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8,745,083 | 1 | 6 | 1. A computer-implemented system for matching personnel with personnel seekers, comprising: a personnel seeker database structured to contain at least one personnel need, as well as at least one personnel need attribute associated with the at least one personnel need; a candidate database structured to contain at least one candidate, as well as at least one candidate attribute associated with the at least one candidate; utilizing at least one matching algorithm to produce a list of candidates having attributes that correspond to the attributes of the at least one personnel need; soliciting feedback regarding the candidates within the list of candidates; modifying the at least one matching algorithm, the at least one candidate attribute, or the at least one personnel need attribute based on the feedback; and producing a next list of candidates utilizing the modified at least one algorithm. | 1. A computer-implemented system for matching personnel with personnel seekers, comprising: a personnel seeker database structured to contain at least one personnel need, as well as at least one personnel need attribute associated with the at least one personnel need; a candidate database structured to contain at least one candidate, as well as at least one candidate attribute associated with the at least one candidate; utilizing at least one matching algorithm to produce a list of candidates having attributes that correspond to the attributes of the at least one personnel need; soliciting feedback regarding the candidates within the list of candidates; modifying the at least one matching algorithm, the at least one candidate attribute, or the at least one personnel need attribute based on the feedback; and producing a next list of candidates utilizing the modified at least one algorithm. 6. The system according to claim 1 , wherein an algorithm weight is assigned to each algorithm. | 0.880353 |
8,843,942 | 1 | 6 | 1. A method implemented in instructions executed by a computer processor of initiating execution of an application's process code, the method comprising: identifying a data type of a document associated with the application being launched; and in response to determining that the document contains the application's process code: instantiating a process object having an execute method for executing at least one process step, wherein one or more operations associated with the process step are defined within the document, and wherein at least one process step includes an operation that, upon execution, directs a flow of execution to another process step; causing the process object to be associated with an application instance; and activating an execute method in the process object and providing the process object the corresponding application instance and the process step to execute the operations associated with the process step. | 1. A method implemented in instructions executed by a computer processor of initiating execution of an application's process code, the method comprising: identifying a data type of a document associated with the application being launched; and in response to determining that the document contains the application's process code: instantiating a process object having an execute method for executing at least one process step, wherein one or more operations associated with the process step are defined within the document, and wherein at least one process step includes an operation that, upon execution, directs a flow of execution to another process step; causing the process object to be associated with an application instance; and activating an execute method in the process object and providing the process object the corresponding application instance and the process step to execute the operations associated with the process step. 6. The method as recited in claim 1 , wherein the instantiated process object allows a second object to register as a listener that is notified in response to process logic of an executing application being modified. | 0.694915 |
8,583,440 | 4 | 5 | 4. An apparatus, comprising: a reduced keyboard comprising: a plurality of character keys, each key corresponding to a plurality of characters, wherein said character keys are for accepting the entry of a key input sequence by a user; a plurality of navigational keys; and at least one selection key; a display for displaying a graphical user interface comprising at least three discrete fields including each of: a text entry field; a character ambiguity field; and a word choice list field; a processor operatively coupled with a memory, wherein said processor is configured for simultaneously displaying, during all aspects of text entry, at least one user-selected word construct in said text entry field, candidate characters in said character ambiguity field, and candidate word constructs in said word choice list field; wherein said processor is further configured for displaying, in the word choice list field a plurality of candidate word constructs in response to successive selection of one or more keys, wherein each key corresponds to at least one character, and wherein at least one key corresponds to a plurality of characters; wherein said processor is further configured for displaying, in the character ambiguity field, all characters for said each key; and wherein said processor is further configured for emphasizing candidate characters that builds the most likely word construct in said character ambiguity field, wherein said most likely word construct is the most frequently used word construct; wherein said processor is further configured for interpreting a pause of given length after said display of said more than one potential disambiguation of the most recent ambiguous key entry in said key input sequence as an indication of said user's intention to enter a character explicitly at the current position in said sequence; wherein the processor is further configured for displaying, after said pause, an interactive explicit candidate selection menu in said character ambiguity field, said explicit candidate selection menu containing all of the potential disambiguations of said most recent ambiguous key entry and a selection indicator associated with one of said potential disambiguations; wherein the processor is further configured for receiving a selection of an explicit candidate via said interactive explicit candidate selection menu; wherein the processor is further configured for truncating said plurality of candidate word constructs based on an explicit character selection at said position in said sequence; wherein said processor is further configured for accepting navigational instructions by said user pressing at least one navigational key, wherein said navigational instructions moves said selection indicator to a different potential disambiguation in said explicit candidate selection menu; wherein said processor is further configured for accepting a selection instruction by said user pressing said at least one selection key, wherein said selection instruction indicates the explicit command to input the potential disambiguation associated with said selection indicator at that time, said selected potential disambiguation taking the form of an explicitly entered character in a particular position with a word construct; wherein said processor is further configured for receiving instructions from said user indicating selection of one word construct from among the plurality of candidate word constructs having said explicitly entered character in a particular position, wherein all of said candidate characters remain visible to said user in said character ambiguity field until said user indication, thereby enabling said user to view alternative disambiguations of said key input sequence; and wherein said processor is further configured for displaying said selected word construct in said text entry field. | 4. An apparatus, comprising: a reduced keyboard comprising: a plurality of character keys, each key corresponding to a plurality of characters, wherein said character keys are for accepting the entry of a key input sequence by a user; a plurality of navigational keys; and at least one selection key; a display for displaying a graphical user interface comprising at least three discrete fields including each of: a text entry field; a character ambiguity field; and a word choice list field; a processor operatively coupled with a memory, wherein said processor is configured for simultaneously displaying, during all aspects of text entry, at least one user-selected word construct in said text entry field, candidate characters in said character ambiguity field, and candidate word constructs in said word choice list field; wherein said processor is further configured for displaying, in the word choice list field a plurality of candidate word constructs in response to successive selection of one or more keys, wherein each key corresponds to at least one character, and wherein at least one key corresponds to a plurality of characters; wherein said processor is further configured for displaying, in the character ambiguity field, all characters for said each key; and wherein said processor is further configured for emphasizing candidate characters that builds the most likely word construct in said character ambiguity field, wherein said most likely word construct is the most frequently used word construct; wherein said processor is further configured for interpreting a pause of given length after said display of said more than one potential disambiguation of the most recent ambiguous key entry in said key input sequence as an indication of said user's intention to enter a character explicitly at the current position in said sequence; wherein the processor is further configured for displaying, after said pause, an interactive explicit candidate selection menu in said character ambiguity field, said explicit candidate selection menu containing all of the potential disambiguations of said most recent ambiguous key entry and a selection indicator associated with one of said potential disambiguations; wherein the processor is further configured for receiving a selection of an explicit candidate via said interactive explicit candidate selection menu; wherein the processor is further configured for truncating said plurality of candidate word constructs based on an explicit character selection at said position in said sequence; wherein said processor is further configured for accepting navigational instructions by said user pressing at least one navigational key, wherein said navigational instructions moves said selection indicator to a different potential disambiguation in said explicit candidate selection menu; wherein said processor is further configured for accepting a selection instruction by said user pressing said at least one selection key, wherein said selection instruction indicates the explicit command to input the potential disambiguation associated with said selection indicator at that time, said selected potential disambiguation taking the form of an explicitly entered character in a particular position with a word construct; wherein said processor is further configured for receiving instructions from said user indicating selection of one word construct from among the plurality of candidate word constructs having said explicitly entered character in a particular position, wherein all of said candidate characters remain visible to said user in said character ambiguity field until said user indication, thereby enabling said user to view alternative disambiguations of said key input sequence; and wherein said processor is further configured for displaying said selected word construct in said text entry field. 5. The apparatus according to claim 4 , further comprising: means for facilitating removal of deemphasis of one or more characters of said at least one character corresponding to said each key, said characters being removed or deemphasized as not part of a candidate word construct. | 0.5 |
7,937,269 | 2 | 3 | 2. The method of claim 1 , wherein classifying comprises classifying each segment by matching the target profile for a given segment to a similar training profile. | 2. The method of claim 1 , wherein classifying comprises classifying each segment by matching the target profile for a given segment to a similar training profile. 3. The method of claim 2 , wherein matching comprises determining a distance between the target profile and each of a plurality of training profiles to determine a training profile that is closest to the target profile. | 0.5 |
8,336,078 | 14 | 15 | 14. The system of claim 13 , wherein the one or more servers are further configured to generate a role assignment record. | 14. The system of claim 13 , wherein the one or more servers are further configured to generate a role assignment record. 15. The system of claim 14 , wherein the role assignment record comprises the actor, the role, and the context parameters. | 0.557971 |
8,832,549 | 1 | 8 | 1. A non-transitory machine readable medium storing a program which when executed by at least one processing unit analyzes a document that comprises a plurality of primitive elements, the primitive elements are present in the document prior to analysis by the program, the program comprising sets of instructions for: identifying a set of potential boundaries as rectilinearly aligned graphical primitive elements that satisfy a set of size constraints; identifying a subset of the potential boundaries as actual boundaries by eliminating from the set (i) potential boundaries identified from the graphical primitive elements that intersect with primitive elements that are not in the set of potential boundaries and (ii) potential boundaries identified from the graphical primitive elements that do not intersect at least two additional potential boundaries; identifying regions bounded by the actual boundaries; and defining a structured document based on the regions and the primitive elements. | 1. A non-transitory machine readable medium storing a program which when executed by at least one processing unit analyzes a document that comprises a plurality of primitive elements, the primitive elements are present in the document prior to analysis by the program, the program comprising sets of instructions for: identifying a set of potential boundaries as rectilinearly aligned graphical primitive elements that satisfy a set of size constraints; identifying a subset of the potential boundaries as actual boundaries by eliminating from the set (i) potential boundaries identified from the graphical primitive elements that intersect with primitive elements that are not in the set of potential boundaries and (ii) potential boundaries identified from the graphical primitive elements that do not intersect at least two additional potential boundaries; identifying regions bounded by the actual boundaries; and defining a structured document based on the regions and the primitive elements. 8. The non-transitory machine readable medium of claim 1 , wherein the set of instructions for identifying the regions comprises sets of instructions for traversing a subset of the actual boundaries to identify a region. | 0.638158 |
7,856,375 | 87 | 112 | 87. A system for preparing personalized communication documents for a plurality of consumer entities comprising: a computing system; one or more electronic databases coupled to the computing system; one or more software routines executing on the computing system which are adapted to: i) process data from a first electronic data file in said one or more electronic databases containing financial product and/or financial service data for the customized communications, which financial product and/or financial service data includes a plurality of separate descriptions, characteristics and/or identifications for at least a first financial product and/or financial service; ii) process data from a second electronic data file in said one or more electronic databases containing customer information for certain of the plurality of consumer entities, said customer information including customer related data in addition to, but not excluding, any one or more of customer name, customer address and customer account information obtained for said certain of the plurality of consumer entities; iii) perform an automated composition process to compose an electronic document file representing a customized communication document for at least one of said certain of the plurality of the consumer entities, said customized communication document comprising information relating to an offering for one or more financial products or services; wherein at least some content included in said customized communication document is customized content generated for said electronic file which includes variable data specifically for a consumer entity automatically derived and/or calculated from said first electronic data file and/or said second electronic data file during said automated composition process for said electronic file and said consumer entity. | 87. A system for preparing personalized communication documents for a plurality of consumer entities comprising: a computing system; one or more electronic databases coupled to the computing system; one or more software routines executing on the computing system which are adapted to: i) process data from a first electronic data file in said one or more electronic databases containing financial product and/or financial service data for the customized communications, which financial product and/or financial service data includes a plurality of separate descriptions, characteristics and/or identifications for at least a first financial product and/or financial service; ii) process data from a second electronic data file in said one or more electronic databases containing customer information for certain of the plurality of consumer entities, said customer information including customer related data in addition to, but not excluding, any one or more of customer name, customer address and customer account information obtained for said certain of the plurality of consumer entities; iii) perform an automated composition process to compose an electronic document file representing a customized communication document for at least one of said certain of the plurality of the consumer entities, said customized communication document comprising information relating to an offering for one or more financial products or services; wherein at least some content included in said customized communication document is customized content generated for said electronic file which includes variable data specifically for a consumer entity automatically derived and/or calculated from said first electronic data file and/or said second electronic data file during said automated composition process for said electronic file and said consumer entity. 112. The system of claim 87 , wherein said customized content is determined for a consumer entity by a routine which analyzes personal data for each consumer entity and evaluates which of said plurality of descriptions, characteristics and/or identifications should be used in presenting said at least first financial product and/or financial service to such consumer entity. | 0.619675 |
9,787,722 | 16 | 17 | 16. The method of claim 15 , wherein: the classifying the security rules into the one or more identical classifications is based on identicality between the security rules; and the classifying the security rules into the one or more similar classifications is based on similarity but not identicality between the rules. | 16. The method of claim 15 , wherein: the classifying the security rules into the one or more identical classifications is based on identicality between the security rules; and the classifying the security rules into the one or more similar classifications is based on similarity but not identicality between the rules. 17. The method of claim 16 , wherein: the generating for display further includes generating for display, for each security rule in the one of the classifications, an indication that the security rule is identical or similar to the selected security rule, and a location in the configuration file where the security rule is found. | 0.5 |
7,849,048 | 1 | 34 | 1. A system for making unstructured data available to structured data tools comprising: a core server computer, wherein the core server computer performs steps comprising: accessing a source of unstructured data; reading the unstructured data from the source of unstructured data; sending the unstructured data to one or more transformation tools; parsing, via a natural-language processing transformation tool, the unstructured data to extract sentences from the unstructured data and then further extract from the extracted sentences sentence-level natural-language processed entities, wherein the sentence-level natural-language processed entities are at least noun phrases; extracting, via a linguistic processing transformation tool, sentence-level linguistically-processed relationships, wherein the sentence-level linguistically-processed relationships comprise associations between the sentence-level natural-language processed entities; sending the sentence-level natural-language processed entities and the sentence-level linguistically-processed relationships from the one or more transformation tools to a categorization tool; determining, via the categorization tool, categorization data elements present in each extracted sentence, wherein the categorization data elements are based on the sentence-level natural-language processed entities and the sentence-level linguistically-processed relationships, and are placed within predetermined categories, and a confidence level for each categorization data element, wherein the confidence level for each categorization data element combines one or more data points linked to the sentence-level natural-language processed entities and the sentence-level linguistically-processed relationships to create a statistically-oriented calculation of confidence assigned to the categorization data element; outputting the confidence level for at least one of the categorization data elements for use in structured data tools; and wherein the one or more data points are selected from the group consisting of: confidence score of value provided by the one or more transformation tools, number of relationships found in the source of unstructured data compared to the size of the source of unstructured data, average number of relationships per kilobyte for relationships of the same type as a selected relationship, number of entities found to be associated with a relationship compared to an average number of entities for relationships in a same hierarchy, number of times similar relationships have been found in the past, number of entities that are grouped together to form a master entity, a number of times an entity occurred in the source of unstructured data compared to the average number of occurrences for entities in the same hierarchy, weighted confidences based on hierarchy of a relationship or entity, measures of data extraction confidence integrated with the system via an analysis schema, measures based on a fullness of a relationship's attributes, measures based on the confluence of a same finding by multiple transformation tools, measures based on the source of the unstructured data, and combinations thereof. | 1. A system for making unstructured data available to structured data tools comprising: a core server computer, wherein the core server computer performs steps comprising: accessing a source of unstructured data; reading the unstructured data from the source of unstructured data; sending the unstructured data to one or more transformation tools; parsing, via a natural-language processing transformation tool, the unstructured data to extract sentences from the unstructured data and then further extract from the extracted sentences sentence-level natural-language processed entities, wherein the sentence-level natural-language processed entities are at least noun phrases; extracting, via a linguistic processing transformation tool, sentence-level linguistically-processed relationships, wherein the sentence-level linguistically-processed relationships comprise associations between the sentence-level natural-language processed entities; sending the sentence-level natural-language processed entities and the sentence-level linguistically-processed relationships from the one or more transformation tools to a categorization tool; determining, via the categorization tool, categorization data elements present in each extracted sentence, wherein the categorization data elements are based on the sentence-level natural-language processed entities and the sentence-level linguistically-processed relationships, and are placed within predetermined categories, and a confidence level for each categorization data element, wherein the confidence level for each categorization data element combines one or more data points linked to the sentence-level natural-language processed entities and the sentence-level linguistically-processed relationships to create a statistically-oriented calculation of confidence assigned to the categorization data element; outputting the confidence level for at least one of the categorization data elements for use in structured data tools; and wherein the one or more data points are selected from the group consisting of: confidence score of value provided by the one or more transformation tools, number of relationships found in the source of unstructured data compared to the size of the source of unstructured data, average number of relationships per kilobyte for relationships of the same type as a selected relationship, number of entities found to be associated with a relationship compared to an average number of entities for relationships in a same hierarchy, number of times similar relationships have been found in the past, number of entities that are grouped together to form a master entity, a number of times an entity occurred in the source of unstructured data compared to the average number of occurrences for entities in the same hierarchy, weighted confidences based on hierarchy of a relationship or entity, measures of data extraction confidence integrated with the system via an analysis schema, measures based on a fullness of a relationship's attributes, measures based on the confluence of a same finding by multiple transformation tools, measures based on the source of the unstructured data, and combinations thereof. 34. The system of claim 1 , further comprising writing the categorization data elements from the categorization tool and the sentence-level natural-language processed entities and the sentence-level linguistically-processed relationships from the one or more transformation tools in a structured database schema. | 0.584 |
