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8,126,718 | 19 | 22 | 19. A computing device comprising: a processor; and memory interconnected with said processor storing instructions for facilitating text-to-speech conversion of a username that, when executed by said processor, cause said device to: retrieve a name of a user associated with said username, said name comprising one of a first name of said user and a last name of said user; and determine a pronunciation of said username based at least in part on whether said name forms at least part of said username, wherein, if said name forms at least part of said username, said determining said pronunciation comprises generating a phonetic representation of said name pronounced as a whole or generating a tokenized representation of said name as a whole suitable for interpretation by a text-to-speech engine, and wherein said determining said pronunciation further comprises calculating a likelihood of pronounceability of a portion of said username that is not said name. | 19. A computing device comprising: a processor; and memory interconnected with said processor storing instructions for facilitating text-to-speech conversion of a username that, when executed by said processor, cause said device to: retrieve a name of a user associated with said username, said name comprising one of a first name of said user and a last name of said user; and determine a pronunciation of said username based at least in part on whether said name forms at least part of said username, wherein, if said name forms at least part of said username, said determining said pronunciation comprises generating a phonetic representation of said name pronounced as a whole or generating a tokenized representation of said name as a whole suitable for interpretation by a text-to-speech engine, and wherein said determining said pronunciation further comprises calculating a likelihood of pronounceability of a portion of said username that is not said name. 22. The computing device of claim 19 wherein said calculating calculates a high likelihood of pronounceability when said portion of said username is determined to be a prefix of the other one of said first name and said last name. | 0.716049 |
9,684,640 | 1 | 8 | 1. A method comprising: creating, by a computer-based system, a linkage data structure corresponding to a second programming language different from a markup language, wherein the linkage data structure includes a field for each tag in a set of tags associated with the markup language, wherein the set of tags is retrieved by parsing a document; generating, by the computer-based system, program code in the second programming language based on the set of tags, wherein the generating comprises: creating a procedure division statement in the second programming language; wherein the procedure division statement is capable of accepting a document written in the markup language, wherein the document is variable length, and wherein the procedure division statement is capable of returning a fixed format data structure corresponding to the linkage data structure, creating a second programming language section to contain the program code in the second programming language; and producing, by the procedure division statement and the second programming language section, the program code in the second programming language, wherein the program code is configured to extract, from the document written in the markup language, the set of tags associated with the markup language and at least one attribute associated with each tag; forming, by the computer-based system, an application programming interface (API) that includes the linkage data structure and the program code; and using, by the computer-based system, the application programming interface (API) to pass content from one or more documents written in the markup language to a program element of a program written in the second programming language. | 1. A method comprising: creating, by a computer-based system, a linkage data structure corresponding to a second programming language different from a markup language, wherein the linkage data structure includes a field for each tag in a set of tags associated with the markup language, wherein the set of tags is retrieved by parsing a document; generating, by the computer-based system, program code in the second programming language based on the set of tags, wherein the generating comprises: creating a procedure division statement in the second programming language; wherein the procedure division statement is capable of accepting a document written in the markup language, wherein the document is variable length, and wherein the procedure division statement is capable of returning a fixed format data structure corresponding to the linkage data structure, creating a second programming language section to contain the program code in the second programming language; and producing, by the procedure division statement and the second programming language section, the program code in the second programming language, wherein the program code is configured to extract, from the document written in the markup language, the set of tags associated with the markup language and at least one attribute associated with each tag; forming, by the computer-based system, an application programming interface (API) that includes the linkage data structure and the program code; and using, by the computer-based system, the application programming interface (API) to pass content from one or more documents written in the markup language to a program element of a program written in the second programming language. 8. The method of claim 1 , wherein the markup language is XML. | 0.871369 |
9,286,886 | 26 | 29 | 26. The system of claim 21 , wherein the comparing comprises selecting the corresponding text fragment based at least in part on a similarity measure between one or more linguistic features of the at least a portion of the input text and the corresponding text fragment. | 26. The system of claim 21 , wherein the comparing comprises selecting the corresponding text fragment based at least in part on a similarity measure between one or more linguistic features of the at least a portion of the input text and the corresponding text fragment. 29. The system of claim 26 , wherein the one or more linguistic features comprise one or more features selected from the group consisting of a named entity feature, a verb semantics feature, a noun semantics feature, an adjective semantics feature, an adverb semantics feature, and a syllable structure feature. | 0.5 |
7,805,391 | 1 | 8 | 1. A computer implemented method for inferring a probability of a first inference, wherein a probability of the first inference relates to anomalous behavior of a member of a cohort, wherein the anomalous behavior comprises a behavior that is more likely not to occur than to occur, and wherein the computer implemented method comprises: receiving a query at a database regarding a fact, wherein the first inference is absent from the database, wherein the database comprises a plurality of divergent data, wherein the plurality of divergent data includes a plurality of cohort data, wherein each datum of the plurality of divergent data in the database is conformed to the dimensions of the database, wherein each datum of the plurality of divergent data has associated metadata and an associated key, wherein the associated metadata comprises data regarding cohorts associated with the corresponding datum, data regarding hierarchies associated with the corresponding datum, data regarding a corresponding source of the datum, and data regarding probabilities associated with integrity, reliability, and importance of each associated datum; establishing the fact as a frame of reference for the query; applying a first set of rules to the query, wherein the first set of rules are determined for the query according to a second set of rules, wherein the frame of reference serves as an anchor for generating associations among the plurality of cohort data and is used to determine rules for limiting the plurality of divergent data that is searched, wherein the first set of rules determine how the plurality of divergent data are to be compared to the fact, and wherein the first set of rules determine a search space for the query; executing the query to create the first inference and the probability of the first inference, wherein the probability of the first inference is determined from comparing the plurality of divergent data according to the first set of rules; storing the probability of the first inference; performing security filtering based on at least one of a significance level of the probability of a first inference, the probability of the first inference exceeding a pre-selected value, and a security level possessed by a user; presenting the probability of the first inference to the user based on whether the user is authorized to receive a set of credit information under a standard selected from a group consisting of a law, a policy, and an institutional review board; and presenting the probability of the first inference to the user even if the user is not authorized to receive the set of credit information under the standard selected from a group consisting of a law, a policy, and an institutional review board, but only responsive to a) restricting the user from accessing the credit information and b) the probability of the first inference exceeding a pre-defined value. | 1. A computer implemented method for inferring a probability of a first inference, wherein a probability of the first inference relates to anomalous behavior of a member of a cohort, wherein the anomalous behavior comprises a behavior that is more likely not to occur than to occur, and wherein the computer implemented method comprises: receiving a query at a database regarding a fact, wherein the first inference is absent from the database, wherein the database comprises a plurality of divergent data, wherein the plurality of divergent data includes a plurality of cohort data, wherein each datum of the plurality of divergent data in the database is conformed to the dimensions of the database, wherein each datum of the plurality of divergent data has associated metadata and an associated key, wherein the associated metadata comprises data regarding cohorts associated with the corresponding datum, data regarding hierarchies associated with the corresponding datum, data regarding a corresponding source of the datum, and data regarding probabilities associated with integrity, reliability, and importance of each associated datum; establishing the fact as a frame of reference for the query; applying a first set of rules to the query, wherein the first set of rules are determined for the query according to a second set of rules, wherein the frame of reference serves as an anchor for generating associations among the plurality of cohort data and is used to determine rules for limiting the plurality of divergent data that is searched, wherein the first set of rules determine how the plurality of divergent data are to be compared to the fact, and wherein the first set of rules determine a search space for the query; executing the query to create the first inference and the probability of the first inference, wherein the probability of the first inference is determined from comparing the plurality of divergent data according to the first set of rules; storing the probability of the first inference; performing security filtering based on at least one of a significance level of the probability of a first inference, the probability of the first inference exceeding a pre-selected value, and a security level possessed by a user; presenting the probability of the first inference to the user based on whether the user is authorized to receive a set of credit information under a standard selected from a group consisting of a law, a policy, and an institutional review board; and presenting the probability of the first inference to the user even if the user is not authorized to receive the set of credit information under the standard selected from a group consisting of a law, a policy, and an institutional review board, but only responsive to a) restricting the user from accessing the credit information and b) the probability of the first inference exceeding a pre-defined value. 8. The computer implemented method of claim 1 , further comprising: presenting the probability of the first inference to the user only if the security level possessed by the user exceeds a pre-determined value. | 0.813499 |
8,702,433 | 12 | 24 | 12. A method of training a user via an interactive electronic training system, the method comprising: providing for display to a trainee via an interactive electronic training system terminal information regarding a real or simulated person, wherein a first portion of the information is presented via a structured form including a plurality of data fields having corresponding data and data field names, and wherein a second portion of the information is formatted as notes including a plurality of sentences; at least partly causing the trainee to be instructed to verbally identify one or more needs of the person based on the information; at least partly causing an indication to be stored in computer readable memory as to whether the trainee correctly identified at least a first need; at least partly causing the trainee to be instructed to verbally identify at least one item that appropriately corresponds to the first need; at least partly causing an indication to be stored in computer readable memory as to whether the trainee correctly identified at least a first item, wherein the first item is a product and/or service, that appropriately corresponds to the first need; at least partly causing the trainee to be instructed to verbally explain why the first item corresponds to the first need; at least partly causing an indication to be stored in computer readable memory as to whether the trainee correctly explained why the first item corresponds to the first need; and calculating and reporting by the interactive electronic training a cumulative score based at least in part on one or more of the stored indications, including at least the indication as to whether the trainee correctly explained why the first item corresponds to the first need. | 12. A method of training a user via an interactive electronic training system, the method comprising: providing for display to a trainee via an interactive electronic training system terminal information regarding a real or simulated person, wherein a first portion of the information is presented via a structured form including a plurality of data fields having corresponding data and data field names, and wherein a second portion of the information is formatted as notes including a plurality of sentences; at least partly causing the trainee to be instructed to verbally identify one or more needs of the person based on the information; at least partly causing an indication to be stored in computer readable memory as to whether the trainee correctly identified at least a first need; at least partly causing the trainee to be instructed to verbally identify at least one item that appropriately corresponds to the first need; at least partly causing an indication to be stored in computer readable memory as to whether the trainee correctly identified at least a first item, wherein the first item is a product and/or service, that appropriately corresponds to the first need; at least partly causing the trainee to be instructed to verbally explain why the first item corresponds to the first need; at least partly causing an indication to be stored in computer readable memory as to whether the trainee correctly explained why the first item corresponds to the first need; and calculating and reporting by the interactive electronic training a cumulative score based at least in part on one or more of the stored indications, including at least the indication as to whether the trainee correctly explained why the first item corresponds to the first need. 24. The method as defined in claim 12 , wherein the first item is a loan. | 0.917978 |
7,606,798 | 1 | 13 | 1. A method performed by a server device, comprising: receiving, by a processor of the server device, a query; determining, by the processor, a geographic location associated with the query; determining, by the processor, a topic corresponding to the query; determining, by the processor, a distance adjustment factor associated with the topic; identifying, by the processor, a set of documents based, at least in part, on the query; determining, by the processor, for each document in the set of documents, a topical score based, at least in part, on the query; determining, by the processor, for each document in the set of documents, a measure of distance between a geographic location associated with the document and the geographic location associated with the query; generating, by the processor, for each document in the set of documents, a distance score based, at least in part, on the measure of distance and the distance adjustment factor, where the distance adjustment factor controls an amount that the distance score changes as a function of the measure of distance; and ordering, by the processor, the set of documents as a function of both the topical scores for the set of documents and the distance scores for the set of documents. | 1. A method performed by a server device, comprising: receiving, by a processor of the server device, a query; determining, by the processor, a geographic location associated with the query; determining, by the processor, a topic corresponding to the query; determining, by the processor, a distance adjustment factor associated with the topic; identifying, by the processor, a set of documents based, at least in part, on the query; determining, by the processor, for each document in the set of documents, a topical score based, at least in part, on the query; determining, by the processor, for each document in the set of documents, a measure of distance between a geographic location associated with the document and the geographic location associated with the query; generating, by the processor, for each document in the set of documents, a distance score based, at least in part, on the measure of distance and the distance adjustment factor, where the distance adjustment factor controls an amount that the distance score changes as a function of the measure of distance; and ordering, by the processor, the set of documents as a function of both the topical scores for the set of documents and the distance scores for the set of documents. 13. The method of claim 1 , where the ordering the set of documents includes; generating an overall score, for each of the documents in the set of documents, as a combination of the topical score and the distance score, and ordering the set of documents based, at least in part, on the overall scores. | 0.779649 |
8,370,147 | 22 | 24 | 22. The system of claim 20 , wherein the one or more processors are configured to resolve the one or more requests by: determining that the one or more requests identified in the natural language utterance include a navigation request to calculate a route to a full or partial address; calculating the route from the current location to a destination having an address that corresponds to the full or partial address; and generating directions from the current location to the destination, wherein the navigation agent generates the directions using the information associated with a navigation-specific information source. | 22. The system of claim 20 , wherein the one or more processors are configured to resolve the one or more requests by: determining that the one or more requests identified in the natural language utterance include a navigation request to calculate a route to a full or partial address; calculating the route from the current location to a destination having an address that corresponds to the full or partial address; and generating directions from the current location to the destination, wherein the navigation agent generates the directions using the information associated with a navigation-specific information source. 24. The system of claim 22 , further comprising one or more domain agents configured to: resolve a subsequent request in a multi-modal input received from the input device subsequent to the navigation agent having calculated the route; and filter results associated with the subsequent request according to the calculated route to resolve the subsequent request. | 0.724085 |
8,521,516 | 5 | 10 | 5. A computer-implemented method executed by one or more processors, the method comprising: receiving a training phrase; normalizing the training phrase according to one or more lexicographic normalization rules; locating the normalized training phrase in a normalized phrase table, the normalized phrase table including a plurality of key-value pairs, each key-value pair having a key that includes a normalized phrase and a value that includes one or more un-normalized phrases associated with the normalized phrase of the key and one or more parameters associated with each un-normalized phrase; associating one or more weights to one or more un-normalized phrases associated with the key-value pair for the identified normalized training phrase in the normalized phrase table based on a relation of each associated un-normalized phrase to the received training phrase; determining a degree of match between the received training phrase and a specific un-normalized phrase associated with the located normalized training phrase, the degree of match being determined according to a similarity measure, wherein associating one or more weights comprises: associating a first weight to the specific un-normalized phrase when the training phrase has a high degree of match with the specific un-normalized phrase, and associating a second weight to the specific un-normalized phrase when the training phrase has a low degree of match with the specific un-normalized phrase; and training a machine learning model using the one or more un-normalized phrases and the associated one or more weights. | 5. A computer-implemented method executed by one or more processors, the method comprising: receiving a training phrase; normalizing the training phrase according to one or more lexicographic normalization rules; locating the normalized training phrase in a normalized phrase table, the normalized phrase table including a plurality of key-value pairs, each key-value pair having a key that includes a normalized phrase and a value that includes one or more un-normalized phrases associated with the normalized phrase of the key and one or more parameters associated with each un-normalized phrase; associating one or more weights to one or more un-normalized phrases associated with the key-value pair for the identified normalized training phrase in the normalized phrase table based on a relation of each associated un-normalized phrase to the received training phrase; determining a degree of match between the received training phrase and a specific un-normalized phrase associated with the located normalized training phrase, the degree of match being determined according to a similarity measure, wherein associating one or more weights comprises: associating a first weight to the specific un-normalized phrase when the training phrase has a high degree of match with the specific un-normalized phrase, and associating a second weight to the specific un-normalized phrase when the training phrase has a low degree of match with the specific un-normalized phrase; and training a machine learning model using the one or more un-normalized phrases and the associated one or more weights. 10. The method of claim 5 , where training the machine learning model includes training a language identification model. | 0.792388 |
9,692,764 | 9 | 11 | 9. A method implemented on a computing device, the method comprising: in a graph database, generating a query vertex comprising a traversal clause that represents a query of the graph database, wherein the traversal clause identifies a vertex type; generating an edge from a user's vertex to the query vertex, wherein the generated edge defines the user's permission to access a set of vertices of the identified vertex type, wherein the set of vertices is specific to the user; identifying, from the graph database, the set of vertices that are specific to the user, by traversing the graph database to locate each vertex of the identified vertex type that is semantically related to the user's vertex; receiving a request to execute the query on behalf of the user; in response to the request, traversing only the identified set of vertices that are specific to the user to generate a report; and displaying the report to the user. | 9. A method implemented on a computing device, the method comprising: in a graph database, generating a query vertex comprising a traversal clause that represents a query of the graph database, wherein the traversal clause identifies a vertex type; generating an edge from a user's vertex to the query vertex, wherein the generated edge defines the user's permission to access a set of vertices of the identified vertex type, wherein the set of vertices is specific to the user; identifying, from the graph database, the set of vertices that are specific to the user, by traversing the graph database to locate each vertex of the identified vertex type that is semantically related to the user's vertex; receiving a request to execute the query on behalf of the user; in response to the request, traversing only the identified set of vertices that are specific to the user to generate a report; and displaying the report to the user. 11. The method of claim 9 , wherein the report includes a reference to the set of vertices that are specific to the user and a reference to the query. | 0.904459 |
7,917,460 | 6 | 7 | 6. The system of claim 5 , the plurality of hypotheses having respective associated sets of key words, and the evidence classifier including a semantic distance classifier that matches words within the one or more text segments to the respective sets of key words associated with the plurality of hypotheses, the semantic classifier determining a hypothesis for the one or more text segments according to a distribution of the words in the one or more text segments among the respective sets of key words associated with the plurality of hypotheses. | 6. The system of claim 5 , the plurality of hypotheses having respective associated sets of key words, and the evidence classifier including a semantic distance classifier that matches words within the one or more text segments to the respective sets of key words associated with the plurality of hypotheses, the semantic classifier determining a hypothesis for the one or more text segments according to a distribution of the words in the one or more text segments among the respective sets of key words associated with the plurality of hypotheses. 7. The system of claim 6 , wherein the occurrence of a key word from a set of key words associated with a given hypothesis within the one or more text segments represents a point associated with that hypothesis on the weighted graph, the selected hypothesis being selected as a weighted mean position of a plurality of points. | 0.5 |
9,811,602 | 13 | 14 | 13. A computing system for facilitating screen readers of online electronic documents, comprising: a processor running computer readable program code configured to generate a structured electronic document including markup language code and scripting language code whose execution is triggered by an occurrence of an event associated with execution of the markup language code, the execution of the scripting language code producing a web presentation unrecognized by a screen reader software program; and memory storing a configuration file that maps of one or more functions of the screen reader software program to one or more functions performed by the scripting language code that produces the unrecognized web presentation when the scripting language code is executed in response to the occurrence of the event, wherein the processor executes computer readable program code to incorporate the configuration file within the structured electronic document when the structured electronic document is initially generated, before the structured electronic document is posted on a web server and made available for subsequent downloading and display, and wherein the configuration file points to text obtained from the unrecognized web presentation in the markup language code to be read aloud by the screen reader software program. | 13. A computing system for facilitating screen readers of online electronic documents, comprising: a processor running computer readable program code configured to generate a structured electronic document including markup language code and scripting language code whose execution is triggered by an occurrence of an event associated with execution of the markup language code, the execution of the scripting language code producing a web presentation unrecognized by a screen reader software program; and memory storing a configuration file that maps of one or more functions of the screen reader software program to one or more functions performed by the scripting language code that produces the unrecognized web presentation when the scripting language code is executed in response to the occurrence of the event, wherein the processor executes computer readable program code to incorporate the configuration file within the structured electronic document when the structured electronic document is initially generated, before the structured electronic document is posted on a web server and made available for subsequent downloading and display, and wherein the configuration file points to text obtained from the unrecognized web presentation in the markup language code to be read aloud by the screen reader software program. 14. The computing system of claim 13 , wherein the configuration file maps a focus function of the screen reader software program to an HTML element produced in response to execution of the scripting language code. | 0.566802 |
