with the outline of the first component glyph and a second control point associated with the outline of the second component glyph, wherein identifying a first control point and a second control point comprises: determining one or more candidate control points for each of the first component glyph and the second component glyph; selecting the first control point from the one or more candidate control points determined for the first component glyph; and selecting the second control point from the one or more candidate control points determined for the second component glyph; receiving, by the computer system, a typographically relevant offset constraint associated with the composite character and relative to the first and second control points of the first and second component glyphs, the offset constraint including a vertical offset and a horizontal offset between the first component glyph and the second component glyph; scaling the outlines of the first and second component glyphs and the vertical and horizontal offsets of the offset constraint based at least in part on an output device resolution; assembling the scaled outline of the first component glyph and the scaled outline of the second component glyph to generate the composite character; and altering the assembled composite character to cause relative positions of the first control point and the second control point in the assembled composite character to satisfy the scaled offset constraint. | 1. A method for use in a computer system of generating a composite character configured for presentation by an output device, the composite character including a first component glyph and a second component glyph, each component glyph being defined by an outline, the method comprising: identifying, by the computer system, a first control point associated with the outline of the first component glyph and a second control point associated with the outline of the second component glyph, wherein identifying a first control point and a second control point comprises: determining one or more candidate control points for each of the first component glyph and the second component glyph; selecting the first control point from the one or more candidate control points determined for the first component glyph; and selecting the second control point from the one or more candidate control points determined for the second component glyph; receiving, by the computer system, a typographically relevant offset constraint associated with the composite character and relative to the first and second control points of the first and second component glyphs, the offset constraint including a vertical offset and a horizontal offset between the first component glyph and the second component glyph; scaling the outlines of the first and second component glyphs and the vertical and horizontal offsets of the offset constraint based at least in part on an output device resolution; assembling the scaled outline of the first component glyph and the scaled outline of the second component glyph to generate the composite character; and altering the assembled composite character to cause relative positions of the first control point and the second control point in the assembled composite character to satisfy the scaled offset constraint. 8. The method of claim 1 wherein the altering operation translates the second component glyph in a horizontal direction relative to the first component glyph. | 0.557556 |
9. A method, comprising: receiving, by a processor, a visualization request relating to information stored in an ontology; parsing, by the processor, the visualization request to generate a search query; submitting, by the processor, the search query to the ontology, wherein the ontology is dynamically created to store retrieved data that is collected from a data source; perform data mitigation to process the retrieved data to detect and resolve conflicts among classified tokens provided by data agent, wherein the data mitigation uses quality score, trust score, and rate of decay for the data source; receiving, by the processor, in response to the query, a result comprising a plurality of instances associated with the classified tokens and a plurality of relationships between the instances; and generating, by the processor, a visual representation of the result using visualization rules stored in a memory, the visualization rules comprising level of detail rules, reduction rules, and rewriting rules, wherein generating the visual representation of the result using the visualization rules comprises: identifying a relationship between a first instance and a second instance from the plurality of instances within the results; removing the relationship between the first instance and the second instance from the results; and removing the first instance from the results when the first instance has no relationships with any other instances from the plurality of instances. | 9. A method, comprising: receiving, by a processor, a visualization request relating to information stored in an ontology; parsing, by the processor, the visualization request to generate a search query; submitting, by the processor, the search query to the ontology, wherein the ontology is dynamically created to store retrieved data that is collected from a data source; perform data mitigation to process the retrieved data to detect and resolve conflicts among classified tokens provided by data agent, wherein the data mitigation uses quality score, trust score, and rate of decay for the data source; receiving, by the processor, in response to the query, a result comprising a plurality of instances associated with the classified tokens and a plurality of relationships between the instances; and generating, by the processor, a visual representation of the result using visualization rules stored in a memory, the visualization rules comprising level of detail rules, reduction rules, and rewriting rules, wherein generating the visual representation of the result using the visualization rules comprises: identifying a relationship between a first instance and a second instance from the plurality of instances within the results; removing the relationship between the first instance and the second instance from the results; and removing the first instance from the results when the first instance has no relationships with any other instances from the plurality of instances. 10. The method of claim 9 , wherein parsing the visualization request to generate the search query comprises reducing the scope of the search query based on the visualization rules. | 0.536223 |
16. The method of claim 1 , further comprising: calculating a score value for each characteristic keyword; receiving a query that includes one or more query keywords; and calculating, based on at least some of the score values, a similarity value for each personalized search engine indicative of a similarity between the query and at least some of the characteristic keywords, wherein selecting a personalized search engine is based at least partly on similarity values. | 16. The method of claim 1 , further comprising: calculating a score value for each characteristic keyword; receiving a query that includes one or more query keywords; and calculating, based on at least some of the score values, a similarity value for each personalized search engine indicative of a similarity between the query and at least some of the characteristic keywords, wherein selecting a personalized search engine is based at least partly on similarity values. 17. The method of claim 16 , further comprising: maintaining separate score values for each characteristic keyword across the multiple personalized search engines. | 0.895679 |
19. The system of claim 13 , further comprising: a Tolerator module that alters the search request by including related terms. | 19. The system of claim 13 , further comprising: a Tolerator module that alters the search request by including related terms. 23. The system of claim 19 , wherein the Normalizer identifies a format of the index and then translates the search request based on the format of the index. | 0.929763 |
1. A computer-implemented method, comprising: storing, at a server system, a set of previously trained predicative models; storing, at the server system, a respective performance indicator associated with each predictive model in the set of predictive models, where each of the respective performance indicators comprises a quantifiable metric determined based on prior usage data corresponding to the associated predictive model; receiving, at the server system, a first feature vector from a first remote computing device, the first feature vector comprising one or more elements; identifying, using the server system, an element type for each of the one or more elements of the first feature vector; selecting, using the server system, a first subset of predictive models from the set of predictive models, where the selection is based on the identified element types of the first feature vector and the stored performance indicators associated with the predictive models of the set; processing the first feature vector using the first subset of predictive models, each predictive model of the first subset of predictive models generating a respective predictive output based on the first feature vector to provide a first plurality of predictive outputs; generating a first combined predictive output based on the first plurality of predictive outputs; in response to generating the first combined predictive output, evaluating a performance of at least one predictive model of the subset; and updating the performance indicator associated with the at least one predictive model based on the evaluated performance. | 1. A computer-implemented method, comprising: storing, at a server system, a set of previously trained predicative models; storing, at the server system, a respective performance indicator associated with each predictive model in the set of predictive models, where each of the respective performance indicators comprises a quantifiable metric determined based on prior usage data corresponding to the associated predictive model; receiving, at the server system, a first feature vector from a first remote computing device, the first feature vector comprising one or more elements; identifying, using the server system, an element type for each of the one or more elements of the first feature vector; selecting, using the server system, a first subset of predictive models from the set of predictive models, where the selection is based on the identified element types of the first feature vector and the stored performance indicators associated with the predictive models of the set; processing the first feature vector using the first subset of predictive models, each predictive model of the first subset of predictive models generating a respective predictive output based on the first feature vector to provide a first plurality of predictive outputs; generating a first combined predictive output based on the first plurality of predictive outputs; in response to generating the first combined predictive output, evaluating a performance of at least one predictive model of the subset; and updating the performance indicator associated with the at least one predictive model based on the evaluated performance. 8. The method of claim 1 , wherein the first plurality of outputs are combined to define a combined feature vector, the combined feature vector being processed by a combining predictive model to generate the combined predictive output. | 0.708211 |
20. The method according to claim 19 , wherein the selecting includes using priority attributes of the alternative media items. | 20. The method according to claim 19 , wherein the selecting includes using priority attributes of the alternative media items. 21. The method according to claim 20 , further comprising using a Par Element of the Adaptation Module for defining a simple time grouping in which multiple elements must be played back at a same time. | 0.951721 |
13. The system of claim 11 , wherein the processor is operable to generate a knowledge feature associated with the externally created object, the knowledge feature operable to define parameter limits placed on the externally created object. | 13. The system of claim 11 , wherein the processor is operable to generate a knowledge feature associated with the externally created object, the knowledge feature operable to define parameter limits placed on the externally created object. 15. The system of claim 13 , wherein the processor is operable to adjust the parameter limits of the knowledge feature in order to alter the scope of changes that can be made to the respective object. | 0.930972 |
1. A method for searching a database of digital media assets, comprising: a processor receiving a search query; the processor analyzing the search query to extract a specialized search condition corresponding to a specialized semantic concept; the processor selecting, from a pre-existing ontology of indexers, a specialized indexer based on a determination that the selected specialized indexer can be used to identify digital media assets that correspond to the specialized semantic concept; the processor analyzing the search query to extract a general search condition corresponding to a related higher level semantic concept; the processor identifying, from a database of digital media assets, a subset of digital media assets that satisfy the general search condition responsive to general metadata for each of the digital media assets; the processor applying the selected specialized indexer to the subset of digital media assets to determine specialized metadata for a digital media asset in the subset of digital media assets, wherein the specialized metadata provides an indication of whether the digital media asset corresponds to the specialized semantic concept; and the processor converting the specialized indexer into a general indexer based at least on a frequency with which the specialized semantic concept is queried, wherein the processor is configured to convert the specialized indexer into the general indexer based further on a computational cost associated with the specialized indexer, wherein the general indexer is applied to the digital media assets independent of a search query, wherein the general indexer is operable to identify a digital media asset that corresponds to a higher level semantic concept and determine the general metadata for the identified digital media asset based at least upon an analysis of pixel data associated with the digital media asset, and wherein the general metadata provides an indication of the higher level semantic concept to which the identified digital media asset corresponds. | 1. A method for searching a database of digital media assets, comprising: a processor receiving a search query; the processor analyzing the search query to extract a specialized search condition corresponding to a specialized semantic concept; the processor selecting, from a pre-existing ontology of indexers, a specialized indexer based on a determination that the selected specialized indexer can be used to identify digital media assets that correspond to the specialized semantic concept; the processor analyzing the search query to extract a general search condition corresponding to a related higher level semantic concept; the processor identifying, from a database of digital media assets, a subset of digital media assets that satisfy the general search condition responsive to general metadata for each of the digital media assets; the processor applying the selected specialized indexer to the subset of digital media assets to determine specialized metadata for a digital media asset in the subset of digital media assets, wherein the specialized metadata provides an indication of whether the digital media asset corresponds to the specialized semantic concept; and the processor converting the specialized indexer into a general indexer based at least on a frequency with which the specialized semantic concept is queried, wherein the processor is configured to convert the specialized indexer into the general indexer based further on a computational cost associated with the specialized indexer, wherein the general indexer is applied to the digital media assets independent of a search query, wherein the general indexer is operable to identify a digital media asset that corresponds to a higher level semantic concept and determine the general metadata for the identified digital media asset based at least upon an analysis of pixel data associated with the digital media asset, and wherein the general metadata provides an indication of the higher level semantic concept to which the identified digital media asset corresponds. 23. The method of claim 1 , wherein analyzing the search query to extract a general search condition comprises selecting general indexers from the pre-existing ontology, and wherein the pre-existing ontology relates the general indexers to the selected specialized indexers. | 0.51489 |
1. A method for building a classifier, comprising: preparing, by a processor, a plurality of pairs for an information retrieval task, each of the plurality of pairs including (i) a training-stage speech recognition result for a respective sequence of training words and (ii) an answer label corresponding to the training-stage speech recognition result; obtaining, by the processor using a search engine, a respective rank for the answer label included in each of the plurality of pairs to obtain a set of ranks; determining, by the processor, for each of the plurality of pairs, an end of question part in the training-stage speech recognition result based on the set of ranks; building, by the processor, the classifier such that the classifier receives a recognition-stage speech recognition result and returns a corresponding end of question part for the recognition-stage speech recognition result, based on the end of question part determined for the plurality of pairs; splitting, by the processor, the recognition-stage speech recognition result at the corresponding end of question part for the recognition-stage speech recognition result; generating, by the processor, a set of answer candidates for replying to the corresponding end of question part for the recognition-stage speech recognition result; and providing, by a display device, the set of answer candidates to a user. | 1. A method for building a classifier, comprising: preparing, by a processor, a plurality of pairs for an information retrieval task, each of the plurality of pairs including (i) a training-stage speech recognition result for a respective sequence of training words and (ii) an answer label corresponding to the training-stage speech recognition result; obtaining, by the processor using a search engine, a respective rank for the answer label included in each of the plurality of pairs to obtain a set of ranks; determining, by the processor, for each of the plurality of pairs, an end of question part in the training-stage speech recognition result based on the set of ranks; building, by the processor, the classifier such that the classifier receives a recognition-stage speech recognition result and returns a corresponding end of question part for the recognition-stage speech recognition result, based on the end of question part determined for the plurality of pairs; splitting, by the processor, the recognition-stage speech recognition result at the corresponding end of question part for the recognition-stage speech recognition result; generating, by the processor, a set of answer candidates for replying to the corresponding end of question part for the recognition-stage speech recognition result; and providing, by a display device, the set of answer candidates to a user. 7. The method of claim 1 , wherein a first encountered word in the training-stage speech recognition result that results in a correct answer label for the training stage speech recognition result is determined as the end of question part in the training-stage speech recognition result. | 0.60585 |
13. A system for training a topic model, comprising: one or more processing units; and memory comprising instructions that when executed by at least one of the one or more processing units, perform a method comprising: for a document within a document corpus: receiving a document representation of the document and features of the document, the document representation comprising a frequency of word occurrences within the document; processing the document representation and the features using a topic model, the processing comprising: specifying a feature/topic parameter for a feature of the document, the feature/topic parameter specifying a probability of the feature being indicative of a first topic, the feature/topic parameter based upon a first uncertainty measure that is associated with a first determination of a first deviation of the feature/topic parameter from a current feature/topic parameter for the topic model; updating the first uncertainty measure based upon a first difference measure between the feature/topic parameter and one or more previously specified feature/topic parameters for the topic model, the first difference measure representing a first range of deviation for the first uncertainty measure; specifying a document/word/topic parameter for a word within the document, the document/word/topic parameter specifying a probability of the word being indicative of a second topic, the document/word/topic parameter based upon a second uncertainty measure that is associated with a second determination of a second deviation of the document/word/topic parameter from a current document/word/topic parameter for the topic model; and updating the second uncertainty measure based upon a second difference measure between the document/word/topic parameter and one or more previously specified document/word/topic parameters for the topic model, the second difference measure representing a second range of deviation for the second uncertainty measure; and training the topic model based upon the feature/topic parameter and the document/word/topic parameter. | 13. A system for training a topic model, comprising: one or more processing units; and memory comprising instructions that when executed by at least one of the one or more processing units, perform a method comprising: for a document within a document corpus: receiving a document representation of the document and features of the document, the document representation comprising a frequency of word occurrences within the document; processing the document representation and the features using a topic model, the processing comprising: specifying a feature/topic parameter for a feature of the document, the feature/topic parameter specifying a probability of the feature being indicative of a first topic, the feature/topic parameter based upon a first uncertainty measure that is associated with a first determination of a first deviation of the feature/topic parameter from a current feature/topic parameter for the topic model; updating the first uncertainty measure based upon a first difference measure between the feature/topic parameter and one or more previously specified feature/topic parameters for the topic model, the first difference measure representing a first range of deviation for the first uncertainty measure; specifying a document/word/topic parameter for a word within the document, the document/word/topic parameter specifying a probability of the word being indicative of a second topic, the document/word/topic parameter based upon a second uncertainty measure that is associated with a second determination of a second deviation of the document/word/topic parameter from a current document/word/topic parameter for the topic model; and updating the second uncertainty measure based upon a second difference measure between the document/word/topic parameter and one or more previously specified document/word/topic parameters for the topic model, the second difference measure representing a second range of deviation for the second uncertainty measure; and training the topic model based upon the feature/topic parameter and the document/word/topic parameter. 18. The system of claim 13 , the features of the document comprising at least one of document length or document type. | 0.547933 |