10,162,815 | 1 | 3 | 1. A dialog knowledge acquisition system comprising: a hardware processor and a memory; an input module and an output module controlled by the hardware processor; the memory storing a dialog manager configured to instantiate a persistent interactive personality (PIP) and a dialog graph having a plurality of linked dialog state nodes; the hardware processor configured to execute the dialog manager to: receive a dialog initiation data via the input module; identify a first state node on the dialog graph corresponding to the dialog initiation data; determine a dialog interaction by the PIP based on the dialog initiation data and the first state node; render the dialog interaction via the output module, wherein the output module renders the dialog interaction as at least one of a speech, a facial expression, and a gesture being performed by one of a robot and an avatar; receive a user response corresponding to the dialog interaction via the input module; perform a strict dialog execution policy such that the user response is only mapped to semantically equivalent children of the first state node; perform a relaxed dialog execution policy such that the user response is mapped to the plurality of linked dialog state nodes outside the children of the first state node, upon registering a dialog failure; identify a second state node on the dialog graph based on the dialog initiation data, the dialog interaction, and the user response; and utilize the dialog initiation data, the first state node, the dialog interaction, the user response, and the second state node to train the dialog graph for subsequent dialog interactions by the PIP. | 1. A dialog knowledge acquisition system comprising: a hardware processor and a memory; an input module and an output module controlled by the hardware processor; the memory storing a dialog manager configured to instantiate a persistent interactive personality (PIP) and a dialog graph having a plurality of linked dialog state nodes; the hardware processor configured to execute the dialog manager to: receive a dialog initiation data via the input module; identify a first state node on the dialog graph corresponding to the dialog initiation data; determine a dialog interaction by the PIP based on the dialog initiation data and the first state node; render the dialog interaction via the output module, wherein the output module renders the dialog interaction as at least one of a speech, a facial expression, and a gesture being performed by one of a robot and an avatar; receive a user response corresponding to the dialog interaction via the input module; perform a strict dialog execution policy such that the user response is only mapped to semantically equivalent children of the first state node; perform a relaxed dialog execution policy such that the user response is mapped to the plurality of linked dialog state nodes outside the children of the first state node, upon registering a dialog failure; identify a second state node on the dialog graph based on the dialog initiation data, the dialog interaction, and the user response; and utilize the dialog initiation data, the first state node, the dialog interaction, the user response, and the second state node to train the dialog graph for subsequent dialog interactions by the PIP. 3. The dialog knowledge acquisition system of claim 1 , wherein the dialog interaction comprises a non-verbal communication by the PIP. | 0.917279 |
7,974,972 | 1 | 16 | 1. A method of providing records to a user, comprising: identifying a set of different languages from an interaction of a user with a server; automatically discerning a set of suspected geographical origins of the user from the interaction with the server; finding a best combination of a language and a suspected geographical origin from the sets of languages and origins; and using the best combination to guide retrieval of search results responsive to a query submitted to the server by the user, and to rank the search results. | 1. A method of providing records to a user, comprising: identifying a set of different languages from an interaction of a user with a server; automatically discerning a set of suspected geographical origins of the user from the interaction with the server; finding a best combination of a language and a suspected geographical origin from the sets of languages and origins; and using the best combination to guide retrieval of search results responsive to a query submitted to the server by the user, and to rank the search results. 16. The method of claim 1 , wherein the step of using the best combination to guide retrieval of search results includes retrieving a single record comprising a combination of at least two other records. | 0.5 |
8,554,754 | 1 | 6 | 1. A method, comprising: populating two or more fields in a plurality of fields of a database of an internet-based system with information by prompting one or more respective candidates in a group of candidates, wherein the information specifies a plurality of skills and a plurality of amounts of experience associated with those skills for each of the one or more respective candidates; searching the database using a search query containing one or more parameters, wherein the one or more parameters include one or more desired lengths of experience for one or more corresponding skills; providing an employer with information specifying one or more matching candidates in response to the query, wherein each of the one or more matching candidates has an amount of experience for each of the one or more skills that is greater than or equal to the desired length of experience corresponding to that skill; and automatically prompting one or more parties to schedule an interview in response to determining that the query is satisfied by the one or more matching candidates. | 1. A method, comprising: populating two or more fields in a plurality of fields of a database of an internet-based system with information by prompting one or more respective candidates in a group of candidates, wherein the information specifies a plurality of skills and a plurality of amounts of experience associated with those skills for each of the one or more respective candidates; searching the database using a search query containing one or more parameters, wherein the one or more parameters include one or more desired lengths of experience for one or more corresponding skills; providing an employer with information specifying one or more matching candidates in response to the query, wherein each of the one or more matching candidates has an amount of experience for each of the one or more skills that is greater than or equal to the desired length of experience corresponding to that skill; and automatically prompting one or more parties to schedule an interview in response to determining that the query is satisfied by the one or more matching candidates. 6. The method of claim 1 , wherein prompting the one or more parties to schedule an interview includes prompting a matching candidate to schedule a telephone interview. | 0.803279 |
9,075,870 | 16 | 17 | 16. The method of claim 15 , wherein said filtering out of the topics that are not included in the competition topics of the target topic comprises removing topics that are different from the competition topics in consideration of property values of the topic template or named-entity tags based on preset removing rules. | 16. The method of claim 15 , wherein said filtering out of the topics that are not included in the competition topics of the target topic comprises removing topics that are different from the competition topics in consideration of property values of the topic template or named-entity tags based on preset removing rules. 17. The method of claim 16 , wherein the preset removing rules are implemented to: remove association words whose named-entity tags are different from the target topic based on the named-entity tags; select a filtering property by the type of the target topic to choose a property to be filtered in dependence on the type of the target topic; search the topic templates for the association words; and remove association words whose filtering property values are the same as the filtering property value of the target topic. | 0.5 |
8,862,475 | 1 | 7 | 1. A computer-implemented method of speech-enabled content navigation and control of a distributed multimodal browser, the distributed multimodal browser providing an execution environment for a multimodal application, the distributed multimodal browser including a graphical user agent and a voice user agent operatively coupled to the graphical user agent, the graphical user agent operating on a multimodal device supporting multiple modes of user interaction including a voice mode and one or more non-voice modes, the voice user agent operating on a voice server, the method comprising: transmitting, by the graphical user agent, a link message to the voice user agent, the link message containing voice commands that control the distributed multimodal browser including at least one grammar associated with the voice commands, the link message also containing an event corresponding to each voice command, wherein at least one of the voice commands is received by the graphical user agent from a voice markup corresponding to the multimodal application; receiving, by the graphical user agent, a voice utterance from a user, the voice utterance specifying a particular voice command; transmitting, by the graphical user agent, the voice utterance to the voice user agent for speech recognition by the voice user agent; receiving, by the graphical user agent, an event message from the voice user agent, the event message specifying a particular event corresponding to the particular voice command specified by the voice utterance; and controlling, by the graphical user agent, the distributed multimodal browser in dependence upon the particular event. | 1. A computer-implemented method of speech-enabled content navigation and control of a distributed multimodal browser, the distributed multimodal browser providing an execution environment for a multimodal application, the distributed multimodal browser including a graphical user agent and a voice user agent operatively coupled to the graphical user agent, the graphical user agent operating on a multimodal device supporting multiple modes of user interaction including a voice mode and one or more non-voice modes, the voice user agent operating on a voice server, the method comprising: transmitting, by the graphical user agent, a link message to the voice user agent, the link message containing voice commands that control the distributed multimodal browser including at least one grammar associated with the voice commands, the link message also containing an event corresponding to each voice command, wherein at least one of the voice commands is received by the graphical user agent from a voice markup corresponding to the multimodal application; receiving, by the graphical user agent, a voice utterance from a user, the voice utterance specifying a particular voice command; transmitting, by the graphical user agent, the voice utterance to the voice user agent for speech recognition by the voice user agent; receiving, by the graphical user agent, an event message from the voice user agent, the event message specifying a particular event corresponding to the particular voice command specified by the voice utterance; and controlling, by the graphical user agent, the distributed multimodal browser in dependence upon the particular event. 7. The method of claim 1 wherein the event message specifies the particular event as a string using dot notation. | 0.881303 |
9,753,922 | 13 | 15 | 13. The system of claim 9 , wherein the one or more modules are further configured to group users based on user attributes to define one or more user groups, and determine interests of the one or more user groups based at least in part on the tracked user interactions of users belonging to the respective user group and the topics associated with the interacted-with document elements. | 13. The system of claim 9 , wherein the one or more modules are further configured to group users based on user attributes to define one or more user groups, and determine interests of the one or more user groups based at least in part on the tracked user interactions of users belonging to the respective user group and the topics associated with the interacted-with document elements. 15. The system of claim 13 , wherein the one or more modules are further configured to generate, for one or more of the users, a recommendation of one or more documents based on a match between topics associated with the one or more documents and the interest determined for the user group to which the user belongs. | 0.5 |
9,971,976 | 10 | 11 | 10. A computer program product comprising: a non-transitory machine readable storage device; and computer code stored on the non-transitory machine readable storage device, with the computer code including instructions for causing a set of one or more hardware processors to perform operations including the following: receiving a first set of input parameters about a first aspect of the candidates in a set of candidates; applying multiple scoring methods to the first set of input parameters to compute, for each scoring method, a score for the first aspect of each candidate; evaluating an agreement between or among the multiple scoring methods by comparing the computed scores of the multiple scoring methods; responsive to the evaluated agreement not meeting or exceeding a threshold level, (i) updating the first set of input parameters by dropping one or more parameters from the first set of input parameters, and (ii) repeating application of the multiple scoring methods with the updated first set of input parameters until the agreement meets the threshold level; and selecting a subset of one or more candidates from the set of candidates by applying one or more selection criteria to a set of the computed scores corresponding to the agreement meeting the threshold for the first aspect of each candidate; wherein: each input parameter in the first set of input parameters is associated with zero or more values for each candidate. | 10. A computer program product comprising: a non-transitory machine readable storage device; and computer code stored on the non-transitory machine readable storage device, with the computer code including instructions for causing a set of one or more hardware processors to perform operations including the following: receiving a first set of input parameters about a first aspect of the candidates in a set of candidates; applying multiple scoring methods to the first set of input parameters to compute, for each scoring method, a score for the first aspect of each candidate; evaluating an agreement between or among the multiple scoring methods by comparing the computed scores of the multiple scoring methods; responsive to the evaluated agreement not meeting or exceeding a threshold level, (i) updating the first set of input parameters by dropping one or more parameters from the first set of input parameters, and (ii) repeating application of the multiple scoring methods with the updated first set of input parameters until the agreement meets the threshold level; and selecting a subset of one or more candidates from the set of candidates by applying one or more selection criteria to a set of the computed scores corresponding to the agreement meeting the threshold for the first aspect of each candidate; wherein: each input parameter in the first set of input parameters is associated with zero or more values for each candidate. 11. The product of claim 10 , wherein each scoring method of the multiple scoring methods: takes as input a same number of input parameters from the first set of input parameters as each other scoring method of the multiple scoring methods; and takes as input same input parameters from the first set of input parameters as each other scoring method of the multiple scoring methods. | 0.667247 |
8,341,108 | 10 | 13 | 10. A method for creating a Kind feature vector, comprising: creating a Kind feature vector for a Kind using a set of initial features and one or more descriptors of a Kind classification characterizing the Kind, the creating comprising: creating one or more dimensions within the Kind feature vector; and for respective dimensions within the Kind feature vector, assigning a probabilistic value to a dimension based upon the probability the Kind relates to a characteristic of the dimension. | 10. A method for creating a Kind feature vector, comprising: creating a Kind feature vector for a Kind using a set of initial features and one or more descriptors of a Kind classification characterizing the Kind, the creating comprising: creating one or more dimensions within the Kind feature vector; and for respective dimensions within the Kind feature vector, assigning a probabilistic value to a dimension based upon the probability the Kind relates to a characteristic of the dimension. 13. The method of claim 10 , comprising: updating the Kind feature vector based upon usage data relating to the Kind classification. | 0.726141 |
4,499,553 | 3 | 16 | 3. A method according to claim 1 wherein the step of comparing the characters forming the determined query word stem portion for acceptable misspelling matches comprises the additional step of forming a match class indication representing one of a plurality of classes in which each of the acceptable misspelling matches may be included; and wherein the step of determining characters forming an ending portion comprises the additional step of utilizing the acceptable match class indication for each of individual candidate words to determine the position of an ending portion in each of individual candidate words. | 3. A method according to claim 1 wherein the step of comparing the characters forming the determined query word stem portion for acceptable misspelling matches comprises the additional step of forming a match class indication representing one of a plurality of classes in which each of the acceptable misspelling matches may be included; and wherein the step of determining characters forming an ending portion comprises the additional step of utilizing the acceptable match class indication for each of individual candidate words to determine the position of an ending portion in each of individual candidate words. 16. A method according to claim 3 wherein the digital data processing means comprises a plurality of stored tables each having digitally coded representations therein, the step of utilizing the suffix class indication comprising the steps of: (a) utilizing representations of the suffix class indications to access and to derive from a first one of the tables a representation of a list of acceptable suffixes, the acceptable suffix list corresponding to a value represented by the suffix class indication; (b) utilizing representations of the at least one of the plural suffix lists to access and to derive from a second one of the tables a representation of the characters forming the at least one acceptable suffix. | 0.696277 |
9,325,508 | 25 | 31 | 25. The method of claim 24 , wherein the at least one of the plurality of policies is indicated in the signature data structure. | 25. The method of claim 24 , wherein the at least one of the plurality of policies is indicated in the signature data structure. 31. The method of claim 25 , wherein the relying party includes evaluating the at least one of the policies under which the signature authority operates in determining whether to trust the digital signing signature. | 0.5 |
9,361,355 | 14 | 19 | 14. The method of claim 1 , wherein identifying candidate data records includes comparing the query to a data store mapping queries to candidate clusters including an entry mapping the query to a first cluster. | 14. The method of claim 1 , wherein identifying candidate data records includes comparing the query to a data store mapping queries to candidate clusters including an entry mapping the query to a first cluster. 19. The method of claim 14 , further including receiving input from a user to approve or modify association of received data records to matched data clusters. | 0.817552 |
8,768,723 | 13 | 15 | 13. Apparatus comprising: at least one processor; and a memory storing processor-executable instructions that, when executed by the at least one processor, perform a method comprising: receiving an original free-form text narrative of a patient encounter provided by a clinician; re-formatting the original free-form text narrative in a manner that facilitates clinical fact extraction performed by a fact extraction engine on a formatted text resulting from the re-formatting; extracting one or more clinical facts from the formatted text using the fact extraction engine, wherein a first fact of the one or more clinical facts is extracted from a first portion of the formatted text, wherein the first portion of the formatted text is a formatted version of a first portion of the original free-form text narrative, the extracting comprising analyzing the formatted text to identify a set of one or more features of at least the first portion of the formatted text, correlating the set of features to one or more abstract semantic concepts, and generating computer-readable data that expresses the one or more abstract semantic concepts as the one or more clinical facts extracted from the formatted text; and providing to a user an indicator that distinguishes the first portion of the original free-form text narrative that resulted in extraction of the first fact, from other portions of the original free-form text narrative that did not result in the extraction of the first fact. | 13. Apparatus comprising: at least one processor; and a memory storing processor-executable instructions that, when executed by the at least one processor, perform a method comprising: receiving an original free-form text narrative of a patient encounter provided by a clinician; re-formatting the original free-form text narrative in a manner that facilitates clinical fact extraction performed by a fact extraction engine on a formatted text resulting from the re-formatting; extracting one or more clinical facts from the formatted text using the fact extraction engine, wherein a first fact of the one or more clinical facts is extracted from a first portion of the formatted text, wherein the first portion of the formatted text is a formatted version of a first portion of the original free-form text narrative, the extracting comprising analyzing the formatted text to identify a set of one or more features of at least the first portion of the formatted text, correlating the set of features to one or more abstract semantic concepts, and generating computer-readable data that expresses the one or more abstract semantic concepts as the one or more clinical facts extracted from the formatted text; and providing to a user an indicator that distinguishes the first portion of the original free-form text narrative that resulted in extraction of the first fact, from other portions of the original free-form text narrative that did not result in the extraction of the first fact. 15. The apparatus of claim 13 , wherein the re-formatting facilitates the clinical fact extraction at least in part by identifying at least one section boundary in the original free-form text narrative, and adding, removing or correcting at least one section heading corresponding to the at least one section boundary. | 0.5 |
9,460,414 | 1 | 6 | 1. A computer-implemented system for providing annotated electronic documents, the annotations which are to be applied to the documents being stored in a first data storage, the documents being stored in a second data storage, the first data storage and the second data storage being at least one of physically separate and logically separate, said system comprising: (A) an annotation component configured to determine, responsive to at least one user, at least one annotation to be applied to at least one document, including a selection resource to select at least a portion of the at least one document and to associate the at least one annotation therewith, and a mark-up resource to at least one of add and edit the at least one annotation, wherein the annotation is image data or text, wherein each annotation can be different from every other annotation; (B) a reference component, responsive to the at least one user, configured to at least one of establish, traverse, indicate, and remove, at least one reference between the at least one portion and at least one of an other portion of the at least one document, an other document, and at least one other portion of the other document; (C) at least one merge component configured to, responsive to a request from a user to retrieve the at least one document: retrieve the at least one document from the second data storage as document data, retrieve the at least one annotation to be applied to said at least one document from a first data storage as annotation data, and combine the annotation data and the document data to form a unitary single logical document, the single logical document displaying the annotation embedded seamlessly in the document data, wherein the annotation data indicates a predetermined section within the document as stored in the second data storage into which the annotation is to be embedded as indicated by an XML data schema; and (D) at least one split component complementary to the merge component, the split component is configured to, responsive to a request from the user to store the document: extract the annotation data and the document data from the single logical document, update the at least one annotation in the first data storage from the extracted annotation data, and update the at least one document in the second data storage from the extracted document data. | 1. A computer-implemented system for providing annotated electronic documents, the annotations which are to be applied to the documents being stored in a first data storage, the documents being stored in a second data storage, the first data storage and the second data storage being at least one of physically separate and logically separate, said system comprising: (A) an annotation component configured to determine, responsive to at least one user, at least one annotation to be applied to at least one document, including a selection resource to select at least a portion of the at least one document and to associate the at least one annotation therewith, and a mark-up resource to at least one of add and edit the at least one annotation, wherein the annotation is image data or text, wherein each annotation can be different from every other annotation; (B) a reference component, responsive to the at least one user, configured to at least one of establish, traverse, indicate, and remove, at least one reference between the at least one portion and at least one of an other portion of the at least one document, an other document, and at least one other portion of the other document; (C) at least one merge component configured to, responsive to a request from a user to retrieve the at least one document: retrieve the at least one document from the second data storage as document data, retrieve the at least one annotation to be applied to said at least one document from a first data storage as annotation data, and combine the annotation data and the document data to form a unitary single logical document, the single logical document displaying the annotation embedded seamlessly in the document data, wherein the annotation data indicates a predetermined section within the document as stored in the second data storage into which the annotation is to be embedded as indicated by an XML data schema; and (D) at least one split component complementary to the merge component, the split component is configured to, responsive to a request from the user to store the document: extract the annotation data and the document data from the single logical document, update the at least one annotation in the first data storage from the extracted annotation data, and update the at least one document in the second data storage from the extracted document data. 6. The system of claim 1 , further comprising: at least one version component, configured to at least one of manage a history of changes and maintain at least one separate version for the at least one document and the at least one annotation applied thereto. | 0.871897 |