8,352,855 | 16 | 18 | 16. The non-transitory computer readable medium of claim 14 , wherein the start point is in a column of text within an article and the initial end point is within a call-out for the article. | 16. The non-transitory computer readable medium of claim 14 , wherein the start point is in a column of text within an article and the initial end point is within a call-out for the article. 18. The non-transitory computer readable medium of claim 16 , wherein the set of instructions for selecting text comprises a set of instructions for selecting all text in the columns of the article. | 0.622137 |
8,713,119 | 12 | 18 | 12. A portable electronic device configured to process voice commands, comprising: one or more input devices; one or more processors; and memory storing one or more programs configured to be executed by the one or more processors, the one or more programs including instructions for: in response to user input, recording at least a portion of a voice command on the portable electronic device; when recording the portion of the voice command on the portable electronic device, storing contextual information of the portable electronic device; after recording the portion of the voice command at the portable electronic device, uploading the portion of the voice command and the stored contextual information from the portable electronic device to remote computing equipment; receiving, from the remote computing equipment, results associated with processing the portion of the voice command and the stored contextual information; and presenting the results. | 12. A portable electronic device configured to process voice commands, comprising: one or more input devices; one or more processors; and memory storing one or more programs configured to be executed by the one or more processors, the one or more programs including instructions for: in response to user input, recording at least a portion of a voice command on the portable electronic device; when recording the portion of the voice command on the portable electronic device, storing contextual information of the portable electronic device; after recording the portion of the voice command at the portable electronic device, uploading the portion of the voice command and the stored contextual information from the portable electronic device to remote computing equipment; receiving, from the remote computing equipment, results associated with processing the portion of the voice command and the stored contextual information; and presenting the results. 18. The portable electronic device of claim 12 , wherein the contextual information is a geographical location of the portable electronic device. | 0.80563 |
7,873,622 | 1 | 7 | 1. A system for presenting a graphical user interface (GUI) for displaying search results, the system comprising: a processor; and memory storing instructions that, when executed by the processor, cause the system to generate the graphical user interface on a display device, the graphical user interface comprising: a window having at least two panes arranged as adjacent columns and separated by at least one adjustable column delimiter that visibly marks the boundaries between adjacent panes, a search element that enables a user to submit a search query; said at least two panes configured to collectively, responsive to submission of the search query, each display search results information from a different respective search category in each of said at least two panes, the different respective search category selected from the group consisting of: general web search results, image search results, book search results, movie search results, map search results, local area search results, reference search results, dictionary search results, address based search results, news search results, diary search results, bookmark search results, search history search results and tracking number search results, and each pane being user configurable to be in either an open state suitable for displaying search results information from a particular search category or a closed state in which a column width of the pane is narrowed to a display identifying the particular search category, wherein moving the at least one adjustable column delimiter to a position where a non-zero column width of an adjacent open pane in the open state is below a designated threshold for closing the open pane causes the open pane to automatically be configured in the closed state. | 1. A system for presenting a graphical user interface (GUI) for displaying search results, the system comprising: a processor; and memory storing instructions that, when executed by the processor, cause the system to generate the graphical user interface on a display device, the graphical user interface comprising: a window having at least two panes arranged as adjacent columns and separated by at least one adjustable column delimiter that visibly marks the boundaries between adjacent panes, a search element that enables a user to submit a search query; said at least two panes configured to collectively, responsive to submission of the search query, each display search results information from a different respective search category in each of said at least two panes, the different respective search category selected from the group consisting of: general web search results, image search results, book search results, movie search results, map search results, local area search results, reference search results, dictionary search results, address based search results, news search results, diary search results, bookmark search results, search history search results and tracking number search results, and each pane being user configurable to be in either an open state suitable for displaying search results information from a particular search category or a closed state in which a column width of the pane is narrowed to a display identifying the particular search category, wherein moving the at least one adjustable column delimiter to a position where a non-zero column width of an adjacent open pane in the open state is below a designated threshold for closing the open pane causes the open pane to automatically be configured in the closed state. 7. The system of claim 1 , wherein the designated threshold for closing the open pane comprises a threshold percentage of the width of the window. | 0.790831 |
9,697,822 | 12 | 13 | 12. The system of claim 11 , the one or more programs further comprising instructions for, after said updating: receiving invocation of the digital assistant; receiving speech input from a second user; generating speech-to-text output; and providing the speech-to-text output to the digital assistant. | 12. The system of claim 11 , the one or more programs further comprising instructions for, after said updating: receiving invocation of the digital assistant; receiving speech input from a second user; generating speech-to-text output; and providing the speech-to-text output to the digital assistant. 13. The system of claim 12 , wherein generating speech-to-text output comprises: comparing the speech input with the adaptive speech recognition model; in accordance with a determination that the second user is the same as the first user, performing automatic speech recognition using the adaptive speech recognition model to generate speech-to-text output; and in accordance with a determination that the second user is distinct from the first user, performing automatic speech recognition using a speaker-independent model to generate speech-to-text output. | 0.5 |
5,488,725 | 15 | 16 | 15. The process according to claim 12 where the maximum probable frequency, f.sub.max, and the minimum probable frequency, f.sub.min, are calculated in accordance with the relationships ##EQU6## where n.sub.i is the number of gaps between documents in the sample containing the selected representation, n.sub.c is the number of documents in the collection, x.sub.i is the number of documents in the sample, s.sub.i is the greater of x.sub.i /n.sub.i or the standard deviation of the n.sub.i gaps, and z is the standard critical value for normal distribution for a preselected reliability. | 15. The process according to claim 12 where the maximum probable frequency, f.sub.max, and the minimum probable frequency, f.sub.min, are calculated in accordance with the relationships ##EQU6## where n.sub.i is the number of gaps between documents in the sample containing the selected representation, n.sub.c is the number of documents in the collection, x.sub.i is the number of documents in the sample, s.sub.i is the greater of x.sub.i /n.sub.i or the standard deviation of the n.sub.i gaps, and z is the standard critical value for normal distribution for a preselected reliability. 16. The process according to claim 15 where the selected representation contains a plurality of terms, the method including setting f.sub.min equal to n.sub.i if the calculated f.sub.min is smaller than n.sub.i, setting f.sub.max equal to n.sub.i +(n.sub.c 31 x.sub.i) if the calculated f.sub.max is smaller than zero or smaller than n.sub.i, and setting f.sub.max equal to an a priori maximum if the calculated f.sub.max is greater than the a priori maximum. | 0.5 |
6,032,116 | 18 | 20 | 18. A method of generating a robust distance measure in a speech recognition system comprising the steps of: generating P order line spectral pair frequencies for a speech input signal; determining a difference, for i=1 to N.sub.1, between the ith line spectral pair frequency and an ith line spectral frequency of a reference speech signal; shifting the difference, for i=1 to N.sub.1, by an ith frequency shifting factor to at least partially compensate for frequency shifting of the ith speech input signal line spectral pair frequency by acoustic noise; and utilizing the shifted difference to classify the speech input signal. | 18. A method of generating a robust distance measure in a speech recognition system comprising the steps of: generating P order line spectral pair frequencies for a speech input signal; determining a difference, for i=1 to N.sub.1, between the ith line spectral pair frequency and an ith line spectral frequency of a reference speech signal; shifting the difference, for i=1 to N.sub.1, by an ith frequency shifting factor to at least partially compensate for frequency shifting of the ith speech input signal line spectral pair frequency by acoustic noise; and utilizing the shifted difference to classify the speech input signal. 20. The method of claim 18 further comprising the steps of: determining respective differences, for i=N.sub.1 +1 to P, between speech input signal line spectral pair frequencies and corresponding reference speech signal line spectral pair frequencies; and weighting of the respective differences, for i=N.sub.1 +1 to P, by respective frequency weighting factors. | 0.5 |
7,505,040 | 1 | 3 | 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. 3. The method of claim 1 wherein the first control point is positioned relative to a defined curve on the outline of the first component glyph. | 0.712851 |
7,970,598 | 12 | 14 | 12. A method for conversing in a plurality of languages, comprising: establishing a plurality of real-time conference areas wherein each real-time conference area supports a language and is operated in parallel with the other conference areas such that each conference area contains the same messages in the same order, said plurality of real-time conference areas managed by an online service conference manager that manages translations of messages from each conference area for broadcast of translated messages to the other conference areas; posting a first message from a first user onto a first of said plurality of real-time conference areas for access by a plurality of computer users currently connected to said first of said plurality of real-time conference areas based on a specified preference for a first language, said first message in said first language and originating from a spoken communication; automatically determining that said first message posted to said first real-time conference area is untranslated; automatically translating said first message into a second language after determining said first message is untranslated; automatically posting said translated message to a second of said real-time conference areas for access by a plurality of computer users currently connected to said second of said plurality of real-time conference areas based on a specified preference for said second language; posting a response to said translated message onto said second of said plurality of real-time conference areas for access by said plurality of users currently connected to said second of said plurality of real-time conference areas, said response in said second language and originating from a spoken communication; automatically determining that said response posted to said second real-time conference area is untranslated; automatically translating said response into said first language after determining said response is untranslated; and automatically posting said translated response in said first language onto said first real-time conference area for access by said plurality of computer users currently connect to said first real-time conference area. | 12. A method for conversing in a plurality of languages, comprising: establishing a plurality of real-time conference areas wherein each real-time conference area supports a language and is operated in parallel with the other conference areas such that each conference area contains the same messages in the same order, said plurality of real-time conference areas managed by an online service conference manager that manages translations of messages from each conference area for broadcast of translated messages to the other conference areas; posting a first message from a first user onto a first of said plurality of real-time conference areas for access by a plurality of computer users currently connected to said first of said plurality of real-time conference areas based on a specified preference for a first language, said first message in said first language and originating from a spoken communication; automatically determining that said first message posted to said first real-time conference area is untranslated; automatically translating said first message into a second language after determining said first message is untranslated; automatically posting said translated message to a second of said real-time conference areas for access by a plurality of computer users currently connected to said second of said plurality of real-time conference areas based on a specified preference for said second language; posting a response to said translated message onto said second of said plurality of real-time conference areas for access by said plurality of users currently connected to said second of said plurality of real-time conference areas, said response in said second language and originating from a spoken communication; automatically determining that said response posted to said second real-time conference area is untranslated; automatically translating said response into said first language after determining said response is untranslated; and automatically posting said translated response in said first language onto said first real-time conference area for access by said plurality of computer users currently connect to said first real-time conference area. 14. The method of claim 12 wherein said second language is selected from the group consisting of English, German, and French. | 0.904726 |
9,594,824 | 11 | 12 | 11. A computer program product, the computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause: receiving, by a processor, a query identifying a source entity, the source entity being of a first entity-type; generating, by the processor, a plurality of candidate entities from an analysis of an entity-relationship graph in response to the query based on the source entity; computing, by the processor, feature values for each candidate entity of the plurality of candidate entities by passing the source entity and the plurality of candidate entities to a type-specific entity recommender particular to the first entity-type; computing, by the processor, an aggregated score for each candidate entity by combining all of the feature values for each candidate entity; generating, by the processor, a plurality of ranked candidate entities by ranking each candidate entity in accordance with the computed aggregate score corresponding to that candidate entity to represent complex interactions between the plurality of candidate entities and leverage the complex interactions in the ranking; and identifying, by the processor, entity and relationship events that alter the entity-relationship graph by monitoring the entity-relationship graph. | 11. A computer program product, the computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause: receiving, by a processor, a query identifying a source entity, the source entity being of a first entity-type; generating, by the processor, a plurality of candidate entities from an analysis of an entity-relationship graph in response to the query based on the source entity; computing, by the processor, feature values for each candidate entity of the plurality of candidate entities by passing the source entity and the plurality of candidate entities to a type-specific entity recommender particular to the first entity-type; computing, by the processor, an aggregated score for each candidate entity by combining all of the feature values for each candidate entity; generating, by the processor, a plurality of ranked candidate entities by ranking each candidate entity in accordance with the computed aggregate score corresponding to that candidate entity to represent complex interactions between the plurality of candidate entities and leverage the complex interactions in the ranking; and identifying, by the processor, entity and relationship events that alter the entity-relationship graph by monitoring the entity-relationship graph. 12. The computer program product of claim 11 , wherein the program instructions are further executable by the processor to cause: presenting the plurality of ranked candidate entities in response to the query. | 0.714481 |
7,996,403 | 8 | 14 | 8. A computer-readable storage medium encoded with a computer program for performing a search request for a name among a database including a plurality of names, the computer program comprising computer executable instructions for: receiving the search request for the name; evaluating the name to assign a first cultural classification as a preliminary cultural classification to the name, wherein the first cultural classification is selected from a plurality of cultural classifications, each encompassing a respective a plurality of cultural sub-classifications; determining a frequency distribution of the name for at least one country associated with at least one of the plurality of cultural sub-classifications; evaluating the preliminary cultural classification associated with the name to assign a final cultural classification to the name, based on the determined frequency distribution of the name for the at least one country, wherein evaluating the preliminary cultural classification comprises: upon determining that only one cultural sub-classification of the plurality of cultural sub-classifications of the preliminary cultural classification is statistically significant, refining the preliminary cultural classification by assigning the one cultural sub-classification to the name as the final cultural classification to the name; upon determining that more than one cultural sub-classification of the plurality of cultural sub-classifications of the preliminary cultural classification is statistically significant, corroborating the preliminary cultural classification by assigning the preliminary cultural classification as the final cultural classification for the name; and upon determining that none of the plurality of cultural sub-classifications of the preliminary cultural classification are statistically significant and that more than one cultural sub-classification of a second cultural classification is statistically significant, overriding the preliminary cultural classification by assigning the second cultural classification to the name as the final cultural classification for the name; and completing the search request by searching for the name among the plurality of names within the database based on the final cultural classification assigned to the name. | 8. A computer-readable storage medium encoded with a computer program for performing a search request for a name among a database including a plurality of names, the computer program comprising computer executable instructions for: receiving the search request for the name; evaluating the name to assign a first cultural classification as a preliminary cultural classification to the name, wherein the first cultural classification is selected from a plurality of cultural classifications, each encompassing a respective a plurality of cultural sub-classifications; determining a frequency distribution of the name for at least one country associated with at least one of the plurality of cultural sub-classifications; evaluating the preliminary cultural classification associated with the name to assign a final cultural classification to the name, based on the determined frequency distribution of the name for the at least one country, wherein evaluating the preliminary cultural classification comprises: upon determining that only one cultural sub-classification of the plurality of cultural sub-classifications of the preliminary cultural classification is statistically significant, refining the preliminary cultural classification by assigning the one cultural sub-classification to the name as the final cultural classification to the name; upon determining that more than one cultural sub-classification of the plurality of cultural sub-classifications of the preliminary cultural classification is statistically significant, corroborating the preliminary cultural classification by assigning the preliminary cultural classification as the final cultural classification for the name; and upon determining that none of the plurality of cultural sub-classifications of the preliminary cultural classification are statistically significant and that more than one cultural sub-classification of a second cultural classification is statistically significant, overriding the preliminary cultural classification by assigning the second cultural classification to the name as the final cultural classification for the name; and completing the search request by searching for the name among the plurality of names within the database based on the final cultural classification assigned to the name. 14. The computer-readable storage medium of claim 8 , wherein each cultural sub-classification is distinct relative to other cultural sub-classifications. | 0.863958 |
8,086,463 | 1 | 3 | 1. A method for dynamically generating a vocal help prompt in a multimodal application, the method comprising: detecting a help-triggering event for an input element of a VoiceXML dialog, wherein the help-triggering event is selected from the group consisting of a request by a user for help, speech input that does not match any active grammar, and no speech input being received for a specified period of time, the detecting implemented with the multimodal application operating on a multimodal device supporting multiple modes of interaction including a voice mode and one or more non-voice modes, the multimodal application operatively coupled to a VoiceXML interpreter; retrieving, by the VoiceXML interpreter from a speech recognition grammar updated based on a changing information source, retrieved help text, wherein the retrieved help text includes first help text associated with at least one non-terminal element of the speech recognition grammar and second help text associated with at least one terminal element of the speech recognition grammar, wherein at least some of the retrieved help text is not hard-coded by a programmer of the multimodal application generating, by the VoiceXML interpreter, a vocal help prompt based, at least in part, on the first help text and the second help text; and presenting by the multimodal application the vocal help prompt through a computer user interface to a user. | 1. A method for dynamically generating a vocal help prompt in a multimodal application, the method comprising: detecting a help-triggering event for an input element of a VoiceXML dialog, wherein the help-triggering event is selected from the group consisting of a request by a user for help, speech input that does not match any active grammar, and no speech input being received for a specified period of time, the detecting implemented with the multimodal application operating on a multimodal device supporting multiple modes of interaction including a voice mode and one or more non-voice modes, the multimodal application operatively coupled to a VoiceXML interpreter; retrieving, by the VoiceXML interpreter from a speech recognition grammar updated based on a changing information source, retrieved help text, wherein the retrieved help text includes first help text associated with at least one non-terminal element of the speech recognition grammar and second help text associated with at least one terminal element of the speech recognition grammar, wherein at least some of the retrieved help text is not hard-coded by a programmer of the multimodal application generating, by the VoiceXML interpreter, a vocal help prompt based, at least in part, on the first help text and the second help text; and presenting by the multimodal application the vocal help prompt through a computer user interface to a user. 3. The method of claim 1 wherein: the grammar includes two or more alternative terminal elements; and retrieving help text further comprises retrieving, for use as help text, text from a number of the alternative terminal elements, the number limited by a predefined maximum. | 0.505396 |
8,209,352 | 31 | 33 | 31. The computer program product of claim 22 further comprising: maintaining one or more indexes. | 31. The computer program product of claim 22 further comprising: maintaining one or more indexes. 33. The computer program product of claim 31 in which the path identifier 20 corresponds to a key to a path entry containing a full path for the node, the path entry resides in a separate table, and the one or more indexes comprise an index on path identifiers or a unique index on reverse paths. | 0.5 |
7,774,353 | 5 | 6 | 5. The method of claim 1 , further comprising: assigning weights to each of the N parameter patterns and to each of the alternatives, wherein microsearches have weights based on the weights assigned to parameter patterns and/or alternatives in the microsearch; based on the assigned weights, determining a weight range for one or more of the templates; and wherein the act of enumerating microsearches is performed for a template in response to weights for microsearches being searched from another template approaching a weight range of the template. | 5. The method of claim 1 , further comprising: assigning weights to each of the N parameter patterns and to each of the alternatives, wherein microsearches have weights based on the weights assigned to parameter patterns and/or alternatives in the microsearch; based on the assigned weights, determining a weight range for one or more of the templates; and wherein the act of enumerating microsearches is performed for a template in response to weights for microsearches being searched from another template approaching a weight range of the template. 6. The method of claim 5 , wherein creating one or more templates comprises creating templates on an as needed basis in determining that weights for microsearches being searched are approaching a weight range of the template. | 0.5 |
10,079,013 | 1 | 10 | 1. A computer-implemented method for developing a semantic understanding of a multi-topic natural language dialog session involving at least two human participants, the method comprising, with a computing system: by a microphone, receiving a plurality of instances of spoken natural language dialog input supplied by a plurality of human participants in the dialog session; for one or more of the instances of dialog input, determining a domain model to apply to the dialog input; developing one or more intents based on the determined domain model, an intent comprising a structured representation of semantic content of the one or more instances of dialog input; storing the intents in a shared dialog context; selecting an intent from the shared dialog context based on an intent mapping, the intent mapping defining a relationship between the selected intent and a current intent, the current intent comprising a structured representation of semantic content of a current instance of the dialog input; creating a shared intent by combining a portion of the selected intent with a portion of the current intent; executing an action by the computing system based on the shared intent; wherein at least one of the intents is based on a domain model relating to a first topic and at least one other of the intents is based on a different domain model relating to a different topic than the first topic; and wherein the selected intent is selected based at least partly on a variable temporal proximity relative to the occurrence of the current intent. | 1. A computer-implemented method for developing a semantic understanding of a multi-topic natural language dialog session involving at least two human participants, the method comprising, with a computing system: by a microphone, receiving a plurality of instances of spoken natural language dialog input supplied by a plurality of human participants in the dialog session; for one or more of the instances of dialog input, determining a domain model to apply to the dialog input; developing one or more intents based on the determined domain model, an intent comprising a structured representation of semantic content of the one or more instances of dialog input; storing the intents in a shared dialog context; selecting an intent from the shared dialog context based on an intent mapping, the intent mapping defining a relationship between the selected intent and a current intent, the current intent comprising a structured representation of semantic content of a current instance of the dialog input; creating a shared intent by combining a portion of the selected intent with a portion of the current intent; executing an action by the computing system based on the shared intent; wherein at least one of the intents is based on a domain model relating to a first topic and at least one other of the intents is based on a different domain model relating to a different topic than the first topic; and wherein the selected intent is selected based at least partly on a variable temporal proximity relative to the occurrence of the current intent. 10. The method of claim 1 , wherein the current intent is a complete intent, the selected intent is a partial intent, and the method further comprises creating the shared intent by combining the selected intent with the current intent. | 0.564815 |