9. A system comprising: memory storing: an information source; business knowledge comprising information about users and business objects comprising; and an application for: receiving a search query from a user, the search query querying information from the information source; identifying features associated with the user and/or the business objects and for which result sets are to include boosting of result items based on business knowledge associated with the user and the information source; identifying, for each identified feature, query terms in the received search query to which a particular feature applies; for each identified query term to which a particular feature applies, adding a significance factor to the identified query term to boost a result item by a particular significance, identifying a weight for one or more feature-value pairs associated with the query term, each weight based on the business knowledge associated with the user and the information source, the business knowledge used to identify one or more feature-value pairs to which the weight is applicable, and using the weights to adjust the effect of significance factors; generating a weighted search query that includes weighted query parts, each weighted query part being a function of a respective query term and an associated weight, wherein the weighted search query is a re-written version of the received search query, the weighted search query including: in-terms having high weights and including selection criteria matching a respective query term and including the values of the one or more feature-value pairs, the in-terms boosting ranks of corresponding result items in the ranked result set according to the weights associated with the one or more feature-value pairs; and out-terms having low weights and including selection criteria matching a respective query term and excluding the values of the one or more feature-value pairs, the out-terms not affecting ranks of corresponding result items in the ranked result set; executing the weighted search query to produce a ranked result set that includes result items ranked according to the weights, the ranked result set including identical, but ranked, result entries that would be produced by executing the received search query; and providing the ranked result set in response to the received search query. | 9. A system comprising: memory storing: an information source; business knowledge comprising information about users and business objects comprising; and an application for: receiving a search query from a user, the search query querying information from the information source; identifying features associated with the user and/or the business objects and for which result sets are to include boosting of result items based on business knowledge associated with the user and the information source; identifying, for each identified feature, query terms in the received search query to which a particular feature applies; for each identified query term to which a particular feature applies, adding a significance factor to the identified query term to boost a result item by a particular significance, identifying a weight for one or more feature-value pairs associated with the query term, each weight based on the business knowledge associated with the user and the information source, the business knowledge used to identify one or more feature-value pairs to which the weight is applicable, and using the weights to adjust the effect of significance factors; generating a weighted search query that includes weighted query parts, each weighted query part being a function of a respective query term and an associated weight, wherein the weighted search query is a re-written version of the received search query, the weighted search query including: in-terms having high weights and including selection criteria matching a respective query term and including the values of the one or more feature-value pairs, the in-terms boosting ranks of corresponding result items in the ranked result set according to the weights associated with the one or more feature-value pairs; and out-terms having low weights and including selection criteria matching a respective query term and excluding the values of the one or more feature-value pairs, the out-terms not affecting ranks of corresponding result items in the ranked result set; executing the weighted search query to produce a ranked result set that includes result items ranked according to the weights, the ranked result set including identical, but ranked, result entries that would be produced by executing the received search query; and providing the ranked result set in response to the received search query. 10. The system of claim 9 , wherein a respective feature-value pair identifies a feature corresponding to a user identifier, a user group, a user role, a department, an organization, a product, a service, a location, or a date-time. | 0.535567 |
1. A system comprising: one or more processors; memory; a map segmentation module maintained in the memory and executable by the one or more processors to segment a map of an area into a set of sections; a function distribution module maintained in the memory and executable by the one or more processors to infer a distribution of functions for each section in the set of sections according to a topic model framework which uses mobility patterns of users leaving from and arriving at each section and uses points of interest (POIs) in each section; a section aggregation module maintained in the memory and executable by the one or more processors to group sections of the set of sections based at least in part on a similarity of the distribution of functions between each section to create functional groups; a functionality intensity estimator maintained in the memory and executable by the one or more processors to estimate a functionality intensity for each of the functional groups; and a group annotation module maintained in the memory and executable by the one or more processors to annotate each of the functional groups on a visual representation of the area. | 1. A system comprising: one or more processors; memory; a map segmentation module maintained in the memory and executable by the one or more processors to segment a map of an area into a set of sections; a function distribution module maintained in the memory and executable by the one or more processors to infer a distribution of functions for each section in the set of sections according to a topic model framework which uses mobility patterns of users leaving from and arriving at each section and uses points of interest (POIs) in each section; a section aggregation module maintained in the memory and executable by the one or more processors to group sections of the set of sections based at least in part on a similarity of the distribution of functions between each section to create functional groups; a functionality intensity estimator maintained in the memory and executable by the one or more processors to estimate a functionality intensity for each of the functional groups; and a group annotation module maintained in the memory and executable by the one or more processors to annotate each of the functional groups on a visual representation of the area. 7. The system of claim 1 , wherein the annotating each of the functional groups comprises annotating each of the functional groups with semantic terms as part of a semi-manual process based on human-labeled portions of the area. | 0.596879 |
1. A method, comprising: accessing, by one or more processing devices, a set of events, wherein each event in the set of events is associated with a time stamp and includes a portion of machine data indicative of performance or operation of an information technology environment; accessing an object-scoring rule that (i) includes a search query that determines when events meet a triggering condition; (ii) identifies an object representing a component of the information technology environment, an application running in the information technology environment, or a person using a component in the information technology environment, and (iii) specifies a numerical contribution to a score for the object, the numerical contribution to be applied to the score based at least on part on a determination that the triggering condition is met; executing the search query of the object-scoring rule against the set of events to determine if the triggering condition of the object-scoring rule is met; based on determining that the triggering condition is met, generating a record of the numerical contribution specified in the object-scoring rule, the record associating the numerical contribution with a time indicator and indicating the object whose score should be affected by the contribution; identifying, using one or more records of numerical contributions, a set of numerical contributions having associated time indicators falling within a defined time period; and calculating the score for the object based on the set of numerical contributions, wherein the score indicates at least one of: an indication of a security risk posed by the component or person that the object represents, an indication of performance of the component of the information technology environment that the object represents, or an indication of performance of the application that the object represents. | 1. A method, comprising: accessing, by one or more processing devices, a set of events, wherein each event in the set of events is associated with a time stamp and includes a portion of machine data indicative of performance or operation of an information technology environment; accessing an object-scoring rule that (i) includes a search query that determines when events meet a triggering condition; (ii) identifies an object representing a component of the information technology environment, an application running in the information technology environment, or a person using a component in the information technology environment, and (iii) specifies a numerical contribution to a score for the object, the numerical contribution to be applied to the score based at least on part on a determination that the triggering condition is met; executing the search query of the object-scoring rule against the set of events to determine if the triggering condition of the object-scoring rule is met; based on determining that the triggering condition is met, generating a record of the numerical contribution specified in the object-scoring rule, the record associating the numerical contribution with a time indicator and indicating the object whose score should be affected by the contribution; identifying, using one or more records of numerical contributions, a set of numerical contributions having associated time indicators falling within a defined time period; and calculating the score for the object based on the set of numerical contributions, wherein the score indicates at least one of: an indication of a security risk posed by the component or person that the object represents, an indication of performance of the component of the information technology environment that the object represents, or an indication of performance of the application that the object represents. 21. The method of claim 1 , wherein the time indicator specifies a time of occurrence of the triggering condition. | 0.911265 |
15. A system for acquiring and processing information about non-human animal diseases, the system comprising: a communication module configured to import veterinary-related text information, wherein the veterinary-related text information comprises information related to one or more non-human animal diseases; a parsing module configured to parse the veterinary-related text information into one or more terms; a relationship determination module configured to determine relations between the one or more terms; a classifying module configured to classify the one or more terms such that each term relates to one of: a non-human animal species, an animal disease, a medical sign and a treatment; and a database table generator configured to generate a database table associated with the imported veterinary-related text information, the database table comprising the one or more classified terms and the relations therebetween. | 15. A system for acquiring and processing information about non-human animal diseases, the system comprising: a communication module configured to import veterinary-related text information, wherein the veterinary-related text information comprises information related to one or more non-human animal diseases; a parsing module configured to parse the veterinary-related text information into one or more terms; a relationship determination module configured to determine relations between the one or more terms; a classifying module configured to classify the one or more terms such that each term relates to one of: a non-human animal species, an animal disease, a medical sign and a treatment; and a database table generator configured to generate a database table associated with the imported veterinary-related text information, the database table comprising the one or more classified terms and the relations therebetween. 18. The system of claim 15 , further comprising: a crawling module configured to craw one or more resources over a network to import the veterinary-related text information. | 0.553877 |
6. The method of claim 1 , wherein generating the parameter tree comprises traversing the first document to extract each parameter included in the first document. | 6. The method of claim 1 , wherein generating the parameter tree comprises traversing the first document to extract each parameter included in the first document. 7. The method of claim 6 , further comprising, for each extracted parameter, setting a value associated with a node included in the parameter tree and corresponding to the extracted parameter based on a value of the parameter. | 0.910741 |
1. A method of automatically configuring a portlet, the method comprising: receiving a portlet with content to be rendered as a portlet window object within a portal; examining the content of the portlet for discovering a contextual aspect; and automatically adjusting at least one attribute of the portlet window object based on the discovered contextual aspect. | 1. A method of automatically configuring a portlet, the method comprising: receiving a portlet with content to be rendered as a portlet window object within a portal; examining the content of the portlet for discovering a contextual aspect; and automatically adjusting at least one attribute of the portlet window object based on the discovered contextual aspect. 9. The method of claim 1 , wherein one of the contextual aspect and the attribute information are user editable. | 0.740826 |
14. A machine-readable storage device, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations comprising: receiving a plurality of photographic images of an object obtained from different positions relative to the object; processing the plurality of photographic images to determine a three-dimensional model of the object; generating a polygon mesh representation of a three-dimensional surface of the object; determining, from the polygon mesh representation of the three-dimensional surface of the object, a graphical representation of a surface of the object comprising a plurality of inscriptions indicative of encoded information; presenting the graphical representation of a surface of the object on a display device; receiving, a selection of a region of the graphical representation of the surface of the object, wherein the region bounds a subset of inscriptions of the plurality of inscriptions; transcribing the subset of inscriptions of the graphical representation of the surface of the object to determine a plurality of markings; associating a group of symbols with the plurality of markings; determining from at least one marking of the plurality of markings a plurality of alternative groups of symbols; determining a plurality of alternative transliterations of a symbol of one of the group of symbols, the plurality of alternative groups of symbols, or both; and determining a plurality of alternative translations of a transliteration of the plurality of alternative transliterations, wherein the graphical representation comprises a flattened surface representation of the three-dimensional surface of the object, and wherein the operations further comprise applying frequency analysis to the polygon mesh representation of the three-dimensional surface of the object, wherein the transcribing of the subset of inscriptions is based on the applying of the frequency analysis. | 14. A machine-readable storage device, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations comprising: receiving a plurality of photographic images of an object obtained from different positions relative to the object; processing the plurality of photographic images to determine a three-dimensional model of the object; generating a polygon mesh representation of a three-dimensional surface of the object; determining, from the polygon mesh representation of the three-dimensional surface of the object, a graphical representation of a surface of the object comprising a plurality of inscriptions indicative of encoded information; presenting the graphical representation of a surface of the object on a display device; receiving, a selection of a region of the graphical representation of the surface of the object, wherein the region bounds a subset of inscriptions of the plurality of inscriptions; transcribing the subset of inscriptions of the graphical representation of the surface of the object to determine a plurality of markings; associating a group of symbols with the plurality of markings; determining from at least one marking of the plurality of markings a plurality of alternative groups of symbols; determining a plurality of alternative transliterations of a symbol of one of the group of symbols, the plurality of alternative groups of symbols, or both; and determining a plurality of alternative translations of a transliteration of the plurality of alternative transliterations, wherein the graphical representation comprises a flattened surface representation of the three-dimensional surface of the object, and wherein the operations further comprise applying frequency analysis to the polygon mesh representation of the three-dimensional surface of the object, wherein the transcribing of the subset of inscriptions is based on the applying of the frequency analysis. 17. The machine-readable storage device of claim 14 , wherein the one of the plurality of alternative groups of symbols, the plurality of alternative transliterations, or the plurality of alternative translations are presented on the display device according to a ranked order. | 0.584619 |
28. The non-transitory computer readable storage medium as claimed in claim 27 , wherein the at least one responsibility type comprises at least one of responsible, accountable, consulted and informed types. | 28. The non-transitory computer readable storage medium as claimed in claim 27 , wherein the at least one responsibility type comprises at least one of responsible, accountable, consulted and informed types. 29. The non-transitory computer readable storage medium as claimed in claim 28 , wherein the responsibility type data is a matrix. | 0.95271 |
12. An article of manufacture comprising: a computer-readable medium having instructions stored thereon, which when executed by a processor on the computer, cause the computer to perform a method of disseminating online promotion campaigns, the instructions comprising: instructions for integrating an online promotion campaign into an application integrated with a social media platform via an application programming interface (API)), wherein the application is configured to enable accessing and harvesting of profile information of users of the social media platform from the social media platform for the online promotion campaign, wherein the social media platform further includes viral features that can be leveraged to disseminate the online promotion campaign, and wherein the online promotion campaign includes distributing coupons or vouchers to participants, wherein the coupons or vouchers are integrated with the API to provide personalized information about participants of the online promotion campaign on the coupons or vouchers; instructions for receiving interest in the online promotion campaign from a participant through an entry point; instructions for harvesting for the online promotion campaign identification information from a social media profile of the participant maintained by the social media platform; instructions in response to the received interest in the online promotion campaign from the participant for generating a personalized coupon or voucher for the participant containing at least some of the participant's identification information; and instructions for distributing the personalized coupon or voucher to the participant, wherein the identification information on the coupon or voucher provides a higher level of security and authenticity than vouchers or coupons that do not contain such identification information. | 12. An article of manufacture comprising: a computer-readable medium having instructions stored thereon, which when executed by a processor on the computer, cause the computer to perform a method of disseminating online promotion campaigns, the instructions comprising: instructions for integrating an online promotion campaign into an application integrated with a social media platform via an application programming interface (API)), wherein the application is configured to enable accessing and harvesting of profile information of users of the social media platform from the social media platform for the online promotion campaign, wherein the social media platform further includes viral features that can be leveraged to disseminate the online promotion campaign, and wherein the online promotion campaign includes distributing coupons or vouchers to participants, wherein the coupons or vouchers are integrated with the API to provide personalized information about participants of the online promotion campaign on the coupons or vouchers; instructions for receiving interest in the online promotion campaign from a participant through an entry point; instructions for harvesting for the online promotion campaign identification information from a social media profile of the participant maintained by the social media platform; instructions in response to the received interest in the online promotion campaign from the participant for generating a personalized coupon or voucher for the participant containing at least some of the participant's identification information; and instructions for distributing the personalized coupon or voucher to the participant, wherein the identification information on the coupon or voucher provides a higher level of security and authenticity than vouchers or coupons that do not contain such identification information. 18. The article of manufacture of claim 12 , wherein the viral features include one or more of newsfeeds, minifeeds or contact invites. | 0.555425 |