8,914,751 | 1 | 14 | 1. A method comprising: outputting, by a computing device and for display at a presence-sensitive display, a graphical keyboard comprising a plurality of keys and a text editor field; responsive to receiving an indication of a continuous gesture detected at the presence-sensitive display that comprises a first path in a first direction from a first key to a second key, outputting, for display at the text editor field of the presence-sensitive display and based at least in part on the continuous gesture, a first character associated with the first key and a second character associated with the second key; determining, by the computing device, that a second path of the continuous gesture from the second key towards the first key retraces at least a portion of the first path from the first key to the second key, wherein the second path is in a second direction that is substantially reverse to the first direction; and responsive to determining that the second path retraces at least the portion of the first path in the second direction that is substantially reverse to the first direction and that the continuous gesture further comprises a third path to a third key, the third key associated with a third character: removing, by the computing device and from display at the presence-sensitive display, the second character, and outputting, by the computing device and for display, the third character in a character string comprising the third character subsequent to first character and without the second character included between the first character and third character. | 1. A method comprising: outputting, by a computing device and for display at a presence-sensitive display, a graphical keyboard comprising a plurality of keys and a text editor field; responsive to receiving an indication of a continuous gesture detected at the presence-sensitive display that comprises a first path in a first direction from a first key to a second key, outputting, for display at the text editor field of the presence-sensitive display and based at least in part on the continuous gesture, a first character associated with the first key and a second character associated with the second key; determining, by the computing device, that a second path of the continuous gesture from the second key towards the first key retraces at least a portion of the first path from the first key to the second key, wherein the second path is in a second direction that is substantially reverse to the first direction; and responsive to determining that the second path retraces at least the portion of the first path in the second direction that is substantially reverse to the first direction and that the continuous gesture further comprises a third path to a third key, the third key associated with a third character: removing, by the computing device and from display at the presence-sensitive display, the second character, and outputting, by the computing device and for display, the third character in a character string comprising the third character subsequent to first character and without the second character included between the first character and third character. 14. The method of claim 1 , wherein removing, from the text editor field of the presence-sensitive display and by the computing device, the second character, is further in response to detecting a substantial change in direction of the second path relative to the first path. | 0.778317 |
7,788,253 | 1 | 6 | 1. A method for building a search index: while building the search index and using the search index to respond to one or more search requests and performing synchronous anchor text processing, maintaining an anchor information store, wherein each entry of the anchor information store identifies a referring document, a target document, and anchor text associated with a link from the referring document to the target document; maintaining a rebuild agenda, wherein each entry of the rebuild agenda identifies a target document that has an entry in the search index and whose anchor text is to be updated in the search index with asynchronous processing because there is at least one new or updated link pointing to the target document; receiving a document for processing; for each outgoing link in the document that points to a target document, adding an entry to the anchor information store that identifies the received document, the target document, and anchor text; and adding an entry to the rebuild agenda for the target document; and for each link pointing from a referring document to the document, locating one or more entries in the anchor information store for which the received document to be processed is identified as the target document; retrieving anchor text from each of the identified entries; and storing the retrieved anchor text in an entry of the search index for the received document; and performing asynchronous anchor text processing to incrementally update current entries in the search index for each document identified in each entry in the rebuild agenda in parallel with the building of the search index and in parallel with using the search index to respond to one or more search requests by: selecting a first target document in the rebuild agenda; using the anchor information store to find anchor text for the first target document by identifying one or more entries in the anchor information store for which the first target document is identified as the target document in the anchor information store; retrieving anchor text from each of the identified entries; and incrementally updating the anchor text in the entry of the search index for the first target document. | 1. A method for building a search index: while building the search index and using the search index to respond to one or more search requests and performing synchronous anchor text processing, maintaining an anchor information store, wherein each entry of the anchor information store identifies a referring document, a target document, and anchor text associated with a link from the referring document to the target document; maintaining a rebuild agenda, wherein each entry of the rebuild agenda identifies a target document that has an entry in the search index and whose anchor text is to be updated in the search index with asynchronous processing because there is at least one new or updated link pointing to the target document; receiving a document for processing; for each outgoing link in the document that points to a target document, adding an entry to the anchor information store that identifies the received document, the target document, and anchor text; and adding an entry to the rebuild agenda for the target document; and for each link pointing from a referring document to the document, locating one or more entries in the anchor information store for which the received document to be processed is identified as the target document; retrieving anchor text from each of the identified entries; and storing the retrieved anchor text in an entry of the search index for the received document; and performing asynchronous anchor text processing to incrementally update current entries in the search index for each document identified in each entry in the rebuild agenda in parallel with the building of the search index and in parallel with using the search index to respond to one or more search requests by: selecting a first target document in the rebuild agenda; using the anchor information store to find anchor text for the first target document by identifying one or more entries in the anchor information store for which the first target document is identified as the target document in the anchor information store; retrieving anchor text from each of the identified entries; and incrementally updating the anchor text in the entry of the search index for the first target document. 6. The computer-implemented method of claim 1 , further comprising: adjusting a search-result weight associated with the document based on an in-link count determined by counting a number of referring documents for the document in the anchor information store. | 0.783694 |
9,345,117 | 11 | 13 | 11. The system of claim 1 , wherein the luminaire further comprises a processor that executes the interpreter module and is coupled to provide the operating parameters to the drive circuit to thereby control time variation of the relative intensities of the light output from the light channels. | 11. The system of claim 1 , wherein the luminaire further comprises a processor that executes the interpreter module and is coupled to provide the operating parameters to the drive circuit to thereby control time variation of the relative intensities of the light output from the light channels. 13. The system of claim 11 , wherein the luminaire further comprises memory storing calibration data, wherein the processor executes a module that uses the calibration data and the script to produce the operating parameters, which are specific to the luminaire. | 0.5 |
8,478,186 | 6 | 7 | 6. The educational system for testing memorization as recited in claim 5 , wherein said network interface is adapted for interfacing the plurality of the educational systems for testing memorization with the central server. | 6. The educational system for testing memorization as recited in claim 5 , wherein said network interface is adapted for interfacing the plurality of the educational systems for testing memorization with the central server. 7. The educational system for testing memorization as recited in claim 6 , wherein the set of digital data stored in the database represents the text of the Qur'an. | 0.5 |
9,654,615 | 8 | 10 | 8. A system for safe messaging while driving comprising: a vehicle in communication with a mobile communication device having: a display with a screen having a user-defined duration of automatic settings' initiation and/or ending; a receiving messages feature; a sounding feature, configured for sounding of sender details and said messages; and a transmitting messages feature, configured for sending text or voice message; wherein said features are configured to be operated using voice commands. | 8. A system for safe messaging while driving comprising: a vehicle in communication with a mobile communication device having: a display with a screen having a user-defined duration of automatic settings' initiation and/or ending; a receiving messages feature; a sounding feature, configured for sounding of sender details and said messages; and a transmitting messages feature, configured for sending text or voice message; wherein said features are configured to be operated using voice commands. 10. The system of claim 8 , wherein said system is deactivated either automatically or manually. | 0.52 |
7,627,096 | 18 | 20 | 18. A system to determine a routing destination based upon speech, the system comprising: an acoustic engine configured to accept a speech input and to output a text based on at least a portion of the speech; a semantic engine coupled to the acoustic engine and operable to identify one or more actions and one or more objects based at least in part on the text; a probability system operable to assign a corresponding confidence level to each of the one or more actions and the one or more objects identified; an action-object pairing system to: identify a dominant entry from the one or more identified actions and the one or more identified objects; and select a complement to the dominant entry from the one or more identified actions and the one or more identified objects, wherein the selection is based at least in part on the dominant entry; and form an action-object pair that includes the dominant entry and the complement to the dominant entry; and an action-object routing table operable to provide a routing destination based at least partially on the action-object pair. | 18. A system to determine a routing destination based upon speech, the system comprising: an acoustic engine configured to accept a speech input and to output a text based on at least a portion of the speech; a semantic engine coupled to the acoustic engine and operable to identify one or more actions and one or more objects based at least in part on the text; a probability system operable to assign a corresponding confidence level to each of the one or more actions and the one or more objects identified; an action-object pairing system to: identify a dominant entry from the one or more identified actions and the one or more identified objects; and select a complement to the dominant entry from the one or more identified actions and the one or more identified objects, wherein the selection is based at least in part on the dominant entry; and form an action-object pair that includes the dominant entry and the complement to the dominant entry; and an action-object routing table operable to provide a routing destination based at least partially on the action-object pair. 20. The system of claim 18 , wherein the object is selected from a list of expected utterances comprising DSL, basic service, call notes, caller ID, bill payment, other providers, coupon specials, names and number, and store locations. | 0.666193 |
7,752,079 | 1 | 6 | 1. A system, comprising: an electronic commerce computer system comprising a plurality of computing devices, the computer system including: a first user interface, generated on a first computing device of the computer system, through which buyers place orders over a network with a merchant and view the status of such orders, the first interface including: a first portion for displaying at least a subset of the negotiated terms between a buyer and the merchant for an accepted order, and a second portion including: a message field for a buyer to generate and submit to the merchant messages that are linked to particular orders, and a display of at least a subset of a record of at least one message previously exchanged between the buyer and the merchant linked to the accepted order, wherein the at least one message is separate from the negotiated terms between the buyer and the merchant for an accepted order; and a second user interface, generated on a second computing device of the computer system, through which the merchant views accepted orders, order-specific messages from buyers, and status information of accepted orders, the second interface including a message field for the merchant to generate and submit messages to the buyers that are linked to specific accepted orders. | 1. A system, comprising: an electronic commerce computer system comprising a plurality of computing devices, the computer system including: a first user interface, generated on a first computing device of the computer system, through which buyers place orders over a network with a merchant and view the status of such orders, the first interface including: a first portion for displaying at least a subset of the negotiated terms between a buyer and the merchant for an accepted order, and a second portion including: a message field for a buyer to generate and submit to the merchant messages that are linked to particular orders, and a display of at least a subset of a record of at least one message previously exchanged between the buyer and the merchant linked to the accepted order, wherein the at least one message is separate from the negotiated terms between the buyer and the merchant for an accepted order; and a second user interface, generated on a second computing device of the computer system, through which the merchant views accepted orders, order-specific messages from buyers, and status information of accepted orders, the second interface including a message field for the merchant to generate and submit messages to the buyers that are linked to specific accepted orders. 6. The system of claim 1 , wherein the first and second user interfaces provide for communications between the merchant and the buyer without use of electronic mail. | 0.788462 |
9,020,932 | 11 | 14 | 11. The system of claim 10 , wherein the processing unit, when executing the program instructions stored on the computer-readable storage medium via the computer readable memory, further identifies the generic occupational identity search data that is associated with occupational identity indicia that is common to the user and at least one other user having the common occupational identity indicia by: recognizing a type of network access used by the user to submit the text string query; determining a predominant occupational identity associated with the recognized type of network access; and retrieving search history common to pluralities of users that share the determined predominant occupational identity as the generic occupational identity search data. | 11. The system of claim 10 , wherein the processing unit, when executing the program instructions stored on the computer-readable storage medium via the computer readable memory, further identifies the generic occupational identity search data that is associated with occupational identity indicia that is common to the user and at least one other user having the common occupational identity indicia by: recognizing a type of network access used by the user to submit the text string query; determining a predominant occupational identity associated with the recognized type of network access; and retrieving search history common to pluralities of users that share the determined predominant occupational identity as the generic occupational identity search data. 14. The system of claim 11 , wherein the processing unit, when executing the program instructions stored on the computer-readable storage medium via the computer readable memory, displays the primary set of search results, the secondary set of search results and the set of peripheral knowledge articles to the user in the different, respective tabbed sheets that are nested on top of one another in a web-based interface dashboard, by: displaying a list of the primary set of results to the user in a primary tabbed sheet of the nested tabbed sheets; listing the secondary set of search results in a secondary tabbed sheet of the nested tabbed sheets, wherein the list of the secondary set of search results is nested behind the primary tabbed sheet and not visible to the user unless selected by the user; and listing the set of peripheral search results in a peripheral tabbed sheet of the nested tabbed sheets, wherein the list of the set of peripheral search results is nested behind the primary and secondary tabbed sheets and not visible to the user unless selected by the user; and wherein selection of one of the secondary tabbed sheet and the peripheral tabbed sheet by the user via a graphical user interface cursor routine causes the processing unit to display the list of results of the selected one of the secondary tabbed sheet and the peripheral tabbed sheet in a tabbed sheet display above each of other unselected ones of the primary tabbed sheet, the secondary tabbed sheet and the peripheral tabbed sheet. | 0.5 |
8,965,886 | 21 | 26 | 21. A nontransitory computer readable medium having embodied thereon a program, the program being executable by a processor to perform a method for providing data based on object relevance, the method comprising: receiving one or more query terms from a user via a communications interface associated with the processor; determining a preliminary relevance of one or more objects associated with an enterprise system based on the one or more query terms; assigning at least one rating to the one or more objects based on the preliminary relevance; propagating the preliminary relevance among the one or more objects; establishing an overall relevance of the one or more objects based on the at least one rating, the preliminary relevance, and the propagation; ranking the one or more objects according to the overall relevance; providing data as search results comprised of the one or more objects according to the ranking to the user; filtering the search results based on at least one selected, dynamically generated filter associated with the one or more objects, the at least one selected dynamically generated filter based on relationships inferred among the one or more objects; ranking the filtered search results; and transmitting to display the filtered search results to the user via the communications interface. | 21. A nontransitory computer readable medium having embodied thereon a program, the program being executable by a processor to perform a method for providing data based on object relevance, the method comprising: receiving one or more query terms from a user via a communications interface associated with the processor; determining a preliminary relevance of one or more objects associated with an enterprise system based on the one or more query terms; assigning at least one rating to the one or more objects based on the preliminary relevance; propagating the preliminary relevance among the one or more objects; establishing an overall relevance of the one or more objects based on the at least one rating, the preliminary relevance, and the propagation; ranking the one or more objects according to the overall relevance; providing data as search results comprised of the one or more objects according to the ranking to the user; filtering the search results based on at least one selected, dynamically generated filter associated with the one or more objects, the at least one selected dynamically generated filter based on relationships inferred among the one or more objects; ranking the filtered search results; and transmitting to display the filtered search results to the user via the communications interface. 26. The nontransitory computer readable medium recited in claim 21 , further comprising receiving at least one further query term from the user for narrowing the search results. | 0.5 |
8,464,234 | 14 | 17 | 14. A system of compilation comprising: a non-transitory storage device; a front-end component comprising: a scanner configured to scan one or more header files on a computing device into a tokenized form and to scan the tokenized form for one or more symbols and create one or more preprocessor symbol tables based upon the one or more symbols; a parser configured to parse the tokenized form on the computing device into one or more abstract syntax tree; and a serialization module configured to: create pre-parsed headers on the non-transitory storage device for the one or more header files by: serializing in a modular form the one or more abstract syntax trees to the storage device; serializing in a modular form the tokenized form to the storage device; and serializing in a modular form the one or more preprocessor symbol tables to the storage device; locate the pre-parsed headers from the storage device based upon directives in one or more source files; and deserialize the one or more abstract syntax trees from the pre-parsed headers on the computing device; and a back-end component configured to convert one or more source files into object code using the one or more abstract syntax trees loaded from the pre-parsed headers. | 14. A system of compilation comprising: a non-transitory storage device; a front-end component comprising: a scanner configured to scan one or more header files on a computing device into a tokenized form and to scan the tokenized form for one or more symbols and create one or more preprocessor symbol tables based upon the one or more symbols; a parser configured to parse the tokenized form on the computing device into one or more abstract syntax tree; and a serialization module configured to: create pre-parsed headers on the non-transitory storage device for the one or more header files by: serializing in a modular form the one or more abstract syntax trees to the storage device; serializing in a modular form the tokenized form to the storage device; and serializing in a modular form the one or more preprocessor symbol tables to the storage device; locate the pre-parsed headers from the storage device based upon directives in one or more source files; and deserialize the one or more abstract syntax trees from the pre-parsed headers on the computing device; and a back-end component configured to convert one or more source files into object code using the one or more abstract syntax trees loaded from the pre-parsed headers. 17. The system of claim 14 , wherein the front-end component further comprises a verifier configured to determine whether the one or more header files are independently compilable. | 0.877551 |
9,712,885 | 1 | 18 | 1. An apparatus, comprising: a processor circuit; and a video display component for execution on the processor circuit to receive video data from a media server, the video data including closed captioning (CC) data; a CC decoder component for execution on the processor circuit to decode the CC data to obtain text; and an ad requesting component for execution on the processor circuit to: determine a future time to display an advertisement; set a time window including the future display time for the advertisement, a first time period before and adjacent to the future display time, a second time period during the future display time, and a third time period adjacent to and after the future display time, the time window comprising less time than an entirety of running time of the video data; select a subset of the video data that will be displayed during the time window; parse the text from the CC data corresponding to the subset of video data to obtain words; provide the words to the media server; receive an advertisement from the media server; and display the advertisement. | 1. An apparatus, comprising: a processor circuit; and a video display component for execution on the processor circuit to receive video data from a media server, the video data including closed captioning (CC) data; a CC decoder component for execution on the processor circuit to decode the CC data to obtain text; and an ad requesting component for execution on the processor circuit to: determine a future time to display an advertisement; set a time window including the future display time for the advertisement, a first time period before and adjacent to the future display time, a second time period during the future display time, and a third time period adjacent to and after the future display time, the time window comprising less time than an entirety of running time of the video data; select a subset of the video data that will be displayed during the time window; parse the text from the CC data corresponding to the subset of video data to obtain words; provide the words to the media server; receive an advertisement from the media server; and display the advertisement. 18. The apparatus of claim 1 , the ad requesting component further to: select a meaning for a parsed word having a plurality of meanings, wherein each of the plurality of meanings is associated with a different set of metadata, wherein the set of metadata for the selected meaning includes a word present in the decoded CC data near the parsed word. | 0.5 |