5,493,507 | 3 | 7 | 3. A digital circuit design assist system comprising a plurality of digital circuit design assist systems according to claim 2, wherein said hardware description language simulation means in each of said digital circuit design assist systems independently verifies each of said hardware units and each of said software units of said digital circuit from said functional model, said structural model, said language model and said software. | 3. A digital circuit design assist system comprising a plurality of digital circuit design assist systems according to claim 2, wherein said hardware description language simulation means in each of said digital circuit design assist systems independently verifies each of said hardware units and each of said software units of said digital circuit from said functional model, said structural model, said language model and said software. 7. A digital circuit design assist system according to claim 3, further comprising: a physical characteristics analysis tool for verifying whether or not said layout for arranging said structural models in a physical space in said digital circuit, for example, on a printed board or in an integrated circuit is appropriate, on the basis of physical characteristics such as wiring lines, delay times, exothermy, etc., of said structural model and/or radio waves, when said structural models are input thereto, and for evaluating the reliability of said digital circuit as an actual device when said layout is physically and actually realized. | 0.5 |
7,954,115 | 1 | 2 | 1. A computer-implementable method, comprising: providing a network-based community portal having a mashup platform integrated therewith; designating at least one pre-negotiated bartering agreement, in response to a particular user input by at least one user of said network-based community portal; and associating a management module with a network-based community portal that permits said at least one user of said network-based community portal to describe to said mashup platform said at least one pre-negotiated bartering agreement in order to permit said network-based community portal to manage the utilization of mashup applications associated with said mashup platform and at least one widget contained by said mashup applications. | 1. A computer-implementable method, comprising: providing a network-based community portal having a mashup platform integrated therewith; designating at least one pre-negotiated bartering agreement, in response to a particular user input by at least one user of said network-based community portal; and associating a management module with a network-based community portal that permits said at least one user of said network-based community portal to describe to said mashup platform said at least one pre-negotiated bartering agreement in order to permit said network-based community portal to manage the utilization of mashup applications associated with said mashup platform and at least one widget contained by said mashup applications. 2. The computer-implementable method of claim 1 wherein said management module comprises a Market Manager. | 0.812721 |
9,911,411 | 6 | 7 | 6. The method of claim 1 , wherein the adjusting step comprises detecting a class of speaker based on the comparison results. | 6. The method of claim 1 , wherein the adjusting step comprises detecting a class of speaker based on the comparison results. 7. The method of claim 6 , wherein the adjusting step further comprises selecting an acoustic model for the detected class of speaker. | 0.5 |
9,384,678 | 3 | 4 | 3. The method of claim 2 , wherein the set of multiple choice answers is a set of text strings, each including one or more words, which are alternatively selectable by the user for answering the generated question. | 3. The method of claim 2 , wherein the set of multiple choice answers is a set of text strings, each including one or more words, which are alternatively selectable by the user for answering the generated question. 4. The method of claim 3 , wherein only one of the set of multiple choice answers corresponds to the correct answer. | 0.5 |
7,814,042 | 102 | 106 | 102. The machine-readable storage medium of claim 101 , wherein the one or more sequences of instructions further comprise instructions which, when executed by the one or more processors, cause the one or more processors to perform the step of determining the alternative query block based on the first query block. | 102. The machine-readable storage medium of claim 101 , wherein the one or more sequences of instructions further comprise instructions which, when executed by the one or more processors, cause the one or more processors to perform the step of determining the alternative query block based on the first query block. 106. The machine-readable storage medium of claim 102 , wherein the query contains a predicate, and the first query block is an inline view; and wherein the instructions that cause determining the alternative query block comprise instructions which, when executed by the one or more processors, cause the one or more processors to perform the step of: pushing the predicate into the inline view. | 0.653509 |
8,903,707 | 1 | 4 | 1. A method for determining a dropped pronoun from a source language, wherein the method comprises: collecting parallel sentences from a source language and a target language; creating at least one word alignment between the parallel sentences in the source language and the target language; mapping at least one pronoun from the target language sentence onto the source language sentence; computing multiple features from the mapping, wherein the multiple features are extracted from both the source language and the at least one pronoun projected from the target language; using the multiple features to train a plurality of classifiers to predict position and spelling of at least one pronoun in the target language when the at least one pronoun is dropped in the source language; updating the plurality of classifiers based on user feedback provided in response to a prediction of position and spelling of at least one pronoun in the target language when the at least one pronoun is dropped in the source language; providing a graphical user interface to enable a user to select one of the plurality of classifiers to apply to a portion of the source language, wherein said plurality of classifiers are displayed to the user via the graphical user interface for selection and comprise (i) a conditional random field classifier, (ii) a tree-relabeling classifier, and (iii) a maximum entropy classifier; wherein each of said collecting, said, creating, said mapping, said computing, said using, said updating, and said providing is carried out by a computer device. | 1. A method for determining a dropped pronoun from a source language, wherein the method comprises: collecting parallel sentences from a source language and a target language; creating at least one word alignment between the parallel sentences in the source language and the target language; mapping at least one pronoun from the target language sentence onto the source language sentence; computing multiple features from the mapping, wherein the multiple features are extracted from both the source language and the at least one pronoun projected from the target language; using the multiple features to train a plurality of classifiers to predict position and spelling of at least one pronoun in the target language when the at least one pronoun is dropped in the source language; updating the plurality of classifiers based on user feedback provided in response to a prediction of position and spelling of at least one pronoun in the target language when the at least one pronoun is dropped in the source language; providing a graphical user interface to enable a user to select one of the plurality of classifiers to apply to a portion of the source language, wherein said plurality of classifiers are displayed to the user via the graphical user interface for selection and comprise (i) a conditional random field classifier, (ii) a tree-relabeling classifier, and (iii) a maximum entropy classifier; wherein each of said collecting, said, creating, said mapping, said computing, said using, said updating, and said providing is carried out by a computer device. 4. The method of claim 1 , wherein using the multiple features to train the plurality of classifiers comprises using at least one mapped feature in conjunction with at least one source language feature and at least one target language feature, comprising: producing an initial translation for the source language sentence into the target language; computing multiple features from the initial translation; and using the multiple features as input to the plurality of classifiers together with the at least one mapped feature. | 0.5 |
9,292,821 | 1 | 4 | 1. A method of consuming a Component Business Model (CBM) Heat Map for Services Oriented Architecture (SOA) based solution development, the method comprising the steps of: identifying a CBM Heat Map; at a first converting step, automatically converting the tabular representation of the CBM Heat Map to a Unified Modeling Language (UML) representation; and subsequent to the first converting step, at a second converting step, converting the UML representation of the CBM Heat Map to a first iteration of input for SOA solution development; wherein the first converting step comprises the steps of: (i) retrieving a plurality of CBM elements; (ii) processing the plurality of CBM elements; (iii) identifying a plurality of UML elements that respectively correspond to the plurality of CBM elements; and (iv) forming the plurality of UML elements into the UML representation of the CBM heat map. | 1. A method of consuming a Component Business Model (CBM) Heat Map for Services Oriented Architecture (SOA) based solution development, the method comprising the steps of: identifying a CBM Heat Map; at a first converting step, automatically converting the tabular representation of the CBM Heat Map to a Unified Modeling Language (UML) representation; and subsequent to the first converting step, at a second converting step, converting the UML representation of the CBM Heat Map to a first iteration of input for SOA solution development; wherein the first converting step comprises the steps of: (i) retrieving a plurality of CBM elements; (ii) processing the plurality of CBM elements; (iii) identifying a plurality of UML elements that respectively correspond to the plurality of CBM elements; and (iv) forming the plurality of UML elements into the UML representation of the CBM heat map. 4. The method of claim 1 further comprising the step of: subsequent to the first converting step, loading the UML representation of the CBM heat map into a modeling tool. | 0.5 |
9,946,709 | 1 | 3 | 1. A computer system for identifying word-senses, the computer system comprising: one or more computer processors, one or more computer-readable storage media, and program instructions stored on the one or more of the computer-readable storage media for execution by at least one of the one or more processors, the program instructions comprising: program instructions to generate a set of domain tables each comprising one or more arrays of aggregated statistical information corresponding to a plurality of words, one or more word-senses corresponding to the plurality of words, and temporal properties corresponding to the plurality of words, wherein the aggregated statistical information comprises a temporal frequency of occurrence value determined using an n-gram viewer; program instructions to receive a word; program instructions to identify the temporal frequency of occurrence value corresponding to the received word from each domain table in the set of domain tables; program instructions to associate the received word with one or more domain tables in the set of domain tables based on the temporal frequency of occurrence value corresponding to the received word in each of the one or more domain tables meeting a threshold value; and program instructions to identify one or more word-senses corresponding to the received word based on one or more corresponding word-senses in the associated one or more domain tables and based on one or more corresponding word-senses in a corresponding domain dictionary. | 1. A computer system for identifying word-senses, the computer system comprising: one or more computer processors, one or more computer-readable storage media, and program instructions stored on the one or more of the computer-readable storage media for execution by at least one of the one or more processors, the program instructions comprising: program instructions to generate a set of domain tables each comprising one or more arrays of aggregated statistical information corresponding to a plurality of words, one or more word-senses corresponding to the plurality of words, and temporal properties corresponding to the plurality of words, wherein the aggregated statistical information comprises a temporal frequency of occurrence value determined using an n-gram viewer; program instructions to receive a word; program instructions to identify the temporal frequency of occurrence value corresponding to the received word from each domain table in the set of domain tables; program instructions to associate the received word with one or more domain tables in the set of domain tables based on the temporal frequency of occurrence value corresponding to the received word in each of the one or more domain tables meeting a threshold value; and program instructions to identify one or more word-senses corresponding to the received word based on one or more corresponding word-senses in the associated one or more domain tables and based on one or more corresponding word-senses in a corresponding domain dictionary. 3. The computer system of claim 1 , wherein program instructions to associate the received word with one or more domain tables in the set of domain tables is further based on: program instructions to receive an input from a user; program instructions to identify a context value; program instructions to receive metadata associated with the received word; and program instructions to receive an input from a computer system. | 0.618705 |
7,882,484 | 17 | 18 | 17. A method of creating a design-specific input/output (I/O) model document, the method comprising: receiving a plurality of I/O pin models corresponding to available I/O pin profiles on a target device; selecting each I/O pin model from the plurality of I/O pin models corresponding to I/O pins specified in a circuit design for inclusion in an I/O specific model document; including, via a processor, each selected I/O pin model within the I/O specific model document; receiving generic package parasitic information for the target device; and including, via the processor, package parasitic information, selected from the generic package parasitic information, for each selected I/O pin model within a design-specific package parasitic document. | 17. A method of creating a design-specific input/output (I/O) model document, the method comprising: receiving a plurality of I/O pin models corresponding to available I/O pin profiles on a target device; selecting each I/O pin model from the plurality of I/O pin models corresponding to I/O pins specified in a circuit design for inclusion in an I/O specific model document; including, via a processor, each selected I/O pin model within the I/O specific model document; receiving generic package parasitic information for the target device; and including, via the processor, package parasitic information, selected from the generic package parasitic information, for each selected I/O pin model within a design-specific package parasitic document. 18. The method of claim 17 , further comprising: providing the I/O specific model document and the design-specific package parasitic document together as a single, unified document. | 0.5 |
5,457,454 | 1 | 4 | 1. An input device utilizing a virtual keyboard, which enables data to be input by designating at least one input-position corresponding to at least one key indicated in an image of a key arrangement displayed on a display means, by using a designating means, said input device comprising: an input-symbol defining means for defining a form and a meaning of each of input-symbols drawn by said designating means across said image of said key arrangement; and a symbol recognition means for discriminating said input-position and related input-symbol drawn across said image of said key arrangement said input-position on the basis of information defined by said input-symbol defining means and for generating at least one input-code corresponding to at least one input-code in a real keyboard, wherein said input device is operative to generate a specified input-code in accordance with a combination of said input-position and said related input-symbol discriminated by said symbol recognition means, and wherein said input device is operative to display characters or symbols corresponding to said specified input-code on said display means. | 1. An input device utilizing a virtual keyboard, which enables data to be input by designating at least one input-position corresponding to at least one key indicated in an image of a key arrangement displayed on a display means, by using a designating means, said input device comprising: an input-symbol defining means for defining a form and a meaning of each of input-symbols drawn by said designating means across said image of said key arrangement; and a symbol recognition means for discriminating said input-position and related input-symbol drawn across said image of said key arrangement said input-position on the basis of information defined by said input-symbol defining means and for generating at least one input-code corresponding to at least one input-code in a real keyboard, wherein said input device is operative to generate a specified input-code in accordance with a combination of said input-position and said related input-symbol discriminated by said symbol recognition means, and wherein said input device is operative to display characters or symbols corresponding to said specified input-code on said display means. 4. An input device as set forth in claim 1, wherein said form of each input-symbol defined by said input-symbol defining means includes a size of a circle drawn by said designating means and a rotational direction in which said circle is drawn. | 0.630303 |
9,652,473 | 8 | 12 | 8. One or more computer-readable storage media comprising instructions stored thereon that, responsive to execution by a computing device, cause the computing device to perform operations comprising: receiving social media data that includes topic information included in the social media data, sentiment values for at least some of the topic information, and location information correlated with the topic information; receiving user selection of a search term, a search term density value, and a sentiment density value for a particular sentiment value; comparing, for locations identified in the location information, a number of occurrences of the search term to a total volume of social media data received to determine a density of the search term at each of the locations; filtering the social media data to identify a set of locations that meet the user selected search term density value; filtering the set of locations to identify a subset of locations that meet the user selected sentiment density value for the particular sentiment value; and populating a map interface with indicia of the search term such that the search term is visually associated with the subset of locations, the indicia of the search term indicating one or more particular locations where the determined density of the particular sentiment value for the search term meets the user selected density value for the particular sentiment value. | 8. One or more computer-readable storage media comprising instructions stored thereon that, responsive to execution by a computing device, cause the computing device to perform operations comprising: receiving social media data that includes topic information included in the social media data, sentiment values for at least some of the topic information, and location information correlated with the topic information; receiving user selection of a search term, a search term density value, and a sentiment density value for a particular sentiment value; comparing, for locations identified in the location information, a number of occurrences of the search term to a total volume of social media data received to determine a density of the search term at each of the locations; filtering the social media data to identify a set of locations that meet the user selected search term density value; filtering the set of locations to identify a subset of locations that meet the user selected sentiment density value for the particular sentiment value; and populating a map interface with indicia of the search term such that the search term is visually associated with the subset of locations, the indicia of the search term indicating one or more particular locations where the determined density of the particular sentiment value for the search term meets the user selected density value for the particular sentiment value. 12. One or more computer-readable storage media as recited in claim 8 , wherein the operations further include: receiving an indication of a change to the search term; and causing one or more changes to the map interface based on the change to the search term. | 0.5 |
9,740,713 | 9 | 12 | 9. A system for processing data, comprising: one or more processors configured to execute an application, the application configured to: enable access to a data model including one or more tables, wherein the one or more tables comprise a set of scalar fields and a derived field associated with at least one of a summary, a formula, and a lookup, and wherein the derived field comprises identifying information of at least one related field or table in the data model or in a relational database; and store the scalar fields in the relational database; and a query builder configured to: use the data model to generate a first query for the relational database to obtain data associated with the derived field using one or more joins of one or more of the scalar fields based at least in part on the identifying information; and enable use of the first query to provide the data to a user during use of the application by the user. | 9. A system for processing data, comprising: one or more processors configured to execute an application, the application configured to: enable access to a data model including one or more tables, wherein the one or more tables comprise a set of scalar fields and a derived field associated with at least one of a summary, a formula, and a lookup, and wherein the derived field comprises identifying information of at least one related field or table in the data model or in a relational database; and store the scalar fields in the relational database; and a query builder configured to: use the data model to generate a first query for the relational database to obtain data associated with the derived field using one or more joins of one or more of the scalar fields based at least in part on the identifying information; and enable use of the first query to provide the data to a user during use of the application by the user. 12. The system of claim 9 , wherein generating the first query to obtain data associated with the derived field involves: using the data model to create one or more views of the one or more tables; and generating the query from the one or more views. | 0.681122 |
8,326,842 | 15 | 19 | 15. A computer-implemented method executed by one or more computing devices, the method comprising: receiving a search query; performing a search using the search query; receiving a plurality of documents snippets from the search; identifying a first set of one or more candidate topics by comparing one or more document snippets to an ontology of topics, wherein comparing each document snippet of the plurality of document snippets to the ontology of topics comprises computing a feature vector for each document snippet based on words contained in each document snippet and comparing each feature vector to topics in the ontology of topics, and wherein positive topic identification for the candidate topics within a document snippet is determined by determining that the feature vector for the document snippet is within a predetermined distance of a given topic; identifying a second set of one or more candidate topics by comparing one or more document snippets to an ontology of partial topics; identifying a third set of one or more candidate topics by generating key-phrase topics from one or more document snippets; ranking candidate topics from the first, second, and third set of candidate topics; selecting one or more topics based on ranking of the candidate topics; and providing a search results page in response to the search query, the search results page having a table of contents containing the one or more topics and a search results area for presenting one or more search results. | 15. A computer-implemented method executed by one or more computing devices, the method comprising: receiving a search query; performing a search using the search query; receiving a plurality of documents snippets from the search; identifying a first set of one or more candidate topics by comparing one or more document snippets to an ontology of topics, wherein comparing each document snippet of the plurality of document snippets to the ontology of topics comprises computing a feature vector for each document snippet based on words contained in each document snippet and comparing each feature vector to topics in the ontology of topics, and wherein positive topic identification for the candidate topics within a document snippet is determined by determining that the feature vector for the document snippet is within a predetermined distance of a given topic; identifying a second set of one or more candidate topics by comparing one or more document snippets to an ontology of partial topics; identifying a third set of one or more candidate topics by generating key-phrase topics from one or more document snippets; ranking candidate topics from the first, second, and third set of candidate topics; selecting one or more topics based on ranking of the candidate topics; and providing a search results page in response to the search query, the search results page having a table of contents containing the one or more topics and a search results area for presenting one or more search results. 19. The computer-implemented method of claim 15 , wherein comparing the one or more document snippets to the ontology of partial topics comprises computing a feature vector for the one or more document snippets based on words contained in each document snippet and comparing each feature vector to partial topics in the ontology of partial topics, and wherein positive partial topic identification for the second of set of one or more candidate topics within a document snippet is determined by determining that the feature vector for the document snippet is within a predetermined distance of a given partial topic. | 0.612579 |
9,705,972 | 16 | 17 | 16. The method of claim 1 , further comprising the at least one processor: storing, in the data repository, the first portion of the input set of data; computing a relevancy score associated with the first portion of the input set of data; determining that the relevancy score is below a relevancy score value threshold; and removing, based on the relevancy score being below the threshold, the first portion of the input set of data from the data repository. | 16. The method of claim 1 , further comprising the at least one processor: storing, in the data repository, the first portion of the input set of data; computing a relevancy score associated with the first portion of the input set of data; determining that the relevancy score is below a relevancy score value threshold; and removing, based on the relevancy score being below the threshold, the first portion of the input set of data from the data repository. 17. The method of claim 16 , wherein the determining the relevancy score to be below the relevancy score value threshold includes the at least one processor using a technique comprising at least one of machine learning techniques, keyword techniques, or embedded link analysis techniques. | 0.5 |
9,189,749 | 10 | 11 | 10. The system of claim 9 , further wherein said criteria of shared features are dynamically determined without the use of a priori classifications and using conditional probability constraints between sets of learned associations. | 10. The system of claim 9 , further wherein said criteria of shared features are dynamically determined without the use of a priori classifications and using conditional probability constraints between sets of learned associations. 11. The system of claim 10 , further wherein said grouping creates a network of conditional probabilities between all encountered natural language artifacts or a subset thereof, determined by consideration of conditional interaction probabilities based on a history of measurements from said data sources. | 0.5 |