6. A method according to claim 5 comprising the step of storing each one of the second and subsequent idiom words together with an idiom tag into the high frequency file, the idiom tag designating that the word is not the first word of the idiom. | 6. A method according to claim 5 comprising the step of storing each one of the second and subsequent idiom words together with an idiom tag into the high frequency file, the idiom tag designating that the word is not the first word of the idiom. 7. A method according to claim 6 comprising the step of storing the first word of an idiom together with a tag into the high frequency file, the tag designating that the word is the first word of an idiom. | 0.924473 |
1. A computer natural language translation system, comprising: means for inputting source language text; means for outputting target language text; and transfer means for generating said target language text from said source language text using stored translation data generated from examples of source and corresponding target language texts, wherein said stored translation data comprises a plurality of translation units, each unit comprising: respective surface data representative of the order of occurrence of language units of said source and target languages; and respective dependency data related to the semantic relationship between said language units of said source and target languages; the dependency data of language units of said source language being aligned with corresponding dependency data of language units of said target language, and wherein said transfer means comprises: (i) analyzing means for analyzing said source language text using said surface data of said source language; (ii) generating means for generating said target language text using said surface data of said target language; and (iii) transforming means for transforming the analysis of said source text into an analysis for said target language using said dependency data. | 1. A computer natural language translation system, comprising: means for inputting source language text; means for outputting target language text; and transfer means for generating said target language text from said source language text using stored translation data generated from examples of source and corresponding target language texts, wherein said stored translation data comprises a plurality of translation units, each unit comprising: respective surface data representative of the order of occurrence of language units of said source and target languages; and respective dependency data related to the semantic relationship between said language units of said source and target languages; the dependency data of language units of said source language being aligned with corresponding dependency data of language units of said target language, and wherein said transfer means comprises: (i) analyzing means for analyzing said source language text using said surface data of said source language; (ii) generating means for generating said target language text using said surface data of said target language; and (iii) transforming means for transforming the analysis of said source text into an analysis for said target language using said dependency data. 6. A system according to claim 1 , in which said translation units include data from which a said source language analysis can be constructed which can be represented in the form of a graph. | 0.538678 |
3. The method of claim 2 , wherein the relationship measures comprise the cohesion value and the correlation value. | 3. The method of claim 2 , wherein the relationship measures comprise the cohesion value and the correlation value. 5. The method of claim 3 , wherein identifying the at least one unknown term as a specialization or a new entity comprises: combining the cohesion value and the correlation value into a single score based on a ranking function; and comparing the single score to one or more threshold scores, wherein whether the at least one unknown term is a specialization or a new entity is based on the comparing. | 0.894195 |
8. The method of claim 1 , wherein the data structure for the dictionary is a prefix tree, the method further comprising: changing a set of characters in a misspelled word; after changing each character, discarding the change when a resulting string is not found in the dictionary prefix tree; and identifying a set of suggestions for the misspelled word when changing the set of characters in the misspelled word results in a valid string in the dictionary prefix tree. | 8. The method of claim 1 , wherein the data structure for the dictionary is a prefix tree, the method further comprising: changing a set of characters in a misspelled word; after changing each character, discarding the change when a resulting string is not found in the dictionary prefix tree; and identifying a set of suggestions for the misspelled word when changing the set of characters in the misspelled word results in a valid string in the dictionary prefix tree. 13. The method of claim 8 further comprising: scoring the set of suggestions for the misspelled word based on a number of characters that are changed in the misspelled word before changing a set of characters in the misspelled word results a valid string in the dictionary prefix tree; and displaying up to a predetermined number of high scored suggestions. | 0.750987 |
1. A computer-implemented method for purchasing an advertisement opportunity based on a knowledge representation, the method comprising: obtaining purchaser-context information associated with at least one purchaser; computing, using at least one processor executing stored program instructions, a value, specific to the at least one purchaser, of an advertisement opportunity associated with at least one advertising word or phrase, the computing comprising: identifying, in a knowledge representation encoded as a data structure, a first concept representing at least a portion of the purchaser-context information, identifying, in the knowledge representation, a second concept with which the at least one advertising word or phrase shares at least one label, determining a semantic relevance between the first concept and the second concept based at least in part on the first and second concepts' relative positions in a topology of the knowledge representation, and computing the value of the advertisement opportunity as a function of the determined semantic relevance between the first and second concepts; comparing the computed value to a purchase price of the advertisement opportunity; and purchasing the advertisement opportunity in response to determining that the computed value exceeds the purchase price. | 1. A computer-implemented method for purchasing an advertisement opportunity based on a knowledge representation, the method comprising: obtaining purchaser-context information associated with at least one purchaser; computing, using at least one processor executing stored program instructions, a value, specific to the at least one purchaser, of an advertisement opportunity associated with at least one advertising word or phrase, the computing comprising: identifying, in a knowledge representation encoded as a data structure, a first concept representing at least a portion of the purchaser-context information, identifying, in the knowledge representation, a second concept with which the at least one advertising word or phrase shares at least one label, determining a semantic relevance between the first concept and the second concept based at least in part on the first and second concepts' relative positions in a topology of the knowledge representation, and computing the value of the advertisement opportunity as a function of the determined semantic relevance between the first and second concepts; comparing the computed value to a purchase price of the advertisement opportunity; and purchasing the advertisement opportunity in response to determining that the computed value exceeds the purchase price. 4. The computer-implemented method of claim 1 , wherein identifying the second concept comprises: obtaining a plurality of concepts semantically relevant to the first concept; computing, for each of one or more concepts in the plurality of concepts, a score indicative of the semantic relevance of the respective concept to the first concept; and selecting the second concept from the one or more concepts based on the computed scores. | 0.52022 |
10. An apparatus comprising at least one processor and at least one memory storing computer program code, wherein the at least one memory and stored computer program code are configured to, with the at least one processor, cause the apparatus to at least: establish a call connection to at least a second apparatus; monitor a conversation during the call in order to detect at least one predetermined context-related keyword received in one of the first or the second apparatus and repeated in the other of the first or the second apparatus; and provide an indication, in response to detecting at least one repeated predetermined context-related keyword, about the detected context-related keyword to a user, said indication enabling opening an application linked to said context-related keyword. | 10. An apparatus comprising at least one processor and at least one memory storing computer program code, wherein the at least one memory and stored computer program code are configured to, with the at least one processor, cause the apparatus to at least: establish a call connection to at least a second apparatus; monitor a conversation during the call in order to detect at least one predetermined context-related keyword received in one of the first or the second apparatus and repeated in the other of the first or the second apparatus; and provide an indication, in response to detecting at least one repeated predetermined context-related keyword, about the detected context-related keyword to a user, said indication enabling opening an application linked to said context-related keyword. 11. The apparatus according to claim 10 , wherein the at least one memory and stored computer program code are further configured to, with the at least one processor, cause the apparatus to: monitor the conversation by a speech recognition application including training data for keywords related to time, a person and/or a location. | 0.605754 |
1. A finite automaton generation system comprising: an NFA conversion unit that increases a number of characters of a transition condition of a finite automaton which has a transition condition with a fixed number of characters, to any specified number of characters; and a result output unit that outputs a finite automaton that has a transition condition with the number of characters thereof increased to any specified number of characters, wherein said NFA conversion unit and the result output unit are configured out of hardware. | 1. A finite automaton generation system comprising: an NFA conversion unit that increases a number of characters of a transition condition of a finite automaton which has a transition condition with a fixed number of characters, to any specified number of characters; and a result output unit that outputs a finite automaton that has a transition condition with the number of characters thereof increased to any specified number of characters, wherein said NFA conversion unit and the result output unit are configured out of hardware. 8. The finite automaton generation system according to claim 1 , comprising: an NFA description matrix storage unit that stores a finite automaton that is described in a matrix form in advance and has a transition condition with a fixed number of characters; the NFA conversion unit that performs conversion in which a number of characters of a transition condition of the finite automaton, stored in the NFA description matrix storage unit and described as a matrix, is increased; an NFA conversion result matrix storage unit that stores a finite automaton description matrix halfway-converted by the NFA conversion unit; and the result output unit that outputs a finite automaton that has a transition condition with the number of characters thereof increased to any specified number of characters. | 0.637993 |
9. The method of claim 3 , wherein at least one instance of adverse event content comprises an implicit adverse event characterization, and the method further comprises deriving an adverse event characterization from the implicit adverse characterization. | 9. The method of claim 3 , wherein at least one instance of adverse event content comprises an implicit adverse event characterization, and the method further comprises deriving an adverse event characterization from the implicit adverse characterization. 10. The method of claim 9 , wherein: the derived adverse event characterization comprises the set of reaction name, and nominal frequency of occurrence. | 0.925584 |
11. The method of claim 10 , further comprising: determining whether a next segment exists as a next segment in the super file if the current segment exists in the super file; and appending the last predetermined number of terms of the current segment to the super file if the next segment does not exist. | 11. The method of claim 10 , further comprising: determining whether a next segment exists as a next segment in the super file if the current segment exists in the super file; and appending the last predetermined number of terms of the current segment to the super file if the next segment does not exist. 12. The method of claim 11 , further comprising: determining whether a next segment of the file exist within the super file if the current segment does not exist in the super file; and appending the first predetermined number of terms of the next segment to the super file if the next segment exists in the super file. | 0.909529 |
5. A non-transitory computer readable storage medium storing computer instructions, which when executed, enable a computer hardware system to process a content source, the processing comprising: analyzing unstructured data contained in the content source to generate a set of structured information about the content source; extracting the set of structured information about the content source; identifying and aggregating a set of documents related to the content source by comparing the set of structured information with metadata stored in a metadata database, wherein the metadata stored in the metadata database is indexed from a set of technical reference publications, wherein a technical reference publication is identified as related to the content source and added to the set of documents related to the content source when the set of structured information extracted from the content source matches an associated metadata of the technical reference publication document; annotating the content source with the structured information extracted from the content source and with metadata associated with each technical reference publication in the set of related documents; and ranking the metadata in the annotated content source, wherein in a case in which more than one technical reference publication in the set of related documents is associated with a piece of metadata, the piece of metadata is assigned a higher rank of importance relative to a piece of metadata which is associated with fewer technical reference publications in the set of related documents. | 5. A non-transitory computer readable storage medium storing computer instructions, which when executed, enable a computer hardware system to process a content source, the processing comprising: analyzing unstructured data contained in the content source to generate a set of structured information about the content source; extracting the set of structured information about the content source; identifying and aggregating a set of documents related to the content source by comparing the set of structured information with metadata stored in a metadata database, wherein the metadata stored in the metadata database is indexed from a set of technical reference publications, wherein a technical reference publication is identified as related to the content source and added to the set of documents related to the content source when the set of structured information extracted from the content source matches an associated metadata of the technical reference publication document; annotating the content source with the structured information extracted from the content source and with metadata associated with each technical reference publication in the set of related documents; and ranking the metadata in the annotated content source, wherein in a case in which more than one technical reference publication in the set of related documents is associated with a piece of metadata, the piece of metadata is assigned a higher rank of importance relative to a piece of metadata which is associated with fewer technical reference publications in the set of related documents. 6. The non-transitory computer readable storage medium of claim 5 , wherein the set of structured information further comprises key words associated with a technology field. | 0.828402 |
1. A method performed by a data processing device, the method comprising: receiving, at the data processing device, spoken language-based content; processing, at the data processing device, the received language-based content to translate the received language-based content into a target language; detecting, at the data processing device, a presence of a cultural sensitivity in the received language-based content; and determining, at the data processing device, guidance for dealing with the detected cultural sensitivity. | 1. A method performed by a data processing device, the method comprising: receiving, at the data processing device, spoken language-based content; processing, at the data processing device, the received language-based content to translate the received language-based content into a target language; detecting, at the data processing device, a presence of a cultural sensitivity in the received language-based content; and determining, at the data processing device, guidance for dealing with the detected cultural sensitivity. 4. The method of claim 1 , further comprising detecting, at the data processing device, a presence of a social sensitivity in the received language-based content. | 0.603521 |
40. The method of claim 39 , wherein the method further comprises the step of determining the alternative query block based on the first query block. | 40. The method of claim 39 , wherein the method further comprises the step of determining the alternative query block based on the first query block. 44. The method of claim 40 , wherein the query contains a predicate, and the first query block is an inline view; and wherein the step of determining the alternative query block comprises: pushing the predicate into the inline view. | 0.965826 |
5. The method of claim 4 , wherein extracting the second-order scatter features from the acoustic input signal further comprises passing at least a portion of the acoustic input signal through a second wavelet transform. | 5. The method of claim 4 , wherein extracting the second-order scatter features from the acoustic input signal further comprises passing at least a portion of the acoustic input signal through a second wavelet transform. 9. The method of claim 5 , wherein the extracting step is performed in a multi-resolution manner based on wavelets having different quality factors. | 0.920888 |
10. A computer-implemented method, comprising: providing parameters with particular code corresponding to a first module selectively designated for inclusion in a personalized container document, wherein the parameters are associated with the first module for use in generating module data for the first module, wherein module data for the first module is adapted for use in a personalized container document, and wherein the parameters of the particular code include a first content element and one or more preference elements; specifying, with additional code, a second module selectively designated for inclusion in the personalized container document, wherein the additional code provides parameters associated with the second module for use in generating module data for the second module, wherein the module data for the second module is adapted for use in the personalized container document, the parameters of the additional code including a second content element; wherein the personalized container document defines an organization for a presentation of content associated with the first module and the second module in a container document display, wherein for each module a portion of the container document display is allocated for the presentation of content corresponding to the module; wherein module data for each module designated for inclusion in the container document is adapted to be served with the container document to a remote browser client, the module data for each module including computer-executable instructions executed on a hardware processor by a remote browser client to render content for the module for presentation in the container document display; and wherein the first content element is different than the second content element and the one or more preference elements include at least one module preference element adapted to specify at least two alternative presentation states of content for the first module, the at least one module preference element defining conditions that change independent of user input in the container document display for dynamically presenting content in one of the at least two presentation states, with content rendered, using the computer-executable instructions executed by the remote browser client, in a first of the at least two presentation states in response to a first condition and rendered, using the computer-executable instructions executed by the remote browser client, in a second of the at least two presentation states in response to a second condition. | 10. A computer-implemented method, comprising: providing parameters with particular code corresponding to a first module selectively designated for inclusion in a personalized container document, wherein the parameters are associated with the first module for use in generating module data for the first module, wherein module data for the first module is adapted for use in a personalized container document, and wherein the parameters of the particular code include a first content element and one or more preference elements; specifying, with additional code, a second module selectively designated for inclusion in the personalized container document, wherein the additional code provides parameters associated with the second module for use in generating module data for the second module, wherein the module data for the second module is adapted for use in the personalized container document, the parameters of the additional code including a second content element; wherein the personalized container document defines an organization for a presentation of content associated with the first module and the second module in a container document display, wherein for each module a portion of the container document display is allocated for the presentation of content corresponding to the module; wherein module data for each module designated for inclusion in the container document is adapted to be served with the container document to a remote browser client, the module data for each module including computer-executable instructions executed on a hardware processor by a remote browser client to render content for the module for presentation in the container document display; and wherein the first content element is different than the second content element and the one or more preference elements include at least one module preference element adapted to specify at least two alternative presentation states of content for the first module, the at least one module preference element defining conditions that change independent of user input in the container document display for dynamically presenting content in one of the at least two presentation states, with content rendered, using the computer-executable instructions executed by the remote browser client, in a first of the at least two presentation states in response to a first condition and rendered, using the computer-executable instructions executed by the remote browser client, in a second of the at least two presentation states in response to a second condition. 11. The computer-implemented method of claim 10 wherein at least one of the particular code or additional code specifies a module using user preferences. | 0.534495 |