7,970,698 | 17 | 20 | 17. A non-transitory computer-readable medium for generating a rule for automated credit request decisioning, comprising program code executable by a processor, the program code comprising: program code for receiving information associated with an applicant through a user interface; program code for receiving data associated with the applicant from at least one data source; program code for configuring the user interface to allow the user to define a plurality of decision rules in a near-natural language; program code for determining information associated with the plurality of decision rules based on the plurality of decision rules in the near-natural language defined by the user; program code for generating a decision flow in response to receiving an identification from the user through the user interface of a flow element, of a decision block, and of a flow path for connecting to at least one of the decision block or the flow element, the decision flow comprising the flow element, the decision block, and the flow path; program code for determining a decision for the applicant based on the information associated with the applicant, the data associated with the applicant from at least one data source, the information associated with the plurality of decision rules determined based on the plurality of decision rules in the near-natural language, and the decision flow, wherein program code for determining the decision for the applicant comprises program code for applying in accordance with the decision flow an executable version of the information associated with the plurality of decision rules in the near-natural language to the data associated with the applicant from at least one data source; and program code for providing the decision to the user interface. | 17. A non-transitory computer-readable medium for generating a rule for automated credit request decisioning, comprising program code executable by a processor, the program code comprising: program code for receiving information associated with an applicant through a user interface; program code for receiving data associated with the applicant from at least one data source; program code for configuring the user interface to allow the user to define a plurality of decision rules in a near-natural language; program code for determining information associated with the plurality of decision rules based on the plurality of decision rules in the near-natural language defined by the user; program code for generating a decision flow in response to receiving an identification from the user through the user interface of a flow element, of a decision block, and of a flow path for connecting to at least one of the decision block or the flow element, the decision flow comprising the flow element, the decision block, and the flow path; program code for determining a decision for the applicant based on the information associated with the applicant, the data associated with the applicant from at least one data source, the information associated with the plurality of decision rules determined based on the plurality of decision rules in the near-natural language, and the decision flow, wherein program code for determining the decision for the applicant comprises program code for applying in accordance with the decision flow an executable version of the information associated with the plurality of decision rules in the near-natural language to the data associated with the applicant from at least one data source; and program code for providing the decision to the user interface. 20. The computer-readable medium of claim 17 , wherein the applicant is one of: an individual, an entity, a business, or a commercial institution. | 0.901882 |
9,881,003 | 16 | 17 | 16. The non-transitory computer-readable storage medium of claim 15 , wherein automatically translating the text comprises: after extracting the text contained in the plurality of speech bubbles, compiling the text into a single piece of text based on the intended reading order; and translating the single piece of text from the first natural language to the second natural language to produce the translated text. | 16. The non-transitory computer-readable storage medium of claim 15 , wherein automatically translating the text comprises: after extracting the text contained in the plurality of speech bubbles, compiling the text into a single piece of text based on the intended reading order; and translating the single piece of text from the first natural language to the second natural language to produce the translated text. 17. The non-transitory computer-readable storage medium of claim 16 , wherein the contextual information further includes a depicted character or object contained in the digital graphic novel content, and automatically translating the text comprises applying a translation algorithm to the text contained in the plurality of speech bubbles that is tailored for the depicted character or object contained in the digital graphic novel content. | 0.5 |
7,735,144 | 25 | 27 | 25. A system comprising a computer readable storage device storing a computer program, a display device, and one or more processors operable to interact with the display device and to execute the computer program and perform operations comprising: reading an electronic document, the electronic document comprising content rules, an existing signed state, and existing content items; authenticating the electronic document one or more times, each authenticating comprising: classifying the existing content items of the electronic document into existing invariant content items and existing modifiable content items, according to the content rules; receiving one or more user actions for one or more of the existing one or more content items; determining whether the one or more user actions are permitted by the content rules; reclassifying one or more existing modifiable content items into new invariant content items in response to the one or more user actions; generating an object digest for an aggregation of the existing invariant content items and the new invariant content items by digesting the existing invariant content items and the new invariant content items, wherein the aggregation includes a simple content item, a semi-complex content item and a complex content item, the object digest is generated according to their complexity and generating the object digest includes ignoring one or more existing modifiable content items in the electronic document that are not reclassified into new invariant content items; generating a saved state of the electronic document; and adding a new signed state to the electronic document, the new signed state comprising the object digest, the saved state, and an electronic signature. | 25. A system comprising a computer readable storage device storing a computer program, a display device, and one or more processors operable to interact with the display device and to execute the computer program and perform operations comprising: reading an electronic document, the electronic document comprising content rules, an existing signed state, and existing content items; authenticating the electronic document one or more times, each authenticating comprising: classifying the existing content items of the electronic document into existing invariant content items and existing modifiable content items, according to the content rules; receiving one or more user actions for one or more of the existing one or more content items; determining whether the one or more user actions are permitted by the content rules; reclassifying one or more existing modifiable content items into new invariant content items in response to the one or more user actions; generating an object digest for an aggregation of the existing invariant content items and the new invariant content items by digesting the existing invariant content items and the new invariant content items, wherein the aggregation includes a simple content item, a semi-complex content item and a complex content item, the object digest is generated according to their complexity and generating the object digest includes ignoring one or more existing modifiable content items in the electronic document that are not reclassified into new invariant content items; generating a saved state of the electronic document; and adding a new signed state to the electronic document, the new signed state comprising the object digest, the saved state, and an electronic signature. 27. The system of claim 25 , wherein each of the content items of the electronic document has a type identifier and a length. | 0.911848 |
9,142,218 | 16 | 17 | 16. The computer-readable storage device of claim 15 , having additional instructions stored which result in operations comprising transmitting access data based on the verifying of the speech as authentic. | 16. The computer-readable storage device of claim 15 , having additional instructions stored which result in operations comprising transmitting access data based on the verifying of the speech as authentic. 17. The computer-readable storage device of claim 16 , wherein the access data provides access to a restricted location. | 0.5 |
8,233,714 | 7 | 11 | 7. The method of claim 1 , wherein the one or more associated search criteria comprises a type of the image object to detect, its physical properties, its search area, and its spatial relationships with other elements. | 7. The method of claim 1 , wherein the one or more associated search criteria comprises a type of the image object to detect, its physical properties, its search area, and its spatial relationships with other elements. 11. The method of claim 7 , wherein the search area for an element is specified in terms of search constraints selected from the group consisting of an absolute search constraint specified in terms of a array of rectangles with specified coordinates; a relative search constraint specified relative to the edges of the image of the paper document; and a relative search constraint specified relative to other search elements. | 0.5 |
7,680,777 | 27 | 29 | 27. A computerized search and presentation system for a set of items, comprising: server coupled to a network; a user terminal coupled to the network; an application running on a computer while stored in the computer's active memory, and stored on the server or the user terminal, wherein the application is configured for: assigning one or more Attributes to each item in the set of items; further assigning to each Attribute, and to no item, a single Kind; creating assignments between Attributes and Kinds, where the assignments assign to each Attribute a single Kind, and to each Kind an arbitrary number of Attributes; assigning to each Kind at least one scheme of transitive implications among the Attributes such that each Kind is associated with several Attributes; determining which Attributes of each Kind are broad Attributes, broad Attributes being Attributes that do not within the set of items imply any other Attribute; presenting to a user a list of Kinds by which the set of items may be grouped; upon accepting the user's selection of one or more Kinds, listing one or more broad Attributes of that Kind; upon accepting the user's selection of one or more of the broad Attributes, presenting information about a subset of items having the selected one or more broad Attributes; wherein Kinds are not assigned to items. | 27. A computerized search and presentation system for a set of items, comprising: server coupled to a network; a user terminal coupled to the network; an application running on a computer while stored in the computer's active memory, and stored on the server or the user terminal, wherein the application is configured for: assigning one or more Attributes to each item in the set of items; further assigning to each Attribute, and to no item, a single Kind; creating assignments between Attributes and Kinds, where the assignments assign to each Attribute a single Kind, and to each Kind an arbitrary number of Attributes; assigning to each Kind at least one scheme of transitive implications among the Attributes such that each Kind is associated with several Attributes; determining which Attributes of each Kind are broad Attributes, broad Attributes being Attributes that do not within the set of items imply any other Attribute; presenting to a user a list of Kinds by which the set of items may be grouped; upon accepting the user's selection of one or more Kinds, listing one or more broad Attributes of that Kind; upon accepting the user's selection of one or more of the broad Attributes, presenting information about a subset of items having the selected one or more broad Attributes; wherein Kinds are not assigned to items. 29. The system of claim 27 , wherein assigning one or more Attributes to each item is recorded in a database by explicit insertion or by automated deduction that the assignment of the Attribute is required from an item's one or more Attributes and the transitive implications among the Attributes. | 0.503344 |
8,881,116 | 1 | 3 | 1. A method, comprising: receiving computer code, the receiving being performed by a device; performing a static verification analysis of the computer code to locate a point in the code that, under at least one set of states of variables in the computer code, causes an error in an execution of the computer code, the static analysis being performed by analyzing a first plurality of possible execution paths of the computer code based on an over-approximation of states, and the performing is being performed by the device; back-propagating, from the located point, through a second plurality of possible execution paths, the second plurality of possible execution paths being obtained based on an under-approximation of the states that were over-approximated, and the back-propagating being performed by the device; determining, based on the back-propagating, a second point in the computer code as a potential cause of the error, the determining being performed by the device; analyzing, using empiric techniques and based on semantic information for the computer code, the computer code to determine a category for the potential cause of the error, the analyzing the computer code being performed by the device, and the category comprising one of: a first category when the cause of the error is determined to be within the computer code, or a second category when the cause of the error is determined to be due to an input to the computer code; and storing output information associated with the second point in the computer code, the storing being performed by the device. | 1. A method, comprising: receiving computer code, the receiving being performed by a device; performing a static verification analysis of the computer code to locate a point in the code that, under at least one set of states of variables in the computer code, causes an error in an execution of the computer code, the static analysis being performed by analyzing a first plurality of possible execution paths of the computer code based on an over-approximation of states, and the performing is being performed by the device; back-propagating, from the located point, through a second plurality of possible execution paths, the second plurality of possible execution paths being obtained based on an under-approximation of the states that were over-approximated, and the back-propagating being performed by the device; determining, based on the back-propagating, a second point in the computer code as a potential cause of the error, the determining being performed by the device; analyzing, using empiric techniques and based on semantic information for the computer code, the computer code to determine a category for the potential cause of the error, the analyzing the computer code being performed by the device, and the category comprising one of: a first category when the cause of the error is determined to be within the computer code, or a second category when the cause of the error is determined to be due to an input to the computer code; and storing output information associated with the second point in the computer code, the storing being performed by the device. 3. The method of claim 1 , where determining the second point in the computer code further includes: determining the second point as a point, in the computer code, that includes an input, a variable assignment, a variable declaration, or a function return value. | 0.748077 |
7,987,151 | 19 | 20 | 19. The system as recited in claim 1 , further comprising: a smell/taste agent configured as processor-readable software code stored on a processor readable medium, and wherein said brain agent is further adapted to selectively interact with said smell/taste agent to interpret the input query and to provide output in response to the input query. | 19. The system as recited in claim 1 , further comprising: a smell/taste agent configured as processor-readable software code stored on a processor readable medium, and wherein said brain agent is further adapted to selectively interact with said smell/taste agent to interpret the input query and to provide output in response to the input query. 20. The system as recited in claim 19 , further comprising: a connector, the connector being embodied as processor-readable software code stored on a processor-readable medium, the connector being configured to facilitate selective interaction with the smell/taste agent with one of an external data structure and an external intelligent system. | 0.5 |
8,073,850 | 9 | 10 | 9. The method of claim 1 , further comprising automatically using the score to determine whether to convert an occurrence of the key phrase on the web page into a link for displaying related content. | 9. The method of claim 1 , further comprising automatically using the score to determine whether to convert an occurrence of the key phrase on the web page into a link for displaying related content. 10. The method of claim 9 , further comprising responding to user selection of the link by causing content related to the key phrase to be displayed in a panel on the web page, said content including an advertisement associated with the key phrase and non-advertisement social media content associated with the key phrase. | 0.5 |
8,620,990 | 1 | 5 | 1. A method for parsing contents of an e-Form, the contents of said e-Form having been divided into more than one section with a different content identification code assigned to each said section of said e-Form, the method comprising: scanning an e-Form submitted for processing to determine, based on said content identification codes, which of said sections have a corresponding parsed result already in a cache system; during said scanning, generating a content identification code for any section that does not have an associated content identification code, in which the content identification code is generated based on contents of the section found not to already have an associated content identification code; parsing contents of said sections without a parsed result already in said cache system; and combining parsed results from said cache system with parsed results from said parsing of sections without a parsed result already in said cache system. | 1. A method for parsing contents of an e-Form, the contents of said e-Form having been divided into more than one section with a different content identification code assigned to each said section of said e-Form, the method comprising: scanning an e-Form submitted for processing to determine, based on said content identification codes, which of said sections have a corresponding parsed result already in a cache system; during said scanning, generating a content identification code for any section that does not have an associated content identification code, in which the content identification code is generated based on contents of the section found not to already have an associated content identification code; parsing contents of said sections without a parsed result already in said cache system; and combining parsed results from said cache system with parsed results from said parsing of sections without a parsed result already in said cache system. 5. The method of claim 1 , further comprising storing parsed results for sections from a number of different e-Forms in said cache system. | 0.72619 |
7,475,016 | 1 | 3 | 1. A method of identifying potentially misaligned speech segments from an ordered sequence of speech segments in order to create an accurate speech database for speech synthesis, the method comprising: identifying a first cluster comprising at least one speech segment selected from the ordered sequence of speech segments if the at least one speech segment satisfies a predetermined filtering test for a misaligned segment; identifying a second cluster comprising at least one different speech segment selected from the ordered sequence of speech segments if the at least one different speech segment satisfies the predetermined filtering test and if there is at least one intervening speech segment occupying a sequential position between the at least one speech segment and the at least one different speech segment, the intervening speech segment failing to satisfy the predetermined filtering test; and combining the first and second clusters and the at least one intervening speech segment to generate an aggregated cluster if the aggregated cluster satisfies a predetermined combining criterion, the aggregated cluster replacing the first and second clusters. | 1. A method of identifying potentially misaligned speech segments from an ordered sequence of speech segments in order to create an accurate speech database for speech synthesis, the method comprising: identifying a first cluster comprising at least one speech segment selected from the ordered sequence of speech segments if the at least one speech segment satisfies a predetermined filtering test for a misaligned segment; identifying a second cluster comprising at least one different speech segment selected from the ordered sequence of speech segments if the at least one different speech segment satisfies the predetermined filtering test and if there is at least one intervening speech segment occupying a sequential position between the at least one speech segment and the at least one different speech segment, the intervening speech segment failing to satisfy the predetermined filtering test; and combining the first and second clusters and the at least one intervening speech segment to generate an aggregated cluster if the aggregated cluster satisfies a predetermined combining criterion, the aggregated cluster replacing the first and second clusters. 3. The method of claim 1 , wherein the predetermined combining criterion is based upon at least one of a breaking test condition and a sizing test condition, the breaking test condition setting a threshold number of intervening speech segments above which the clusters bracketing the intervening speech segments remain broken up into distinct clusters, the sizing test condition requiring a number of speech segments contained in an aggregated cluster to be greater than a predetermined number. | 0.5 |
8,645,300 | 3 | 4 | 3. The method according to claim 1 wherein processing the at least one input parameter comprises determining a ranked list of intents for the webpage. | 3. The method according to claim 1 wherein processing the at least one input parameter comprises determining a ranked list of intents for the webpage. 4. The method according to claim 3 comprising: selecting the highest ranked intent for the webpage; and causing at least the highest ranked intent to be displayed on the webpage in the web browser. | 0.5 |
6,122,361 | 24 | 25 | 24. An automated directory assistance system comprising: a) a speech recognition dictionary including a plurality of vocabulary items potentially recognizable on a basis of a spoken utterance by a user of said automated directory assistance system, each vocabulary item being indicative of a locality in which an entity whose telephone number potentially sought by the user may reside; b) first processing unit for detecting at least a portion of a telephone number dialed by the user to access a directory assistance call function; c) second processing unit responsive to said at least a portion of said telephone number dialed by the user and to the spoken utterance for determining a probability value for at least one of said vocabulary items, the probability value being indicative of a likelihood of match between said at least one of said vocabulary items and the spoken utterance. | 24. An automated directory assistance system comprising: a) a speech recognition dictionary including a plurality of vocabulary items potentially recognizable on a basis of a spoken utterance by a user of said automated directory assistance system, each vocabulary item being indicative of a locality in which an entity whose telephone number potentially sought by the user may reside; b) first processing unit for detecting at least a portion of a telephone number dialed by the user to access a directory assistance call function; c) second processing unit responsive to said at least a portion of said telephone number dialed by the user and to the spoken utterance for determining a probability value for at least one of said vocabulary items, the probability value being indicative of a likelihood of match between said at least one of said vocabulary items and the spoken utterance. 25. An automated directory assistance system as defined in claim 24, wherein said at least a portion of a telephone number dialed by the user is an NPA portion of a telephone number permitting access to the directory assistance function. | 0.5 |
9,066,285 | 6 | 7 | 6. The modem of claim 1 , wherein the processing apparatus is configured to establish the first channel being of a type dedicated to only the first version. | 6. The modem of claim 1 , wherein the processing apparatus is configured to establish the first channel being of a type dedicated to only the first version. 7. The modem of claim 6 , wherein the processing apparatus is configured to establish the second channel being of a type dedicated to the second version. | 0.5 |
4,623,985 | 5 | 6 | 5. The electronic translator of claim 1 further comprising: display means, responsive to said input means, for displaying said first word or words, said display means being further responsive to said access means for subsequently displaying said retrieved equivalent second word or words, said display means being further responsive to said holding means for displaying said particular first word or words in the form originally introduced by said input means. | 5. The electronic translator of claim 1 further comprising: display means, responsive to said input means, for displaying said first word or words, said display means being further responsive to said access means for subsequently displaying said retrieved equivalent second word or words, said display means being further responsive to said holding means for displaying said particular first word or words in the form originally introduced by said input means. 6. The electronic translator of claim 5 wherein said first word or words comprises a multiword phrase including at least one particular said first word having no equivalent second word stored in said second memory means and at least one translatable first word having an equivalent second word or words stored in said second memory means; said access means retrieving the second word or words from said second memory means equivalent in meaning to said at least one translatable first word while retaining a space for said particular first word; said display means displaying said second word or words from said second memory means and said particular first word from said holding means with said particular first word occupying said space retained by said access means to form a translated multiword phrase including said particular first word not translated by said translator. | 0.5 |