7,769,591 | 1 | 11 | 1. A local device comprising: a primary functionality component; an input component configured to receive speech input; a processing component coupled to the input component, the processing component configured to: identify keywords in the speech input, determine whether the local device is capable of processing the speech input based on whether one or more keywords are identified in the speech input, if the local device is capable of processing the speech input, process the speech input, generate corresponding local control signals, and transmit the local control signals to the primary functionality component to direct an action in the primary functionality component, and if the local device is not capable of processing the speech input, extract feature parameters from the speech input for processing at a remote system, receive remote control signals from the remote system responsive to the remote system performing speech recognition on the feature parameters by storing an acoustic model of the feature parameters and recognizing a command based on a previously stored acoustic model associated with the local device to address specific characteristics of the feature parameters, and send the remote control signals to the primary functionality component; and a transceiver coupled to the processing component and configured to establish communications between the local device and the remote system, wherein the communications comprise: high bandwidth communications configured to return data supporting audio or video output at the local device, and low bandwidth communications configured to return data supporting the remote control signals. | 1. A local device comprising: a primary functionality component; an input component configured to receive speech input; a processing component coupled to the input component, the processing component configured to: identify keywords in the speech input, determine whether the local device is capable of processing the speech input based on whether one or more keywords are identified in the speech input, if the local device is capable of processing the speech input, process the speech input, generate corresponding local control signals, and transmit the local control signals to the primary functionality component to direct an action in the primary functionality component, and if the local device is not capable of processing the speech input, extract feature parameters from the speech input for processing at a remote system, receive remote control signals from the remote system responsive to the remote system performing speech recognition on the feature parameters by storing an acoustic model of the feature parameters and recognizing a command based on a previously stored acoustic model associated with the local device to address specific characteristics of the feature parameters, and send the remote control signals to the primary functionality component; and a transceiver coupled to the processing component and configured to establish communications between the local device and the remote system, wherein the communications comprise: high bandwidth communications configured to return data supporting audio or video output at the local device, and low bandwidth communications configured to return data supporting the remote control signals. 11. The local device of claim 1 , wherein the processing component is further configured to replace a keyword in the set of known keywords based on the update from the remote system. | 0.643137 |
7,839,511 | 1 | 2 | 1. A method for enhancement with a print data processing computer of an input document data stream which comprises at least one input format formdef file as an advanced function presentation (AFP) format definition resource file (formdef) comprising format definitions and an input document data file structured in ranges and sub-ranges and containing variable data, comprising the steps of: in a control file defining finishing commands and enhancing the data stream with said finishing commands, said control file being generated by analyzing a data structure of the input document data file and mapping said data structure into said control file, and said finishing commands being automatically inserted into said formdef file which are invoked instead of original calls contained in the document data file; in the control file also defining document levels that correspond to at least one of the ranges and the sub-ranges of the input document data file, the document levels on which specific ones of the finishing commands are to be applied being defined independent of said data structure of the document input file, said data processing computer comprising a first computer program module providing a graphical user interface by which a user specifies said levels within the data stream; in the control file also by use of said graphical user interface the user also associating the finishing commands with the levels, and registering which finishing commands are executed in which levels; in the control file also associating a first of the finishing commands with one of the ranges and associating a second of the finishing commands with one of the sub-ranges; and using the control file, input format file, and the input document data file, with said processing computer automatically generating and outputting by a second computer program module to a printing device for creating a printed document an output format file as an advanced function presentation (AFP) format definition resource file (formdef) that contains the finishing commands in callable groups, the output format formdef file being provided with modified medium maps relative to the input formdef file, and an output document data file containing the variable data and group calls associated by at least one of range-by-range and sub-range-by-sub-range. | 1. A method for enhancement with a print data processing computer of an input document data stream which comprises at least one input format formdef file as an advanced function presentation (AFP) format definition resource file (formdef) comprising format definitions and an input document data file structured in ranges and sub-ranges and containing variable data, comprising the steps of: in a control file defining finishing commands and enhancing the data stream with said finishing commands, said control file being generated by analyzing a data structure of the input document data file and mapping said data structure into said control file, and said finishing commands being automatically inserted into said formdef file which are invoked instead of original calls contained in the document data file; in the control file also defining document levels that correspond to at least one of the ranges and the sub-ranges of the input document data file, the document levels on which specific ones of the finishing commands are to be applied being defined independent of said data structure of the document input file, said data processing computer comprising a first computer program module providing a graphical user interface by which a user specifies said levels within the data stream; in the control file also by use of said graphical user interface the user also associating the finishing commands with the levels, and registering which finishing commands are executed in which levels; in the control file also associating a first of the finishing commands with one of the ranges and associating a second of the finishing commands with one of the sub-ranges; and using the control file, input format file, and the input document data file, with said processing computer automatically generating and outputting by a second computer program module to a printing device for creating a printed document an output format file as an advanced function presentation (AFP) format definition resource file (formdef) that contains the finishing commands in callable groups, the output format formdef file being provided with modified medium maps relative to the input formdef file, and an output document data file containing the variable data and group calls associated by at least one of range-by-range and sub-range-by-sub-range. 2. A method according to claim 1 wherein the output document data file is fed to a data production system that comprises said printing device and at least one device for processing of a print product at least one of before and after the printing event, and wherein the finishing commands activate at least one of the devices for processing of the print product at least one of before and after said printing event. | 0.5 |
8,607,138 | 23 | 25 | 23. The computer usable storage medium of claim 22 , wherein the report processed by the OLAP system includes one or more formatting macros, and wherein executing the computer readable program code on the one or more physical computing devices further causes the one or more physical computing devices to: process the one or more formatting macros at the server system, wherein the server system processes the one or more formatting macros to format the report for presentation in the spreadsheet application displayed within the instance of the web browser. | 23. The computer usable storage medium of claim 22 , wherein the report processed by the OLAP system includes one or more formatting macros, and wherein executing the computer readable program code on the one or more physical computing devices further causes the one or more physical computing devices to: process the one or more formatting macros at the server system, wherein the server system processes the one or more formatting macros to format the report for presentation in the spreadsheet application displayed within the instance of the web browser. 25. The computer usable storage medium of claim 23 , wherein the spreadsheet application applies the one or more formatting macros upon receipt at the user system to format the report for presentation in the spreadsheet application displayed within the instance of the web browser. | 0.737873 |
9,063,971 | 1 | 4 | 1. A method comprising: receiving, by a processing device, user information associated with a query for information in a lightweight directory access protocol (LDAP) repository, the query received in an abstraction format; determining, by the processing device, a computing domain in view of the user information; retrieving, by the processing device, a configuration file associated with the computing domain, the configuration file comprising a mapping for the query between an abstraction format and a vendor specific format; and converting, by the processing device, the query to the vendor specific format in view of the mapping in the configuration file. | 1. A method comprising: receiving, by a processing device, user information associated with a query for information in a lightweight directory access protocol (LDAP) repository, the query received in an abstraction format; determining, by the processing device, a computing domain in view of the user information; retrieving, by the processing device, a configuration file associated with the computing domain, the configuration file comprising a mapping for the query between an abstraction format and a vendor specific format; and converting, by the processing device, the query to the vendor specific format in view of the mapping in the configuration file. 4. The method of claim 1 , wherein the computing domain comprises a directory server, an authentication server, and the LDAP repository. | 0.825641 |
8,819,078 | 1 | 13 | 1. A method of event processing for graph structured data, comprising: storing graph-structured data in a store, the graph-structured data including a plurality of vertex, edge, and/or property graph elements; defining a first graph view of a characteristic of vertex, edge, and/or property graph elements; determining a subgraph as a subset of the plurality of vertex, edge, and/or property graph elements that have the characteristic of vertex, edge, and/or property graph elements defined by the first graph view; and processing the vertex, edge, and/or property graph elements of the subgraph responsive to a predefined event that occurs on at least one of the vertex, edge, and/or property graph elements of the subgraph. | 1. A method of event processing for graph structured data, comprising: storing graph-structured data in a store, the graph-structured data including a plurality of vertex, edge, and/or property graph elements; defining a first graph view of a characteristic of vertex, edge, and/or property graph elements; determining a subgraph as a subset of the plurality of vertex, edge, and/or property graph elements that have the characteristic of vertex, edge, and/or property graph elements defined by the first graph view; and processing the vertex, edge, and/or property graph elements of the subgraph responsive to a predefined event that occurs on at least one of the vertex, edge, and/or property graph elements of the subgraph. 13. The method of claim 1 , wherein storing graph-structured data includes storing the graph-structured data in a distributed store across a plurality of computing devices, and the method further comprising migrating at least some of the subset of the plurality of vertex, edge, and/or property graph elements that have the characteristic of vertex, edge, and/or property graph elements defined by the first graph view across machines of the distributed store in response to events to spread load or change graph partitioning for performance optimization. | 0.630984 |
6,018,617 | 11 | 12 | 11. A method of producing a set of examinations having both dynamic content and dynamic presentation, the method comprising the steps of: creating a collection of question data files, each question data file containing at least one content item, at least one corresponding label item for the at least one content item, and a set of initial variables whose values are determined according to a set of variation rules, selecting a number of the question data files; sequencing through the selected number of question data files; determining the placement of the content and label items of the selected number of question data files on a printed sheet; measuring the dimension of each content and each label item; and processing the variation rules and replacing each of the initial variables with a result value. | 11. A method of producing a set of examinations having both dynamic content and dynamic presentation, the method comprising the steps of: creating a collection of question data files, each question data file containing at least one content item, at least one corresponding label item for the at least one content item, and a set of initial variables whose values are determined according to a set of variation rules, selecting a number of the question data files; sequencing through the selected number of question data files; determining the placement of the content and label items of the selected number of question data files on a printed sheet; measuring the dimension of each content and each label item; and processing the variation rules and replacing each of the initial variables with a result value. 12. A method as in claim 11, wherein the variation rules for each question specify a mathematical calculation, a logical calculation, a constraint, or a call to a function. | 0.50289 |
6,155,834 | 61 | 68 | 61. A method of teaching a student to read, comprising: constructing sight reading development tests that include one or more interactive processes each with one or more interactive process types; presenting the sight reading development tests to the student; inputting a response from the student; determining student performance according to a response time measured between said presenting and said inputting steps; adjusting the difficulty level according to the student performance; and iterating said constructing, presenting, inputting, determining and adjusting wherein said constructing step constructs a next sight reading development test according to the adjusted difficulty level. | 61. A method of teaching a student to read, comprising: constructing sight reading development tests that include one or more interactive processes each with one or more interactive process types; presenting the sight reading development tests to the student; inputting a response from the student; determining student performance according to a response time measured between said presenting and said inputting steps; adjusting the difficulty level according to the student performance; and iterating said constructing, presenting, inputting, determining and adjusting wherein said constructing step constructs a next sight reading development test according to the adjusted difficulty level. 68. The method according to claim 61, said step adjusting including adjusting the difficulty level by adjusting a number of words utilized by the sight development tests. | 0.732704 |
7,814,125 | 12 | 13 | 12. A method in a data processing system for presenting a response based upon an application of business logic in accordance with a request, where the data processing system comprises a modified model-view-controller architecture that includes a user interface controller, a user interface builder implementing a first interface, an application layer implementing a second interface, a data access manager implementing a third interface, and at least one data access utility implementing a fourth interface, wherein the interface controller interacts with the user interface builder via the first interface, the user interface builder interacts with the application layer via the second interface, the application layer interacts with the data access manager via the third interface, the method comprising: initializing, by the data processing system, a page token in response to receiving a request, the page token comprising an abstract model component containing a specification for obtaining data designated to be sent in a response to the request or a specification for updating data obtained from the request, and a view component for providing referential format specifications for the data provided in the abstract model component, wherein the response comprises an entire web page including a current panel; passing, by the data processing system, the page token between at least two components of the modified model-view-controller architecture via an interface; applying, by the application layer, business logic to contents of the page token as a result of the passing; and presenting, by the data processing system, a response to the request based upon results of the applying business logic; wherein the data access manager sends at least a portion of the page token, including the specification, to the at least one data access utility via the fourth interface for use by the at least one data access utility in accessing data in a data store. | 12. A method in a data processing system for presenting a response based upon an application of business logic in accordance with a request, where the data processing system comprises a modified model-view-controller architecture that includes a user interface controller, a user interface builder implementing a first interface, an application layer implementing a second interface, a data access manager implementing a third interface, and at least one data access utility implementing a fourth interface, wherein the interface controller interacts with the user interface builder via the first interface, the user interface builder interacts with the application layer via the second interface, the application layer interacts with the data access manager via the third interface, the method comprising: initializing, by the data processing system, a page token in response to receiving a request, the page token comprising an abstract model component containing a specification for obtaining data designated to be sent in a response to the request or a specification for updating data obtained from the request, and a view component for providing referential format specifications for the data provided in the abstract model component, wherein the response comprises an entire web page including a current panel; passing, by the data processing system, the page token between at least two components of the modified model-view-controller architecture via an interface; applying, by the application layer, business logic to contents of the page token as a result of the passing; and presenting, by the data processing system, a response to the request based upon results of the applying business logic; wherein the data access manager sends at least a portion of the page token, including the specification, to the at least one data access utility via the fourth interface for use by the at least one data access utility in accessing data in a data store. 13. The method of claim 12 , wherein the request includes at least one of: a browser-initiated hypertext transport protocol request; a request for at least one of data, a document, an image, and multi-media content; a search request; and a request to launch an application. | 0.671875 |
7,711,573 | 7 | 8 | 7. The system of claim 1 , wherein the received resume is one of a number of resumes. | 7. The system of claim 1 , wherein the received resume is one of a number of resumes. 8. The system of claim 7 , wherein the receipt of the number of resumes is automatic, scheduled, or periodic. | 0.5 |
9,424,440 | 10 | 12 | 10. A computer program embodied on a non-transitory computer executable medium and configured to be executed by a processor, the program comprising: code for receiving a digital document in a word processing format into a non-transitory computer readable medium; code for generating a first baseline data sequence from the digital document, the first baseline data sequence having a first printable element and a second printable element following the first printable element of the first baseline data sequence; code for generating a first modified data sequence from the first baseline data sequence in accordance with a set of modification rules, the first modified data sequence having a first printable element and a second printable element following the first printable element of the first modified data sequence, wherein the first printable element of the first modified data sequence is identical to the first printable element of the first baseline data sequence, wherein the second printable element of the first modified data sequence is identical to the second element of the first baseline data sequence, wherein at least one unprintable element of the first baseline data sequence, between the first and final printable elements of the first baseline data sequence, is not in the first modified data sequence, so that the first modified data sequence is shorter than the first baseline data sequence, and wherein an integrity verification code (IVC) generated for the first modified data sequence will differ from an IVC generated for the first baseline data sequence; code for generating a first original IVC, wherein generating a first original IVC comprises performing a one-way operation on the first modified data sequence, and wherein the modification rules render tampering of the digital document undetectable for the at least one unprintable element within the first baseline data sequence; code for generating a second baseline data sequence from the digital document, the second baseline data sequence having a first printable element and a second printable element following the first printable element of the second baseline data sequence; wherein the second baseline data sequence is different than the first baseline data sequence, code for generating a second modified data sequence from the second baseline data sequence in accordance with the set of modification rules, the second modified data sequence having a first printable element and a second printable element following the first printable element of the second modified data sequence, wherein the first printable element of the second modified data sequence is identical to the first printable element of the second baseline data sequence, wherein the second printable element of the second modified data sequence is identical to the second element of the second baseline data sequence, wherein at least one unprintable element of the second baseline data sequence, between the first and final printable elements of the second baseline data sequence, is not in the second modified data sequence, so that the second modified data sequence is shorter than the second baseline data sequence, and wherein an IVC generated for the second modified data sequence will differ from an IVC generated for the second baseline data sequence; code for generating a second original IVC, wherein generating a second original IVC comprises performing a one-way operation on the second modified data sequence, and wherein the modification rules render tampering of the digital document undetectable for the at least one unprintable element within the second baseline data sequence; and code for publishing the digital document with at least a portion of the first original IVC and the second original IVC rendered on a face of the published document. | 10. A computer program embodied on a non-transitory computer executable medium and configured to be executed by a processor, the program comprising: code for receiving a digital document in a word processing format into a non-transitory computer readable medium; code for generating a first baseline data sequence from the digital document, the first baseline data sequence having a first printable element and a second printable element following the first printable element of the first baseline data sequence; code for generating a first modified data sequence from the first baseline data sequence in accordance with a set of modification rules, the first modified data sequence having a first printable element and a second printable element following the first printable element of the first modified data sequence, wherein the first printable element of the first modified data sequence is identical to the first printable element of the first baseline data sequence, wherein the second printable element of the first modified data sequence is identical to the second element of the first baseline data sequence, wherein at least one unprintable element of the first baseline data sequence, between the first and final printable elements of the first baseline data sequence, is not in the first modified data sequence, so that the first modified data sequence is shorter than the first baseline data sequence, and wherein an integrity verification code (IVC) generated for the first modified data sequence will differ from an IVC generated for the first baseline data sequence; code for generating a first original IVC, wherein generating a first original IVC comprises performing a one-way operation on the first modified data sequence, and wherein the modification rules render tampering of the digital document undetectable for the at least one unprintable element within the first baseline data sequence; code for generating a second baseline data sequence from the digital document, the second baseline data sequence having a first printable element and a second printable element following the first printable element of the second baseline data sequence; wherein the second baseline data sequence is different than the first baseline data sequence, code for generating a second modified data sequence from the second baseline data sequence in accordance with the set of modification rules, the second modified data sequence having a first printable element and a second printable element following the first printable element of the second modified data sequence, wherein the first printable element of the second modified data sequence is identical to the first printable element of the second baseline data sequence, wherein the second printable element of the second modified data sequence is identical to the second element of the second baseline data sequence, wherein at least one unprintable element of the second baseline data sequence, between the first and final printable elements of the second baseline data sequence, is not in the second modified data sequence, so that the second modified data sequence is shorter than the second baseline data sequence, and wherein an IVC generated for the second modified data sequence will differ from an IVC generated for the second baseline data sequence; code for generating a second original IVC, wherein generating a second original IVC comprises performing a one-way operation on the second modified data sequence, and wherein the modification rules render tampering of the digital document undetectable for the at least one unprintable element within the second baseline data sequence; and code for publishing the digital document with at least a portion of the first original IVC and the second original IVC rendered on a face of the published document. 12. The program of claim 10 wherein the second baseline data sequence is a subset, less than all, of the first baseline data sequence. | 0.969697 |
7,844,457 | 2 | 3 | 2. The method of claim 1 , wherein training the acoustic model comprises training so as to add an indication of an unaccented vowel of a word that has been classified as a function word. | 2. The method of claim 1 , wherein training the acoustic model comprises training so as to add an indication of an unaccented vowel of a word that has been classified as a function word. 3. The method of claim 2 , further comprising training the acoustic model so as to add an indication of an accented vowel of a word that has been classified as a content word. | 0.5 |
8,639,678 | 4 | 7 | 4. A system according to claim 3 , wherein said data processor device is further operative with the computer readable instructions to predict likelihood of existence of said particular relationship type in response to said number of occurrences. | 4. A system according to claim 3 , wherein said data processor device is further operative with the computer readable instructions to predict likelihood of existence of said particular relationship type in response to said number of occurrences. 7. A system according to claim 4 , wherein said data processor device is further operative with the computer readable instructions to predict a likelihood of existence of said particular relationship type in response to a weighted combination of different types of sentence structural relationship identified between said first term and said different second term. | 0.5 |