12. A method comprising: receiving, at a server, from a client, one or more input search terms; determining a category for the one or more input search terms; and in response to the server receiving the one or more input search terms: the server retrieving a plurality of coupons from a database based on a search conducted based on the received one or more input search terms, wherein the database maps at least one search term to at least one coupon; identifying at least a particular coupon of the plurality of coupons in the database based on a particular search term within the determined category, wherein the particular search term is a term other than the one or more input search terms; the server filtering the retrieved plurality of coupons to select which coupons in the plurality of coupons to return as one or more search results for the one or more input search terms, wherein the filtering is based at least partially upon a first criterion, wherein the first criterion is one of: a number of times a given coupon in the plurality of coupons has been previously printed, or a number of times a given coupon in the plurality of coupons has been previously redeemed; and the server returning to the client, as the one or more search results for the one or more input search terms, only a filtered set of one or more coupons comprising coupons selected as a result of the filtering; wherein the filtered set of one or more coupons returned as the one or more search results is smaller than the plurality of coupons retrieved from the database based on the one or more input search terms; wherein the method is performed by one or more computing devices. | 12. A method comprising: receiving, at a server, from a client, one or more input search terms; determining a category for the one or more input search terms; and in response to the server receiving the one or more input search terms: the server retrieving a plurality of coupons from a database based on a search conducted based on the received one or more input search terms, wherein the database maps at least one search term to at least one coupon; identifying at least a particular coupon of the plurality of coupons in the database based on a particular search term within the determined category, wherein the particular search term is a term other than the one or more input search terms; the server filtering the retrieved plurality of coupons to select which coupons in the plurality of coupons to return as one or more search results for the one or more input search terms, wherein the filtering is based at least partially upon a first criterion, wherein the first criterion is one of: a number of times a given coupon in the plurality of coupons has been previously printed, or a number of times a given coupon in the plurality of coupons has been previously redeemed; and the server returning to the client, as the one or more search results for the one or more input search terms, only a filtered set of one or more coupons comprising coupons selected as a result of the filtering; wherein the filtered set of one or more coupons returned as the one or more search results is smaller than the plurality of coupons retrieved from the database based on the one or more input search terms; wherein the method is performed by one or more computing devices. 16. The method of claim 12 , wherein the first criterion is the number of times the given coupon in the plurality of coupons has been previously redeemed. | 0.645909 |
13. A computer program product comprising: a computer-usable storage medium having computer-usable program code stored thereon that, when executed by a system comprising a processor and a memory, causes the system to perform a method of processing an Extensible Markup Language (XML) document, the method comprising: loading, via the processor, an execution plan into a virtual machine, wherein the execution plan represents an XML schema and specifies a hierarchy of XML components, and wherein the virtual machine comprises a plurality of dedicated XML processing functions specifically corresponding to the XML components specified by the XML schema; selectively invoking, via the processor, the XML processing functions available within the virtual machine according to the execution plan, wherein the XML processing functions operate upon an XML document; and outputting, via the processor, an indication of whether the XML document is valid according to the XML processing functions. | 13. A computer program product comprising: a computer-usable storage medium having computer-usable program code stored thereon that, when executed by a system comprising a processor and a memory, causes the system to perform a method of processing an Extensible Markup Language (XML) document, the method comprising: loading, via the processor, an execution plan into a virtual machine, wherein the execution plan represents an XML schema and specifies a hierarchy of XML components, and wherein the virtual machine comprises a plurality of dedicated XML processing functions specifically corresponding to the XML components specified by the XML schema; selectively invoking, via the processor, the XML processing functions available within the virtual machine according to the execution plan, wherein the XML processing functions operate upon an XML document; and outputting, via the processor, an indication of whether the XML document is valid according to the XML processing functions. 15. The computer program product of claim 13 , wherein the virtual machine consists only of functions that process XML documents. | 0.782689 |
8. The system of claim 4 , wherein the clustering module further comprising a product graph module to combine the linegraphs generated for each merger input into a single product graph comprising a unique node for each combination of nodes from the linegraphs. | 8. The system of claim 4 , wherein the clustering module further comprising a product graph module to combine the linegraphs generated for each merger input into a single product graph comprising a unique node for each combination of nodes from the linegraphs. 11. The system of claim 8 , wherein clustering module further comprises a clique module to identify a largest clique in the single product graph, the largest clique comprising a largest combined graph pattern obtainable from the merger input graph patterns of the selected pair of merger inputs; the clustering module configured to use a size of the largest clique to determine if the selected pair of merger inputs comprise sufficient structural similarities. | 0.881635 |
7. The method according to claim 1 further comprising: importing the content package file to a portal server computer. | 7. The method according to claim 1 further comprising: importing the content package file to a portal server computer. 8. The method according to claim 7 further comprising: storing the content package file and the first level content files on the portal server computer, wherein the first level content files overwrite duplicative files that are stored on the portal server computer. | 0.896819 |
1. A computer network-based messaging system including a multiple-layer chat filtering system for controlling the content of messages sent by users in the messaging system, the messaging system comprising: a user computer that receives a message, the message including a plurality of words entered by a sender; one or more data storage devices including: a word database including a plurality of permitted words which are allowed to be transmitted, and a phrase database including a plurality of prohibited phrases which are not allowed to be transmitted, at least one of the plurality of prohibited phrases consisting of a plurality of individual words that are each included in the word database, wherein said phrase database is not part of the user computer in the computer network and said user computer is operative to send information addressed to a messaging server system that is remote from the user computer, and said user computer receives an indication from the phrase database of whether said information includes the prohibited phrases; and a message sending part that transmits the message over a computer network only if all of the plurality of words entered by the sender are contained in the word database and none of the plurality of prohibited phrases are contained in the message, wherein the user computer provides a display of the message to the sender prior to the message sending part transmitting the message, and wherein the display highlights words included in the message that must be removed before the message sending part will transmit the message over the computer network. | 1. A computer network-based messaging system including a multiple-layer chat filtering system for controlling the content of messages sent by users in the messaging system, the messaging system comprising: a user computer that receives a message, the message including a plurality of words entered by a sender; one or more data storage devices including: a word database including a plurality of permitted words which are allowed to be transmitted, and a phrase database including a plurality of prohibited phrases which are not allowed to be transmitted, at least one of the plurality of prohibited phrases consisting of a plurality of individual words that are each included in the word database, wherein said phrase database is not part of the user computer in the computer network and said user computer is operative to send information addressed to a messaging server system that is remote from the user computer, and said user computer receives an indication from the phrase database of whether said information includes the prohibited phrases; and a message sending part that transmits the message over a computer network only if all of the plurality of words entered by the sender are contained in the word database and none of the plurality of prohibited phrases are contained in the message, wherein the user computer provides a display of the message to the sender prior to the message sending part transmitting the message, and wherein the display highlights words included in the message that must be removed before the message sending part will transmit the message over the computer network. 3. The messaging system of claim 1 , wherein the display highlights phrases included in the message that are contained in the phrase database. | 0.649824 |
7. A method of managing data to support usage of the data in content, the method comprising: describing concepts of a plurality of models of the data in an ontology; structuring content in accordance with a semantic-independent schema that does not model the subject matter of the data, the structuring including embedding in the content one or more data-centric components transformable to incorporate the data into the content without affecting other components of the content structure; deriving a validation schema from the ontology; and using the validation schema, creating a transform for extracting one or more objects from the data-centric components and validating the one or more extracted objects to validate the content. | 7. A method of managing data to support usage of the data in content, the method comprising: describing concepts of a plurality of models of the data in an ontology; structuring content in accordance with a semantic-independent schema that does not model the subject matter of the data, the structuring including embedding in the content one or more data-centric components transformable to incorporate the data into the content without affecting other components of the content structure; deriving a validation schema from the ontology; and using the validation schema, creating a transform for extracting one or more objects from the data-centric components and validating the one or more extracted objects to validate the content. 14. The method of claim 7 , further comprising generating a report of results of the validating. | 0.617581 |
1. An intrusion masquerade detection method, comprising: applying a compression algorithm to one or more sets of user input content data to build a set of user grammars; forming at least one model for the set of user grammars using characteristics of the set of user grammars, the characteristics including a first estimated algorithmic minimum sufficient statistic for the set of user grammars; storing the at least one model for the set of user grammars in a database; calculating a second estimated algorithmic minimum sufficient statistic for at least one target block of other input content data by applying the compression algorithm to the at least one target block of the other input content data; determining a distance between the at least one target block of the other input content data and the at least model for the set of user grammars by comparing the first estimated algorithmic minimum sufficient statistic and the second estimated algorithmic minimum sufficient statistic; determining an intrusion masquerade based on the determined distance; and outputting an indication of the determined intrusion masquerade. | 1. An intrusion masquerade detection method, comprising: applying a compression algorithm to one or more sets of user input content data to build a set of user grammars; forming at least one model for the set of user grammars using characteristics of the set of user grammars, the characteristics including a first estimated algorithmic minimum sufficient statistic for the set of user grammars; storing the at least one model for the set of user grammars in a database; calculating a second estimated algorithmic minimum sufficient statistic for at least one target block of other input content data by applying the compression algorithm to the at least one target block of the other input content data; determining a distance between the at least one target block of the other input content data and the at least model for the set of user grammars by comparing the first estimated algorithmic minimum sufficient statistic and the second estimated algorithmic minimum sufficient statistic; determining an intrusion masquerade based on the determined distance; and outputting an indication of the determined intrusion masquerade. 3. The intrusion masquerade detection method of claim 1 , the determining an intrusion masquerade including: calculating an inverse compression ratio over a time period for the at least one target block of the other input content data; comparing the calculated inverse compression ratio for the at least one target block of the other input content data to at least one inverse compression ratio associated with a compressed data set of the at least one model for the set of user grammars; and determining an intrusion masquerade event based on a difference between the calculated inverse compression ratio for the at least one target block of the other input content data and the at least one inverse compression ratio associated with the compressed data set of the at least one model for the set of user grammars exceeding a threshold. | 0.532275 |
3. The method of claim 2 , wherein the subset of the content related to the post object comprises one or more social network conversations. | 3. The method of claim 2 , wherein the subset of the content related to the post object comprises one or more social network conversations. 5. The method of claim 3 , wherein analyzing the content from the plurality of data sources comprises performing semantic analysis on the one or more social network conversations, and applying one or more tags to the one or more social network conversations based at least in part upon a result of the semantic analysis. | 0.959462 |
1. A computer system which generates user profiles in a computerized application, comprising: at least one memory which contains at least one program comprising the steps of: providing a user with a questionnaire to determine at least one of the user's intelligence, personality, emotional state, computer experience, sensory skills, motor skills, education, or training; determining a type of user profile based on information received from said questionnaire; creating a test case for the user based upon user responses to said questionnaire; receiving and storing unique user preferences received from the user in response to said test case, in a database, for future utilization by the user; compiling a comprehensive user profile based on said unique user preferences, which is assigned specifically to the user; tracking and storing all computer functions, tools and commands executed by the user, in said database, to create user and task-specific statistical patterns of utilization of said comprehensive user profile; modifying computer programs used by said user, said user preferences, and said comprehensive user profiles, based on results of said tracking step; receiving affective measurements on said user to determine an emotional state of the user; modifying user profiles of said computer programs in response to said affective measurements; analyzing said information from said questionnaire and responses relative to groups of users to determine similarities in profiles of different groups of users; and initiating a response when said affective measurements exceed a predetermined threshold, said response including at least one of an automated time-out from use of the computerized application, a decrease in a number of computer system tools available for use by the user, a simplification of the user interface, environmental changes, or a decrease in navigation speed of input means; and a processor which executes the program. | 1. A computer system which generates user profiles in a computerized application, comprising: at least one memory which contains at least one program comprising the steps of: providing a user with a questionnaire to determine at least one of the user's intelligence, personality, emotional state, computer experience, sensory skills, motor skills, education, or training; determining a type of user profile based on information received from said questionnaire; creating a test case for the user based upon user responses to said questionnaire; receiving and storing unique user preferences received from the user in response to said test case, in a database, for future utilization by the user; compiling a comprehensive user profile based on said unique user preferences, which is assigned specifically to the user; tracking and storing all computer functions, tools and commands executed by the user, in said database, to create user and task-specific statistical patterns of utilization of said comprehensive user profile; modifying computer programs used by said user, said user preferences, and said comprehensive user profiles, based on results of said tracking step; receiving affective measurements on said user to determine an emotional state of the user; modifying user profiles of said computer programs in response to said affective measurements; analyzing said information from said questionnaire and responses relative to groups of users to determine similarities in profiles of different groups of users; and initiating a response when said affective measurements exceed a predetermined threshold, said response including at least one of an automated time-out from use of the computerized application, a decrease in a number of computer system tools available for use by the user, a simplification of the user interface, environmental changes, or a decrease in navigation speed of input means; and a processor which executes the program. 3. The computer system according to claim 1 , wherein said steps further comprise: weighting various factors in said comprehensive user profile such that an educational profile is created for said user which is appropriate for on-line learning of said user; and exporting said educational profile to all internet sites queried by said user. | 0.5 |
21. A method according to claim 20 , wherein the placing step includes the step of placing in said table additional data associated with said exported elements. | 21. A method according to claim 20 , wherein the placing step includes the step of placing in said table additional data associated with said exported elements. 22. A method according to claim 21 , wherein said additional data includes said frame sequence numbers, said positions, and said settings. | 0.96911 |