9,460,712 | 13 | 23 | 13. A system, comprising: one or more processors; and memory storing instructions that when executed by at least some of the one or more processors effectuates operations comprising: receiving, over the Internet, from a user terminal, a query spoken by a user, wherein the query spoken by the user includes a speech utterance representing a category of businesses and a speech utterance representing a geographic location; recognizing the geographic location with a speech recognition engine based on the speech utterance representing the geographic location; recognizing the category of businesses with the speech recognition engine based on the speech utterance representing the category of businesses; searching a business listing for businesses within both the recognized category of businesses and the recognized geographic location to select businesses responsive to the query spoken by the user; and sending to the user terminal information related to at least some of the responsive businesses. | 13. A system, comprising: one or more processors; and memory storing instructions that when executed by at least some of the one or more processors effectuates operations comprising: receiving, over the Internet, from a user terminal, a query spoken by a user, wherein the query spoken by the user includes a speech utterance representing a category of businesses and a speech utterance representing a geographic location; recognizing the geographic location with a speech recognition engine based on the speech utterance representing the geographic location; recognizing the category of businesses with the speech recognition engine based on the speech utterance representing the category of businesses; searching a business listing for businesses within both the recognized category of businesses and the recognized geographic location to select businesses responsive to the query spoken by the user; and sending to the user terminal information related to at least some of the responsive businesses. 23. The system of claim 13 , wherein the user terminal is a computer. | 0.942975 |
7,657,507 | 16 | 18 | 16. One or more computer-readable media having computer-readable instructions thereon which, when executed by one or more processors, cause the one or more processors to perform the following: extracting from a digital corpus an object, wherein extracting the object comprises: extracting from the digital corpus a plurality of pseudo-URLs; constructing a plurality of feature strings from the plurality of pseudo-URLs, at least one feature string for each pseudo-URL; applying a hash function to the plurality of feature strings to calculate a hash value of each feature string; placing the plurality of pseudo-URLs into slots of a hash-table according to the hash value of the at least one feature string of each pseudo-URL, such that each slot of the hash table contains pseudo-URLs each having at least one feature string with the same hash value; calculating similarity of the pseudo-URLs in each slot of the hash-table using a similarity function; selecting pseudo-URLs having similarity that satisfies a specified threshold to be members of a subgroup of pseudo-URLs; and associating the subgroup of the pseudo-URLs with the object; extracting from the digital corpus a pseudo-anchor text associated with the object; and making the pseudo-anchor text available for searching. | 16. One or more computer-readable media having computer-readable instructions thereon which, when executed by one or more processors, cause the one or more processors to perform the following: extracting from a digital corpus an object, wherein extracting the object comprises: extracting from the digital corpus a plurality of pseudo-URLs; constructing a plurality of feature strings from the plurality of pseudo-URLs, at least one feature string for each pseudo-URL; applying a hash function to the plurality of feature strings to calculate a hash value of each feature string; placing the plurality of pseudo-URLs into slots of a hash-table according to the hash value of the at least one feature string of each pseudo-URL, such that each slot of the hash table contains pseudo-URLs each having at least one feature string with the same hash value; calculating similarity of the pseudo-URLs in each slot of the hash-table using a similarity function; selecting pseudo-URLs having similarity that satisfies a specified threshold to be members of a subgroup of pseudo-URLs; and associating the subgroup of the pseudo-URLs with the object; extracting from the digital corpus a pseudo-anchor text associated with the object; and making the pseudo-anchor text available for searching. 18. The computer-readable media as recited in claim 16 , wherein the extracting a pseudo-anchor text associated with the object comprises: selecting a set of candidate anchor blocks each comprising a digital content including a reference point associated with the object; and extracting the pseudo-anchor text from the set of candidate anchor blocks. | 0.5 |
10,037,456 | 1 | 14 | 1. An attribute-assigning image-processing system comprising: a distributed computing system that includes multiple computer systems, each having one or more processors, one or more memories, one or more mass-storage devices, and one or more network interconnections; an input-image memory for receiving an input image provided by one or more of the multiple computer systems; a set of first-level feature detectors, provided by one or more of the multiple computer systems, that detect face-containing subimages within the input image and, for each face-containing subimage, generate a set of normalized regions; a set of second-level feature detectors that generate a set of feature vectors from each normalized region; a set of attribute classifiers that each outputs an attribute value and an associated probability that reflects a likelihood that the attribute value returned in response to input of one or more feature vectors reflects the attribute value that would be assigned to the image of the facial feature contained in the normalized region from which the set of second-level feature detectors produced the input feature vectors; and a controller that submits the input image to the first-level feature detectors in order to generate sets of normalized regions for each face-containing subimage in the input image; submits each set of normalized regions to generate a set of feature vectors; and submits subsets of feature vectors to each attribute classifier in an ordered list of attribute classifiers to assign attributes to each face-containing subimage. | 1. An attribute-assigning image-processing system comprising: a distributed computing system that includes multiple computer systems, each having one or more processors, one or more memories, one or more mass-storage devices, and one or more network interconnections; an input-image memory for receiving an input image provided by one or more of the multiple computer systems; a set of first-level feature detectors, provided by one or more of the multiple computer systems, that detect face-containing subimages within the input image and, for each face-containing subimage, generate a set of normalized regions; a set of second-level feature detectors that generate a set of feature vectors from each normalized region; a set of attribute classifiers that each outputs an attribute value and an associated probability that reflects a likelihood that the attribute value returned in response to input of one or more feature vectors reflects the attribute value that would be assigned to the image of the facial feature contained in the normalized region from which the set of second-level feature detectors produced the input feature vectors; and a controller that submits the input image to the first-level feature detectors in order to generate sets of normalized regions for each face-containing subimage in the input image; submits each set of normalized regions to generate a set of feature vectors; and submits subsets of feature vectors to each attribute classifier in an ordered list of attribute classifiers to assign attributes to each face-containing subimage. 14. The attribute-assigning image-processing system of claim 1 wherein each attribute classifier is assigned a reliability. | 0.894692 |
8,732,112 | 4 | 6 | 4. The method of claim 1 , comprising: receiving a vehicle malfunction record comprising one or more fleet vehicle system fault codes, one or more fleet vehicle component fault codes, and one or more fleet vehicle failure modes; categorizing the vehicle malfunction record based on the probability inferences, wherein determining, using the graphical model, one or more probability inferences comprises determining marginal probabilities based on the fleet vehicle system fault codes, the fleet vehicle component fault codes, and the fleet vehicle failure modes; and outputting the categorized vehicle malfunction record to a user. | 4. The method of claim 1 , comprising: receiving a vehicle malfunction record comprising one or more fleet vehicle system fault codes, one or more fleet vehicle component fault codes, and one or more fleet vehicle failure modes; categorizing the vehicle malfunction record based on the probability inferences, wherein determining, using the graphical model, one or more probability inferences comprises determining marginal probabilities based on the fleet vehicle system fault codes, the fleet vehicle component fault codes, and the fleet vehicle failure modes; and outputting the categorized vehicle malfunction record to a user. 6. The method of claim 4 , wherein outputting the one or more categorized vehicle trouble records to a user comprises outputting, if the categorized vehicle malfunction record comprises a probability below a threshold, the one or more vehicle trouble records to a diagnostic engineer. | 0.5 |
9,779,293 | 9 | 12 | 9. An apparatus comprising: a memory; and at least one processor coupled to the memory, the at least one processor configured to: capture at least one screenshot of a display screen including an initial screenshot; remove text from the initial screenshot to generate a base image; identify a background of the initial screenshot as a closed region; and for each of the at least one screenshot: store a time of capturing the screenshot in the memory; identify text, text color, and text location in the screenshot; identify each closed region in the screenshot that is different from the background of the initial screenshot, and a region color and region location for each identified closed region in the screenshot; store, in the memory, the region color and the region location for each identified closed region; and store, in the memory, the text color and the text location of the identified text. | 9. An apparatus comprising: a memory; and at least one processor coupled to the memory, the at least one processor configured to: capture at least one screenshot of a display screen including an initial screenshot; remove text from the initial screenshot to generate a base image; identify a background of the initial screenshot as a closed region; and for each of the at least one screenshot: store a time of capturing the screenshot in the memory; identify text, text color, and text location in the screenshot; identify each closed region in the screenshot that is different from the background of the initial screenshot, and a region color and region location for each identified closed region in the screenshot; store, in the memory, the region color and the region location for each identified closed region; and store, in the memory, the text color and the text location of the identified text. 12. The apparatus of claim 9 , wherein the at least one processor is further configured to capture the at least one screenshot of the display screen by: periodically capturing an additional screenshot according to a specified interval of time. | 0.826429 |
9,116,643 | 6 | 10 | 6. A method for managing a first version and a second version of an electronic document (ED), comprising: obtaining a request to print the first version of the ED; generating, by a computer processor and in response to obtaining the request, a print job based on the first version of the ED and identifying a storage location of the second version of the ED, of which at least a part of contents is different from the first version of the ED; and sending the print job to a printer to generate a hardcopy document (HD) based on the first version of the ED, wherein the HD comprises a graphical element specifying the storage location of the second version of the ED, and wherein the second version of the ED is accessible to a reader of the HD by visiting the storage location specified in the graphical element of the HD. | 6. A method for managing a first version and a second version of an electronic document (ED), comprising: obtaining a request to print the first version of the ED; generating, by a computer processor and in response to obtaining the request, a print job based on the first version of the ED and identifying a storage location of the second version of the ED, of which at least a part of contents is different from the first version of the ED; and sending the print job to a printer to generate a hardcopy document (HD) based on the first version of the ED, wherein the HD comprises a graphical element specifying the storage location of the second version of the ED, and wherein the second version of the ED is accessible to a reader of the HD by visiting the storage location specified in the graphical element of the HD. 10. The method of claim 6 , wherein the first version of the ED and the second version of the ED are written in different languages. | 0.869307 |
9,734,454 | 4 | 5 | 4. The method of claim 1 wherein fact checking the target information based on the format of the target information includes analyzing and learning from previous fact checks of an entity providing the target information. | 4. The method of claim 1 wherein fact checking the target information based on the format of the target information includes analyzing and learning from previous fact checks of an entity providing the target information. 5. The method of claim 4 wherein if, based on learning, the entity responds with the second question when attempting to avoid answering the first question, then determining the status of the target information as non-responsive. | 0.5 |
8,467,713 | 1 | 4 | 1. A method for mentally investing a reader in a story comprising: providing the reader with a hybrid book comprising the story, the hybrid book including: pages of text embodying a text portion of the story, a computer readable medium with stored spoken presentation data corresponding to an audible portion of the story, the audible portion being wholly distinct from the text portion of the story, and an audio device configured to make audible the spoken presentation data; providing the reader with a summary document which summarizes aspects of the story, the summary document including at least a partial list of people, animals, or creatures discussed in the story; and making audible the stored spoken presentation date, with the audio device after the reader has received the summary document and prior to the reader reading the pages of text embodying the text portion of the story, wherein the audible portion represents a beginning portion of the story and corresponds to a given number of pages of text omitted from the text portion of the story. | 1. A method for mentally investing a reader in a story comprising: providing the reader with a hybrid book comprising the story, the hybrid book including: pages of text embodying a text portion of the story, a computer readable medium with stored spoken presentation data corresponding to an audible portion of the story, the audible portion being wholly distinct from the text portion of the story, and an audio device configured to make audible the spoken presentation data; providing the reader with a summary document which summarizes aspects of the story, the summary document including at least a partial list of people, animals, or creatures discussed in the story; and making audible the stored spoken presentation date, with the audio device after the reader has received the summary document and prior to the reader reading the pages of text embodying the text portion of the story, wherein the audible portion represents a beginning portion of the story and corresponds to a given number of pages of text omitted from the text portion of the story. 4. The method of claim 1 , wherein the audible portion corresponds to a given number of pages of text omitted from the text portion requiring not more than 45 minutes to provide the audible portion. | 0.846986 |
9,852,379 | 1 | 12 | 1. A computer-implemented method of generating a model for predicting whether a content word is being used figuratively, comprising: accessing, with a processing system, a plurality of training texts, each training text including content words, and each content word having a corresponding annotation indicating whether the content word is being used figuratively in the associated training text; accessing, with the processing system, a plurality of topic models generated from a first corpus; for each content word in the plurality of training texts, assigning, with the processing system, a plurality of topic-model feature scores to the content word, each topic-model feature score being associated with one of the plurality of topic models and determined based on: determining, with the processing system, a first probability of the content word being used in a topic represented by the topic model; determining, with the processing system, a second probability of the content word being used in a second corpus; and computing, with the processing system, the topic-model feature score using the first probability and the second probability; and generating, with the processing system, a prediction model for predicting whether a content word is being used figuratively, the generating being based on at least one of the plurality of topic-model feature scores and the annotations. | 1. A computer-implemented method of generating a model for predicting whether a content word is being used figuratively, comprising: accessing, with a processing system, a plurality of training texts, each training text including content words, and each content word having a corresponding annotation indicating whether the content word is being used figuratively in the associated training text; accessing, with the processing system, a plurality of topic models generated from a first corpus; for each content word in the plurality of training texts, assigning, with the processing system, a plurality of topic-model feature scores to the content word, each topic-model feature score being associated with one of the plurality of topic models and determined based on: determining, with the processing system, a first probability of the content word being used in a topic represented by the topic model; determining, with the processing system, a second probability of the content word being used in a second corpus; and computing, with the processing system, the topic-model feature score using the first probability and the second probability; and generating, with the processing system, a prediction model for predicting whether a content word is being used figuratively, the generating being based on at least one of the plurality of topic-model feature scores and the annotations. 12. The method of claim 1 , comprising: for each sentence in a corpus: comparing, with the processing system, the sentence to one or more predetermined patterns for identifying similes; if the sentence matches any of the one or more predetermined patterns, identifying, with the processing system, at least one simile source word in the sentence and at least one contextual content word in the sentence other than the simile source word; and creating, in a data source, a record representing an observance of the at least one simile source word and the at least one contextual content word occurring in the same sentence; for each content word in the plurality of training texts: identifying, with the processing system, a sentence in which the content word occurs; identifying, with the processing system, at least one other content word in the sentence; determining, with the processing system, at least one frequency value based on using the data source, each frequency value representing a frequency of the content word and one of the at least one other content word occurring in a same sentence where the content word is being used as a simile source word; and determining, with the processing system, a non-literal-context feature score based on the at least one frequency value; wherein the generating of the prediction model is further based on the non-literal-context feature scores. | 0.58047 |
8,160,879 | 1 | 3 | 1. A computer-implemented method of enhancing speech recognition accuracy, the method comprising: dividing a system dialog turn into segments based on timing of probable user responses; generating a weighted grammar for each segment; exclusively activating the weighted grammar generated for a respective segment of the dialog turn during the respective segment of the dialog turn; and recognizing user speech received during the respective segment using the activated weighted grammar generated for the respective segment. | 1. A computer-implemented method of enhancing speech recognition accuracy, the method comprising: dividing a system dialog turn into segments based on timing of probable user responses; generating a weighted grammar for each segment; exclusively activating the weighted grammar generated for a respective segment of the dialog turn during the respective segment of the dialog turn; and recognizing user speech received during the respective segment using the activated weighted grammar generated for the respective segment. 3. The computer-implemented method of claim 1 , wherein weighted grammars are based on a user profile. | 0.844985 |
9,971,838 | 2 | 3 | 2. The processor-implemented method of claim 1 , further comprising: receiving, by one or more processors, ratings of levels of how disturbing the subjectively disturbing synthetic context-based object is for a particular demographic group; applying, by one or more processors, the ratings to define a subjective disturbance weight for the subjectively disturbing synthetic context-based object; and populating, by one or more processors, the n-tuple with the subjective disturbance weight. | 2. The processor-implemented method of claim 1 , further comprising: receiving, by one or more processors, ratings of levels of how disturbing the subjectively disturbing synthetic context-based object is for a particular demographic group; applying, by one or more processors, the ratings to define a subjective disturbance weight for the subjectively disturbing synthetic context-based object; and populating, by one or more processors, the n-tuple with the subjective disturbance weight. 3. The processor-implemented method of claim 2 , further comprising: selectively pulling, by one or more processors, the parsed subjectively disturbing content into the context-based data gravity well based on the subjective disturbance weight in the n-tuple. | 0.5 |
8,781,836 | 1 | 4 | 1. A method performed by a portable computing device for providing consistent speech, wherein the portable computing device includes a processor, memory and an audio processing component coupled with an audio input device, comprising: receiving human speech at the audio input device, the human speech including one or more inconsistent components; converting the human speech into consistent speech, including: converting the human speech to text; and converting the text directly to consistent speech based at least in part on a text to speech protocol that renders at least one of the one or more inconsistent components consistent, wherein the one or more inconsistent components include varying speech patterns or speech artifacts; and passing the consistent speech to an output device. | 1. A method performed by a portable computing device for providing consistent speech, wherein the portable computing device includes a processor, memory and an audio processing component coupled with an audio input device, comprising: receiving human speech at the audio input device, the human speech including one or more inconsistent components; converting the human speech into consistent speech, including: converting the human speech to text; and converting the text directly to consistent speech based at least in part on a text to speech protocol that renders at least one of the one or more inconsistent components consistent, wherein the one or more inconsistent components include varying speech patterns or speech artifacts; and passing the consistent speech to an output device. 4. The method as recited in claim 1 , wherein the output device is incorporated into a hearing aid, and wherein the portable computing device is in wireless communication with the hearing aid. | 0.5 |
9,721,559 | 1 | 7 | 1. A method of augmenting training data comprising: receiving, by a computer system, a training set of sampled audio data including speech of a plurality of source speakers; for each of the plurality of source speakers; converting, by the computer system, a feature sequence of a source speaker, of the plurality of source speakers, determined from a plurality of utterances of scripted speech within the training set, to a feature sequence of a respective target speaker under the same scripted speech, wherein the feature sequence of the respective target speaker is added to the training set; training, by the computer system, a speaker-dependent acoustic model for the respective target speaker for corresponding speaker-specific acoustic characteristics; and estimating, by the computer system, a mapping function between the feature sequence of the source speaker and the speaker-dependent acoustic model of the respective target speaker; and for each of the plurality of source speakers: mapping, by the computer system, each of the utterances from each of the plurality of source speakers in the training set using the mapping function to a plurality of other source speakers of the plurality of source speakers, wherein the mapping is added to the training set to generate augmented training data configured to train an automatic system recognition computer system. | 1. A method of augmenting training data comprising: receiving, by a computer system, a training set of sampled audio data including speech of a plurality of source speakers; for each of the plurality of source speakers; converting, by the computer system, a feature sequence of a source speaker, of the plurality of source speakers, determined from a plurality of utterances of scripted speech within the training set, to a feature sequence of a respective target speaker under the same scripted speech, wherein the feature sequence of the respective target speaker is added to the training set; training, by the computer system, a speaker-dependent acoustic model for the respective target speaker for corresponding speaker-specific acoustic characteristics; and estimating, by the computer system, a mapping function between the feature sequence of the source speaker and the speaker-dependent acoustic model of the respective target speaker; and for each of the plurality of source speakers: mapping, by the computer system, each of the utterances from each of the plurality of source speakers in the training set using the mapping function to a plurality of other source speakers of the plurality of source speakers, wherein the mapping is added to the training set to generate augmented training data configured to train an automatic system recognition computer system. 7. The method of claim 1 , wherein the respective target speaker is a new speaker created by perturbing a vocal tract length of the source speaker. | 0.685897 |