8,200,661 | 1 | 8 | 1. A method for providing a recommendation, comprising: training a supervised-learning model based on portions of content in a support document that are accessed by one or more users during multiple separate sessions, wherein the support document comprises content for multiple support topics; configuring the supervised-learning model so that when the supervised-learning model is used to identify one or more support topics, the supervised-learning model assigns a larger weight to a first portion of the content that is accessed by a given user during a single session than a second portion of the content that is accessed by the given user during multiple separate sessions; monitoring user actions while a user interacts with the support document using a computer during a current session; based on the user actions, using the supervised-learning model to identify one or more support topics in the multiple support topics; and providing the recommendation, which includes the one or more identified support topics, to the user. | 1. A method for providing a recommendation, comprising: training a supervised-learning model based on portions of content in a support document that are accessed by one or more users during multiple separate sessions, wherein the support document comprises content for multiple support topics; configuring the supervised-learning model so that when the supervised-learning model is used to identify one or more support topics, the supervised-learning model assigns a larger weight to a first portion of the content that is accessed by a given user during a single session than a second portion of the content that is accessed by the given user during multiple separate sessions; monitoring user actions while a user interacts with the support document using a computer during a current session; based on the user actions, using the supervised-learning model to identify one or more support topics in the multiple support topics; and providing the recommendation, which includes the one or more identified support topics, to the user. 8. The method of claim 1 , wherein there is at least partial overlap of the first portion of the content and the second portion of the content. | 0.80411 |
8,578,263 | 31 | 32 | 31. The computer program product of claim 30 wherein the filtering of the structured document comprises extracting, from the structured document, structural elements having classification identifiers corresponding to the second user classification of the session copy of the second user profile and writing the extracted structural elements into the session structured document in the session document. | 31. The computer program product of claim 30 wherein the filtering of the structured document comprises extracting, from the structured document, structural elements having classification identifiers corresponding to the second user classification of the session copy of the second user profile and writing the extracted structural elements into the session structured document in the session document. 32. The computer program product of claim 31 further comprising: computer instructions, recorded on the storage device, for filtering the presentation grammar, in dependence upon the extracted structural elements, into a session grammar in the session document. | 0.5 |
9,092,459 | 2 | 3 | 2. The method of claim 1 , further comprising: providing one or more of the suggested answer for presentation to the first user. | 2. The method of claim 1 , further comprising: providing one or more of the suggested answer for presentation to the first user. 3. The method of claim 2 , wherein providing one or more of the suggested answers includes providing one or more ranked suggested answers and wherein providing one or more ranked suggested answers comprises: grouping similar suggested answers based on a semantic similarity measure; and ranking the suggested answers where each group of similar suggested answers is given a combined ranking. | 0.5 |
9,195,639 | 13 | 17 | 13. A computer-implemented method of automatically analyzing text documents in which a computer performs the following steps: comparing text from a subject text to text of a plurality of given text templates, each text template containing at least one paragraph of text; determining which given text template or text templates has text that matches the text from the subject text document to a given degree of correspondence; generating a report of the differences between the text from the subject text document and the text of the matching text template or text templates; comparing a family of specimen text documents; identifying one paragraph of text within one of the family of specimen text documents that most closely matches a paragraph of text in all of the other specimen text documents, as compared to all of the other paragraphs in the one specimen text document; and generating one of the text templates containing at least the one identified paragraph of text. | 13. A computer-implemented method of automatically analyzing text documents in which a computer performs the following steps: comparing text from a subject text to text of a plurality of given text templates, each text template containing at least one paragraph of text; determining which given text template or text templates has text that matches the text from the subject text document to a given degree of correspondence; generating a report of the differences between the text from the subject text document and the text of the matching text template or text templates; comparing a family of specimen text documents; identifying one paragraph of text within one of the family of specimen text documents that most closely matches a paragraph of text in all of the other specimen text documents, as compared to all of the other paragraphs in the one specimen text document; and generating one of the text templates containing at least the one identified paragraph of text. 17. The computer-implemented method of claim 13 wherein the determining matches to a degree of correspondence step uses a longest common subsequence algorithm. | 0.506211 |
8,249,858 | 1 | 8 | 1. A method for multilingual administration of enterprise data, the method comprising: retrieving, by at least one device, enterprise data; extracting, by the at least one device, text from the enterprise data for rendering from a digital media file, the extracted text being in a source language; selecting, by the at least one device, a predetermined default target language from among a plurality of target languages based on a data type for the enterprise data; identifying, by the at least one device, that the source language is not the predetermined default target language for rendering the enterprise data; translating, by the at least one device, the extracted text in the source language to translated text in the predetermined default target language; converting, by the at least one device, the translated text to synthesized speech in the predetermined default target language; and recording, by the at least one device, the synthesized speech in the predetermined default target language in a digital media file. | 1. A method for multilingual administration of enterprise data, the method comprising: retrieving, by at least one device, enterprise data; extracting, by the at least one device, text from the enterprise data for rendering from a digital media file, the extracted text being in a source language; selecting, by the at least one device, a predetermined default target language from among a plurality of target languages based on a data type for the enterprise data; identifying, by the at least one device, that the source language is not the predetermined default target language for rendering the enterprise data; translating, by the at least one device, the extracted text in the source language to translated text in the predetermined default target language; converting, by the at least one device, the translated text to synthesized speech in the predetermined default target language; and recording, by the at least one device, the synthesized speech in the predetermined default target language in a digital media file. 8. The method of claim 1 , wherein the translating comprises: decoding meaning of the text in the source language, wherein the decoding comprises decoding derived from at least one of grammar, semantics, syntax, and idioms; and re-encoding the meaning of the text into the predetermined default target language based on the decoding derived from the at least one of grammar, semantics, syntax, and idioms. | 0.5 |
9,213,891 | 1 | 3 | 1. An information processing device including a processor comprising: a recognition result acquiring unit implemented by the processor, for acquiring respective recognition result information outputted by a plurality of recognition engines executing different recognition processes on recognition target data; and an integration recognition result outputting unit implemented by the processor, for outputting a new recognition result obtained by integrating the respective recognition result information acquired from the plurality of recognition engines, wherein: the recognition result acquiring unit is configured to acquire the respective recognition result information in a graph-structured or tree-structured data format being previously defined, from the plurality of recognition engines; and the integration recognition result outputting unit is configured to, when the respective recognition result information include information corresponding to each other in accordance with a predetermined standard, integrate each part of the respective recognition result information so as to be related to each other, and output in the graph structured or tree-structured data format as the new recognition result, and further, the recognition result acquiring unit is configured to acquire the recognition result information from an object recognition engine that recognizes an object from the recognition target data and a flow recognition engine that recognizes a flow representing a trajectory of a predetermined object from the recognition target data respectively; and the integration recognition result outputting unit is configured to, based on the recognition result information acquired from the object recognition engine and the recognition result information acquired from the flow recognition engine, examine an identity of a person identification information and an object identification information, the person identification information being included in the recognition result information acquired from the object recognition engine and the object identification information being included in the recognition result information acquired from the flow recognition engine, in a case that both the recognition result information are coincident with each other integrate by relating flow information of a predetermined object with object information and output as the new recognition result, the flow information being the recognition result information acquired from the flow recognition engine, and the object information being the recognition result information acquired from the object recognition engine. | 1. An information processing device including a processor comprising: a recognition result acquiring unit implemented by the processor, for acquiring respective recognition result information outputted by a plurality of recognition engines executing different recognition processes on recognition target data; and an integration recognition result outputting unit implemented by the processor, for outputting a new recognition result obtained by integrating the respective recognition result information acquired from the plurality of recognition engines, wherein: the recognition result acquiring unit is configured to acquire the respective recognition result information in a graph-structured or tree-structured data format being previously defined, from the plurality of recognition engines; and the integration recognition result outputting unit is configured to, when the respective recognition result information include information corresponding to each other in accordance with a predetermined standard, integrate each part of the respective recognition result information so as to be related to each other, and output in the graph structured or tree-structured data format as the new recognition result, and further, the recognition result acquiring unit is configured to acquire the recognition result information from an object recognition engine that recognizes an object from the recognition target data and a flow recognition engine that recognizes a flow representing a trajectory of a predetermined object from the recognition target data respectively; and the integration recognition result outputting unit is configured to, based on the recognition result information acquired from the object recognition engine and the recognition result information acquired from the flow recognition engine, examine an identity of a person identification information and an object identification information, the person identification information being included in the recognition result information acquired from the object recognition engine and the object identification information being included in the recognition result information acquired from the flow recognition engine, in a case that both the recognition result information are coincident with each other integrate by relating flow information of a predetermined object with object information and output as the new recognition result, the flow information being the recognition result information acquired from the flow recognition engine, and the object information being the recognition result information acquired from the object recognition engine. 3. The information processing device according to claim 1 , wherein the recognition result information acquired by the recognition result acquiring unit from each of the plurality of recognition engines is in a graph-structured or tree-structured data format. | 0.895733 |
9,235,909 | 1 | 2 | 1. A method for simplifying the presentation of a visually complex semantic model comprising: within a presentation area of a graphical modeling application running on a computing device, visually displaying on a display screen a visually complex semantic model, comprising a first quantity of visual objects and a first quantity of associations between the objects, wherein each visual object is visually connected to another of the objects by at least one association, wherein each association visually represents a relationship between the objects shown on the display screen; the graphical modeling application running on the computing device receiving a user-selected command to simplify a presentation of the displayed visually complex semantic model by a model simplification tool determining an association status for each object representation in the visually complex semantic model according to a plurality of simplification rules, wherein the association status determined for each of the objects is one of an autonomous object, a containment object, and a subordinate object; and transforming the presentation of the visually complex semantic model into a different visual representation referred to as a simplified semantic model, which is visually displayed in the presentation area, wherein the simplified semantic model comprises a second quantity of objects and a second quantity of simplified associations, wherein each displayed object of the second quantity is connected to another object of the second quantity by at least one of the simplified associations, wherein at least a portion of the objects of the second quantity are containment objects comprising at least one subordinate object visually nested within the containment object, wherein the second quantity of objects is less than the first quantity of objects, wherein the second quantity of associations is less than the first quantity of associations, and wherein each object of the visually complex semantic model is represented within the simplified semantic model by an object of the second quantity or by at least one subordinate object, wherein the simplified semantic model is rendered within the presentation area of the graphical modeling application. | 1. A method for simplifying the presentation of a visually complex semantic model comprising: within a presentation area of a graphical modeling application running on a computing device, visually displaying on a display screen a visually complex semantic model, comprising a first quantity of visual objects and a first quantity of associations between the objects, wherein each visual object is visually connected to another of the objects by at least one association, wherein each association visually represents a relationship between the objects shown on the display screen; the graphical modeling application running on the computing device receiving a user-selected command to simplify a presentation of the displayed visually complex semantic model by a model simplification tool determining an association status for each object representation in the visually complex semantic model according to a plurality of simplification rules, wherein the association status determined for each of the objects is one of an autonomous object, a containment object, and a subordinate object; and transforming the presentation of the visually complex semantic model into a different visual representation referred to as a simplified semantic model, which is visually displayed in the presentation area, wherein the simplified semantic model comprises a second quantity of objects and a second quantity of simplified associations, wherein each displayed object of the second quantity is connected to another object of the second quantity by at least one of the simplified associations, wherein at least a portion of the objects of the second quantity are containment objects comprising at least one subordinate object visually nested within the containment object, wherein the second quantity of objects is less than the first quantity of objects, wherein the second quantity of associations is less than the first quantity of associations, and wherein each object of the visually complex semantic model is represented within the simplified semantic model by an object of the second quantity or by at least one subordinate object, wherein the simplified semantic model is rendered within the presentation area of the graphical modeling application. 2. The method of claim 1 , wherein the determining and consolidating steps are performed by a model simplification tool utilizing a plurality of simplification rules defining object and association transformations. | 0.632302 |
8,832,197 | 1 | 16 | 1. A method for creating a collaborative discussion on a user device, the method comprising executing instructions stored in memory, wherein the execution of the instructions by the processor: stores the media content in one or more servers; provides a collaborative discussion interface, wherein the collaborative discussion interface displays a plurality of structured discussions for starting and participating in a discussion; creates a collaborative discussion using a user-selected structured discussion of the plurality of structured discussions; and posts the created collaborative discussion over a network, such that the posted collaborative discussion is accessible by a plurality of users by way of respective other user devices, wherein the collaborative discussion includes the media content, which is updated dynamically, and wherein the collaborative discussion includes a platform on which the plurality of users are capable of engaging in real-time collaborations or discussions concerning the media content. | 1. A method for creating a collaborative discussion on a user device, the method comprising executing instructions stored in memory, wherein the execution of the instructions by the processor: stores the media content in one or more servers; provides a collaborative discussion interface, wherein the collaborative discussion interface displays a plurality of structured discussions for starting and participating in a discussion; creates a collaborative discussion using a user-selected structured discussion of the plurality of structured discussions; and posts the created collaborative discussion over a network, such that the posted collaborative discussion is accessible by a plurality of users by way of respective other user devices, wherein the collaborative discussion includes the media content, which is updated dynamically, and wherein the collaborative discussion includes a platform on which the plurality of users are capable of engaging in real-time collaborations or discussions concerning the media content. 16. The method of claim 1 , wherein the execution of instructions by the processor further receives discussion input from one or more other users of the plurality of users via one or more of the respective other user devices, the discussion input prompted by the created collaborative discussion. | 0.525641 |
8,379,830 | 4 | 8 | 4. A computerized method as claimed in claim 3 wherein said threshold value may be dynamically reset based on a lifetime value of a relationship with said user. | 4. A computerized method as claimed in claim 3 wherein said threshold value may be dynamically reset based on a lifetime value of a relationship with said user. 8. A computerized method as claimed in claim 4 wherein said log likelihood ratio is re-calculated for each turn in said automated interaction and wherein said re-calculation takes place after each turn in real time during said automated interaction. | 0.5 |
5,559,898 | 29 | 34 | 29. The method of claim 28, wherein the vector elements in the input vector and the template vector have numerical values, and the sort is by numerical value. | 29. The method of claim 28, wherein the vector elements in the input vector and the template vector have numerical values, and the sort is by numerical value. 34. The method of claim 29, wherein elements forming the foreground of the template vectors have discrimination power, and the q pairs of corresponding elements are double sorted; first by the numerical values of the elements forming the template vector; and second by the discrimination power of the foreground elements forming the template vector. | 0.5 |
9,910,829 | 1 | 11 | 1. A method for automatically separating documents represented within a plurality of images by delineating document boundaries and identifying document types in accordance with classification rules, the method comprising: automatically generating classification rules that predict a document type or subdocument type for each of the plurality of images based on textual information and/or graphical information represented in each respective one of the plurality of images, wherein the classification rules are generated based on analyzing textual information and/or graphical information of a plurality of training images using one or more of: a probabilistic network; relational algebra; and machine learning techniques automatically generating one or more identifiers for identifying which of a plurality of document images belongs to which of a plurality of categories; automatically categorizing a plurality of document images into a plurality of predetermined categories based on analyzing textual information and/or image characteristics of each of the plurality of document images using the classification rules, wherein the step of automatically categorizing comprises: producing an output score for each document image based on the analysis thereof using the classification rules, wherein each output score represents an estimated document type probability or a subdocument type probability; and using a graph search algorithm to determine an optimum categorization sequence from a plurality of possible categorization sequences for the plurality of document images based on the output scores; and separating documents within the plurality of document images from one another by either: electronically associating at least one computer-generated label with at least some of the plurality of document images, each label corresponding to a different one of the plurality of categories and comprising one of the one or more identifiers generated for identifying which of the plurality of document images belongs to which of the plurality of categories; or inserting one or more computer-generated separation pages between at least some of the plurality of document images to delineate images belonging to different ones of the plurality of categories, each separation page comprising one of the one or more identifiers generated for identifying which of the plurality of document images belongs to which of the plurality of categories; or both electronically associating the at least one computer-generated label with at least some of the plurality of document images and inserting the one or more computer-generated separation pages between at least some of the plurality of document images. | 1. A method for automatically separating documents represented within a plurality of images by delineating document boundaries and identifying document types in accordance with classification rules, the method comprising: automatically generating classification rules that predict a document type or subdocument type for each of the plurality of images based on textual information and/or graphical information represented in each respective one of the plurality of images, wherein the classification rules are generated based on analyzing textual information and/or graphical information of a plurality of training images using one or more of: a probabilistic network; relational algebra; and machine learning techniques automatically generating one or more identifiers for identifying which of a plurality of document images belongs to which of a plurality of categories; automatically categorizing a plurality of document images into a plurality of predetermined categories based on analyzing textual information and/or image characteristics of each of the plurality of document images using the classification rules, wherein the step of automatically categorizing comprises: producing an output score for each document image based on the analysis thereof using the classification rules, wherein each output score represents an estimated document type probability or a subdocument type probability; and using a graph search algorithm to determine an optimum categorization sequence from a plurality of possible categorization sequences for the plurality of document images based on the output scores; and separating documents within the plurality of document images from one another by either: electronically associating at least one computer-generated label with at least some of the plurality of document images, each label corresponding to a different one of the plurality of categories and comprising one of the one or more identifiers generated for identifying which of the plurality of document images belongs to which of the plurality of categories; or inserting one or more computer-generated separation pages between at least some of the plurality of document images to delineate images belonging to different ones of the plurality of categories, each separation page comprising one of the one or more identifiers generated for identifying which of the plurality of document images belongs to which of the plurality of categories; or both electronically associating the at least one computer-generated label with at least some of the plurality of document images and inserting the one or more computer-generated separation pages between at least some of the plurality of document images. 11. The method of claim 1 , wherein automatically generating the one or more identifiers is based at least in part on information selected from a group consisting of: subdocument sequence information; a subdocument frequency; and a subdocument length distribution. | 0.625 |
8,140,347 | 10 | 12 | 10. A program storage device readable by computer, tangibly embodying a program of instructions executable by said computer to perform a method for constructing extensible markup language (XML) transactions comprising an XML format run on a computer system, said method comprising: establishing an original pre-defined data type definition format for an XML transaction; creating a copy of said original pre-defined data type definition format for said XML transaction; pre-building static structures of said XML transaction, wherein said static structures comprise a pre-built XML data structure with pre-filled values based on a transaction type of said XML transaction and a predetermined trading partner profile; classifying dynamic structures of said XML transaction with empty tags and single occurrence classifiers for repeating dynamic structures; building a list of a sequence of said static and dynamic structures; linking said list to the type of XML transaction and said predetermined trading partner profile; combining said static structures with said dynamic structures at a runtime of said XML transaction based on said sequence, said type of XML transaction, said trading partner profile, and said dynamic structures of said XML transaction, wherein an occurrence of said runtime of said XML transaction occurs when said XML transaction is sent to a trading partner, wherein said combining comprises filling the empty tags of said dynamic structures; and constructing a final XML structure based on the combining process, wherein said final XML structure comprises fully built dynamic structures that comprise completely filled tags, and wherein said final XML structure is validated by comparing said final XML structure against said copy of said original pre-defined data type definition format for said XML transaction. | 10. A program storage device readable by computer, tangibly embodying a program of instructions executable by said computer to perform a method for constructing extensible markup language (XML) transactions comprising an XML format run on a computer system, said method comprising: establishing an original pre-defined data type definition format for an XML transaction; creating a copy of said original pre-defined data type definition format for said XML transaction; pre-building static structures of said XML transaction, wherein said static structures comprise a pre-built XML data structure with pre-filled values based on a transaction type of said XML transaction and a predetermined trading partner profile; classifying dynamic structures of said XML transaction with empty tags and single occurrence classifiers for repeating dynamic structures; building a list of a sequence of said static and dynamic structures; linking said list to the type of XML transaction and said predetermined trading partner profile; combining said static structures with said dynamic structures at a runtime of said XML transaction based on said sequence, said type of XML transaction, said trading partner profile, and said dynamic structures of said XML transaction, wherein an occurrence of said runtime of said XML transaction occurs when said XML transaction is sent to a trading partner, wherein said combining comprises filling the empty tags of said dynamic structures; and constructing a final XML structure based on the combining process, wherein said final XML structure comprises fully built dynamic structures that comprise completely filled tags, and wherein said final XML structure is validated by comparing said final XML structure against said copy of said original pre-defined data type definition format for said XML transaction. 12. The program storage device of claim 10 , wherein said method further comprises predefining said trading partner profile associated with a predetermined trading entity. | 0.652439 |