9. A medium storing processor-executable instructions that when executed by a processor perform a method, the method comprising: receiving time-series data of each of a plurality of real-time data streams from one or more sensors, the plurality of real-time data streams comprising a primary data stream representing a measured value, a low limit data stream representing a low limit of the measured value, a high limit data stream representing a high limit of the measured value, and a target value data stream representing a target value of the measured value, wherein the time-series data of the primary data stream includes a plurality of time stamps and a plurality of data values that are separate from the plurality of time stamps, each of the plurality of data values of the primary data stream being associated with a respective one of the plurality of time stamps and representing the measured value, wherein the time-series data of the low limit data stream includes a plurality of data values that are separate from the plurality of time stamps and the plurality of data values of the primary data stream, each of the plurality of data values of the low limit data stream being associated with a respective one of the plurality of time stamps and representing a low limit of the measured value, wherein the time-series data of the high limit data stream includes a plurality of data values that are separate from the plurality of time stamps, the plurality of data values of the primary data stream and the plurality of data values of the low limit data stream, each of the plurality of data values of the high limit data stream being associated with a respective one of the plurality of time stamps and representing a high limit of the measured value, and wherein the time-series data of the target value data stream includes a plurality of data values that are separate from the plurality of time stamps, the plurality of data values of the primary data stream, the plurality of data values of the low limit data stream and the plurality of data values of the high limit data stream, each of the plurality of data values of the target value data stream being associated with a respective one of the plurality of time stamps and representing a target value of the measured value; and creating an electronic file based on the plurality of real-time data streams, the electronic file including a data portion and a metadata portion, wherein the metadata portion of the electronic file comprises, for each of the plurality of data streams, one or more data stream identifiers, a time range, a source type, and a data type, and wherein the data portion comprises the time-series data of each of the plurality of real-time data streams. | 9. A medium storing processor-executable instructions that when executed by a processor perform a method, the method comprising: receiving time-series data of each of a plurality of real-time data streams from one or more sensors, the plurality of real-time data streams comprising a primary data stream representing a measured value, a low limit data stream representing a low limit of the measured value, a high limit data stream representing a high limit of the measured value, and a target value data stream representing a target value of the measured value, wherein the time-series data of the primary data stream includes a plurality of time stamps and a plurality of data values that are separate from the plurality of time stamps, each of the plurality of data values of the primary data stream being associated with a respective one of the plurality of time stamps and representing the measured value, wherein the time-series data of the low limit data stream includes a plurality of data values that are separate from the plurality of time stamps and the plurality of data values of the primary data stream, each of the plurality of data values of the low limit data stream being associated with a respective one of the plurality of time stamps and representing a low limit of the measured value, wherein the time-series data of the high limit data stream includes a plurality of data values that are separate from the plurality of time stamps, the plurality of data values of the primary data stream and the plurality of data values of the low limit data stream, each of the plurality of data values of the high limit data stream being associated with a respective one of the plurality of time stamps and representing a high limit of the measured value, and wherein the time-series data of the target value data stream includes a plurality of data values that are separate from the plurality of time stamps, the plurality of data values of the primary data stream, the plurality of data values of the low limit data stream and the plurality of data values of the high limit data stream, each of the plurality of data values of the target value data stream being associated with a respective one of the plurality of time stamps and representing a target value of the measured value; and creating an electronic file based on the plurality of real-time data streams, the electronic file including a data portion and a metadata portion, wherein the metadata portion of the electronic file comprises, for each of the plurality of data streams, one or more data stream identifiers, a time range, a source type, and a data type, and wherein the data portion comprises the time-series data of each of the plurality of real-time data streams. 22. The medium of claim 9 , wherein one of the plurality of data values of the primary data stream, one of the plurality of data values of the low limit data stream, one of the plurality of data values of the high limit data stream and one of the plurality of data values of the target value data stream are each associated with a particular one of the plurality of time stamps and are recorded at a time represented by the particular one of the plurality of time stamps. | 0.5 |
13. A system according to claim 12 , wherein the model identifies specific relationships between the elements of the model, and each of the model elements has defined properties, and wherein: at least some of the documents include first and second types of text; the first type of text is retrieved from the properties of the model elements; the second type of text is reproduced verbatim from a template; and the structure of the documents is derived from the structure of the model. | 13. A system according to claim 12 , wherein the model identifies specific relationships between the elements of the model, and each of the model elements has defined properties, and wherein: at least some of the documents include first and second types of text; the first type of text is retrieved from the properties of the model elements; the second type of text is reproduced verbatim from a template; and the structure of the documents is derived from the structure of the model. 17. A system according to claim 13 , further comprising: a graph editor; and wherein: when the user selects a portion of the document, said graph editor opens on the portion of the model represented by said selected portion of the document; and when the user uses the graph editor to make changes to the model, the changed model content is refreshed wherever said model content appears in all of the documents. | 0.807803 |
35. The computer system of claim 32 , wherein: the practice gesture to be performed is presented in the first display area; and wherein the executable instructions further cause the computer system to: compare the detected practice gesture to the gesture to be performed; and if the detected practice gesture does not correspond to the gesture to be performed, provide a negative feedback indicator; and if the detected gesture does correspond to the gesture to be performed, provide a positive feedback indicator. | 35. The computer system of claim 32 , wherein: the practice gesture to be performed is presented in the first display area; and wherein the executable instructions further cause the computer system to: compare the detected practice gesture to the gesture to be performed; and if the detected practice gesture does not correspond to the gesture to be performed, provide a negative feedback indicator; and if the detected gesture does correspond to the gesture to be performed, provide a positive feedback indicator. 37. The computer system of claim 35 wherein the computer system comprises at least one of a handheld computer, a personal digital assistant, a media player, and a mobile telephone. | 0.753469 |
11. A method of ranking entities using reputation or influence scores, comprising: using a processor to determine reputation scores for one or more subjects based on connections, the reputation scores indicating reputations of the subjects; providing a plurality of citations, each citation representing an online posting of an expression of opinion by a subject on an object, wherein the subject is representative of a user; using a processor to select a subset of citations for each object from the citations citing each object, the content of the citations in the selected subset matching one or more of search terms for a search query; assigning citation scores to a subset of a plurality of objects, the citation scores indicating relevance of the objects cited by citations and are determined based at least in part on matching one or more search terms with the content of the citations of the objects by the one or more subjects, the selection scores for an object being determined for each search query based on a subset of subjects citing the object; combining the citation scores for the objects and the reputation scores for the subjects citing the objects to calculate selection scores for the objects determined based on matching of the one or more search terms with the content of the citations, the selection scores for an object determined for each search query based on a subset of subjects citing the object, with the subjects in the subset being the subjects of previously selected subsets of citations to each object, a different selection score computed for the same object when a different search query is provided; and selecting and ranking the objects based on the selection scores of the objects, a different ranking computed for a same set or overlapping sets of objects when the search query is different. | 11. A method of ranking entities using reputation or influence scores, comprising: using a processor to determine reputation scores for one or more subjects based on connections, the reputation scores indicating reputations of the subjects; providing a plurality of citations, each citation representing an online posting of an expression of opinion by a subject on an object, wherein the subject is representative of a user; using a processor to select a subset of citations for each object from the citations citing each object, the content of the citations in the selected subset matching one or more of search terms for a search query; assigning citation scores to a subset of a plurality of objects, the citation scores indicating relevance of the objects cited by citations and are determined based at least in part on matching one or more search terms with the content of the citations of the objects by the one or more subjects, the selection scores for an object being determined for each search query based on a subset of subjects citing the object; combining the citation scores for the objects and the reputation scores for the subjects citing the objects to calculate selection scores for the objects determined based on matching of the one or more search terms with the content of the citations, the selection scores for an object determined for each search query based on a subset of subjects citing the object, with the subjects in the subset being the subjects of previously selected subsets of citations to each object, a different selection score computed for the same object when a different search query is provided; and selecting and ranking the objects based on the selection scores of the objects, a different ranking computed for a same set or overlapping sets of objects when the search query is different. 14. The method recited in claim 11 , wherein the objects include books, films, music, documents, websites, objects for sale, objects that are reviewed or recommended or cited, or any entities that are associated with a Uniform Resource Identifier (URI), wherein the subjects include entities representing authors of Internet content or users of social media services. | 0.613445 |
13. The method of claim 12 , further comprising: converting the phoneme graph to a word-phoneme graph, the word-phoneme graph assigning a word and associated phonetic transcription to each edge. | 13. The method of claim 12 , further comprising: converting the phoneme graph to a word-phoneme graph, the word-phoneme graph assigning a word and associated phonetic transcription to each edge. 18. The method according to claim 13 , further comprising: converting the word-phoneme graph to the word graph, the word graph assigning a word to each edge. | 0.908671 |
1. A method for encoding a plurality of key matching rules grouped in a chunk, each of the key matching rules beginning with a header and having at least one dimension, the method comprising: in a rule encoding engine, communicatively coupled to memory and provided with a chunk of key matching rules, building a multi-rule corresponding to the chunk comprising: storing in the memory a multi-rule header of the multi-rule, the multi-rule header representing, collectively, a plurality of headers stored one after the other, the multi-rule header being decoded by a rule matching engine in a single decode operation to extract the plurality of headers of the key matching rules, wherein the plurality of headers include values which control the rule matching engine processing of the key matching rules, including dimensions, the rule matching engine formats the key matching rules based on a key and matches the key matching rules against the key to find a match based on the values stored in the plurality of headers. | 1. A method for encoding a plurality of key matching rules grouped in a chunk, each of the key matching rules beginning with a header and having at least one dimension, the method comprising: in a rule encoding engine, communicatively coupled to memory and provided with a chunk of key matching rules, building a multi-rule corresponding to the chunk comprising: storing in the memory a multi-rule header of the multi-rule, the multi-rule header representing, collectively, a plurality of headers stored one after the other, the multi-rule header being decoded by a rule matching engine in a single decode operation to extract the plurality of headers of the key matching rules, wherein the plurality of headers include values which control the rule matching engine processing of the key matching rules, including dimensions, the rule matching engine formats the key matching rules based on a key and matches the key matching rules against the key to find a match based on the values stored in the plurality of headers. 10. The method of claim 1 further comprising, given a key matching rule having at least one dimension, storing in the memory a value associated with the at least one dimension, the value being stored as dimension data of the multi-rule. | 0.588045 |
13. A non-transitory computer-readable recording medium, storing instructions that when executed, cause a processor configured to: receive an input of a first text; receive a voice input; transform the voice input into a second text by performing voice recognition; determine at least a part of the second text corresponding to the first text; correct the second text by replacing at least a part of the second text with the first text; and output the corrected second text. | 13. A non-transitory computer-readable recording medium, storing instructions that when executed, cause a processor configured to: receive an input of a first text; receive a voice input; transform the voice input into a second text by performing voice recognition; determine at least a part of the second text corresponding to the first text; correct the second text by replacing at least a part of the second text with the first text; and output the corrected second text. 18. The non-transitory computer-readable recording medium of claim 13 , wherein the determine the at least a part of the second text comprises determining the at least a part of the second text based on a similarity to the first text word. | 0.512605 |
1. A method for search string expansion, the method comprising: receiving a list of reserved phrases, each reserved phrase in the list being related to a content and wherein each reserved phrase is associated with a portion of the content; categorizing each reserved phrase according to linguistic characteristics; generating a candidate list of synonyms for each reserved phrase in the list; filtering the candidate list of synonyms by: removing synonym duplicates; and comparing synonyms to a synonym rule and removing synonyms that do not comply with the synonym rule; categorizing each synonym in the filtered candidate list of synonyms according to linguistic characteristics of the associated reserved phrase; receiving a query string; identifying a matching synonym from the filtered list of candidate synonyms that matches a part of the query string; and determining if the part of the query string matches the linguistic characteristics of the matching synonym. | 1. A method for search string expansion, the method comprising: receiving a list of reserved phrases, each reserved phrase in the list being related to a content and wherein each reserved phrase is associated with a portion of the content; categorizing each reserved phrase according to linguistic characteristics; generating a candidate list of synonyms for each reserved phrase in the list; filtering the candidate list of synonyms by: removing synonym duplicates; and comparing synonyms to a synonym rule and removing synonyms that do not comply with the synonym rule; categorizing each synonym in the filtered candidate list of synonyms according to linguistic characteristics of the associated reserved phrase; receiving a query string; identifying a matching synonym from the filtered list of candidate synonyms that matches a part of the query string; and determining if the part of the query string matches the linguistic characteristics of the matching synonym. 4. The method of claim 1 , wherein generating a candidate list of synonyms comprises: analyzing the content; and determining alternate words used to refer to each of the reserved phrases in the content. | 0.729333 |
3. The computer-based system of claim 1 , wherein said software module for providing translation of voiceover, text, or voiceover and text is further adapted for use by said learner. | 3. The computer-based system of claim 1 , wherein said software module for providing translation of voiceover, text, or voiceover and text is further adapted for use by said learner. 4. The computer-based system of claim 3 , wherein access to said software module for providing translation of voiceover, text, or voiceover and text is regulated by said learner. | 0.952545 |
7. The computer-readable, non-transitory medium according to claim 1 , wherein the translating includes finding a graphical symbol dataset whose content label represents a status, and furnishing the found graphical symbol dataset with state values that the status can take. | 7. The computer-readable, non-transitory medium according to claim 1 , wherein the translating includes finding a graphical symbol dataset whose content label represents a status, and furnishing the found graphical symbol dataset with state values that the status can take. 8. The computer-readable, non-transitory medium according to claim 7 , wherein the procedure further comprises displaying one or more diagrams based on the graphical symbol datasets and relationship link datasets produced by the translating, the diagram including first graphical symbols each having a content label indicating content thereof and second graphical symbols each having both a content label indicating content thereof and state values given by the translating, the diagram further including one or more relationship links connecting two or more of the first and second graphical symbols and each having a particular end shape to indicate the type of relationship between the connected graphical symbols. | 0.741354 |
2. A method for processing an input file or stream, comprising: receiving a definition of an original finite state machine; transforming the original finite state machine to a plurality of corresponding finite state machines, each corresponding finite state machine having an output equivalent to the original finite state machine subject to a different respective starting state; receiving a data object subject to a plurality of possible starting states; processing the data object with the plurality of corresponding finite state machines; selecting an output of one corresponding finite state machines corresponding to a proper starting state for the data object; and applying a set of remnants from state transitions of an earlier data object to resolve a later data object. | 2. A method for processing an input file or stream, comprising: receiving a definition of an original finite state machine; transforming the original finite state machine to a plurality of corresponding finite state machines, each corresponding finite state machine having an output equivalent to the original finite state machine subject to a different respective starting state; receiving a data object subject to a plurality of possible starting states; processing the data object with the plurality of corresponding finite state machines; selecting an output of one corresponding finite state machines corresponding to a proper starting state for the data object; and applying a set of remnants from state transitions of an earlier data object to resolve a later data object. 4. The method according to claim 2 , further comprising interpreting the output of the selected corresponding finite state machine dependent on the set of remnants, representing unmatched results from state transitions from antecedent data objects. | 0.862583 |