9,922,645 | 8 | 13 | 8. A system comprising: one or more computers; and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving, by a computing device that includes (i) a text-to-speech engine, (ii) an automated speech recognizer, and (iii) a barge-in detection model that is trained to output an indication of whether given audio data comprises synthesized speech, an audio signal corresponding to a user's utterance that is spoken while the computing device is outputting synthesized speech; processing, using the barge-in detection model that is trained to output an indication of whether given audio data comprises synthesized speech, particular audio data that comprises data corresponding to the audio signal and data corresponding to the synthesized speech; in response to receiving an indication from the barge-in model that the particular audio data comprises synthesized speech, suppressing a further output of the text-to-speech engine; and outputting, by the automated speech recognizer, a transcription of the user's utterance without the synthesized speech. | 8. A system comprising: one or more computers; and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving, by a computing device that includes (i) a text-to-speech engine, (ii) an automated speech recognizer, and (iii) a barge-in detection model that is trained to output an indication of whether given audio data comprises synthesized speech, an audio signal corresponding to a user's utterance that is spoken while the computing device is outputting synthesized speech; processing, using the barge-in detection model that is trained to output an indication of whether given audio data comprises synthesized speech, particular audio data that comprises data corresponding to the audio signal and data corresponding to the synthesized speech; in response to receiving an indication from the barge-in model that the particular audio data comprises synthesized speech, suppressing a further output of the text-to-speech engine; and outputting, by the automated speech recognizer, a transcription of the user's utterance without the synthesized speech. 13. The system of claim 8 , wherein the barge-in model is trained to identify whether given audio data comprises a user's voice. | 0.869388 |
9,928,296 | 11 | 13 | 11. The method of claim 10 , wherein the extracted document pattern shared by the first documents and the second documents includes the word “restaurant”. | 11. The method of claim 10 , wherein the extracted document pattern shared by the first documents and the second documents includes the word “restaurant”. 13. The method of claim 11 , wherein the word “restaurant” appears in both: first URLs of the first documents, and second URLs of the second documents. | 0.525157 |
7,580,836 | 8 | 12 | 8. A method comprising: (a) recognizing utterances of a speaker through converting the utterances into a recognized string, the utterances being received by a speaker input and converted to digital signals; (b) comparing the recognized string with a reference string to determine errors; (c) calculating estimated weights for sections of the utterances; (d) marking the errors in the utterances and providing corresponding estimated weights to form adaptation enrollment data; and (e) using the adaptation enrollment data to convert a speaker independent model to a speaker dependent model; wherein calculating the estimated weights comprises computing an average likelihood difference per frame and then computing a weight value by averaging the average likelihood difference over all error words. | 8. A method comprising: (a) recognizing utterances of a speaker through converting the utterances into a recognized string, the utterances being received by a speaker input and converted to digital signals; (b) comparing the recognized string with a reference string to determine errors; (c) calculating estimated weights for sections of the utterances; (d) marking the errors in the utterances and providing corresponding estimated weights to form adaptation enrollment data; and (e) using the adaptation enrollment data to convert a speaker independent model to a speaker dependent model; wherein calculating the estimated weights comprises computing an average likelihood difference per frame and then computing a weight value by averaging the average likelihood difference over all error words. 12. The method of claim 8 , wherein for a particular speaker, different misrecognized words have different weights. | 0.828358 |
8,086,441 | 17 | 18 | 17. The non-transitory machine-accessible medium of claim 15 , wherein simultaneously searching for the plurality of N-grams in the string of bytes further comprises: inputting the string of bytes into the finite state machine; and tracing operations of the finite state machine over the string of bytes. | 17. The non-transitory machine-accessible medium of claim 15 , wherein simultaneously searching for the plurality of N-grams in the string of bytes further comprises: inputting the string of bytes into the finite state machine; and tracing operations of the finite state machine over the string of bytes. 18. The non-transitory machine-accessible medium of claim 17 , wherein collecting the statistical information comprises: counting a number of occurrences of each of the N-grams using the output table while tracing the operations of the finite state machine. | 0.5 |
8,760,451 | 1 | 6 | 1. A computer-implemented method for rendering a text image on a display device, comprising: accessing, using a computer device, an image lookup table having a set of encoded data for two or more text characters associated with a text string of two or more text characters to be rendered in an image; reading, using a computer device, the set of encoded data from the image lookup table to determine first and second encoding data for one of the text characters associated with the text string, the first encoding data for the one of the text characters defining information about the position of the text character in the text string and the second encoding data for the one of the text characters defining a reference to a field of pixels in the image lookup table storing a set of pixel values defining the image to be rendered for the one of the text characters; determining, using the computer device, pixel values for the one of the text characters from the referenced field of pixels for the one of the text characters as stored within the image lookup table to analyze for rendering at a particular position within the image; determining, using the computer device, a center position of the one of the text characters stored in the field of pixels defined by the second encoding data for the one of the text characters based on the first encoding data for the one of the text characters; and rendering, using the computer device, pixels of the one of the text characters at a particular position within the image based on the location of the center position of the one of the text characters. | 1. A computer-implemented method for rendering a text image on a display device, comprising: accessing, using a computer device, an image lookup table having a set of encoded data for two or more text characters associated with a text string of two or more text characters to be rendered in an image; reading, using a computer device, the set of encoded data from the image lookup table to determine first and second encoding data for one of the text characters associated with the text string, the first encoding data for the one of the text characters defining information about the position of the text character in the text string and the second encoding data for the one of the text characters defining a reference to a field of pixels in the image lookup table storing a set of pixel values defining the image to be rendered for the one of the text characters; determining, using the computer device, pixel values for the one of the text characters from the referenced field of pixels for the one of the text characters as stored within the image lookup table to analyze for rendering at a particular position within the image; determining, using the computer device, a center position of the one of the text characters stored in the field of pixels defined by the second encoding data for the one of the text characters based on the first encoding data for the one of the text characters; and rendering, using the computer device, pixels of the one of the text characters at a particular position within the image based on the location of the center position of the one of the text characters. 6. The computer-implemented method of claim 1 , wherein reading the set of encoding data includes reading first encoding data stored in the image lookup table at a position adjacent to the second encoding data. | 0.876616 |
8,156,160 | 8 | 12 | 8. A system comprising: a processing unit; and a storage device storing a process to generate a poet personality by configuring the processing unit to: analyze a plurality of poems, each of the poems containing a plurality of words; generate a plurality of analysis models, each of said analysis models representing one of said plurality of poems; mark words in the poems with rhyme numbers with words that rhyme with each other having the same rhyme number; generate a data structure that specifies n-grams found in the text, with each analysis model having a set of weights, bigram, trigram and quadgram exponents; and store in computer storage the plurality of analysis models in a personality data structure including a set of parameters that control poetry generation using the personality data structure. | 8. A system comprising: a processing unit; and a storage device storing a process to generate a poet personality by configuring the processing unit to: analyze a plurality of poems, each of the poems containing a plurality of words; generate a plurality of analysis models, each of said analysis models representing one of said plurality of poems; mark words in the poems with rhyme numbers with words that rhyme with each other having the same rhyme number; generate a data structure that specifies n-grams found in the text, with each analysis model having a set of weights, bigram, trigram and quadgram exponents; and store in computer storage the plurality of analysis models in a personality data structure including a set of parameters that control poetry generation using the personality data structure. 12. The system of claim 8 wherein the processing unit is further configured to: define values of exponents for each of a bigram, trigram and quadgram structures for an author represented within the poet personality. | 0.509132 |
8,751,485 | 13 | 14 | 13. The search result providing system of claim 1 , wherein the related loan word providing unit is configured to add the at least one related loan word being found to a webpage that provides the search result corresponding to the query, and provide the webpage comprising the at least one related loan word to a terminal. | 13. The search result providing system of claim 1 , wherein the related loan word providing unit is configured to add the at least one related loan word being found to a webpage that provides the search result corresponding to the query, and provide the webpage comprising the at least one related loan word to a terminal. 14. The search result providing system of claim 13 , wherein the webpage comprising the at least one related loan word comprises at least one of a meaning showing operation and a pronunciation playing operation associated with the at least one related loan word. | 0.5 |
9,195,972 | 9 | 10 | 9. A computerized method for managing schedule items in an electronic calendar system, comprising: storing activity data associated with a user, wherein the activity data includes items scheduled for the user; receiving and storing profile data associated with the user, wherein the profile data includes metadata associated with potentially scheduled items; selecting and interfacing with a plurality of external knowledge databases to obtain knowledge data associated with the user; integrating the profile data, activity data, and knowledge data into a collection of integrated scheduling information; and outputting at least one of a suggested modification to at least one of the items scheduled for the user and to a potential conflict in the items scheduled for the user, based on the integrated scheduling information, wherein the activity data further includes global positioning system (GPS) data providing location and route information of the user, wherein the GPS data is utilized to predict future location and route information, wherein the location and route information predicted is utilized to suggest rescheduling an item to improve travel efficiency for the user. | 9. A computerized method for managing schedule items in an electronic calendar system, comprising: storing activity data associated with a user, wherein the activity data includes items scheduled for the user; receiving and storing profile data associated with the user, wherein the profile data includes metadata associated with potentially scheduled items; selecting and interfacing with a plurality of external knowledge databases to obtain knowledge data associated with the user; integrating the profile data, activity data, and knowledge data into a collection of integrated scheduling information; and outputting at least one of a suggested modification to at least one of the items scheduled for the user and to a potential conflict in the items scheduled for the user, based on the integrated scheduling information, wherein the activity data further includes global positioning system (GPS) data providing location and route information of the user, wherein the GPS data is utilized to predict future location and route information, wherein the location and route information predicted is utilized to suggest rescheduling an item to improve travel efficiency for the user. 10. The method of claim 9 , wherein the location and route information predicted is utilized to identify a conflict with a scheduled event based on a travel time required by the user. | 0.5 |
7,555,478 | 1 | 9 | 1. A computer-implemented method for providing search results, the method comprising: receiving a query term; determining contextual usages of the query term; and providing as the search results a search result entry for each of the determined contextual usages, the search result entry comprising at least one image content and at least one textual content. | 1. A computer-implemented method for providing search results, the method comprising: receiving a query term; determining contextual usages of the query term; and providing as the search results a search result entry for each of the determined contextual usages, the search result entry comprising at least one image content and at least one textual content. 9. The method of claim 1 , wherein the determined contextual usages are not associated with price. | 0.874359 |
9,177,557 | 10 | 11 | 10. The method of claim 8 , further comprising: identifying speaker independent feature data associated with a second speaker subspace associated with a second human speaker; and performing a second speech recognition operation restricted to a second one of the multiple human speaker subspaces. | 10. The method of claim 8 , further comprising: identifying speaker independent feature data associated with a second speaker subspace associated with a second human speaker; and performing a second speech recognition operation restricted to a second one of the multiple human speaker subspaces. 11. The method of providing speech recognition functionality based on a spoken utterance according to claim 10 , further comprising dividing the input speech energy by contribution from each of the at least two speakers. | 0.5 |
8,566,698 | 1 | 8 | 1. A computer-implemented method for processing forms on a personal computer while isolated from a computer server which has a document management system, comprising the steps of: storing Hypertext Markup Language (HTML) forms on the personal computer wherein the forms have data fields for data submission; receiving data as input to at least one first data field on a first HTML form while the personal computer is physically disconnected from the computer server; receiving a data submit command with respect to the first HTML form field input data; storing the first HTML form field input data on the personal computer in response to the received data submit command; wherein the HTML form field input data is stored in a first file on the personal computer in response to the received data submit command; after the personal computer is connected to a network having a data connection to the document management system, identifying second completed HTML forms including the first HTML form in response to a user command, second HTML form field input data associated with the second completed HTML forms being stored on the personal computer in second files; using a graphical user interface to indicate that the second HTML form field input data are to be sent from the second files on the personal computer to the document management system; converting each of the second files from a binary format into a character string; combining the character strings corresponding to the second files into a single data stream; sending the single data stream over the network to the document management system; wherein after being successfully sent to the document management system, the first HTML form and the stored first HTML form field input data are removed from the personal computer; wherein the document management system facilitates electronic capturing of documents and for storing and managing the documents. | 1. A computer-implemented method for processing forms on a personal computer while isolated from a computer server which has a document management system, comprising the steps of: storing Hypertext Markup Language (HTML) forms on the personal computer wherein the forms have data fields for data submission; receiving data as input to at least one first data field on a first HTML form while the personal computer is physically disconnected from the computer server; receiving a data submit command with respect to the first HTML form field input data; storing the first HTML form field input data on the personal computer in response to the received data submit command; wherein the HTML form field input data is stored in a first file on the personal computer in response to the received data submit command; after the personal computer is connected to a network having a data connection to the document management system, identifying second completed HTML forms including the first HTML form in response to a user command, second HTML form field input data associated with the second completed HTML forms being stored on the personal computer in second files; using a graphical user interface to indicate that the second HTML form field input data are to be sent from the second files on the personal computer to the document management system; converting each of the second files from a binary format into a character string; combining the character strings corresponding to the second files into a single data stream; sending the single data stream over the network to the document management system; wherein after being successfully sent to the document management system, the first HTML form and the stored first HTML form field input data are removed from the personal computer; wherein the document management system facilitates electronic capturing of documents and for storing and managing the documents. 8. The method of claim 1 wherein the storing of the HTML form field data on the personal computer includes collecting the entered data and saving the entered data to a self-contained file on the personal computer. | 0.580709 |
8,694,888 | 4 | 8 | 4. A non-transitory computer readable medium storing a computer program for presenting computer-generated characters, the computer program executable by at least one processor, the computer program comprising sets of instructions for: receiving a plurality of frames as input; assigning the plurality of frames to represent a character in a font, wherein each frame depicts a particular representation of said character; defining the font by storing the plurality of frames in at least one file associated with said font; and associating the character with a keystroke on a keyboard, so that when the font is selected and the keystroke is received, the plurality of frames is sequentially displayed on a display screen to provide an animated presentation of the character. | 4. A non-transitory computer readable medium storing a computer program for presenting computer-generated characters, the computer program executable by at least one processor, the computer program comprising sets of instructions for: receiving a plurality of frames as input; assigning the plurality of frames to represent a character in a font, wherein each frame depicts a particular representation of said character; defining the font by storing the plurality of frames in at least one file associated with said font; and associating the character with a keystroke on a keyboard, so that when the font is selected and the keystroke is received, the plurality of frames is sequentially displayed on a display screen to provide an animated presentation of the character. 8. The non-transitory computer readable medium of claim 4 , the computer program further comprising sets of instructions for: assigning more than one set of frames from the plurality of frames to represent more than one character in a font, wherein each frame in a set of frames depicts a particular representation of the particular character assigned to said set of frames; defining the font by storing the more than one set of frames in at least one file associated with said font; and associating the more than one character with more than one keystroke on the keyboard, so that when the font is selected and the more than one keystroke is received, the more than one set of frames is sequentially displayed on a display screen to provide an animated presentation of the more than one character. | 0.5 |
9,507,834 | 1 | 3 | 1. A method comprising: comparing, by a first server, a search query against a first collection of data storing an instance of co-occurrence of an entity in a second collection of data, wherein the search query is received from a client; identifying, by the first server, a type of entity corresponding to the entity based on the comparing; extracting, by the first server in real-time, the entity from the search query based on the identifying; selecting, by a second server in real-time, a fuzzy matching technique corresponding to the type of the entity, wherein at least one of the first server or the second server comprises a main memory hosting a database storing the first collection of data, wherein the database stores at least one of an entity-to-entity index, an entity-to-topic index, or an entity-to-fact index; searching, by the second server in real-time, the first collection of data via the fuzzy matching technique; identifying, by the second server, data associated with the search query based on the searching; generating, by the second server, a plurality of suggested search queries from the data based on the identifying; outputting, by the second server, the suggested search queries to the client such that the suggested search queries are sorted based on match proximity to the search query. | 1. A method comprising: comparing, by a first server, a search query against a first collection of data storing an instance of co-occurrence of an entity in a second collection of data, wherein the search query is received from a client; identifying, by the first server, a type of entity corresponding to the entity based on the comparing; extracting, by the first server in real-time, the entity from the search query based on the identifying; selecting, by a second server in real-time, a fuzzy matching technique corresponding to the type of the entity, wherein at least one of the first server or the second server comprises a main memory hosting a database storing the first collection of data, wherein the database stores at least one of an entity-to-entity index, an entity-to-topic index, or an entity-to-fact index; searching, by the second server in real-time, the first collection of data via the fuzzy matching technique; identifying, by the second server, data associated with the search query based on the searching; generating, by the second server, a plurality of suggested search queries from the data based on the identifying; outputting, by the second server, the suggested search queries to the client such that the suggested search queries are sorted based on match proximity to the search query. 3. The method of claim 1 , wherein the searching is after the search query is finalized. | 0.768421 |