7,856,472 | 1 | 58 | 1. In a computer system including a plurality of n-tuples, each of the plurality of n-tuples including n>1 text strings, a computer-implemented method comprising: displaying at least one window in connection with a website; displaying, utilizing the at least one window, a stock-related field; receiving a plurality of characters of text from a user as the user is typing the text utilizing the stock-related field; after the user types each character in the received text, dynamically determining whether the characters typed so far match one or more text strings in a first one of a plurality of n-tuples including n>1 text strings, each of the plurality of n-tuples including first text representing a stock ticker symbol and second text representing a company name corresponding to the stock ticker symbol; if it is determined that the characters typed so far match the one or more text strings in the one of the plurality of n-tuples, indicating to the user that a match has been found, utilizing the at least one window; displaying, utilizing the at least one window, a plurality of message summaries; wherein the plurality of message summaries comprise first information derived from a first message of a plurality of first messages and second information derived from a second message of a plurality of second messages associated with at least one online forum; displaying, utilizing the at least one window, a first set of representations; receiving first input from the user indicating a selection of one of the first set of representations; displaying a second set of representations representing a set of hyperlinks, utilizing the at least one window, in response to receiving the first input; receiving second input from the user indicating a selection of one of the second set of representations; and navigating to a destination specified by the selected one of the second set of representations, in response to receiving the second input. | 1. In a computer system including a plurality of n-tuples, each of the plurality of n-tuples including n>1 text strings, a computer-implemented method comprising: displaying at least one window in connection with a website; displaying, utilizing the at least one window, a stock-related field; receiving a plurality of characters of text from a user as the user is typing the text utilizing the stock-related field; after the user types each character in the received text, dynamically determining whether the characters typed so far match one or more text strings in a first one of a plurality of n-tuples including n>1 text strings, each of the plurality of n-tuples including first text representing a stock ticker symbol and second text representing a company name corresponding to the stock ticker symbol; if it is determined that the characters typed so far match the one or more text strings in the one of the plurality of n-tuples, indicating to the user that a match has been found, utilizing the at least one window; displaying, utilizing the at least one window, a plurality of message summaries; wherein the plurality of message summaries comprise first information derived from a first message of a plurality of first messages and second information derived from a second message of a plurality of second messages associated with at least one online forum; displaying, utilizing the at least one window, a first set of representations; receiving first input from the user indicating a selection of one of the first set of representations; displaying a second set of representations representing a set of hyperlinks, utilizing the at least one window, in response to receiving the first input; receiving second input from the user indicating a selection of one of the second set of representations; and navigating to a destination specified by the selected one of the second set of representations, in response to receiving the second input. 58. The method of claim 1 , wherein the displaying the plurality of message summaries is carried out utilizing the website, and a reply message is capable of being generated by the user utilizing the website. | 0.744472 |
8,831,929 | 1 | 4 | 1. A computer-implemented method, comprising: receiving, at a computing device including one or more processors, a composition input including one or more characters in a source language; determining, at the computing device, candidate selections for two or more target languages based on the composition input; determining, at the computing device, a language context value for each of the two or more target languages by evaluating the candidate selections against language models for the two or more target languages, wherein each language model includes a rule set for a corresponding target language; selecting, at the computing device, a set of the candidate selections based on the language context values, the set of candidate selections including at least one candidate selection in each of the two or more target languages; and outputting, from the computing device, the set of candidate selections in a single, interleaved list of candidate selections arranged based on a relative likelihood that a specific candidate selection was intended from the composition input. | 1. A computer-implemented method, comprising: receiving, at a computing device including one or more processors, a composition input including one or more characters in a source language; determining, at the computing device, candidate selections for two or more target languages based on the composition input; determining, at the computing device, a language context value for each of the two or more target languages by evaluating the candidate selections against language models for the two or more target languages, wherein each language model includes a rule set for a corresponding target language; selecting, at the computing device, a set of the candidate selections based on the language context values, the set of candidate selections including at least one candidate selection in each of the two or more target languages; and outputting, from the computing device, the set of candidate selections in a single, interleaved list of candidate selections arranged based on a relative likelihood that a specific candidate selection was intended from the composition input. 4. The computer-implemented method of claim 1 , wherein the language models include a Chinese language model that includes a grammar rule set for the Chinese language, and wherein the Chinese language model is based at least one of (i) serial verb construction, (ii) aspects of perfectives, and (iii) aspects of imperfectives. | 0.8696 |
9,703,775 | 1 | 9 | 1. A method comprising, by one or more computing devices: selecting, by one or more of the computing devices, a first text string from a set of text strings to be translated, wherein each text string of the set of text strings is associated with a priority value, and wherein the first text string is selected based on its priority value, wherein the priority value of the first text string is determined based on one or more previously calculated reliability-values of one or more translations for the first text string; sending, to a client system of a first user, instructions configured to present a translation prompt comprising the first text string and a translation-input field, wherein the first user is associated with a credibility-score based on prior translation activity of the first user; receiving, from the client system, an input by the first user at the translation-input field, wherein the input corresponds to a first translation for the first text string; and calculating, by one or more of the computing devices, a reliability-value for the first translation based on the input and the credibility-score of the first user. | 1. A method comprising, by one or more computing devices: selecting, by one or more of the computing devices, a first text string from a set of text strings to be translated, wherein each text string of the set of text strings is associated with a priority value, and wherein the first text string is selected based on its priority value, wherein the priority value of the first text string is determined based on one or more previously calculated reliability-values of one or more translations for the first text string; sending, to a client system of a first user, instructions configured to present a translation prompt comprising the first text string and a translation-input field, wherein the first user is associated with a credibility-score based on prior translation activity of the first user; receiving, from the client system, an input by the first user at the translation-input field, wherein the input corresponds to a first translation for the first text string; and calculating, by one or more of the computing devices, a reliability-value for the first translation based on the input and the credibility-score of the first user. 9. The method of claim 1 , further comprising publishing the first translation to other users if the first translation has a reliability-value exceeding a threshold reliability-value. | 0.863433 |
9,507,876 | 2 | 3 | 2. The method of claim 1 , further comprising: sending, to the client device of the first user, a query-initiation page responsive to a selection of a query field by the first user, the query-initiation page comprising one or more query-domain elements corresponding to one or more query-domains, respectively, wherein each query-domain element is activatable to generate a search query comprising a selection of the associated query-domain. | 2. The method of claim 1 , further comprising: sending, to the client device of the first user, a query-initiation page responsive to a selection of a query field by the first user, the query-initiation page comprising one or more query-domain elements corresponding to one or more query-domains, respectively, wherein each query-domain element is activatable to generate a search query comprising a selection of the associated query-domain. 3. The method of claim 2 , wherein receiving the first search query comprises receiving an indication the first user has activated a first query-domain element corresponding to the first query-domain. | 0.5 |
7,681,147 | 1 | 29 | 1. A computer-implemented method for determining probable meanings of inputted search query terms, the method comprising: receiving an input of at least one search query word by a networked server; determining a probable meaning of the at least one word by a processor of the networked server in accordance with a prior probability and a context frequency probability of probable meanings of the word, wherein the context frequency probability consists of the meaning of the word in a context of a plurality of terms immediately preceding or immediately following the word, wherein the prior probability comprises a probability that the word refers to a predetermined meaning irregardless of a query context in which the word is used, which prior probability is derived from previous analysis of documents containing the word, wherein determining the probable meaning comprises: estimating an expected final probability for the at least one word given the prior probability of the at least one word; deriving an inverse combine function that uses the prior probability and the expected final probability to determine the context frequency probability of the at least one word; using a combine mathematical function to combine the prior probability and the context frequency probability to produce a final probability of the probable meaning of the at least one search query word; and using the final probability of the probable meaning of the at least one word by the networked server to influence search results returned in response to the inputted at least one word. | 1. A computer-implemented method for determining probable meanings of inputted search query terms, the method comprising: receiving an input of at least one search query word by a networked server; determining a probable meaning of the at least one word by a processor of the networked server in accordance with a prior probability and a context frequency probability of probable meanings of the word, wherein the context frequency probability consists of the meaning of the word in a context of a plurality of terms immediately preceding or immediately following the word, wherein the prior probability comprises a probability that the word refers to a predetermined meaning irregardless of a query context in which the word is used, which prior probability is derived from previous analysis of documents containing the word, wherein determining the probable meaning comprises: estimating an expected final probability for the at least one word given the prior probability of the at least one word; deriving an inverse combine function that uses the prior probability and the expected final probability to determine the context frequency probability of the at least one word; using a combine mathematical function to combine the prior probability and the context frequency probability to produce a final probability of the probable meaning of the at least one search query word; and using the final probability of the probable meaning of the at least one word by the networked server to influence search results returned in response to the inputted at least one word. 29. The method of claim 1 , further comprising: ignoring the context frequency probability if it is within a predetermined range of a neutral context frequency. | 0.77208 |
8,959,083 | 11 | 15 | 11. A social video search system comprising: a processor, and; a memory storing instructions that, when executed, cause the system to: receiving a query from a user; obtaining social context information about interactions of the user from a social graph; obtaining a search result using the query; modifying a ranking of the search result based on an association with a particular source and a relation to the user to produce a modified search result; generating an annotation for at least one search result where the annotation indicates the association with the particular source and the relation to the user; and providing, for display to the user, the modified search result and the annotation for presentation. | 11. A social video search system comprising: a processor, and; a memory storing instructions that, when executed, cause the system to: receiving a query from a user; obtaining social context information about interactions of the user from a social graph; obtaining a search result using the query; modifying a ranking of the search result based on an association with a particular source and a relation to the user to produce a modified search result; generating an annotation for at least one search result where the annotation indicates the association with the particular source and the relation to the user; and providing, for display to the user, the modified search result and the annotation for presentation. 15. The system of claim 11 , wherein the social context information includes information about interaction of the user with other users. | 0.682243 |
7,720,804 | 48 | 52 | 48. The system as claimed in claim 29 , wherein the data warehouse solution computing system providing data management services comprises: managing creation and modification of warehouse tables in the data warehouse; and managing loading of the data into the warehouse tables from the source systems. | 48. The system as claimed in claim 29 , wherein the data warehouse solution computing system providing data management services comprises: managing creation and modification of warehouse tables in the data warehouse; and managing loading of the data into the warehouse tables from the source systems. 52. The system as claimed in claim 48 , wherein the data warehouse solution computing system managing creation and modification of warehouse tables further comprises indexing materialized views in the data warehouse. | 0.659306 |
8,538,752 | 1 | 9 | 1. A method for predicting a word accuracy, comprising: obtaining, by a processor, an utterance in speech data, wherein the utterance comprises an actual word string; processing, by the processor, the utterance for generating an interpretation of the actual word string; processing, by the processor, the utterance to identify an utterance frame; and calculating, by the processor, a prediction of a word accuracy associated with the interpretation based on a stationary signal-to-noise ratio and a non-stationary signal-to-noise ratio, wherein the stationary signal-to-noise ratio and the non-stationary signal-to-noise ratio are determined according to a frame energy associated with the utterance frame, and wherein the calculating comprises: computing the stationary signal-to-noise ratio for the utterance; computing the non-stationary signal-to-noise ratio for the utterance; and computing the prediction of the word accuracy associated with the interpretation using the stationary signal-to-noise ratio and the non-stationary signal-to-noise ratio. | 1. A method for predicting a word accuracy, comprising: obtaining, by a processor, an utterance in speech data, wherein the utterance comprises an actual word string; processing, by the processor, the utterance for generating an interpretation of the actual word string; processing, by the processor, the utterance to identify an utterance frame; and calculating, by the processor, a prediction of a word accuracy associated with the interpretation based on a stationary signal-to-noise ratio and a non-stationary signal-to-noise ratio, wherein the stationary signal-to-noise ratio and the non-stationary signal-to-noise ratio are determined according to a frame energy associated with the utterance frame, and wherein the calculating comprises: computing the stationary signal-to-noise ratio for the utterance; computing the non-stationary signal-to-noise ratio for the utterance; and computing the prediction of the word accuracy associated with the interpretation using the stationary signal-to-noise ratio and the non-stationary signal-to-noise ratio. 9. The method of claim 1 , wherein the computing the non-stationary signal-to-noise ratio comprises: computing an average signal power for the utterance; determining a noise power for each of a plurality of utterance frames of the utterance classified as silence frames; computing a noise signal-to-noise ratio for each of the plurality of utterance frames, wherein the noise signal-to-noise ratio for one of the plurality of utterance frames is computed using the average signal power and the noise power of the one of the plurality of utterance frames; and computing a standard deviation of the noise signal-to-noise ratios for the plurality of utterance frames. | 0.5 |
8,150,736 | 1 | 9 | 1. A method in a computing system having a processor, the method comprising: receiving a request for a web page, the request including a locale identifier value, the locale identifier value referencing a geographic location associated with a referral website and a language associated with a webpage of the referral website containing a link used to generate the request; with the processor, retrieving a version of marketing information identified by processing the locale identifier value included in the request for the web page; with the processor, generating the requested web page to include information representative of the retrieved version of the marketing information; and transmitting the generated web page. | 1. A method in a computing system having a processor, the method comprising: receiving a request for a web page, the request including a locale identifier value, the locale identifier value referencing a geographic location associated with a referral website and a language associated with a webpage of the referral website containing a link used to generate the request; with the processor, retrieving a version of marketing information identified by processing the locale identifier value included in the request for the web page; with the processor, generating the requested web page to include information representative of the retrieved version of the marketing information; and transmitting the generated web page. 9. The method of claim 1 , wherein the generating is performed such that the generated web page contains information representative of the marketing information, the generated web page including an offer to sell the product for a price expressed in a currency that corresponds to the geographic location referenced by the locale identifier value. | 0.599537 |
9,405,834 | 1 | 3 | 1. A computer-implemented method for identifying related search queries, performed on a server having at least one processor and memory storing at least one program for execution by the at least one processor to perform the method, comprising: receiving a search query from a user; identifying a set of ranked search results satisfying the search query; identifying, using historical search query data, at least one last related search query in at least one chain of related search queries that is related to the search query and that includes at least one search result that was selected by users who issued the search query, wherein the identifying includes querying a query database to identify the at least one last related search query in the at least one chain of related search queries that are related to the search query, wherein a respective chain of related search queries is a sequence of consecutive search queries that are issued by a respective user and that include an initial search query that is successively refined; the query database includes a plurality of records, wherein each respective record includes: a respective search query; a number of times the respective search query was issued; a respective search result that was selected by users who issued the respective search query and that corresponds to at least one respective related search query in at least one respective chain of related search queries that are related to the respective search query; the at least one respective related search query; and a number of times the respective search query led to a selection of the respective search result; and returning the set of ranked search results and the at least one last related search query to the user. | 1. A computer-implemented method for identifying related search queries, performed on a server having at least one processor and memory storing at least one program for execution by the at least one processor to perform the method, comprising: receiving a search query from a user; identifying a set of ranked search results satisfying the search query; identifying, using historical search query data, at least one last related search query in at least one chain of related search queries that is related to the search query and that includes at least one search result that was selected by users who issued the search query, wherein the identifying includes querying a query database to identify the at least one last related search query in the at least one chain of related search queries that are related to the search query, wherein a respective chain of related search queries is a sequence of consecutive search queries that are issued by a respective user and that include an initial search query that is successively refined; the query database includes a plurality of records, wherein each respective record includes: a respective search query; a number of times the respective search query was issued; a respective search result that was selected by users who issued the respective search query and that corresponds to at least one respective related search query in at least one respective chain of related search queries that are related to the respective search query; the at least one respective related search query; and a number of times the respective search query led to a selection of the respective search result; and returning the set of ranked search results and the at least one last related search query to the user. 3. The computer-implemented method of claim 1 , wherein each respective related search query in the at least one chain of related search queries, except for the at least one last related search query in the at least one chain of related search queries, violates a timing criterion with respect to user-selection of search results. | 0.630872 |
9,400,987 | 27 | 31 | 27. A non-transitory computer-readable storage medium tangibly encoded with computer-executable instructions, that when executed by at least a processor of a computing device, perform the method comprising: receiving, over a network, a user context query from a user, wherein the user context query is formatted as a parameter of a uniform resource locator (URL) and comprises at least one user context criteria; formulating a network data query based on the at least one user context criteria, said formulating comprises configuring the network data query based on user data relating to the querying user corresponding to a context of most interest to the user; identifying at least one entry from a plurality of entries in a context query bid database that relates to the at least one user context criteria based on the formulated network data query, wherein each of the plurality of entries comprises at least one bid context criteria, a bid amount, an identification of an advertiser, and an identification of at least one advertisement; selecting one of the identified at least one of the plurality of entries on the context query bid database, wherein the selected one of the plurality of entries on the context query bid database has the highest bid amount; retrieving, over the network, at least one entry from an advertisement database such that an identification of an advertiser and an identification of an advertisement on the at least one entry from the advertisement database matches the identification of the advertiser and the identification of the at least one advertisement on the selected one of the plurality of entries on the context query bid database, wherein each entry on the advertisement database comprises an identification of an advertiser, an identification of an advertisement, and at least one advertisement data object; and generating a dynamic webpage having content relating to the user context query. | 27. A non-transitory computer-readable storage medium tangibly encoded with computer-executable instructions, that when executed by at least a processor of a computing device, perform the method comprising: receiving, over a network, a user context query from a user, wherein the user context query is formatted as a parameter of a uniform resource locator (URL) and comprises at least one user context criteria; formulating a network data query based on the at least one user context criteria, said formulating comprises configuring the network data query based on user data relating to the querying user corresponding to a context of most interest to the user; identifying at least one entry from a plurality of entries in a context query bid database that relates to the at least one user context criteria based on the formulated network data query, wherein each of the plurality of entries comprises at least one bid context criteria, a bid amount, an identification of an advertiser, and an identification of at least one advertisement; selecting one of the identified at least one of the plurality of entries on the context query bid database, wherein the selected one of the plurality of entries on the context query bid database has the highest bid amount; retrieving, over the network, at least one entry from an advertisement database such that an identification of an advertiser and an identification of an advertisement on the at least one entry from the advertisement database matches the identification of the advertiser and the identification of the at least one advertisement on the selected one of the plurality of entries on the context query bid database, wherein each entry on the advertisement database comprises an identification of an advertiser, an identification of an advertisement, and at least one advertisement data object; and generating a dynamic webpage having content relating to the user context query. 31. The non-transitory computer-readable storage medium of claim 27 wherein in the formulating step, entries in the context query bid database are identified wherein the at least one bid context criteria on the identified one of the plurality of entries on the context query bid database relate to at least one entity that is closely related to a second at least one entity that relates to the at least one context criteria on the user context query. | 0.536082 |