10. A computer-readable storage medium comprising a set of instructions for acquiring a lock of a data structure in a network file system (“NFS”) environment, the set of instructions to direct a processor to perform acts of: creating a text file in a management library of a data structure, wherein a name of the text file comprises an identifier of the lock, an identifier of a process attempting to acquire the lock, and an identifier of a machine on which the process attempting to acquire the lock is running; storing the identifier of the process attempting to acquire the lock in the contents of the text file; storing the identifier of the machine on which the process attempting to acquire the lock is running in the contents of the text file; creating a hard link that points to the text file; determining a number of links pointing to the text file; locking the data structure based on the number of links pointing to the text file; reading the contents of the text file; determining whether the contents of the text file comprise the identifier of the process that acquired the lock; determining whether the contents of the text file comprise the identifier of the machine on which the process that acquired the lock is running; and determining whether to release the lock of the data structure based on whether the contents of the text file comprise the identifier of the process that acquired the lock and the identifier of the machine on which the process that acquired the lock is running, wherein determining whether to release the lock of the data structure based on whether the contents of the text file comprise the identifier of the process that acquired the lock and the identifier of the machine on which the process that acquired the lock is running comprises: releasing the lock of the data structure in response to determining the contents of the text file comprises both the identifier of the process that acquired the lock and the identifier of the machine on which the process that acquired the lock is running, and maintaining the lock of the data structure in response to determining the contents of the text file does not comprise at least one of the identifier of the process that acquired the lock or the identifier of the machine on which the process that acquired the lock is running. | 10. A computer-readable storage medium comprising a set of instructions for acquiring a lock of a data structure in a network file system (“NFS”) environment, the set of instructions to direct a processor to perform acts of: creating a text file in a management library of a data structure, wherein a name of the text file comprises an identifier of the lock, an identifier of a process attempting to acquire the lock, and an identifier of a machine on which the process attempting to acquire the lock is running; storing the identifier of the process attempting to acquire the lock in the contents of the text file; storing the identifier of the machine on which the process attempting to acquire the lock is running in the contents of the text file; creating a hard link that points to the text file; determining a number of links pointing to the text file; locking the data structure based on the number of links pointing to the text file; reading the contents of the text file; determining whether the contents of the text file comprise the identifier of the process that acquired the lock; determining whether the contents of the text file comprise the identifier of the machine on which the process that acquired the lock is running; and determining whether to release the lock of the data structure based on whether the contents of the text file comprise the identifier of the process that acquired the lock and the identifier of the machine on which the process that acquired the lock is running, wherein determining whether to release the lock of the data structure based on whether the contents of the text file comprise the identifier of the process that acquired the lock and the identifier of the machine on which the process that acquired the lock is running comprises: releasing the lock of the data structure in response to determining the contents of the text file comprises both the identifier of the process that acquired the lock and the identifier of the machine on which the process that acquired the lock is running, and maintaining the lock of the data structure in response to determining the contents of the text file does not comprise at least one of the identifier of the process that acquired the lock or the identifier of the machine on which the process that acquired the lock is running. 17. The computer-readable storage medium of claim 10 , further comprising a set of instructions to direct the computer system to perform acts of: after a predetermined period of time, determining for a second time whether the contents of the text file comprise the identifier of the process that acquired the lock; after the predetermined period of time, determining for a second time whether the contents of the text file comprise the identifier of the machine on which the process that acquired the lock is running; and after the predetermined period of time, determining whether to release the lock of the data structure based on whether the contents of the text file comprise the identifier of the process that acquired the lock and the identifier of the machine on which the process that acquired the lock is running. | 0.5 |
1. A method for providing a name pronunciation guide, comprising: storing a plurality of audio files in a database, each audio file representing a particular pronunciation of one of a plurality of names; receiving user information from a first user via a communication network, the user information including name information of the first user; searching the database to find one or more audio files corresponding to the name information; providing the first user with one or more audio files in the database corresponding to the name information via the communication network for the first user's listening and selection; creating a user profile for the first user, the user profile comprising the user information and the selected audio file; storing the user profile in the database; receiving an inquiry for searching a name of the first user from a second user via the communication network; searching the database for one or more user profiles corresponding to the searched name; and providing to the second user a display of the one or more user profiles corresponding to the searched name via the communication network, wherein the display includes a link to one or more audio files associated with the one or more user profiles, wherein the name information comprises one or more call-me-this names. | 1. A method for providing a name pronunciation guide, comprising: storing a plurality of audio files in a database, each audio file representing a particular pronunciation of one of a plurality of names; receiving user information from a first user via a communication network, the user information including name information of the first user; searching the database to find one or more audio files corresponding to the name information; providing the first user with one or more audio files in the database corresponding to the name information via the communication network for the first user's listening and selection; creating a user profile for the first user, the user profile comprising the user information and the selected audio file; storing the user profile in the database; receiving an inquiry for searching a name of the first user from a second user via the communication network; searching the database for one or more user profiles corresponding to the searched name; and providing to the second user a display of the one or more user profiles corresponding to the searched name via the communication network, wherein the display includes a link to one or more audio files associated with the one or more user profiles, wherein the name information comprises one or more call-me-this names. 21. The method of claim 1 , wherein the one or more audio files is professionally recorded. | 0.683033 |
14. A non-transitory memory storing a plurality of processor-issuable processing instructions to provide an interaction interface having a plurality of interaction interface mechanisms comprising: a display interface mechanism configured to display a graphical representation of a threat confidence score to a user, the threat confidence score being calculated based on one or more cyber threat characteristics, the one or more cyber threat characteristics including a threat tag score and a threat asset score, the threat asset score being calculated by propagating a threat tag through a web of assets and integrating threat tag scores of the assets; and a user configurable interface mechanism including one or more user input elements configured to receive from a user a weight associated with a cyber threat characteristic, the threat confidence score being re-calculated based on the cyber threat characteristic scaled by the weight to produce an updated threat confidence score, the display element configured to display the graphical representation of the threat confidence score being dynamically updated with the updated threat confidence score. | 14. A non-transitory memory storing a plurality of processor-issuable processing instructions to provide an interaction interface having a plurality of interaction interface mechanisms comprising: a display interface mechanism configured to display a graphical representation of a threat confidence score to a user, the threat confidence score being calculated based on one or more cyber threat characteristics, the one or more cyber threat characteristics including a threat tag score and a threat asset score, the threat asset score being calculated by propagating a threat tag through a web of assets and integrating threat tag scores of the assets; and a user configurable interface mechanism including one or more user input elements configured to receive from a user a weight associated with a cyber threat characteristic, the threat confidence score being re-calculated based on the cyber threat characteristic scaled by the weight to produce an updated threat confidence score, the display element configured to display the graphical representation of the threat confidence score being dynamically updated with the updated threat confidence score. 15. The interaction interface of claim 14 , wherein the user interface input element is configured to control a sliding bar. | 0.854651 |
6. The method of claim 5 , wherein the edit to the first document is applied to the second document via the bridge, and is applied from the second document to the copy via the additional bridge. | 6. The method of claim 5 , wherein the edit to the first document is applied to the second document via the bridge, and is applied from the second document to the copy via the additional bridge. 9. The method of claim 6 , wherein a first user editing the first document collaborates in real time with a second user editing one of the plurality of first copies. | 0.95003 |
15. A system residing in a computer-readable medium for folder and file based management of layers and classes of a layered software application that is being developed into a runtime environment, said system comprising: a folder manager coupled to a storage area, said folder manager for: receiving inputs to said system including receiving a portion of code representing a method performed by a class of the layered software application and receiving instructions for storing said class of said layered software application, and opening a layer file folder within said storage area, said layer file folder for storing information related to a software development layer of said layered software application, said software development layer representing a unique version of said layered software application; a file manager coupled to said storage area, said file manager for opening a class file stored within said layer file folder, said class file represents said class of said layered software application and said class file for storing a software application class implemented in said software development layer; and a write manager coupled to said storage area, said write manager for writing textual information into said class file of said layer file folder, said textual information defining a said method implemented as part of said class in said software development layer. | 15. A system residing in a computer-readable medium for folder and file based management of layers and classes of a layered software application that is being developed into a runtime environment, said system comprising: a folder manager coupled to a storage area, said folder manager for: receiving inputs to said system including receiving a portion of code representing a method performed by a class of the layered software application and receiving instructions for storing said class of said layered software application, and opening a layer file folder within said storage area, said layer file folder for storing information related to a software development layer of said layered software application, said software development layer representing a unique version of said layered software application; a file manager coupled to said storage area, said file manager for opening a class file stored within said layer file folder, said class file represents said class of said layered software application and said class file for storing a software application class implemented in said software development layer; and a write manager coupled to said storage area, said write manager for writing textual information into said class file of said layer file folder, said textual information defining a said method implemented as part of said class in said software development layer. 16. The system of claim 15 , further comprising: a load manager coupled to said storage area and to a runtime environment, said load manager for reading said textual information from said class file and selectively providing an output from said system to said runtime environment, said output comprising a portion of said textual information. | 0.505076 |
5. The method of claim 1 , wherein the received data corresponding to a plurality of segment comments associated with the video includes data corresponding to a particular segment comment, and wherein the data corresponding to the particular segment comment includes an identification of a starting point of a segment of the video associated with the particular segment comment. | 5. The method of claim 1 , wherein the received data corresponding to a plurality of segment comments associated with the video includes data corresponding to a particular segment comment, and wherein the data corresponding to the particular segment comment includes an identification of a starting point of a segment of the video associated with the particular segment comment. 7. The method of claim 5 , wherein the data corresponding to the particular segment comment includes an identification of an end point of a segment of the video associated with the particular segment comment. | 0.96012 |
1. A computationally-implemented method, comprising: managing adaptation data that is stored at a reference location, wherein the adaptation data is at least partly based on at least one speech interaction of a particular party; determining an availability of the adaptation data by comparing a property of the adaptation data located at the referenced location with an expected value of the property of the adaptation data; facilitating transmission of the adaptation data to a target device when there is an indication of a speech-facilitated transaction between the target device and the particular party, wherein the adaptation data is configured to be applied to the target device to assist in execution of the speech-facilitated transaction; and facilitating acquisition of adaptation result data that is based on at least one aspect of the speech-facilitated transaction and configured to be used in determining whether to modify the adaptation data, upon receipt of an indication from the target device of a status of the speech-facilitated transaction between the target device and the particular party, wherein said status includes an indicator of a success in determining speech of the speech-facilitated transaction. | 1. A computationally-implemented method, comprising: managing adaptation data that is stored at a reference location, wherein the adaptation data is at least partly based on at least one speech interaction of a particular party; determining an availability of the adaptation data by comparing a property of the adaptation data located at the referenced location with an expected value of the property of the adaptation data; facilitating transmission of the adaptation data to a target device when there is an indication of a speech-facilitated transaction between the target device and the particular party, wherein the adaptation data is configured to be applied to the target device to assist in execution of the speech-facilitated transaction; and facilitating acquisition of adaptation result data that is based on at least one aspect of the speech-facilitated transaction and configured to be used in determining whether to modify the adaptation data, upon receipt of an indication from the target device of a status of the speech-facilitated transaction between the target device and the particular party, wherein said status includes an indicator of a success in determining speech of the speech-facilitated transaction. 6. The computationally-implemented method of claim 1 , wherein said facilitating transmission of the adaptation data to a target device when there is an indication of a speech-facilitated transaction between the target device and the particular party, wherein the adaptation data is configured to be applied to the target device to assist in execution of the speech-facilitated transaction comprises: facilitating transmission of the adaptation data to the target device upon receipt of an indication from the target device that a speech recognition component of the target device is processing speech of the particular party below a particular success rate. | 0.772404 |
8. A message correlation method comprising: determining, by a processor, whether there are two or more outstanding queries to which a response from a user to any of the outstanding queries that includes no context would render the response uncorrelatable to one of the outstanding queries; and Correlating, by the processor, a keyword associated with a response to an outstanding query, wherein, when there is a conflict where the keyword is used in at least two of the two or more outstanding queries or the keyword is not used in the two or more outstanding queries, a supplemental query is sent to the user requesting clarification. | 8. A message correlation method comprising: determining, by a processor, whether there are two or more outstanding queries to which a response from a user to any of the outstanding queries that includes no context would render the response uncorrelatable to one of the outstanding queries; and Correlating, by the processor, a keyword associated with a response to an outstanding query, wherein, when there is a conflict where the keyword is used in at least two of the two or more outstanding queries or the keyword is not used in the two or more outstanding queries, a supplemental query is sent to the user requesting clarification. 12. The method of claim 8 , further comprising matching the keyword in the response to a same keyword in an outstanding query. | 0.589928 |
1. A method of identifying and associating individuals comprising: providing a first set of records associated with one or more individuals at a defined geographic location and a defined period of time; providing a second set of records associated with individuals across multiple geographic locations and defined time periods; wherein the first or second set of records include identifications of locations of origin of an individual and wherein the identified set of individuals are in contemporaneous geospatial proximity, and further including identifying the preponderance of the location of origin of the identified set individuals, and using the preponderance of the location of origin to associate the location of origin relationships among the set of identified individuals who previously had no identified location of origin relationships in the first and second sets of records; selecting a desired demarcated area of the Earth; selecting a desired date range; searching, identifying, and returning a set of individuals corresponding to the selected demarcated area and the desired date range; and associating relationships among the set of returned individuals who previously had no identified relationships in the first and second sets of records corresponding to the demarcated area and date range. | 1. A method of identifying and associating individuals comprising: providing a first set of records associated with one or more individuals at a defined geographic location and a defined period of time; providing a second set of records associated with individuals across multiple geographic locations and defined time periods; wherein the first or second set of records include identifications of locations of origin of an individual and wherein the identified set of individuals are in contemporaneous geospatial proximity, and further including identifying the preponderance of the location of origin of the identified set individuals, and using the preponderance of the location of origin to associate the location of origin relationships among the set of identified individuals who previously had no identified location of origin relationships in the first and second sets of records; selecting a desired demarcated area of the Earth; selecting a desired date range; searching, identifying, and returning a set of individuals corresponding to the selected demarcated area and the desired date range; and associating relationships among the set of returned individuals who previously had no identified relationships in the first and second sets of records corresponding to the demarcated area and date range. 12. The method of claim 1 wherein the method is performed on and the results displayed on a social networking site. | 0.684315 |
1. A method for testing a voice enabled application on a target device, the method comprising conducting one or more interactions with the target device, at least some of the interactions comprising: selecting one of a plurality of input modes for sending input to the target device; presenting an acoustic utterance in an acoustic environment to the target device, including presenting a noise signal to the target device, using the selected input mode; determining one of a plurality of response modes for responding to an output of the target device; receiving an output of the target device in response to the acoustic utterance and the noise signal according to the determined response mode; and comparing the output to an output expected from the acoustic utterance; wherein the selected input mode and the determined response mode depend on input/output capabilities of the target device wherein presenting the acoustic utterance further comprises generating the acoustic utterance using an acoustic speaker; wherein the speaker comprises an artificial human mouth; wherein the acoustic environment is produced using an acoustic noise source that generates the noise signal, the noise signal representing one or more environmental noises of a natural environment. | 1. A method for testing a voice enabled application on a target device, the method comprising conducting one or more interactions with the target device, at least some of the interactions comprising: selecting one of a plurality of input modes for sending input to the target device; presenting an acoustic utterance in an acoustic environment to the target device, including presenting a noise signal to the target device, using the selected input mode; determining one of a plurality of response modes for responding to an output of the target device; receiving an output of the target device in response to the acoustic utterance and the noise signal according to the determined response mode; and comparing the output to an output expected from the acoustic utterance; wherein the selected input mode and the determined response mode depend on input/output capabilities of the target device wherein presenting the acoustic utterance further comprises generating the acoustic utterance using an acoustic speaker; wherein the speaker comprises an artificial human mouth; wherein the acoustic environment is produced using an acoustic noise source that generates the noise signal, the noise signal representing one or more environmental noises of a natural environment. 18. The method of claim 1 wherein the target device is a cell phone or a personal digital assistant. | 0.636594 |