9,361,325 | 16 | 18 | 16. A computer system comprising a computer processor coupled to a computer-readable memory unit, said memory unit comprising instructions that when executed by the computer processor implements a method comprising: receiving, by said computer processor, first data defining a scope and context of an information governance project, said computing system storing a meta-model profile associated with a meta-model, wherein said meta-model comprises an implementation mechanism, and wherein said implementation mechanism comprises a software tool that includes: a user interface configured to capture information requirements from stakeholders for customizing the meta-model profile by adding or removing concepts and relationships; a UML interpreter configured to instantiate or run a specification of said meta-model; a method interpreter configured to instantiate phases, activities, and tasks defined in a method to ensure that requirements are captured in a correct order following a format defined by said meta-model; a connector system configured to connect to and operate common technical systems used to manage databases; and a controller configured to mediate interactions between components; displaying, by said computer processor, high level subject areas comprising a scope/context for extending said the high level subject areas by adding or removing said concepts and relationships; importing, by said computer processor, existing logical information/data model formats used in existing enterprise systems; linking, by said computer processor, said existing logical information/data model formats to at least one concept of said concepts; integrating, by said computer processor, business and technical stakeholders across diverse models; mapping, by said computer processor, information management requirements with respect to an organization onto publicly available information models and technical systems; receiving, by said computer processor, information requirements data associated with said first data, said information requirements data conforming to semantics and syntax specified in said meta-model profile; classifying, by said computer processor, said information requirements data into concepts in accordance with said meta-model profile; generating, by said computer processor in accordance with a sequence for generating models, conceptual models from said concepts and in accordance with said meta-model profile, wherein said conceptual models define a structure and meaning of said first data, and wherein each concept model of said conceptual models comprises a subject area and a concept instance; generating, by said computer processor in accordance with said sequence, realization models from said concepts and in accordance with said meta-model profile, wherein said realization models are configured to deliver requirements using existing or planned technical software and hardware systems and associated interfaces, messages, and data storage structures, and wherein each realization model of said realization models comprises a role, a realization user interface, a realizing system interface, a canonical message model comprising message attributes, and a deployment system; associating, by said computer processor executing said realization user interface, realizing systems and associated interfaces with message standards and canonical message models of said data realization models; defining, by said computer processor, a stewardship model configured to govern said conceptual models and said realization models; assigning, by said computer processor, governance roles associated with said meta-model profile to informational assets within said conceptual models; generating, by said computer processor in accordance with said sequence, policy models in accordance with said governance roles, said informational assets, and said meta-model profile, wherein said policy models are configured to determine how specific instances of said conceptual models, said realization models, and said stewardship models are configured for specific customer needs; modifying, by said computer processor in response to user commands, elements of said policy models resulting in said modified policy models; and deploying, by said computer processor, a final architecture option associated with said modified policy models and said information assets. | 16. A computer system comprising a computer processor coupled to a computer-readable memory unit, said memory unit comprising instructions that when executed by the computer processor implements a method comprising: receiving, by said computer processor, first data defining a scope and context of an information governance project, said computing system storing a meta-model profile associated with a meta-model, wherein said meta-model comprises an implementation mechanism, and wherein said implementation mechanism comprises a software tool that includes: a user interface configured to capture information requirements from stakeholders for customizing the meta-model profile by adding or removing concepts and relationships; a UML interpreter configured to instantiate or run a specification of said meta-model; a method interpreter configured to instantiate phases, activities, and tasks defined in a method to ensure that requirements are captured in a correct order following a format defined by said meta-model; a connector system configured to connect to and operate common technical systems used to manage databases; and a controller configured to mediate interactions between components; displaying, by said computer processor, high level subject areas comprising a scope/context for extending said the high level subject areas by adding or removing said concepts and relationships; importing, by said computer processor, existing logical information/data model formats used in existing enterprise systems; linking, by said computer processor, said existing logical information/data model formats to at least one concept of said concepts; integrating, by said computer processor, business and technical stakeholders across diverse models; mapping, by said computer processor, information management requirements with respect to an organization onto publicly available information models and technical systems; receiving, by said computer processor, information requirements data associated with said first data, said information requirements data conforming to semantics and syntax specified in said meta-model profile; classifying, by said computer processor, said information requirements data into concepts in accordance with said meta-model profile; generating, by said computer processor in accordance with a sequence for generating models, conceptual models from said concepts and in accordance with said meta-model profile, wherein said conceptual models define a structure and meaning of said first data, and wherein each concept model of said conceptual models comprises a subject area and a concept instance; generating, by said computer processor in accordance with said sequence, realization models from said concepts and in accordance with said meta-model profile, wherein said realization models are configured to deliver requirements using existing or planned technical software and hardware systems and associated interfaces, messages, and data storage structures, and wherein each realization model of said realization models comprises a role, a realization user interface, a realizing system interface, a canonical message model comprising message attributes, and a deployment system; associating, by said computer processor executing said realization user interface, realizing systems and associated interfaces with message standards and canonical message models of said data realization models; defining, by said computer processor, a stewardship model configured to govern said conceptual models and said realization models; assigning, by said computer processor, governance roles associated with said meta-model profile to informational assets within said conceptual models; generating, by said computer processor in accordance with said sequence, policy models in accordance with said governance roles, said informational assets, and said meta-model profile, wherein said policy models are configured to determine how specific instances of said conceptual models, said realization models, and said stewardship models are configured for specific customer needs; modifying, by said computer processor in response to user commands, elements of said policy models resulting in said modified policy models; and deploying, by said computer processor, a final architecture option associated with said modified policy models and said information assets. 18. The computer system of claim 16 , wherein said defining said governance roles comprises: defining governance operations associated with said governance roles. | 0.732673 |
7,725,414 | 34 | 36 | 34. The computer implemented method of claim 33 , wherein (d) and (e), and (f) includes selecting electronic communications for labeling by the user targeted to build the electronic communications classifier within known performance bounds. | 34. The computer implemented method of claim 33 , wherein (d) and (e), and (f) includes selecting electronic communications for labeling by the user targeted to build the electronic communications classifier within known performance bounds. 36. The computer implemented method of claim 34 , wherein selecting electronic communications for labeling by the user selects electronic communications from groups of electronic communications including: a training set group of electronic communications selected by an active learning algorithm; a system-labeled set of electronic communications previously labeled by the system; a test set group of electronic communications for testing the accuracy of a current state of the classifier being developed; a faulty set of electronic communications suspected to be previously mis-labeled by the user; and a random set of electronic communications previously labeled by the user. | 0.5 |
9,141,867 | 8 | 16 | 8. A method comprising: under control of one or more processors configured with executable instructions, identifying a plurality of segments of content in a content item; determining that a segment boundary hint is associated with the content item; adjusting a segment of the plurality of segments based on the segment boundary hint; and presenting the content of the content item based at least in part on the plurality of segments. | 8. A method comprising: under control of one or more processors configured with executable instructions, identifying a plurality of segments of content in a content item; determining that a segment boundary hint is associated with the content item; adjusting a segment of the plurality of segments based on the segment boundary hint; and presenting the content of the content item based at least in part on the plurality of segments. 16. The method as recited in claim 8 , further comprising determining that the segment boundary hint comprises location information that identifies a location in the content of a particular segment of the plurality of segments. | 0.596085 |
4,847,766 | 2 | 3 | 2. The word processing system of claim 1, wherein pressing of said further key has caused said at least one alternative word and discrete definition to be displayed, and further including program controls for inserting and removing data from text presented in said display, and a fourth control responsive to the operator's pressing of yet another one of said depressible keys when said discrete definition corresponding to the at least one alternative word, displayed in response to pressing the further key, more nearly matches the desired definition, said fourth control being operable to replace said entered word in said text by the at least one alternative word. | 2. The word processing system of claim 1, wherein pressing of said further key has caused said at least one alternative word and discrete definition to be displayed, and further including program controls for inserting and removing data from text presented in said display, and a fourth control responsive to the operator's pressing of yet another one of said depressible keys when said discrete definition corresponding to the at least one alternative word, displayed in response to pressing the further key, more nearly matches the desired definition, said fourth control being operable to replace said entered word in said text by the at least one alternative word. 3. The word-processing system of claim 2, wherein said display includes a cursor movable relative to characters therein and has a capacity less in number than the totality of characters in said alternative word and the definition thereof, such that a terminal portion of said totality of characters is displayable only by exclusion of said alternative word from said display upon scrolling of said terminal portion relative to said cursor to bring about display of said terminal portion; and means operating said third control independently of said exclusion of the at least one alternative word from the display. | 0.5 |
7,761,287 | 16 | 18 | 16. A computer-readable medium encoded with computer-executable instructions for controlling a computing device to determine an opinion expressed in a sentence, by a method comprising: calculating a probability that the sentence contains a sequence of parts of speech that indicate an expression of the opinion; determining a probability that the sentence contains an opinion word that expresses the opinion given the sequence; determining a probability that a word of the sentence expresses the opinion given the opinion word; and calculating an overall probability that the sentence expresses the opinion by combining the probabilities. | 16. A computer-readable medium encoded with computer-executable instructions for controlling a computing device to determine an opinion expressed in a sentence, by a method comprising: calculating a probability that the sentence contains a sequence of parts of speech that indicate an expression of the opinion; determining a probability that the sentence contains an opinion word that expresses the opinion given the sequence; determining a probability that a word of the sentence expresses the opinion given the opinion word; and calculating an overall probability that the sentence expresses the opinion by combining the probabilities. 18. The computer-readable medium of claim 16 wherein the overall probability is represented by the following:
P ( o|s )= P ( o|ow )* P ( ow|sp )* P ( sp|s ) where P(x|y) represents the conditional probability of x given y, s represents the target words, o represents the opinion, ow represents an opinion word that may express an opinion, and sp represents a sequence of parts of speech that may express an opinion. | 0.5 |
9,189,462 | 1 | 9 | 1. A computer-implemented method for checking bidirectional text in text content, comprising: for each text part of a plurality of text parts in the text content: determining an intrinsic text direction of the text part, wherein the intrinsic text direction of the text part is determined to be: (i) left-to-right when the text part has a Unicode value indicating left-to-right, and (ii) right-to-left when the text part has a Unicode value indicating right-to-left; determining a declared text direction associated with the text part, wherein determining the declared text direction includes: applying a Unicode Bidirectional Algorithm to the text part, and noting a lowest explicit embedding level assigned by the Unicode Bidirectional Algorithm to any character of the text part and whether the Unicode Bidirectional Algorithm assigned a neutral override status to any of the characters with that lowest explicit embedding level, wherein the declared direction is left-to-right when that lowest explicit embedding level was even and had a neutral override status, and the declared direction is right-to-left when that lowest override status was odd and had a neutral override status, and otherwise declaring the declared as undefined; generating, with a computing device, a bidirectional text error based on a mismatch between the intrinsic text direction and the declared text direction; and reporting the bidirectional text error. | 1. A computer-implemented method for checking bidirectional text in text content, comprising: for each text part of a plurality of text parts in the text content: determining an intrinsic text direction of the text part, wherein the intrinsic text direction of the text part is determined to be: (i) left-to-right when the text part has a Unicode value indicating left-to-right, and (ii) right-to-left when the text part has a Unicode value indicating right-to-left; determining a declared text direction associated with the text part, wherein determining the declared text direction includes: applying a Unicode Bidirectional Algorithm to the text part, and noting a lowest explicit embedding level assigned by the Unicode Bidirectional Algorithm to any character of the text part and whether the Unicode Bidirectional Algorithm assigned a neutral override status to any of the characters with that lowest explicit embedding level, wherein the declared direction is left-to-right when that lowest explicit embedding level was even and had a neutral override status, and the declared direction is right-to-left when that lowest override status was odd and had a neutral override status, and otherwise declaring the declared as undefined; generating, with a computing device, a bidirectional text error based on a mismatch between the intrinsic text direction and the declared text direction; and reporting the bidirectional text error. 9. The method of claim 1 , further comprising dividing the text content into the plurality of the text parts such that in each text part, all the characters of the text part have the same explicit embedding level and override status. | 0.738202 |
8,131,714 | 1 | 6 | 1. A method for determining a linguistic preference between two or more phrases each of the two or more phrases including a plurality of words, comprising: submitting each of the two or more phrases simultaneously, and one or more linguistic categories, as a search string to at least one search engine, wherein a search performed by the at least one search engine is restricted to the one or more linguistic categories; receiving search results from each instance of the at least one search engine for each submitted search string; comparing total hit values of each search result with each other; and displaying, to a user, one of the phrases associated with a greatest total hit value as a preferred phrase, wherein the step of displaying comprises displaying, to the user, two or more of display phrases where a difference between the total hit values of the two or more phrases is less than a predefined percentage. | 1. A method for determining a linguistic preference between two or more phrases each of the two or more phrases including a plurality of words, comprising: submitting each of the two or more phrases simultaneously, and one or more linguistic categories, as a search string to at least one search engine, wherein a search performed by the at least one search engine is restricted to the one or more linguistic categories; receiving search results from each instance of the at least one search engine for each submitted search string; comparing total hit values of each search result with each other; and displaying, to a user, one of the phrases associated with a greatest total hit value as a preferred phrase, wherein the step of displaying comprises displaying, to the user, two or more of display phrases where a difference between the total hit values of the two or more phrases is less than a predefined percentage. 6. The method of claim 1 , the steps of submitting and receiving comprising communicating with the at least one search engine via a local area network. | 0.5 |
8,549,485 | 13 | 15 | 13. A system comprising: a memory; and a processing device coupled with the memory to execute an interpreted language software application to execute an expression in the interpreted language software application to evaluate an object of a first class that comprises one or more objects and an object of a second class, the second class being a concise expression class, execute the expression in the interpreted language software application to determine whether data associated with the object of the first class exists, return a result of the execution of the expression when data associated with the object of the first class exists within the software application, and return an instance of an object of the concise expression class when data associated with the object of the first class does not exist within the interpreted language software application, wherein when the concise expression class comprises an attribute of the object of the second class, an attribute of the returned instance of the object of the concise expression class corresponds to the attribute of the object of the second class. | 13. A system comprising: a memory; and a processing device coupled with the memory to execute an interpreted language software application to execute an expression in the interpreted language software application to evaluate an object of a first class that comprises one or more objects and an object of a second class, the second class being a concise expression class, execute the expression in the interpreted language software application to determine whether data associated with the object of the first class exists, return a result of the execution of the expression when data associated with the object of the first class exists within the software application, and return an instance of an object of the concise expression class when data associated with the object of the first class does not exist within the interpreted language software application, wherein when the concise expression class comprises an attribute of the object of the second class, an attribute of the returned instance of the object of the concise expression class corresponds to the attribute of the object of the second class. 15. The system of claim 13 , wherein when the processing device defines the concise expression class, the processing device to define a class-specific operator overloading for objects and methods within the concise expression class. | 0.522634 |
7,975,217 | 4 | 6 | 4. The method of claim 1 , wherein the metadata is an index identifying the displayable data. | 4. The method of claim 1 , wherein the metadata is an index identifying the displayable data. 6. The method of claim 4 , wherein the displayable data represents a translation of a source message suggested by a user, and further comprising: (d) receiving a request including the index; and (e) storing the translation according to the index. | 0.5 |
5,588,056 | 1 | 5 | 1. A method implemented on a computing device for forming a pronounceable security password using a plurality of first word segment portions, each having at least one character, and a plurality of second word segment portions, each having at least one character, with each of said plurality of first word segment portions (i) having an associated set of one or more said second word segment portions, each said second word segment portion within said associated set being different from others within said set and being combinable with said associated first word segment portion to form a pronounceable word segment and (ii) having a transition number corresponding to the number of said second word segment portions within the associated set of second word segment portions, comprising the steps of: selecting a first one of said plurality of first word segment portions, wherein selection of any one of said plurality of first word segment portions is of substantially equal probability; selecting a first one of said plurality of second word segment portions from said set of second word segment portions associated with said selected first word segment portion, wherein selection of any one of said second word segment portions within said associated set of second word segment portions is of substantially equal probability; combining said selected first word segment portion and said selected second word segment portion to form a first pronounceable word segment; and generating a pronounceable security password including said first pronounceable word segment only if consecutive characters of said first pronounceable word segment fail to correspond to those of said plurality of first word segment portions having a transition number less than a first threshold transition number. | 1. A method implemented on a computing device for forming a pronounceable security password using a plurality of first word segment portions, each having at least one character, and a plurality of second word segment portions, each having at least one character, with each of said plurality of first word segment portions (i) having an associated set of one or more said second word segment portions, each said second word segment portion within said associated set being different from others within said set and being combinable with said associated first word segment portion to form a pronounceable word segment and (ii) having a transition number corresponding to the number of said second word segment portions within the associated set of second word segment portions, comprising the steps of: selecting a first one of said plurality of first word segment portions, wherein selection of any one of said plurality of first word segment portions is of substantially equal probability; selecting a first one of said plurality of second word segment portions from said set of second word segment portions associated with said selected first word segment portion, wherein selection of any one of said second word segment portions within said associated set of second word segment portions is of substantially equal probability; combining said selected first word segment portion and said selected second word segment portion to form a first pronounceable word segment; and generating a pronounceable security password including said first pronounceable word segment only if consecutive characters of said first pronounceable word segment fail to correspond to those of said plurality of first word segment portions having a transition number less than a first threshold transition number. 5. A method for forming a pronounceable security password according to claim 1, further comprising the steps of: identifying a first word segment portion, within said plurality of first word segment portions, corresponding to one or more characters at an end portion of said first pronounceable word segment; selecting a second one of said plurality of second word segment portions from the set of second word segment portions associated with said corresponding first word segment portion, wherein selection of any one of said second word segment portions within said associated set of second word segment portions is of substantially equal probability; and combining said first pronounceable word segment with said second selected second word segment portion to form a part of the password; wherein said generated pronounceable security password includes said second selected second word segment only if consecutive characters of said formed password part fail to correspond to those of said plurality of first word segment portions having a transition number less than a second threshold transition number. | 0.549227 |
9,072,966 | 6 | 7 | 6. The method of claim 3 , further comprising: receiving input indicating a selection of a new clue or solution: replacing the primary information with new primary information describing a new primary subject related to the new clue or solution, wherein the new primary subject is designated as the primary subject; and replacing the secondary information with new secondary information describing a new secondary subject related to the new primary subject. | 6. The method of claim 3 , further comprising: receiving input indicating a selection of a new clue or solution: replacing the primary information with new primary information describing a new primary subject related to the new clue or solution, wherein the new primary subject is designated as the primary subject; and replacing the secondary information with new secondary information describing a new secondary subject related to the new primary subject. 7. The method of claim 6 , further comprising: replacing the second advertisement with a third advertisement relating to the new primary subject. | 0.5 |
8,965,922 | 6 | 7 | 6. The method of claim 5 , wherein the step of ranking the retrieved templates uses a multi-source scoring method to generate a ranking score S of each template. | 6. The method of claim 5 , wherein the step of ranking the retrieved templates uses a multi-source scoring method to generate a ranking score S of each template. 7. The method of claim 6 , wherein said score S of each template is computed as follows:
S =Æ’( S InputAnalysis , S TemplateMatching , S Context , S InteractionHistory ) where Æ’ is a function used to combine scores from four sources, S InputAnalysis is a result of matching the user input with entries in a dictionary, S TemplateMatching is a degree of match between the template queries and the content-recommendation template retrieved from the template database, S Context is proportional to a probability of the retrieved template given a context and a content model, and S InteractionHistory is a likelihood of the retrieved template given an interaction history. | 0.5 |