9,094,455 | 11 | 12 | 11. A tangible and non-transitory machine-readable storage medium encoded with instructions for execution by a network node, the tangible and non-transitory machine-readable storage medium comprising: instructions for determining identification of a current version of a Diameter communication protocol associated with a communication session including determining a major version and a minor version including accessing provisioned information on said network node associated with a Diameter identity associated with said session; instructions for retrieving version spans for said current version from a protocol versioning dictionary at said network node; instructions for determining if an attribute definition exists having a version span matching said current version and if so, determining if said current version satisfies time constraints of said version span and if so applying the associated attribute definition. | 11. A tangible and non-transitory machine-readable storage medium encoded with instructions for execution by a network node, the tangible and non-transitory machine-readable storage medium comprising: instructions for determining identification of a current version of a Diameter communication protocol associated with a communication session including determining a major version and a minor version including accessing provisioned information on said network node associated with a Diameter identity associated with said session; instructions for retrieving version spans for said current version from a protocol versioning dictionary at said network node; instructions for determining if an attribute definition exists having a version span matching said current version and if so, determining if said current version satisfies time constraints of said version span and if so applying the associated attribute definition. 12. The tangible and non-transitory machine-readable storage medium of claim 11 , comprising instructions so that if no attribute definition exists having a version span matching said current version, then determining if an older version applies and if so applying the associated attribute definition. | 0.5 |
9,262,394 | 5 | 6 | 5. The requirement acquisition method according to claim 4 , further comprising: calculating a distance between each of the plurality of character strings and the document; and extracting a character string among the plurality of character strings the distance thereof is minimum as the candidate character string. | 5. The requirement acquisition method according to claim 4 , further comprising: calculating a distance between each of the plurality of character strings and the document; and extracting a character string among the plurality of character strings the distance thereof is minimum as the candidate character string. 6. The requirement acquisition method according to claim 5 , wherein the distance between each of the plurality of character strings and the document is calculated based on an approximation calculation method of a Kolmogorov complexity. | 0.5 |
8,543,654 | 1 | 9 | 1. A method executed at least in part by a computing device for providing a contextual conversation framework, the method comprising: receiving a request to initiate a conversation through a communication application user interface; compiling metadata associated with the conversation, wherein the metadata includes one or more of: an origination of the request, a conversation history, and a plurality of user attributes; determining an initiating user's context based on the compiled metadata associated with the conversation; providing the initiating user's context to a receiving user as a conversation context; defining a context definition including a definition file describing application-specific context data; defining a receiving user's context based on the initiating user's context; defining a plurality of steps for building a conversation context; transmitting the receiving user's context along with a conversation invite, the plurality of steps for building the conversation context, and the context definition as a package; establishing a context channel between the initiating user and the receiving user activated at a context application at the receiving user's client such that real time data, including one of: contextual data and a document, is exchanged for sharing a conversation context while the conversation is being facilitated; verifying the conversation context at a context trust model for establishing application trust of one or more applications associated with the initiating user and the receiving user, wherein the trust is determined from registration of an application, associated behavior and supporting URLs and paths used; upon conclusion of the conversation updating the conversation context; and enabling one or more applications to store a conversation history, publish and subscribe to the updated conversation context. | 1. A method executed at least in part by a computing device for providing a contextual conversation framework, the method comprising: receiving a request to initiate a conversation through a communication application user interface; compiling metadata associated with the conversation, wherein the metadata includes one or more of: an origination of the request, a conversation history, and a plurality of user attributes; determining an initiating user's context based on the compiled metadata associated with the conversation; providing the initiating user's context to a receiving user as a conversation context; defining a context definition including a definition file describing application-specific context data; defining a receiving user's context based on the initiating user's context; defining a plurality of steps for building a conversation context; transmitting the receiving user's context along with a conversation invite, the plurality of steps for building the conversation context, and the context definition as a package; establishing a context channel between the initiating user and the receiving user activated at a context application at the receiving user's client such that real time data, including one of: contextual data and a document, is exchanged for sharing a conversation context while the conversation is being facilitated; verifying the conversation context at a context trust model for establishing application trust of one or more applications associated with the initiating user and the receiving user, wherein the trust is determined from registration of an application, associated behavior and supporting URLs and paths used; upon conclusion of the conversation updating the conversation context; and enabling one or more applications to store a conversation history, publish and subscribe to the updated conversation context. 9. The method of claim 1 , further comprising: storing the updated conversation context in a context application data store such that a subsequent conversation session is initiated using the updated conversation context. | 0.688385 |
7,577,655 | 2 | 7 | 2. The method of claim 1 where the determining includes: determining, by the processor, a plurality of metric values for the news source. | 2. The method of claim 1 where the determining includes: determining, by the processor, a plurality of metric values for the news source. 7. The method of claim 2 where the generating includes: adding, by the processor, the plurality of metric values for the news source to produce a total value, obtaining, by the processor, the quality value by dividing the total value by a quantity of metric values in the plurality of metric values. | 0.5 |
8,849,693 | 1 | 6 | 1. A method for advertising in electronic commerce comprising: creating an electronic advertisement while connected to a computer network, wherein creating the electronic advertisement comprises: accessing a server included in said computer network; selecting at least one advertising service from a plurality of advertising services provided in a menu of advertising services, each of said advertising services including: an advertisement format specific to the advertising service, an associated display area in which the advertising service is to be presented, the associated display area corresponding to one of a plurality of display areas, where the plurality of display areas include: a priority placement area, and at least one other placement area, an associated advertiser priority indicating a particular order for presenting the advertising service relative to other advertising services, and an associated cost; establishing the advertisement format as including presentation information specific to the advertising service such that the plurality of advertising services collectively include a plurality of different advertisement formats; and paying at least a portion of the associated cost via said server while connected to said computer network. | 1. A method for advertising in electronic commerce comprising: creating an electronic advertisement while connected to a computer network, wherein creating the electronic advertisement comprises: accessing a server included in said computer network; selecting at least one advertising service from a plurality of advertising services provided in a menu of advertising services, each of said advertising services including: an advertisement format specific to the advertising service, an associated display area in which the advertising service is to be presented, the associated display area corresponding to one of a plurality of display areas, where the plurality of display areas include: a priority placement area, and at least one other placement area, an associated advertiser priority indicating a particular order for presenting the advertising service relative to other advertising services, and an associated cost; establishing the advertisement format as including presentation information specific to the advertising service such that the plurality of advertising services collectively include a plurality of different advertisement formats; and paying at least a portion of the associated cost via said server while connected to said computer network. 6. The method of claim 1 , wherein said computer network is an intranet. | 0.912621 |
8,244,539 | 1 | 6 | 1. A method, comprising: performing by one or more computers: receiving an indication of a request for content; identifying the requested content, wherein the requested content includes first audio data having a first set of phonemes; matching one or more of a plurality of advertising files to the requested content, wherein the one or more of the plurality of advertising files includes second audio data having a second set of phonemes, and wherein the matching is based, at least in part, upon a comparison between the first and second sets of phonemes; and causing the one or more of the plurality of advertising files to be delivered in response to the request. | 1. A method, comprising: performing by one or more computers: receiving an indication of a request for content; identifying the requested content, wherein the requested content includes first audio data having a first set of phonemes; matching one or more of a plurality of advertising files to the requested content, wherein the one or more of the plurality of advertising files includes second audio data having a second set of phonemes, and wherein the matching is based, at least in part, upon a comparison between the first and second sets of phonemes; and causing the one or more of the plurality of advertising files to be delivered in response to the request. 6. The method of claim 1 , wherein the one or more of the plurality of advertising files belongs to a corresponding one of a plurality of product categories. | 0.5 |
8,230,354 | 1 | 3 | 1. A system for providing branded display elements in an application for one or more clients, said system comprising: a branding content database for storing branded display elements, and said branded display elements being stored according to a database schema, said branding display elements including one or more default files, and said branding content database being configured to store one or more versions of said branded display elements, wherein said one or more versions are defined according to said database schema and retrievable in response to a branding request; and a branding module having, a component configured for retrieving one of said branded display elements from said branding content database in response to a branding request from the application and component being configured to provide one of said default files in response to a branding request when said one or more default files remain unaltered; a component configured for configuring said branded display element for a graphical display associated with the application; and a component configured for associating said branded display element with the application according to said database schema and storing said branded display element in said branding content database. | 1. A system for providing branded display elements in an application for one or more clients, said system comprising: a branding content database for storing branded display elements, and said branded display elements being stored according to a database schema, said branding display elements including one or more default files, and said branding content database being configured to store one or more versions of said branded display elements, wherein said one or more versions are defined according to said database schema and retrievable in response to a branding request; and a branding module having, a component configured for retrieving one of said branded display elements from said branding content database in response to a branding request from the application and component being configured to provide one of said default files in response to a branding request when said one or more default files remain unaltered; a component configured for configuring said branded display element for a graphical display associated with the application; and a component configured for associating said branded display element with the application according to said database schema and storing said branded display element in said branding content database. 3. The system as claimed in claim 1 , wherein said branding request comprises one or more of a message branding request, a plug-in branding request, a notification template images request and an installer branding request. | 0.619863 |
9,069,838 | 17 | 18 | 17. A computer system comprising: a processor, a computer readable memory, and a computer readable storage medium; first program instructions to associate a non-contextual data object with a context object to define a synthetic context-based object, wherein the non-contextual data object ambiguously relates to multiple subject-matters, and wherein the context object provides a context that identifies a specific subject-matter, from the multiple subject-matters, of the non-contextual data object; second program instructions to associate the synthetic context-based object with at least one specific data store, wherein said at least one specific data store comprises data that is associated with data contained in the non-contextual data object and the context object; and third program instructions to construct a dimensionally constrained hierarchical synthetic context-based object library for multiple synthetic context-based objects, wherein synthetic context-based objects within a same dimension of the dimensionally constrained hierarchical synthetic context-based object library share data from a same context object, and wherein synthetic context-based objects within the same dimension of the dimensionally constrained hierarchical synthetic context-based object library contain disparate data from different non-contextual data objects; and wherein the first, second, and third program instructions are stored on the computer readable storage medium for execution by the processor via the computer readable memory. | 17. A computer system comprising: a processor, a computer readable memory, and a computer readable storage medium; first program instructions to associate a non-contextual data object with a context object to define a synthetic context-based object, wherein the non-contextual data object ambiguously relates to multiple subject-matters, and wherein the context object provides a context that identifies a specific subject-matter, from the multiple subject-matters, of the non-contextual data object; second program instructions to associate the synthetic context-based object with at least one specific data store, wherein said at least one specific data store comprises data that is associated with data contained in the non-contextual data object and the context object; and third program instructions to construct a dimensionally constrained hierarchical synthetic context-based object library for multiple synthetic context-based objects, wherein synthetic context-based objects within a same dimension of the dimensionally constrained hierarchical synthetic context-based object library share data from a same context object, and wherein synthetic context-based objects within the same dimension of the dimensionally constrained hierarchical synthetic context-based object library contain disparate data from different non-contextual data objects; and wherein the first, second, and third program instructions are stored on the computer readable storage medium for execution by the processor via the computer readable memory. 18. The computer system of claim 17 , further comprising: fourth program instructions to data mine a data structure for the non-contextual data object and the context object, wherein said data mining locates said at least one specific data store that comprises data contained in the non-contextual data object and the context object; and wherein the fourth program instructions are stored on the computer readable storage medium for execution by the processor via the computer readable memory. | 0.5 |
8,949,718 | 14 | 15 | 14. The electronic device of claim 13 wherein the visual audio links comprise hyperlinks that are displayed as at least one of a text, an icon, and a graphic that scroll across the display as the digital audio content is played. | 14. The electronic device of claim 13 wherein the visual audio links comprise hyperlinks that are displayed as at least one of a text, an icon, and a graphic that scroll across the display as the digital audio content is played. 15. The electronic device of claim 14 when preference value settings in a user profile control a speed at which the visual audio links scroll. | 0.5 |
9,858,252 | 1 | 7 | 1. A database system, comprising: a processing system; and a memory device coupled to the processing system, the memory device having instructions stored thereon that, in response to execution by the processing system, cause the processing system to perform operations comprising: storing, by the processing system of the database system, a plurality of subscriptions, each of the subscriptions associated with a different subscriber of a plurality of subscribers, each subscriber associated with a permission level of a plurality of permission levels; receiving first information uploaded from a first remote user system over a network, wherein the first information comprises annotation information including annotations and positioning information; determining whether to grant one of the subscribers access to the annotations; and in response to determining to grant the one of the subscribers access to the annotations, downloading second information to be used to graphically annotate content on a second remote user system without modification of said content on the second remote user system, wherein the second information that is different from the received first information and the second remote user system that is different from the first remote user system; wherein said content comprises at least one of text, an image, flash based images or video, and the content is created using at least one of a plurality of document creation applications; and wherein the second different information comprises an overlay having a transparent work area to align to an arbitrary format display corresponding to the content. | 1. A database system, comprising: a processing system; and a memory device coupled to the processing system, the memory device having instructions stored thereon that, in response to execution by the processing system, cause the processing system to perform operations comprising: storing, by the processing system of the database system, a plurality of subscriptions, each of the subscriptions associated with a different subscriber of a plurality of subscribers, each subscriber associated with a permission level of a plurality of permission levels; receiving first information uploaded from a first remote user system over a network, wherein the first information comprises annotation information including annotations and positioning information; determining whether to grant one of the subscribers access to the annotations; and in response to determining to grant the one of the subscribers access to the annotations, downloading second information to be used to graphically annotate content on a second remote user system without modification of said content on the second remote user system, wherein the second information that is different from the received first information and the second remote user system that is different from the first remote user system; wherein said content comprises at least one of text, an image, flash based images or video, and the content is created using at least one of a plurality of document creation applications; and wherein the second different information comprises an overlay having a transparent work area to align to an arbitrary format display corresponding to the content. 7. The database system of claim 1 , wherein the positioning information includes a first value corresponding to at least one of a size or resolution of a document that corresponds to the annotation information. | 0.773218 |
8,108,509 | 30 | 34 | 30. An interactive network system, comprising; a first computer; a second computer, the second computer receiving content data from the first computer, wherein the content data is altered in accordance with content data output characteristics specified by the first computer, the interactive network system further comprising, a voice recognition component, the voice recognition component converts an audio component of the content data to text data; a text conversion component, the text conversion component processes the text data to converted text data, and a voice synthesis component, the voice synthesis component synthesizes the converted text data to audio data for output in the second computer; wherein audio data to be output at the second computer includes the application of an expression alteration that does not include performing language translation. | 30. An interactive network system, comprising; a first computer; a second computer, the second computer receiving content data from the first computer, wherein the content data is altered in accordance with content data output characteristics specified by the first computer, the interactive network system further comprising, a voice recognition component, the voice recognition component converts an audio component of the content data to text data; a text conversion component, the text conversion component processes the text data to converted text data, and a voice synthesis component, the voice synthesis component synthesizes the converted text data to audio data for output in the second computer; wherein audio data to be output at the second computer includes the application of an expression alteration that does not include performing language translation. 34. An interactive network system as recited in claim 30 , wherein the first and second computers are networked together and a server assists in the communication and data handling between the first and second computers. | 0.74537 |
7,487,084 | 10 | 18 | 10. A method of testing speech recognition in a new vehicle, said method comprising: propagating a speech output via a speaker arrangement based on a known text previously recorded by a human; wherein the text is comprised of a set of commands of interest, said speech output being stored digitally in a laptop computer disposed within the new vehicle; testing the acceptability of installed speech recognition systems in vehicles via a testing arrangement while the vehicles are being operated on a roadway at speeds of 0, 30, and 60 miles per hour, wherein the acceptability of a particular vehicular speech recognition system is based upon a comparison of pre-specified standards of recognition accuracy and signal-to-noise ratio values with a recognition accuracy value and a signal-to-noise ratio value produced by the particular vehicular speech recognition system, based on the text recognized from the speech output and the testing arrangement is located separate from the vehicle being tested, the propagated speech output being transmitted to the testing arrangement via a cellular transmission unit located within the vehicle being tested; wherein said speaker arrangement is configured to simulate the propagation of a human voice and is calibrated with respect to an audio input of an installed speech recognition system in a vehicle such that the speech output is propagated at the same pressure and distance from a microphone used to record the human speech; wherein the speech output comprises three recordings of the human voice, one recording having been made while the human wore a headset playing noise typical of an automobile operating on a roadway at a speed of 0 miles per hour, one recording having been made while the human wore a headset playing noise typical of an automobile operating on a roadway at a speed of 30 miles, and one recording having been made while the human wore a headset playing noise typical of an automobile operating on a roadway at a speed of 60 miles per hour; wherein the recording having been made while the human wore a headset playing noise typical of an automobile operating on a roadway at a speed of 0 miles per hour is played during the test when the vehicle is being operated on a roadway at a speed of 0miles per hour; wherein the recording having been made while the human wore a headset playing noise typical of an automobile operating on a roadway at a speed of 30 miles per hour is played during the test when the vehicle is being operated on a roadway at a speed of 30 miles per hour; wherein the recording having been made while the human wore a headset playing noise typical of an automobile operating on a roadway at a speed of 60 miles per hour is played during the test when the vehicle is being operated on a roadway at a speed of 60 miles per hour; and wherein the method is used to test every one-hundredth car that leaves an assembly line. | 10. A method of testing speech recognition in a new vehicle, said method comprising: propagating a speech output via a speaker arrangement based on a known text previously recorded by a human; wherein the text is comprised of a set of commands of interest, said speech output being stored digitally in a laptop computer disposed within the new vehicle; testing the acceptability of installed speech recognition systems in vehicles via a testing arrangement while the vehicles are being operated on a roadway at speeds of 0, 30, and 60 miles per hour, wherein the acceptability of a particular vehicular speech recognition system is based upon a comparison of pre-specified standards of recognition accuracy and signal-to-noise ratio values with a recognition accuracy value and a signal-to-noise ratio value produced by the particular vehicular speech recognition system, based on the text recognized from the speech output and the testing arrangement is located separate from the vehicle being tested, the propagated speech output being transmitted to the testing arrangement via a cellular transmission unit located within the vehicle being tested; wherein said speaker arrangement is configured to simulate the propagation of a human voice and is calibrated with respect to an audio input of an installed speech recognition system in a vehicle such that the speech output is propagated at the same pressure and distance from a microphone used to record the human speech; wherein the speech output comprises three recordings of the human voice, one recording having been made while the human wore a headset playing noise typical of an automobile operating on a roadway at a speed of 0 miles per hour, one recording having been made while the human wore a headset playing noise typical of an automobile operating on a roadway at a speed of 30 miles, and one recording having been made while the human wore a headset playing noise typical of an automobile operating on a roadway at a speed of 60 miles per hour; wherein the recording having been made while the human wore a headset playing noise typical of an automobile operating on a roadway at a speed of 0 miles per hour is played during the test when the vehicle is being operated on a roadway at a speed of 0miles per hour; wherein the recording having been made while the human wore a headset playing noise typical of an automobile operating on a roadway at a speed of 30 miles per hour is played during the test when the vehicle is being operated on a roadway at a speed of 30 miles per hour; wherein the recording having been made while the human wore a headset playing noise typical of an automobile operating on a roadway at a speed of 60 miles per hour is played during the test when the vehicle is being operated on a roadway at a speed of 60 miles per hour; and wherein the method is used to test every one-hundredth car that leaves an assembly line. 18. The method according to claim 10 , wherein said propagating step comprises propagating prerecorded speech output. | 0.764113 |
7,977,562 | 12 | 15 | 12. The at least one computer storage medium of claim 11 , wherein the lyrics are provided in a text file. | 12. The at least one computer storage medium of claim 11 , wherein the lyrics are provided in a text file. 15. The at least one computer storage medium of claim 12 , wherein synthesizing the lyrics with the melody comprises: breaking down words in the lyrics into sub-phonemic units; converting the sub-phonemic units into a sequence of contextual labels; and determining a matching contextual parametric model for each contextual label, wherein the sequence of contextual parametric models is comprised of the matching contextual model for each contextual label. | 0.5 |