13. A method for information source alignment, the method comprising: generating, by a computer, a source category hierarchy tree for a source class in a first information source and a target category hierarchy tree for a target class in a second information source, wherein the source and target category hierarchy trees are constructed from a class hierarchy of a knowledge source; determining a similarity between the source and target classes by comparing the source and target category hierarchy trees, and identifying common nodes between the source and target category hierarchy trees for calculating a class-similarity value based on the category hierarchy tree comparison; determining contextual similarity between the source and target classes by determining a similarity between superclasses of the source and target classes, the superclasses being ascertained from the respective first and second information sources, and determining a number of superclasses of the source and target classes that are supported by the source and target category hierarchy trees; and determining an alignment between the source and target classes based on the comparison of the source and target category hierarchy trees, and based on the determined contextual similarity. | 13. A method for information source alignment, the method comprising: generating, by a computer, a source category hierarchy tree for a source class in a first information source and a target category hierarchy tree for a target class in a second information source, wherein the source and target category hierarchy trees are constructed from a class hierarchy of a knowledge source; determining a similarity between the source and target classes by comparing the source and target category hierarchy trees, and identifying common nodes between the source and target category hierarchy trees for calculating a class-similarity value based on the category hierarchy tree comparison; determining contextual similarity between the source and target classes by determining a similarity between superclasses of the source and target classes, the superclasses being ascertained from the respective first and second information sources, and determining a number of superclasses of the source and target classes that are supported by the source and target category hierarchy trees; and determining an alignment between the source and target classes based on the comparison of the source and target category hierarchy trees, and based on the determined contextual similarity. 14. The method of claim 13 , further comprising aligning the source and target classes if a calculated class-similarity value based on the category hierarchy tree comparison and a calculated contextual-similarity value based on the determined contextual similarity exceed a predetermined overall-similarity threshold. | 0.569783 |
4. The method of claim 3 , wherein the method further comprises the steps of: converting extracted entities and attributes into sets of entity-attribute-value and entity-entity-relationship; automatically determining the number of kinds of objects contained in the entity-attribute-value set; assigning attributes to the kind of object described; and, automatically determining rules which govern entities in the entity-entity-relationship set. | 4. The method of claim 3 , wherein the method further comprises the steps of: converting extracted entities and attributes into sets of entity-attribute-value and entity-entity-relationship; automatically determining the number of kinds of objects contained in the entity-attribute-value set; assigning attributes to the kind of object described; and, automatically determining rules which govern entities in the entity-entity-relationship set. 5. The method of claim 4 , wherein the method further comprises the step of: converting segments into object-oriented Bayesian Networks. | 0.815353 |
1. A method of organizing information, the method comprising: providing for a knowledgebase; providing for a first set of tags configured to be applied to items in the knowledgebase; providing for a second set of tags configured to be applied to items in the knowledgebase; searching the knowledgebase based on at least one of the first set of tags and the second set of tags; and ranking result items in a search result favoring the result items tagged with terms for the first set of tags. | 1. A method of organizing information, the method comprising: providing for a knowledgebase; providing for a first set of tags configured to be applied to items in the knowledgebase; providing for a second set of tags configured to be applied to items in the knowledgebase; searching the knowledgebase based on at least one of the first set of tags and the second set of tags; and ranking result items in a search result favoring the result items tagged with terms for the first set of tags. 4. The method of claim 1 , further comprising providing access to the sub-plurality of items tagged with at least one tag from the second set of tags to the first set of users for additional tagging with the first set of tags. | 0.752183 |
30. The system according to claim 17 , wherein the voice server receives the first text data transmitted from the transmitting terminal, conducts a search to determine whether information that matches information associated with the sender of the first text data is present in the voice database, extracts, from the voice database, the first voice data corresponding to the first text data transmitted from the transmitting terminal based on a result of the conducting a search, and transmits the extracted voice data to the receiving terminal. | 30. The system according to claim 17 , wherein the voice server receives the first text data transmitted from the transmitting terminal, conducts a search to determine whether information that matches information associated with the sender of the first text data is present in the voice database, extracts, from the voice database, the first voice data corresponding to the first text data transmitted from the transmitting terminal based on a result of the conducting a search, and transmits the extracted voice data to the receiving terminal. 31. The system according to claim 30 , wherein the voice server extracts voice data of the sender of the first text data stored in the voice database when information that matches information associated with the sender of the first text data is present in the voice database as a result of the conducting the search. | 0.806928 |
1. A game apparatus comprising: a plurality of playing boards each having (comprising) a series of words (pertaining to the same topic) listed in a plurality of rows horizontally (and) disposed on the front face thereof, said words all comprising an equal number of letters and arranged in columns with said letters which occupy the same serial position in each of said words falling in the same column, each of said columns labelled with a numerical value, the rear face of said boards having said words and the definitions thereof disposed thereon the playing boards being divided into a plurality of sets wherein the playing boards within a set contain words pertaining to one specific topic, each playing board within a set having a unique arrangement of words; a master board comprising a plurality of juxtaposed (justaposed) columns each having one of said numerical values at the top of each of said columns and the letters of the alphabet in sequential order listed in each column; a score board with a grid disposed thereon the vertical rows thereof labelled with said topics (categories) and horizontal rows adapted to receive therein the players names, the vertical column furthermost from said names labelled "total"; a plurality of markers; and a plurality of cards comprising random combinations of said numerical values and the letters of the alphabet disposed thereon. | 1. A game apparatus comprising: a plurality of playing boards each having (comprising) a series of words (pertaining to the same topic) listed in a plurality of rows horizontally (and) disposed on the front face thereof, said words all comprising an equal number of letters and arranged in columns with said letters which occupy the same serial position in each of said words falling in the same column, each of said columns labelled with a numerical value, the rear face of said boards having said words and the definitions thereof disposed thereon the playing boards being divided into a plurality of sets wherein the playing boards within a set contain words pertaining to one specific topic, each playing board within a set having a unique arrangement of words; a master board comprising a plurality of juxtaposed (justaposed) columns each having one of said numerical values at the top of each of said columns and the letters of the alphabet in sequential order listed in each column; a score board with a grid disposed thereon the vertical rows thereof labelled with said topics (categories) and horizontal rows adapted to receive therein the players names, the vertical column furthermost from said names labelled "total"; a plurality of markers; and a plurality of cards comprising random combinations of said numerical values and the letters of the alphabet disposed thereon. 9. The game apparatus as claimed in claim 1, wherein said score board comprises a material which may be marked, erased, and reused. | 0.552399 |
9. A system for ranking content, comprising: a hardware processor that: receives a search query; generates a plurality of search results in response to the search query; determines one or more entity types associated with a content class within the plurality of search results, wherein the content class indicates a type of media content of a content item associated with at least one search result of the plurality of search results, and wherein the one or more entity types indicate information associated with the type of media content; determines whether the search query is a query for content belonging to the content class based on a plurality of criteria that includes: (i) determining whether at least one of the plurality of search results is associated with the one or more determined entity types; (ii) determining whether entities shared between the plurality of search results are associated with content corresponding to the one or more determined entity types, wherein each entity includes metadata indicating at least a topic of a corresponding search result; and (iii) determining whether the plurality of search results includes one or more authoritative result candidates having an entity with an entity type corresponding to the one or more entity types; and in response to determining that the plurality of criteria have been met, promotes at least one search result of the plurality of search results belonging to the content class. | 9. A system for ranking content, comprising: a hardware processor that: receives a search query; generates a plurality of search results in response to the search query; determines one or more entity types associated with a content class within the plurality of search results, wherein the content class indicates a type of media content of a content item associated with at least one search result of the plurality of search results, and wherein the one or more entity types indicate information associated with the type of media content; determines whether the search query is a query for content belonging to the content class based on a plurality of criteria that includes: (i) determining whether at least one of the plurality of search results is associated with the one or more determined entity types; (ii) determining whether entities shared between the plurality of search results are associated with content corresponding to the one or more determined entity types, wherein each entity includes metadata indicating at least a topic of a corresponding search result; and (iii) determining whether the plurality of search results includes one or more authoritative result candidates having an entity with an entity type corresponding to the one or more entity types; and in response to determining that the plurality of criteria have been met, promotes at least one search result of the plurality of search results belonging to the content class. 14. The system of claim 9 , further comprising promoting the one or more authoritative result candidates belonging to the content class within a list of at least a portion of the plurality of search results in response to determining that the plurality of criteria have been met. | 0.836257 |
21. A method of identifying end-of-speech within an audio stream, comprising: step for analyzing each window of the audio stream in a speech discriminator; step for assigning a classification to said each window based on speech discriminator output corresponding to said each window, the classification being selected from a classification set comprising a first classification label corresponding to presence of speech within said each window, a second classification label corresponding to silence within said each window, and a third classification label corresponding to noise in said each window; incrementing a speech counter in response to said each window being assigned the first classification label; incrementing a silence counter in response to said each window being assigned the second classification label; incrementing a noise counter in response to said each window being assigned the third classification label; step for determining when the speech counter exceeds a first limit; clearing the speech counter, the silence counter, and the noise counter in response to the speech counter exceeds a first limit; step for weighting at least one of the silence counter and the noise counter to obtain weighted silence and noise values; step for combining the weighted silence and noise values in a result; step for comparing the result to a second limit; and step for identifying end-of-speech within the audio stream in response to the result reaching the second limit; wherein the steps for analyzing, assigning are performed by at least one processor. | 21. A method of identifying end-of-speech within an audio stream, comprising: step for analyzing each window of the audio stream in a speech discriminator; step for assigning a classification to said each window based on speech discriminator output corresponding to said each window, the classification being selected from a classification set comprising a first classification label corresponding to presence of speech within said each window, a second classification label corresponding to silence within said each window, and a third classification label corresponding to noise in said each window; incrementing a speech counter in response to said each window being assigned the first classification label; incrementing a silence counter in response to said each window being assigned the second classification label; incrementing a noise counter in response to said each window being assigned the third classification label; step for determining when the speech counter exceeds a first limit; clearing the speech counter, the silence counter, and the noise counter in response to the speech counter exceeds a first limit; step for weighting at least one of the silence counter and the noise counter to obtain weighted silence and noise values; step for combining the weighted silence and noise values in a result; step for comparing the result to a second limit; and step for identifying end-of-speech within the audio stream in response to the result reaching the second limit; wherein the steps for analyzing, assigning are performed by at least one processor. 22. A method according to claim 21 , further comprising delimiting end of an audio section within the audio stream when end-of-speech is identified to obtain a delimited audio section. | 0.62517 |
10. The computer-implemented method of claim 1 , wherein determining the first segmented result and the second segmented result comprises: segmenting the string of characters into a plurality of segmented results; and selecting the first segmented result and the second segmented result from the plurality of segmented results. | 10. The computer-implemented method of claim 1 , wherein determining the first segmented result and the second segmented result comprises: segmenting the string of characters into a plurality of segmented results; and selecting the first segmented result and the second segmented result from the plurality of segmented results. 11. The computer-implemented method of claim 10 , wherein selecting the first segmented result and the second segmented result comprises calculating a probability value for each of the plurality of segmented results. | 0.917083 |
13. An apparatus, comprising: a wearable device, comprising: a processor; a support vector machine; and a memory comprising computer-readable instructions which when executed by the processor cause the processor and the support vector machine to perform the steps comprising: receiving an audio input from a first person comprising spoken words through a microphone communicatively coupled to the processor; sampling the audio input into a sample of a predetermined length of time; applying by the support vector machine an algorithm to the sample; determining, by the support vector machine, an emotional content of the sample by accessing a database comprising audio samples with predetermined emotional content and determining a closest emotional match to the sample from the predetermined emotional content such that the determining the closest emotional match trains the algorithm to optimize accuracy in determining the closest emotional match for subsequent samples; and outputting, by the support vector machine, the closest emotional match to the emotional content of the sample for use by a second person having an autism spectrum disorder. | 13. An apparatus, comprising: a wearable device, comprising: a processor; a support vector machine; and a memory comprising computer-readable instructions which when executed by the processor cause the processor and the support vector machine to perform the steps comprising: receiving an audio input from a first person comprising spoken words through a microphone communicatively coupled to the processor; sampling the audio input into a sample of a predetermined length of time; applying by the support vector machine an algorithm to the sample; determining, by the support vector machine, an emotional content of the sample by accessing a database comprising audio samples with predetermined emotional content and determining a closest emotional match to the sample from the predetermined emotional content such that the determining the closest emotional match trains the algorithm to optimize accuracy in determining the closest emotional match for subsequent samples; and outputting, by the support vector machine, the closest emotional match to the emotional content of the sample for use by a second person having an autism spectrum disorder. 22. The apparatus of claim 13 , wherein the processing is performed remotely from the wearable device over the Internet or a cloud-based computer network. | 0.535249 |
5. A system for computerized searching comprising: an input unit to receive a search query including a plurality of features; a processor to: assign to each of said plurality of features a respective weight, and retrieve a first plurality of retrieved items based on said features and said weights, a display unit for displaying at least a portion of said retrieved items to a user, wherein said input unit is further to receive a user selection of at least one of said first plurality of retrieved items, and wherein said processor is further to: modify said weights based on features of said at least one selected retrieved item, and retrieve a second plurality of retrieved items based on said modified weights, wherein after receiving said user selection of said retrieved item, said processor is further to: suggest at least one additional feature based on features of said selected retrieved item; and assign at least one respective weight to said at least one additional feature, wherein said processor is to retrieve said second plurality of retrieved items by retrieving said second plurality of retrieved items on said features and modified weights, and based on said additional features and said weights. | 5. A system for computerized searching comprising: an input unit to receive a search query including a plurality of features; a processor to: assign to each of said plurality of features a respective weight, and retrieve a first plurality of retrieved items based on said features and said weights, a display unit for displaying at least a portion of said retrieved items to a user, wherein said input unit is further to receive a user selection of at least one of said first plurality of retrieved items, and wherein said processor is further to: modify said weights based on features of said at least one selected retrieved item, and retrieve a second plurality of retrieved items based on said modified weights, wherein after receiving said user selection of said retrieved item, said processor is further to: suggest at least one additional feature based on features of said selected retrieved item; and assign at least one respective weight to said at least one additional feature, wherein said processor is to retrieve said second plurality of retrieved items by retrieving said second plurality of retrieved items on said features and modified weights, and based on said additional features and said weights. 6. The system of claim 5 , wherein said search query is a free-text query, and wherein said processor is to parse said free-text query to obtain said plurality of features. | 0.701754 |
1. A method of querying a collection of electronic documents, comprising: defining a query for retrieving a numerical answer, said query comprising one or more search terms and a tolerance for said numerical answer; defining a set of document portions from said collection, each document portion in said set being extracted from an electronic document and comprising at least one term relevant to at least one of the one or more search terms and a numerical value associated with the at least one term; arranging the associated numerical values contained in said set in an order; defining a plurality of results groups, each results group comprising an interval of the arranged numerical values, wherein each interval is determined based on a difference between two of the arranged numerical values in the set, and wherein each interval has a range not exceeding the tolerance; ranking the plurality of results groups; and returning at least one interval of a highest ranked results group as a response to said query. | 1. A method of querying a collection of electronic documents, comprising: defining a query for retrieving a numerical answer, said query comprising one or more search terms and a tolerance for said numerical answer; defining a set of document portions from said collection, each document portion in said set being extracted from an electronic document and comprising at least one term relevant to at least one of the one or more search terms and a numerical value associated with the at least one term; arranging the associated numerical values contained in said set in an order; defining a plurality of results groups, each results group comprising an interval of the arranged numerical values, wherein each interval is determined based on a difference between two of the arranged numerical values in the set, and wherein each interval has a range not exceeding the tolerance; ranking the plurality of results groups; and returning at least one interval of a highest ranked results group as a response to said query. 3. The method of claim 1 , wherein the step of defining the plurality of results groups comprises repeating the steps of: selecting a previously unselected first numerical value from the arranged numerical values; and defining all results groups intervals ranging from the selected first numerical value to a subsequent numerical value in the arranged numerical values for which the interval range does not exceed the tolerance; until all but the last numerical value of the arranged numerical values have been selected as the first numerical value. | 0.561317 |