6,167,393 | 28 | 31 | 28. A method of querying arbitrarily structured records in a database, the method being implemented in a query engine, the query engine being executable by a processor operably connected to a memory device, and the method comprising the following steps executed in an order and repetition determined by the query engine: building, in a memory device, a query structure containing a selection criterion and information defining a query; retrieving indicia of records from the database in accordance with the query structure; evaluating each of the indicia according to the selection criterion to determine whether the indicia satisfy the selection criterion; and returning selected indicia that satisfy the selection criterion. | 28. A method of querying arbitrarily structured records in a database, the method being implemented in a query engine, the query engine being executable by a processor operably connected to a memory device, and the method comprising the following steps executed in an order and repetition determined by the query engine: building, in a memory device, a query structure containing a selection criterion and information defining a query; retrieving indicia of records from the database in accordance with the query structure; evaluating each of the indicia according to the selection criterion to determine whether the indicia satisfy the selection criterion; and returning selected indicia that satisfy the selection criterion. 31. The method of claim 28, wherein retrieving further comprises using an index to find the indicia of records. | 0.866906 |
9,396,726 | 1 | 2 | 1. A method performed on at least one processor of speech recognition wherein the speech recognition system replaces a generic language model with a user specific language model, comprising: receiving analog audio from a specific speaker; digitizing the analog audio to generate a corpus of material associated with the specific speaker; receiving a user specific language model that is formed from the corpus of material associated with the specific speaker; determining whether a continuous speech recognition system should use a generic language model or the user specific language model, wherein the step of determining comprises: estimating an asymptotic size of the user specific language model; calculating a number of user specific n-grams missing from the generic language model; calculating a number of user specific n-grams missing from the user specific language model; and determining whether the number of user specific n-grams missing from the user specific language model is less than a parameterization factor times the number of user specific n-grams missing from the generic language model; and if it is determined that the continuous speech recognition system should use the generic language model, repeating the receiving step and the determining step until it is determined that the continuous speech recognition system should use the user specific language model; and if it is determined that the continuous speech recognition system should use the user specific language model, causing the continuous speech recognition system to replace the generic language model with the user specific language model. | 1. A method performed on at least one processor of speech recognition wherein the speech recognition system replaces a generic language model with a user specific language model, comprising: receiving analog audio from a specific speaker; digitizing the analog audio to generate a corpus of material associated with the specific speaker; receiving a user specific language model that is formed from the corpus of material associated with the specific speaker; determining whether a continuous speech recognition system should use a generic language model or the user specific language model, wherein the step of determining comprises: estimating an asymptotic size of the user specific language model; calculating a number of user specific n-grams missing from the generic language model; calculating a number of user specific n-grams missing from the user specific language model; and determining whether the number of user specific n-grams missing from the user specific language model is less than a parameterization factor times the number of user specific n-grams missing from the generic language model; and if it is determined that the continuous speech recognition system should use the generic language model, repeating the receiving step and the determining step until it is determined that the continuous speech recognition system should use the user specific language model; and if it is determined that the continuous speech recognition system should use the user specific language model, causing the continuous speech recognition system to replace the generic language model with the user specific language model. 2. The method of claim 1 further comprising the step of updating the user specific language model. | 0.88361 |
9,966,089 | 1 | 7 | 1. A method of improving communication in a speech communication system between at least a first user and a second user, the speech communication system comprising at least one processor communicatively connected to non-transitory memory, the method comprising: a) transcribing, by the system, a recorded portion of a speech communication between the at least first and second user to form a transcribed portion, b) selecting and marking, by the system, at least one of the words of the transcribed portion which is considered to be a keyword of the speech communication, c) performing, by the system, a search for each keyword and produces at least one definition for each keyword that identifies a meaning of that keyword, d) calculating, by the system, a trustworthiness factor for each keyword, each trustworthiness factor indicating a calculated validity of a respective definition for a respective keyword; and e) displaying, by the system, the transcribed portion such that, in response to a pointer being positioned over at least one keyword of the displayed transcribed portion or selection of at least one displayed keyword, displaying the at least one definition of the keyword together with the calculated trustworthiness factor for each displayed definition is actuated. | 1. A method of improving communication in a speech communication system between at least a first user and a second user, the speech communication system comprising at least one processor communicatively connected to non-transitory memory, the method comprising: a) transcribing, by the system, a recorded portion of a speech communication between the at least first and second user to form a transcribed portion, b) selecting and marking, by the system, at least one of the words of the transcribed portion which is considered to be a keyword of the speech communication, c) performing, by the system, a search for each keyword and produces at least one definition for each keyword that identifies a meaning of that keyword, d) calculating, by the system, a trustworthiness factor for each keyword, each trustworthiness factor indicating a calculated validity of a respective definition for a respective keyword; and e) displaying, by the system, the transcribed portion such that, in response to a pointer being positioned over at least one keyword of the displayed transcribed portion or selection of at least one displayed keyword, displaying the at least one definition of the keyword together with the calculated trustworthiness factor for each displayed definition is actuated. 7. The method of claim 1 , wherein: step d) comprises the system performing a semantic search using taxonomies in order to generate a taxonomy correction factor for modifying the trustworthiness factor. | 0.92097 |
8,781,829 | 1 | 12 | 1. A computer-implemented method performed by at least one computer processor, the method comprising: (A) applying automatic speech recognition to an audio signal to produce a structured document representing contents of the audio signal; (B) determining whether the structured document includes an indication of compliance for each of a plurality of best practices to produce a conclusion; (C) inserting content into the structured document, based on the conclusion, to produce a modified structured document; (D) generating a first indication that a user should provide additional input of a first type to conform the structured document to a first best practice in the plurality of best practices; and (E) generating a second indication that the user should provide additional input of a type to conform the structured document to a second best practice in the plurality of best practices. | 1. A computer-implemented method performed by at least one computer processor, the method comprising: (A) applying automatic speech recognition to an audio signal to produce a structured document representing contents of the audio signal; (B) determining whether the structured document includes an indication of compliance for each of a plurality of best practices to produce a conclusion; (C) inserting content into the structured document, based on the conclusion, to produce a modified structured document; (D) generating a first indication that a user should provide additional input of a first type to conform the structured document to a first best practice in the plurality of best practices; and (E) generating a second indication that the user should provide additional input of a type to conform the structured document to a second best practice in the plurality of best practices. 12. The method of claim 1 , further comprising generating a prompt for additional input, based on the conclusion, the prompt rendered separately from a rendering of the structured document. | 0.7 |
7,865,354 | 19 | 22 | 19. A computer-implemented method of analyzing opinions in a text document, said method comprising: using a computer, establishing a predetermined set of regular expressions, each regular expression of said set of regular expressions corresponding to a specific parts-of-speech (POS) tag sequence; using said computer, inputting and parsing said text document to provide a plurality of POS tag sequences; using said computer, matching said predetermined set of regular expressions to said plurality of POS tag sequences from said text document to provide one or more extracted opinions; using said computer, lexically analyzing each word of said one or more extracted opinions to group said one or more extracted opinions into clusters of extracted opinions; and any of: using said computer, marking said one or more extracted opinions in said text document with classification tags, wherein said classification tags correspond to said clusters of extracted opinions; and using said computer, graphically displaying said clusters of extracted opinions, wherein said graphically displaying comprises any of: displaying relative proportions of said extracted opinions in said clusters of extracted opinions; and displaying said clusters of extracted opinions using any of a pie-chart and a bar-chart. | 19. A computer-implemented method of analyzing opinions in a text document, said method comprising: using a computer, establishing a predetermined set of regular expressions, each regular expression of said set of regular expressions corresponding to a specific parts-of-speech (POS) tag sequence; using said computer, inputting and parsing said text document to provide a plurality of POS tag sequences; using said computer, matching said predetermined set of regular expressions to said plurality of POS tag sequences from said text document to provide one or more extracted opinions; using said computer, lexically analyzing each word of said one or more extracted opinions to group said one or more extracted opinions into clusters of extracted opinions; and any of: using said computer, marking said one or more extracted opinions in said text document with classification tags, wherein said classification tags correspond to said clusters of extracted opinions; and using said computer, graphically displaying said clusters of extracted opinions, wherein said graphically displaying comprises any of: displaying relative proportions of said extracted opinions in said clusters of extracted opinions; and displaying said clusters of extracted opinions using any of a pie-chart and a bar-chart. 22. The method of claim 19 , further comprising organizing said clusters of extracted opinions into groups, wherein said one or more extracted opinions within each of said groups comprises a similar topic. | 0.581633 |
9,710,538 | 17 | 21 | 17. The processing device of claim 1 , wherein the at least one boolean operator includes at least one from among AND operator (AND co-occurrence frequency), and OR operator (OR co-occurrence frequency). | 17. The processing device of claim 1 , wherein the at least one boolean operator includes at least one from among AND operator (AND co-occurrence frequency), and OR operator (OR co-occurrence frequency). 21. The processing device of claim 17 , wherein: second candidate condition is satisfied if there is no category immediately below the category of interest and the OR co-occurrence frequencies acquired for the category of interest and the categories above the category of interest excluding the topmost one are higher than a frequency presenting the absence of co-occurrence with reference to a given comparison criterion, the distribution of the OR co-occurrence frequencies of the on-the-path categories is in conformity with the given distribution pattern associated with the second candidate condition. | 0.501645 |
8,260,619 | 3 | 5 | 3. The system of claim 1 , wherein said plurality of grammars are determined based on a website, and wherein the set of computer executable instructions are further operable to configure the computer to achieve the goal during the interaction with the user without requiring the interaction to follow the structure of the website. | 3. The system of claim 1 , wherein said plurality of grammars are determined based on a website, and wherein the set of computer executable instructions are further operable to configure the computer to achieve the goal during the interaction with the user without requiring the interaction to follow the structure of the website. 5. The system of claim 3 , wherein the plurality of grammars are determined based on records of use of a multimodal user interface, and wherein the goal is determined based on a flow derived based on the records of use of the multimodal user interface. | 0.5 |
8,793,201 | 4 | 12 | 4. A computer-implemented method, comprising: generating a set of seed rules comprising: one or more rules resulting from one or more previously performed machine learning operations, and one or more randomly or pseudo-randomly generated rules; performing one or more machine learning operations on the set of seed rules to generate a new set of rules for determining whether a given pair of information items have a specific relationship, wherein: said performing the one or more machine learning operations on the set of seed rules is performed in regard to a set of information items including one or more differences from another set of information items used in said generating the set of seed rules, or the specific relationship is different than another specific relationship for which the set of seed rules was generated; and applying the new set of rules to one or more pairs of information items to identify at least one pair of information items as having the specific relationship. | 4. A computer-implemented method, comprising: generating a set of seed rules comprising: one or more rules resulting from one or more previously performed machine learning operations, and one or more randomly or pseudo-randomly generated rules; performing one or more machine learning operations on the set of seed rules to generate a new set of rules for determining whether a given pair of information items have a specific relationship, wherein: said performing the one or more machine learning operations on the set of seed rules is performed in regard to a set of information items including one or more differences from another set of information items used in said generating the set of seed rules, or the specific relationship is different than another specific relationship for which the set of seed rules was generated; and applying the new set of rules to one or more pairs of information items to identify at least one pair of information items as having the specific relationship. 12. The computer-implemented method of claim 4 , wherein performing the one or more machine learning operations comprises generating based on training data a machine learning model for labeling item pairs as having or not having the specific relationship, wherein the method further comprises adding distinguishing item pair attributes to the training data to improve labeling accuracy of a new model generated based on the updated training data. | 0.780512 |
7,925,610 | 30 | 31 | 30. A system comprising: one or more computers programmed to perform operations comprising: receiving data identifying a knowledge item; retrieving, from a database, document-based information to be used in selecting a meaning for the knowledge item, where the document-based information comprises one or more documents related to the knowledge item; determining one or more document term meanings of the document-based information, the determining comprising, for each document in the document-based information; selecting one or more meanings for terms in the document from meanings associated with the terms in a computer-readable data collection that associates at least one meaning with each of the terms, and then determining one or more document term meanings of the document from the one or more meanings for the terms in the document; determining a plurality of knowledge item term meanings of the knowledge item by selecting a plurality of meanings from meanings associated with one or more terms of the knowledge item in the computer-readable data collection; determining a strength of relationship between each knowledge item meaning and each document term meaning of the document-based information, and determining a respective selection probability for each knowledge item term meaning from the strengths; designating knowledge item term meaning from the plurality of knowledge item meanings as a meaning of the knowledge item according to the respective selection probability associated with each knowledge item term meaning; and storing the designated meaning of the knowledge item. | 30. A system comprising: one or more computers programmed to perform operations comprising: receiving data identifying a knowledge item; retrieving, from a database, document-based information to be used in selecting a meaning for the knowledge item, where the document-based information comprises one or more documents related to the knowledge item; determining one or more document term meanings of the document-based information, the determining comprising, for each document in the document-based information; selecting one or more meanings for terms in the document from meanings associated with the terms in a computer-readable data collection that associates at least one meaning with each of the terms, and then determining one or more document term meanings of the document from the one or more meanings for the terms in the document; determining a plurality of knowledge item term meanings of the knowledge item by selecting a plurality of meanings from meanings associated with one or more terms of the knowledge item in the computer-readable data collection; determining a strength of relationship between each knowledge item meaning and each document term meaning of the document-based information, and determining a respective selection probability for each knowledge item term meaning from the strengths; designating knowledge item term meaning from the plurality of knowledge item meanings as a meaning of the knowledge item according to the respective selection probability associated with each knowledge item term meaning; and storing the designated meaning of the knowledge item. 31. The system of claim 30 , wherein the knowledge item comprises a keyword received as input to a search engine. | 0.906146 |
9,626,745 | 1 | 5 | 1. A multi-band image fusion method, comprising: receiving three or more images, wherein each image includes a plurality of channel types, and wherein each image has at least one channel of each of the plurality of channel types; selecting one of the three or more images to serve as a reference image, wherein each of the other images not selected as the reference image is selected to serve as an input image; registering each of the input images to the reference image; applying multi-band noise reduction to the reference image to generate a filtered pyramidal representation of each of the reference image's channels, wherein the pyramidal representation of each of the reference image's channels comprises a plurality of levels; applying multi-band noise reduction to each input image to generate a filtered pyramidal representation of each channel of each input image, wherein the pyramidal representation of each channel of each input image comprises a plurality of levels; fusing, on a level-by-level basis, each of the reference image's filtered pyramidal representations with a corresponding filtered pyramidal representation of each input image to generate a fused image channel for each channel of the reference image; and saving the fused image channels to a memory. | 1. A multi-band image fusion method, comprising: receiving three or more images, wherein each image includes a plurality of channel types, and wherein each image has at least one channel of each of the plurality of channel types; selecting one of the three or more images to serve as a reference image, wherein each of the other images not selected as the reference image is selected to serve as an input image; registering each of the input images to the reference image; applying multi-band noise reduction to the reference image to generate a filtered pyramidal representation of each of the reference image's channels, wherein the pyramidal representation of each of the reference image's channels comprises a plurality of levels; applying multi-band noise reduction to each input image to generate a filtered pyramidal representation of each channel of each input image, wherein the pyramidal representation of each channel of each input image comprises a plurality of levels; fusing, on a level-by-level basis, each of the reference image's filtered pyramidal representations with a corresponding filtered pyramidal representation of each input image to generate a fused image channel for each channel of the reference image; and saving the fused image channels to a memory. 5. The method of claim 1 , wherein fusing is based on a similarity metric, and wherein the similarity metric is based on a gradient between a reference image pixel at a first level of a first one of the reference image's pyramidal representations and a corresponding input image pixel at each level of a first input image's corresponding pyramidal representation. | 0.528571 |
8,954,458 | 3 | 8 | 3. The computer-implemented method of claim 1 , wherein creating the first set of tokens comprises: processing the incoming query by changing an upper-case letter into a lower-case letter and removing a space; and creating the first set of tokens from the processed incoming query, each token having a predetermined length and overlapping a previous token by including one or more characters from the previous token. | 3. The computer-implemented method of claim 1 , wherein creating the first set of tokens comprises: processing the incoming query by changing an upper-case letter into a lower-case letter and removing a space; and creating the first set of tokens from the processed incoming query, each token having a predetermined length and overlapping a previous token by including one or more characters from the previous token. 8. The computer-implemented method of claim 3 , wherein the predetermined length of each token is three. | 0.623188 |
7,580,924 | 16 | 17 | 16. The server system of claim 15 wherein the fabrication set and the family set have a many-to-many relationship. | 16. The server system of claim 15 wherein the fabrication set and the family set have a many-to-many relationship. 17. The server system of claim 16 wherein a family of the family set and corresponding device set form a parent-child relationship, respectively and a device of the device set and corresponding lot set form another parent-child relationship, respectively. | 0.5 |
9,519,688 | 8 | 11 | 8. A computer program product stored in a computer readable storage medium, comprising computer instructions that, when executed by an information handling system, causes the information handling system to utilize unstructured corpora by performing actions comprising: receiving, from a first user, a question at a question answering (QA) system; identifying a set of one or more second users that are subjects of the question; generating a private corpora associated with the first user, wherein the private corpora is generated from a first set of one or more private data stores associated with a first user and a second set of one or more private data stores associated with the set of one or more second users, wherein access to the second set of private data stores is provided to the first user by the set of second users; retrieving responsive data to the question from the private corpora, wherein the responsive data is ranked based on a relevance to the question; identifying one or more likely answers based on the ranked responsive data; and providing the one or more likely answers to the first user. | 8. A computer program product stored in a computer readable storage medium, comprising computer instructions that, when executed by an information handling system, causes the information handling system to utilize unstructured corpora by performing actions comprising: receiving, from a first user, a question at a question answering (QA) system; identifying a set of one or more second users that are subjects of the question; generating a private corpora associated with the first user, wherein the private corpora is generated from a first set of one or more private data stores associated with a first user and a second set of one or more private data stores associated with the set of one or more second users, wherein access to the second set of private data stores is provided to the first user by the set of second users; retrieving responsive data to the question from the private corpora, wherein the responsive data is ranked based on a relevance to the question; identifying one or more likely answers based on the ranked responsive data; and providing the one or more likely answers to the first user. 11. The computer program product of claim 8 wherein the actions further comprise: creating a security profile associated with a selected one of the set of the second users, wherein the security profile restricts access by the first user to one or more types of data from the selected second user's private data sources; and inhibiting inclusion in the responsive data of the one or more types of data from the selected second user's private data sources. | 0.5 |
8,015,144 | 9 | 10 | 9. In a computing environment, a system comprising: a segmentation mechanism configured to separate GPS data into a plurality of segments; a feature extraction mechanism configured to extract features for respective segments; and an inference model configured to use at least some of the features extracted for respective segments to infer a transportation mode for respective segments. | 9. In a computing environment, a system comprising: a segmentation mechanism configured to separate GPS data into a plurality of segments; a feature extraction mechanism configured to extract features for respective segments; and an inference model configured to use at least some of the features extracted for respective segments to infer a transportation mode for respective segments. 10. The system of claim 9 , the inference model comprising a conditional random field framework. | 0.84858 |
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