9,218,819 | 11 | 13 | 11. A system comprising: one or more computers configured to perform operations comprising: receiving a voice input from a user; determining a context from the voice input, wherein the context identifies a subject of the voice input, and wherein the context is associated with an action; identifying, from previously stored data specific to the user, one or more potentially relevant current or near-term events related to the user and the subject of the voice input based on the context and the voice input; determining a level of confidence for an association of the one or more potentially relevant current or near-term events and the context; and when the level of confidence is sufficiently high, performing the action based on the voice input, and on the one or more potentially relevant current or near-term events, wherein the one or more potentially relevant current or near-term events are used to customize the action. | 11. A system comprising: one or more computers configured to perform operations comprising: receiving a voice input from a user; determining a context from the voice input, wherein the context identifies a subject of the voice input, and wherein the context is associated with an action; identifying, from previously stored data specific to the user, one or more potentially relevant current or near-term events related to the user and the subject of the voice input based on the context and the voice input; determining a level of confidence for an association of the one or more potentially relevant current or near-term events and the context; and when the level of confidence is sufficiently high, performing the action based on the voice input, and on the one or more potentially relevant current or near-term events, wherein the one or more potentially relevant current or near-term events are used to customize the action. 13. The system of claim 11 , wherein the one or more potentially relevant current or near-term events are identified from the group consisting of calendar data associated with the user, social network data associated with the user, email data associated with the user, text message data associated with the user, telephone conversation data associated with the user, internet activity data associated with the user, and any combination thereof. | 0.5 |
9,792,277 | 18 | 21 | 18. A computer system for determining an impact of text in a document using linguistic analysis, without using statistical methods, and frequency or word pattern based methods, wherein the same text can have different impact for different topics of interest, the system comprising: a processor within the computer system; a cluster generating module configured to generate at least one cluster from a plurality of semantically related clauses across multiple sentences of text of a single document through co-referencing, without statistical methods or frequency or word pattern based methods or any manual review and by applying semantic relationship strength rules based on the type of co-referential relationship in the text; a cluster concept identifier to identify at least one representative concept for the at least one cluster using linguistic rules, wherein the representative concept can be from any sentence in the cluster; wherein other non-representative concepts are not considered for further analysis and where the same representative concept may not be representative in another document; and an impact analyzer including: a semantic parameter identifier configured to determine at least one semantic parameter for a first representative clause of the at least one cluster; at least one configurable linguistic impact analysis rule using parts of speech roles of the words in the clause to determine an impact of the first representative clause of the at least one cluster using the at least one semantic parameter; and an impact engine configured to compute the impact of the first representative clause of the at least one cluster using the representative concept and the at least one configurable linguistic impact analysis rule using parts of speech roles of the words in the clause, on the topic of interest, without statistical methods and without frequency and word patterns, and where the impact can be different for every instance of the clause across different documents or for the same clause for different topics of interest. | 18. A computer system for determining an impact of text in a document using linguistic analysis, without using statistical methods, and frequency or word pattern based methods, wherein the same text can have different impact for different topics of interest, the system comprising: a processor within the computer system; a cluster generating module configured to generate at least one cluster from a plurality of semantically related clauses across multiple sentences of text of a single document through co-referencing, without statistical methods or frequency or word pattern based methods or any manual review and by applying semantic relationship strength rules based on the type of co-referential relationship in the text; a cluster concept identifier to identify at least one representative concept for the at least one cluster using linguistic rules, wherein the representative concept can be from any sentence in the cluster; wherein other non-representative concepts are not considered for further analysis and where the same representative concept may not be representative in another document; and an impact analyzer including: a semantic parameter identifier configured to determine at least one semantic parameter for a first representative clause of the at least one cluster; at least one configurable linguistic impact analysis rule using parts of speech roles of the words in the clause to determine an impact of the first representative clause of the at least one cluster using the at least one semantic parameter; and an impact engine configured to compute the impact of the first representative clause of the at least one cluster using the representative concept and the at least one configurable linguistic impact analysis rule using parts of speech roles of the words in the clause, on the topic of interest, without statistical methods and without frequency and word patterns, and where the impact can be different for every instance of the clause across different documents or for the same clause for different topics of interest. 21. The system of claim 18 , wherein the impact engine computes an impact of a second representative clause of the at least one cluster with respect to the category of the document and the at least one impact analysis rule using parts of speech roles of the words in the clause, without statistical methods and without frequency and word patterns, such that the first clause and the second clause of the at least one cluster corresponds to at least one sentence of the at least one cluster from a single document. | 0.5 |
9,183,199 | 1 | 7 | 1. A communication device for a multiple language translation system comprising: a main housing for holding electrical components and circuitry utilized for operation of the communication device; a wireless transmitter receiver module for communicating with other devices, the wireless transmitter receiver module comprising: a bluetooth transceiver for connecting with other communication devices for the multiple language translation system and allowing the communication device to communicate using the multiple language translation system with the other communication devices; and a wi-fi transceiver for connecting to the Internet; the communication device receiving an original language input from an originating user via the bluetooth transceiver; translating the original language input into base language text; converting the base language text into at least one word code; determining at least one target language word using the at least one word code; searching for the at least one target language word on the Internet via the wi-fi transceiver; ranking search result entries based on user preferred translations submitted by other users; selecting a search result entry from search results based on user preferred translations ranking; performing a reverse translation process on the selected search result entry to translate the selected search result entry back into a language of the original language input; presenting result of the reverse translation process to the originating user in the language of the original language input via the bluetooth transceiver; receiving via the bluetooth transceiver an indication from the originating user that the result of the reverse translation process is correct; selecting another search result entry from the search results if the originating user indicates that the result of the reverse translation process is not correct; performing the reverse translation process and presenting the result of the reverse translation process to the originating user until the originating user indicates that the result of the reverse translation process is correct; and updating a database of the communication device with results of the reverse translation process, search result entry rankings, and indications made by the originating user about the results of the reverse translation process; an earphone for providing audible translations; at least one operation switch for controlling operation settings of the communication device; and at least one signal indicator for visually indicating status of the communication device, the at least one signal indicator visually indicating which communication devices are connected together and allowing other communication device users to see the status of the communication device. | 1. A communication device for a multiple language translation system comprising: a main housing for holding electrical components and circuitry utilized for operation of the communication device; a wireless transmitter receiver module for communicating with other devices, the wireless transmitter receiver module comprising: a bluetooth transceiver for connecting with other communication devices for the multiple language translation system and allowing the communication device to communicate using the multiple language translation system with the other communication devices; and a wi-fi transceiver for connecting to the Internet; the communication device receiving an original language input from an originating user via the bluetooth transceiver; translating the original language input into base language text; converting the base language text into at least one word code; determining at least one target language word using the at least one word code; searching for the at least one target language word on the Internet via the wi-fi transceiver; ranking search result entries based on user preferred translations submitted by other users; selecting a search result entry from search results based on user preferred translations ranking; performing a reverse translation process on the selected search result entry to translate the selected search result entry back into a language of the original language input; presenting result of the reverse translation process to the originating user in the language of the original language input via the bluetooth transceiver; receiving via the bluetooth transceiver an indication from the originating user that the result of the reverse translation process is correct; selecting another search result entry from the search results if the originating user indicates that the result of the reverse translation process is not correct; performing the reverse translation process and presenting the result of the reverse translation process to the originating user until the originating user indicates that the result of the reverse translation process is correct; and updating a database of the communication device with results of the reverse translation process, search result entry rankings, and indications made by the originating user about the results of the reverse translation process; an earphone for providing audible translations; at least one operation switch for controlling operation settings of the communication device; and at least one signal indicator for visually indicating status of the communication device, the at least one signal indicator visually indicating which communication devices are connected together and allowing other communication device users to see the status of the communication device. 7. The communication device for a multiple language translation system of claim 1 , further comprising: a memory for storing a conversation, a translation, and a translation database. | 0.816265 |
8,838,435 | 1 | 5 | 1. A method of processing a plurality of linguistic expressions, each linguistic expression originating from an originator, and each linguistic expression being a linguistic expression in which the originator of that linguistic expression expresses a respective sentiment with respect to one or more topics, the method comprising: for each linguistic expression, detecting, by one or more processors, one or more topics of interest addressed in that linguistic expression by the originator of that linguistic expression; for each linguistic expression, for each topic detected in that linguistic expression, assessing, by the one or more processors, a sentiment of the originator of that linguistic expression with respect to that topic; grouping, by the one or more processors, the originators into one or more groups based on similarities between the originators' respective sets of detected topic-sentiment pairs; associating with a given group, by the one or more processors, semantic information, wherein the semantic information associated with the given group relates to one or more features selected from a group of features consisting of: properties of one or more members of that given group and the sets of topic-sentiment pairs of one or more of the members of that given group; and for a given originator, creating or updating, by the one or more processors, a profile; wherein the given originator is a member of the given group; wherein the profile is created or updated such that it comprises attributes of the given originator; and wherein the attributes are dependent upon the given originator's membership in the given group and the semantic information associated with the given group. | 1. A method of processing a plurality of linguistic expressions, each linguistic expression originating from an originator, and each linguistic expression being a linguistic expression in which the originator of that linguistic expression expresses a respective sentiment with respect to one or more topics, the method comprising: for each linguistic expression, detecting, by one or more processors, one or more topics of interest addressed in that linguistic expression by the originator of that linguistic expression; for each linguistic expression, for each topic detected in that linguistic expression, assessing, by the one or more processors, a sentiment of the originator of that linguistic expression with respect to that topic; grouping, by the one or more processors, the originators into one or more groups based on similarities between the originators' respective sets of detected topic-sentiment pairs; associating with a given group, by the one or more processors, semantic information, wherein the semantic information associated with the given group relates to one or more features selected from a group of features consisting of: properties of one or more members of that given group and the sets of topic-sentiment pairs of one or more of the members of that given group; and for a given originator, creating or updating, by the one or more processors, a profile; wherein the given originator is a member of the given group; wherein the profile is created or updated such that it comprises attributes of the given originator; and wherein the attributes are dependent upon the given originator's membership in the given group and the semantic information associated with the given group. 5. A method according to claim 1 further comprising: for one or more of the groups of which the given originator is a member, determining, by the one or more processors, a respective membership value, the respective membership value being dependent on the degree of affiliation of the given originator with a respective group; and including, by the one or more processors, in the profile of a second originator, the one or more membership values. | 0.5 |
8,250,079 | 7 | 8 | 7. A computer program product for identifying near and exact-duplicate documents in a document collection and including one or more computer readable instructions embedded on a non-transitory computer readable medium and configured to cause one or more computer processors to perform the steps of: for each document in the collection: reading textual content from the document; filtering the textual content based on user settings; determining N most frequent words from the filtered textual content of the document to generate a first most frequent, word- 1 , to an Nth most frequent word, word-N, sorted from highest to lowest frequency; performing a quorum search using the N most frequent words from the filtered textual content in the document with a threshold M, wherein the threshold M is used to retrieve documents from the document collection having a number M of the N most frequent words; sorting results from the quorum search based on relevancy, whereby based on the values of N and M near and exact-duplicate documents are identified in the document collection; and determining the relevancy by taking a number of hits for the quorum search in the document and dividing the number of hits by a size of the document in kilobytes of text in the document or a size in kilobytes for the entire document. | 7. A computer program product for identifying near and exact-duplicate documents in a document collection and including one or more computer readable instructions embedded on a non-transitory computer readable medium and configured to cause one or more computer processors to perform the steps of: for each document in the collection: reading textual content from the document; filtering the textual content based on user settings; determining N most frequent words from the filtered textual content of the document to generate a first most frequent, word- 1 , to an Nth most frequent word, word-N, sorted from highest to lowest frequency; performing a quorum search using the N most frequent words from the filtered textual content in the document with a threshold M, wherein the threshold M is used to retrieve documents from the document collection having a number M of the N most frequent words; sorting results from the quorum search based on relevancy, whereby based on the values of N and M near and exact-duplicate documents are identified in the document collection; and determining the relevancy by taking a number of hits for the quorum search in the document and dividing the number of hits by a size of the document in kilobytes of text in the document or a size in kilobytes for the entire document. 8. The computer program product of claim 7 , further comprising associating a respective XML wrapper for each document in the collection, wherein the XML wrapper includes a unique document identification for the document, and unique document identifications for near and exact-duplicate documents of the document, and wherein the reading of the textual content from the document includes reading the XML wrapper for the document. | 0.5 |
8,239,750 | 41 | 42 | 41. The computer-readable medium of claim 33 , further comprising: (D) determining whether the plurality of values are consistent with the plurality of names. | 41. The computer-readable medium of claim 33 , further comprising: (D) determining whether the plurality of values are consistent with the plurality of names. 42. The computer-readable medium of claim 41 , wherein (D) comprises determining whether the plurality of values are consistent with the plurality of names based on semantic values of the second subset. | 0.5 |
9,189,483 | 2 | 3 | 2. The method of claim 1 , wherein the information comprises demographic features. | 2. The method of claim 1 , wherein the information comprises demographic features. 3. The method of claim 2 , wherein the demographic features comprise one of age, gender, socio-economic group, nationality, and origin. | 0.5 |
9,031,834 | 2 | 4 | 2. Method as claimed in claim 1 , where extracting a rapidly varying input component includes generating the slowly varying input component, at least in part, through smoothing of the spectral envelope input representation, where the smoothing attenuates the magnitude of at least one of the formant and the spectral trough and preserves a non-constant coarse shape of the spectral envelope input representation and deriving the rapidly varying input component by subtracting the slowly varying input component from the spectral envelope input representation. | 2. Method as claimed in claim 1 , where extracting a rapidly varying input component includes generating the slowly varying input component, at least in part, through smoothing of the spectral envelope input representation, where the smoothing attenuates the magnitude of at least one of the formant and the spectral trough and preserves a non-constant coarse shape of the spectral envelope input representation and deriving the rapidly varying input component by subtracting the slowly varying input component from the spectral envelope input representation. 4. Method as claimed in claim 2 , where the step of generating the slowly varying input component includes deriving the average of a first interpolation function E max (n) interpolating the maxima of the spectral envelope input representation and a second interpolation function E min (n) interpolating the minima of the spectral envelope input representation. | 0.5 |
8,990,087 | 17 | 18 | 17. A method for obtaining and rendering audio based on text in an electronic book (eBook), the method comprising: sending, from an eBook reader device, a request to download the eBook; receiving, at the eBook reader device, the eBook, a supplemental pronunciation database, and specified voice information for synthesizing speech in a specified voice; synthesizing a first speech for a first portion of text in the eBook based at least in part on a pronunciation from the supplemental pronunciation database for portions of text which have pronunciations in the supplemental pronunciation database; synthesizing a second speech for a second portion of text in the eBook based at least in part on a pronunciation from a default pronunciation database for portions of text which do not have pronunciations in the supplemental pronunciation database; synthesizing a third speech for a third portion of text in the eBook based at least in part on the specified voice for portions of text which are specified to be synthesized with the specified voice; and synthesizing a fourth speech for a fourth portion of text based at least in part on a default voice for portions of text which do not have any specified voice. | 17. A method for obtaining and rendering audio based on text in an electronic book (eBook), the method comprising: sending, from an eBook reader device, a request to download the eBook; receiving, at the eBook reader device, the eBook, a supplemental pronunciation database, and specified voice information for synthesizing speech in a specified voice; synthesizing a first speech for a first portion of text in the eBook based at least in part on a pronunciation from the supplemental pronunciation database for portions of text which have pronunciations in the supplemental pronunciation database; synthesizing a second speech for a second portion of text in the eBook based at least in part on a pronunciation from a default pronunciation database for portions of text which do not have pronunciations in the supplemental pronunciation database; synthesizing a third speech for a third portion of text in the eBook based at least in part on the specified voice for portions of text which are specified to be synthesized with the specified voice; and synthesizing a fourth speech for a fourth portion of text based at least in part on a default voice for portions of text which do not have any specified voice. 18. The method of claim 17 , wherein the supplemental pronunciation database is restricted to be used with the eBook and not with at least one other eBook. | 0.63615 |
10,163,451 | 5 | 6 | 5. A method comprising: determining first combined audio characteristics of a first accent by combining first sample audio characteristics of audio samples associated with the first accent; determining second combined audio characteristics of a second accent by combining second sample audio characteristics of audio samples associated with the second accent; determining third combined audio characteristics of a third accent by combining the first combined audio characteristics of the first accent and the second combined audio characteristics of the second accent; comparing the third combined audio characteristics of the third accent to other audio characteristics associated with one or more other accents; generating a translation model between a fourth accent and the third accent; receiving an input audio portion substantially associated with the fourth accent in a first spoken language; and outputting, based at least in part on the translation model, an output audio portion substantially associated with the third accent in the first spoken language. | 5. A method comprising: determining first combined audio characteristics of a first accent by combining first sample audio characteristics of audio samples associated with the first accent; determining second combined audio characteristics of a second accent by combining second sample audio characteristics of audio samples associated with the second accent; determining third combined audio characteristics of a third accent by combining the first combined audio characteristics of the first accent and the second combined audio characteristics of the second accent; comparing the third combined audio characteristics of the third accent to other audio characteristics associated with one or more other accents; generating a translation model between a fourth accent and the third accent; receiving an input audio portion substantially associated with the fourth accent in a first spoken language; and outputting, based at least in part on the translation model, an output audio portion substantially associated with the third accent in the first spoken language. 6. The method of claim 5 , further comprising determining that the input audio portion is substantially associated with the fourth accent. | 0.759582 |
9,607,610 | 3 | 5 | 3. The method of claim 1 , further comprising: determining, based on the input, the acoustic feature parameters including spectral parameters associated with the speech, aperiodicity parameters associated with the speech, and phase parameters associated with the speech. | 3. The method of claim 1 , further comprising: determining, based on the input, the acoustic feature parameters including spectral parameters associated with the speech, aperiodicity parameters associated with the speech, and phase parameters associated with the speech. 5. The method of claim 3 , further comprising: receiving, by the device, a selection indicative of selected types of the acoustic feature parameters from one or more of Cepstrum, Mel-Cepstrum, Generalized-Mel-Cepstrum, Discrete Mel-Cepstrum, Log-Spectral, Auto-Regressive, Line-Spectrum-Pairs, Line-Spectrum-Frequencies, Mel-Line-Spectrum-Pairs, Reflection Coefficients, Log-Area-Ratio Coefficients, minimum-phase, maximum-phase, sum-of-cosines pulse, sum-of-sines pulse, constant random pulse, log-aperiodicity, filterbank-based quantization, or maximum voiced frequency, wherein determining the acoustic feature parameters is based on the selection. | 0.5 |
8,311,827 | 22 | 23 | 22. A computer-implemented method of controlling a vehicle, comprising: receiving one or more instructions issued as speech; analyzing the speech using speech recognition software to provide a sequence of words and a word confidence measure for each word so recognized; analyzing the sequence of words to identify a semantic concept corresponding to an instruction based on the analysis, and a semantic confidence level for the identified semantic concept derived at least in part with reference to the word confidence measures of the words associated with the semantic concept; providing a spoken confirmation of the semantic concept so identified based on the semantic confidence level, the spoken confirmation having one of a speaking rate or a pitch adjusted according to an indicated response length; and using the semantic concept so identified to provide a control input for the vehicle; wherein the step of providing the spoken confirmation of the semantic concept comprises indicating that the instruction was not understood when the semantic confidence level is below a threshold, or the step of using the semantic concept comprises providing the control input for the vehicle when the semantic confidence level exceeds the threshold. | 22. A computer-implemented method of controlling a vehicle, comprising: receiving one or more instructions issued as speech; analyzing the speech using speech recognition software to provide a sequence of words and a word confidence measure for each word so recognized; analyzing the sequence of words to identify a semantic concept corresponding to an instruction based on the analysis, and a semantic confidence level for the identified semantic concept derived at least in part with reference to the word confidence measures of the words associated with the semantic concept; providing a spoken confirmation of the semantic concept so identified based on the semantic confidence level, the spoken confirmation having one of a speaking rate or a pitch adjusted according to an indicated response length; and using the semantic concept so identified to provide a control input for the vehicle; wherein the step of providing the spoken confirmation of the semantic concept comprises indicating that the instruction was not understood when the semantic confidence level is below a threshold, or the step of using the semantic concept comprises providing the control input for the vehicle when the semantic confidence level exceeds the threshold. 23. The method of claim 22 , wherein the step of analyzing the speech to provide a sequence of words comprises using continuous hidden Markov models. | 0.664414 |
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