7. The method of claim 6 , further comprising: inputting a second said URLv into said individual's consolidated entity's URLv access; deriving identities of said first and second inputted URLvs; confirming said identities are same; activating said database's said second-inputted URLv's associated API access script; receiving said second-inputted URLv's API's returned data; activating said database's said second-inputted URLv's associated script for limiting said second-inputted URLv's API's returned data; inputting said limited returned data of said second-inputted URLv into said individual's consolidated entity; whereby, a user can selectively input different URLvs of same identity in order to add differing portions of an API website's data into an individual's consolidated entity in a web exchange and wherein the web exchange can confirm these inputted URLvs belong to same identity. | 7. The method of claim 6 , further comprising: inputting a second said URLv into said individual's consolidated entity's URLv access; deriving identities of said first and second inputted URLvs; confirming said identities are same; activating said database's said second-inputted URLv's associated API access script; receiving said second-inputted URLv's API's returned data; activating said database's said second-inputted URLv's associated script for limiting said second-inputted URLv's API's returned data; inputting said limited returned data of said second-inputted URLv into said individual's consolidated entity; whereby, a user can selectively input different URLvs of same identity in order to add differing portions of an API website's data into an individual's consolidated entity in a web exchange and wherein the web exchange can confirm these inputted URLvs belong to same identity. 9. The method of claim 7 , wherein said inputted limited returned data of first and second URLvs are further consolidated, organized, and presented. | 0.863977 |
1. A method comprising, by one or more computing systems: identifying a shared visual concept in two or more visual-media items, wherein each visual-media item comprises one or more images, each image comprising one or more visual features, and wherein each visual-media item comprises one or more visual concepts, the shared visual concept being identified based on one or more shared visual features in the respective images of the visual-media items; extracting, for each of the visual-media items, one or more n-grams from one or more communications associated with the visual-media item; generating, in a d-dimensional space, an embedding for each of the visual-media items, wherein a location of the embedding for the visual-media item is based on the one or more visual concepts included in the visual-media item; generating, in the d-dimensional space, an embedding for each of the extracted n-grams, wherein a location of the embedding for the n-gram is based on a frequency of occurrence of the n-gram in the communications associated with the visual-media items; associating with the shared visual concept, one or more of the extracted n-grams that have embeddings within a threshold area of the embeddings for the identified visual-media items; populating a visual-concept index that indexes visual concepts with their respective associated n-grams; receiving, from a client system of a user, a search query comprising one or more n-grams; determining, based on the visual-concept index, one or more visual concepts associated with the n-grams of the search query; and sending, to the client system of the user, one or more search results comprising visual-media items in which the determined visual concepts are identified. | 1. A method comprising, by one or more computing systems: identifying a shared visual concept in two or more visual-media items, wherein each visual-media item comprises one or more images, each image comprising one or more visual features, and wherein each visual-media item comprises one or more visual concepts, the shared visual concept being identified based on one or more shared visual features in the respective images of the visual-media items; extracting, for each of the visual-media items, one or more n-grams from one or more communications associated with the visual-media item; generating, in a d-dimensional space, an embedding for each of the visual-media items, wherein a location of the embedding for the visual-media item is based on the one or more visual concepts included in the visual-media item; generating, in the d-dimensional space, an embedding for each of the extracted n-grams, wherein a location of the embedding for the n-gram is based on a frequency of occurrence of the n-gram in the communications associated with the visual-media items; associating with the shared visual concept, one or more of the extracted n-grams that have embeddings within a threshold area of the embeddings for the identified visual-media items; populating a visual-concept index that indexes visual concepts with their respective associated n-grams; receiving, from a client system of a user, a search query comprising one or more n-grams; determining, based on the visual-concept index, one or more visual concepts associated with the n-grams of the search query; and sending, to the client system of the user, one or more search results comprising visual-media items in which the determined visual concepts are identified. 4. The method of claim 1 , wherein one or more of the communications associated with the visual-media items are communications that include one or more of the visual-media items or one or more references to one or more of the visual-media items. | 0.62085 |
12. A system comprising: one or more processors; memory; and machine-readable instructions stored in the memory, that upon execution by the one or more processors cause the system to carry out operations comprising: transforming an audio input signal into a first sequence of feature vectors and a second sequence of feature vectors, wherein both the first and second sequences of feature vectors correspond in common to a sequence of temporal frames of the audio input signal, and wherein each respective feature vector of the first sequence and a corresponding respective feature vector of the second sequence bear quantitative measures of acoustic properties of a corresponding, respective temporal frame of the sequence of temporal frames of the audio input signal, processing the first sequence of feature vectors with a neural network (NN) implemented by the system to generate a NN-based set of emission probabilities for a plurality of hidden Markov models (HMMs) implemented by the system, processing the second sequence of feature vectors with a Gaussian mixture model (GMM) implemented by the system to generate a GMM-based set of emission probabilities for the plurality of HMMs, by computing, for each temporal frame, weighted sums of the NN-based emission probabilities and the GMM-based emission probabilities, merging the NN-based set of emission probabilities with the GMM-based set of emission probabilities to generate a merged set of emission probabilities for the plurality of HMMs, and applying the merged set of emission probabilities to the plurality of HMMs to determine speech content corresponding to the sequence of temporal frames of the audio input signal, wherein the weighted sums are computed according to weights computationally-determined by at least one processor during to a training process that minimizes a computationally-determined difference between computationally-predicted speech in training temporal frames and predetermined speech in the training temporal frames. | 12. A system comprising: one or more processors; memory; and machine-readable instructions stored in the memory, that upon execution by the one or more processors cause the system to carry out operations comprising: transforming an audio input signal into a first sequence of feature vectors and a second sequence of feature vectors, wherein both the first and second sequences of feature vectors correspond in common to a sequence of temporal frames of the audio input signal, and wherein each respective feature vector of the first sequence and a corresponding respective feature vector of the second sequence bear quantitative measures of acoustic properties of a corresponding, respective temporal frame of the sequence of temporal frames of the audio input signal, processing the first sequence of feature vectors with a neural network (NN) implemented by the system to generate a NN-based set of emission probabilities for a plurality of hidden Markov models (HMMs) implemented by the system, processing the second sequence of feature vectors with a Gaussian mixture model (GMM) implemented by the system to generate a GMM-based set of emission probabilities for the plurality of HMMs, by computing, for each temporal frame, weighted sums of the NN-based emission probabilities and the GMM-based emission probabilities, merging the NN-based set of emission probabilities with the GMM-based set of emission probabilities to generate a merged set of emission probabilities for the plurality of HMMs, and applying the merged set of emission probabilities to the plurality of HMMs to determine speech content corresponding to the sequence of temporal frames of the audio input signal, wherein the weighted sums are computed according to weights computationally-determined by at least one processor during to a training process that minimizes a computationally-determined difference between computationally-predicted speech in training temporal frames and predetermined speech in the training temporal frames. 16. The system of claim 12 , wherein determining speech content is at least one of generating a text string of the speech content, or identifying a computer-executable command based on the speech content. | 0.711601 |
14. A computer program product comprising a computer-readable storage medium including a computer readable program, wherein the computer-readable medium is a member of a set of computer readable media consisting of a semiconductor or solid state memory, a magnetic tape, a removable computer diskette, a rigid magnetic disk, an optical disk, a magnetic storage medium, and volatile and non-volatile memory devices, wherein the computer readable program when executed by a processor on a computer causes the computer to: store one or more entries for valid documents and one or more entries for error documents in a documents portion of a text index and storing tokens from the valid documents in a posting lists portion of the text index, wherein each of the entries includes a first field and a second field, wherein the first field for a valid document is indexable and stores one or more tokens forming document text, wherein the first field for an error document is not indexable and stores an error message, and wherein the second field stores an indication of whether the document is an error document; fetch a document with a document identifier; and in response to determining that the document is an error document, add an entry in the documents portion of the text index that includes an error message in a field that is not searchable in response to a search request using one or more tokens to locate one or more documents indexed by the text index. | 14. A computer program product comprising a computer-readable storage medium including a computer readable program, wherein the computer-readable medium is a member of a set of computer readable media consisting of a semiconductor or solid state memory, a magnetic tape, a removable computer diskette, a rigid magnetic disk, an optical disk, a magnetic storage medium, and volatile and non-volatile memory devices, wherein the computer readable program when executed by a processor on a computer causes the computer to: store one or more entries for valid documents and one or more entries for error documents in a documents portion of a text index and storing tokens from the valid documents in a posting lists portion of the text index, wherein each of the entries includes a first field and a second field, wherein the first field for a valid document is indexable and stores one or more tokens forming document text, wherein the first field for an error document is not indexable and stores an error message, and wherein the second field stores an indication of whether the document is an error document; fetch a document with a document identifier; and in response to determining that the document is an error document, add an entry in the documents portion of the text index that includes an error message in a field that is not searchable in response to a search request using one or more tokens to locate one or more documents indexed by the text index. 19. The computer program product of claim 14 , wherein the document is a first document with a first document identifier and wherein the computer readable program when executed on a computer causes the computer to: receive a second document with the first document identifier; determine that the second document has an entry in the text index; determine that the second document is an error document; delete the entry in the text index; and add a new entry in the documents portion of the text index for the second document, wherein the new entry includes an error message, the document identifier, and an indication that the second document is an error document. | 0.514159 |
15. At least one non-transitory computer-readable medium having encoded thereon executable instructions which, when executed by at least one processor, cause the at least one processor to perform a method comprising acts of: receiving, via at least one network, adaptation data generated at least in part by performing statistical processing on audio data comprising at least one user utterance; and using the adaptation data to update at least one acoustic model for use in speech recognition processing, wherein the adaptation data is in a format that prevents reconstruction of the audio data. | 15. At least one non-transitory computer-readable medium having encoded thereon executable instructions which, when executed by at least one processor, cause the at least one processor to perform a method comprising acts of: receiving, via at least one network, adaptation data generated at least in part by performing statistical processing on audio data comprising at least one user utterance; and using the adaptation data to update at least one acoustic model for use in speech recognition processing, wherein the adaptation data is in a format that prevents reconstruction of the audio data. 17. The at least one non-transitory computer-readable medium of claim 15 , wherein: the adaptation data comprises first adaptation data received from a first device and second adaptation data received from a second device different from the first device; and the act of using the adaptation data comprises aggregating the first adaptation data and the second adaptation data. | 0.635989 |
8. A computer-implemented method comprising: accessing a speech lattice representing a plurality of speech recognition hypotheses for a portion of speech data, the plurality of speech recognition hypotheses including a plurality of word hypotheses for a plurality of words in the portion of speech data, each word hypothesis of the plurality of word hypotheses including an n-tuple representing a start time associated with the word hypothesis, an end time associated with the word hypothesis, a word TD that identifies a particular word represented by the word hypothesis, and an associated probability for the word hypothesis; selecting a set of word hypotheses, from the plurality of word hypotheses, that are hypotheses for a same word in the portion of speech data and that have start and end times that satisfy a criteria, the set of word hypotheses being selected using the word IDs, start times, and end times of the n-tuples for the plurality of word hypotheses, wherein each word hypothesis in the set that satisfy the criteria has an associated start time within a first predetermined range of the start times of all other word hypotheses in the set and has an associated end time within a second predetermined range of the end times of all other word hypotheses in the set, and at least two word hypotheses in the set have different associated start times and/or different associated end times from each other; and generating, using a processor of a computer, a merged word hypothesis for the same word in the portion of speech data by merging the set of word hypotheses, wherein generating comprises: merging the at least two word hypotheses in the set having different associated start times and/or different associated end times from each other; assigning start and end times to the merged word hypothesis that are the same as the start and end times associated with the word hypothesis in the set having a highest probability; and assigning a probability to the merged word hypothesis by combining the associated probabilities of the merged set of word hypotheses; and storing an index entry to represent the merged word hypothesis. | 8. A computer-implemented method comprising: accessing a speech lattice representing a plurality of speech recognition hypotheses for a portion of speech data, the plurality of speech recognition hypotheses including a plurality of word hypotheses for a plurality of words in the portion of speech data, each word hypothesis of the plurality of word hypotheses including an n-tuple representing a start time associated with the word hypothesis, an end time associated with the word hypothesis, a word TD that identifies a particular word represented by the word hypothesis, and an associated probability for the word hypothesis; selecting a set of word hypotheses, from the plurality of word hypotheses, that are hypotheses for a same word in the portion of speech data and that have start and end times that satisfy a criteria, the set of word hypotheses being selected using the word IDs, start times, and end times of the n-tuples for the plurality of word hypotheses, wherein each word hypothesis in the set that satisfy the criteria has an associated start time within a first predetermined range of the start times of all other word hypotheses in the set and has an associated end time within a second predetermined range of the end times of all other word hypotheses in the set, and at least two word hypotheses in the set have different associated start times and/or different associated end times from each other; and generating, using a processor of a computer, a merged word hypothesis for the same word in the portion of speech data by merging the set of word hypotheses, wherein generating comprises: merging the at least two word hypotheses in the set having different associated start times and/or different associated end times from each other; assigning start and end times to the merged word hypothesis that are the same as the start and end times associated with the word hypothesis in the set having a highest probability; and assigning a probability to the merged word hypothesis by combining the associated probabilities of the merged set of word hypotheses; and storing an index entry to represent the merged word hypothesis. 9. The computer-implemented method of claim 8 , wherein the portion of speech data comprises a spoken sentence. | 0.580467 |
1. A system, comprising: at least one knowledge base of entities, entity types, and entity attributes; a features component configured to compute a plurality of intermediate features of the entities and attributes for each entity, and aggregate the intermediate features to output a final feature set of features having an entity-attribute tuple corresponding to the respective plurality of computed intermediate features; a relevance component configured to generate a relevance score for a given entity and associated attributes based on the feature set, wherein the relevance of each attribute of the entity is based on a set of judgments from human judges; a ranking component configured to rank the attributes of the given entity based on the relevance scores; and a microprocessor that executes computer-executable instructions associated with at least one of the features component, relevance component, or the ranking component. | 1. A system, comprising: at least one knowledge base of entities, entity types, and entity attributes; a features component configured to compute a plurality of intermediate features of the entities and attributes for each entity, and aggregate the intermediate features to output a final feature set of features having an entity-attribute tuple corresponding to the respective plurality of computed intermediate features; a relevance component configured to generate a relevance score for a given entity and associated attributes based on the feature set, wherein the relevance of each attribute of the entity is based on a set of judgments from human judges; a ranking component configured to rank the attributes of the given entity based on the relevance scores; and a microprocessor that executes computer-executable instructions associated with at least one of the features component, relevance component, or the ranking component. 3. The system of claim 1 , further comprising a machine learned classifier model that is trained using the feature set to output the relevance scores for the given entity and the associated attributes. | 0.560473 |
15. The medium of claim 14 , wherein the template includes two concepts and the relationship from the knowledge model. | 15. The medium of claim 14 , wherein the template includes two concepts and the relationship from the knowledge model. 16. The medium of claim 15 , wherein the instantiation of the template replaces one of the two concepts with an instance from the knowledge model. | 0.970092 |
14. A system, comprising at least one automatic speech recognition component configured to analyze at least one voice interaction by at least one agent that follows at least one script in at least one of a plurality of panels and to determine whether the at least one agent has adequately followed the at least one script using confidence level thresholds assigned to each of the plurality of panels. | 14. A system, comprising at least one automatic speech recognition component configured to analyze at least one voice interaction by at least one agent that follows at least one script in at least one of a plurality of panels and to determine whether the at least one agent has adequately followed the at least one script using confidence level thresholds assigned to each of the plurality of panels. 16. The system of claim 14 , comprising an action component operable to cause at least one action to be taken. | 0.723702 |
14. The method of claim 13 , wherein copying or cut-and-pasting a section of the spatial arrangement to a different location in the spatial arrangement creates a set of display elements whose corresponding queries correspond to query elements of the copied, or cut-and-pasted section, appended to a query corresponding to the location from which the section was moved. | 14. The method of claim 13 , wherein copying or cut-and-pasting a section of the spatial arrangement to a different location in the spatial arrangement creates a set of display elements whose corresponding queries correspond to query elements of the copied, or cut-and-pasted section, appended to a query corresponding to the location from which the section was moved. 15. The method of claim 14 , wherein the section of the spatial arrangement is part of the interactive tree descended from a selected node, the pasting is to a different node, and the query corresponding to the Original location is the query corresponding to the different node. | 0.816784 |
Subsets and Splits
No saved queries yet
Save your SQL queries to embed, download, and access them later. Queries will appear here once saved.