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8,510,350 | 7 | 8 | 7. The method of claim 6 , further including presenting to the user a list of possible file matches during said file find operation. | 7. The method of claim 6 , further including presenting to the user a list of possible file matches during said file find operation. 8. The method of claim 7 , further including decreasing possible numbers of matches in the presented list of possible file matches as the user increases search terms during the file find operation. | 0.5 |
9,424,278 | 9 | 11 | 9. A method of identifying customers of productive assets, comprising: machine-searching a number of Uniform Commercial Code financing statements filed for finance transactions wherein at least some of the financing statements include an image within a field for collateral information on specific productive assets that are supplied as collateral for a transaction underlying a particular financing statement, and corresponding party information of the collateral information; machine-recording the corresponding party information from each financing statement; machine-retrieving the collateral information image from the field within the at least some of the financing statements and performing an Optical Character Recognition (OCR) process on the retrieved collateral image to derive digital text representing the collateral information; machine-organizing the collateral information in conjunction with the corresponding party information into records in a searchable database, including keyword mapping the collateral information into particular equipment specific categories; and machine-presenting the records in a format with hyperlinks for the collateral information and the corresponding party information, wherein one of the hyperlinks for the corresponding party information includes a web-based interactive map. | 9. A method of identifying customers of productive assets, comprising: machine-searching a number of Uniform Commercial Code financing statements filed for finance transactions wherein at least some of the financing statements include an image within a field for collateral information on specific productive assets that are supplied as collateral for a transaction underlying a particular financing statement, and corresponding party information of the collateral information; machine-recording the corresponding party information from each financing statement; machine-retrieving the collateral information image from the field within the at least some of the financing statements and performing an Optical Character Recognition (OCR) process on the retrieved collateral image to derive digital text representing the collateral information; machine-organizing the collateral information in conjunction with the corresponding party information into records in a searchable database, including keyword mapping the collateral information into particular equipment specific categories; and machine-presenting the records in a format with hyperlinks for the collateral information and the corresponding party information, wherein one of the hyperlinks for the corresponding party information includes a web-based interactive map. 11. The method of claim 9 , wherein each financing statement includes initial filing information and addendum information, and the method further includes adding the addendum information to the initial filing information to create a single record. | 0.611635 |
9,152,678 | 21 | 27 | 21. A non-transitory machine-readable storage device having instructions stored thereon which, when executed by at least one data processing apparatus, cause the at least one data processing apparatus to perform operations comprising: determining historical click-through rates over a plurality of time periods for a first search result responsive to a query and for a second search result responsive to the query wherein the first and second search results refer to different respective web pages; calculating click-fractions for one or more of the plurality of time periods based on the determined historical click-through rates of the first and second search results; determining that a particular click-fraction of the calculated click-fractions in a first time period of the plurality of time periods exceeds a minimum change threshold; receiving the query from a user during a second time period that chronologically follows the plurality of time periods; obtaining search results responsive to the query; adjusting a ranking of the first search result in the obtained search results during the second time period; and providing the search results including the adjusted ranking of the first search result to the user. | 21. A non-transitory machine-readable storage device having instructions stored thereon which, when executed by at least one data processing apparatus, cause the at least one data processing apparatus to perform operations comprising: determining historical click-through rates over a plurality of time periods for a first search result responsive to a query and for a second search result responsive to the query wherein the first and second search results refer to different respective web pages; calculating click-fractions for one or more of the plurality of time periods based on the determined historical click-through rates of the first and second search results; determining that a particular click-fraction of the calculated click-fractions in a first time period of the plurality of time periods exceeds a minimum change threshold; receiving the query from a user during a second time period that chronologically follows the plurality of time periods; obtaining search results responsive to the query; adjusting a ranking of the first search result in the obtained search results during the second time period; and providing the search results including the adjusted ranking of the first search result to the user. 27. The storage device of claim 21 , wherein the first time period and the second time period are corresponding periods based on a lunar calendar or a Gregorian calendar. | 0.738462 |
8,370,869 | 5 | 23 | 5. The computer readable media of claim 3 , wherein said visual features are further defined by at least one feature description selected from the group consisting of color, texture, position, size, shape, motion, camera motion, editing effect and orientation. | 5. The computer readable media of claim 3 , wherein said visual features are further defined by at least one feature description selected from the group consisting of color, texture, position, size, shape, motion, camera motion, editing effect and orientation. 23. The computer readable media of claim 5 , wherein feature descriptions include pointers to extraction and matching code to facilitate code downloading. | 0.5 |
9,015,575 | 1 | 4 | 1. A computer implemented method comprising: identifying a host setting related to a property of a document data structure and a property of a fragment data structure, the document data structure reflecting an organization of a document that includes a reference to the fragment data structure separate from the document, and the fragment data structure reflecting an organization of a portion of the document; retrieving the fragment data structure that is separate from the document; and overriding the property of the fragment data structure using the property of the document data structure, if the host setting includes a command that the property of the fragment data structure is to be overridden by the property of the document data structure. | 1. A computer implemented method comprising: identifying a host setting related to a property of a document data structure and a property of a fragment data structure, the document data structure reflecting an organization of a document that includes a reference to the fragment data structure separate from the document, and the fragment data structure reflecting an organization of a portion of the document; retrieving the fragment data structure that is separate from the document; and overriding the property of the fragment data structure using the property of the document data structure, if the host setting includes a command that the property of the fragment data structure is to be overridden by the property of the document data structure. 4. The computer implemented method of claim 1 , further comprising building the document using the property of the document data structure and the property of the fragment data structure. | 0.600427 |
9,934,215 | 12 | 13 | 12. A method of associating a text transcription of an audio file with a spreadsheet, comprising: receiving at least one audio file; associating the at least one audio file with a cell of the spreadsheet; transcribing the at least one audio file into text; creating a compound data type including the at least one audio file and the text transcription; and associating the compound data type with the cell of the spreadsheet. | 12. A method of associating a text transcription of an audio file with a spreadsheet, comprising: receiving at least one audio file; associating the at least one audio file with a cell of the spreadsheet; transcribing the at least one audio file into text; creating a compound data type including the at least one audio file and the text transcription; and associating the compound data type with the cell of the spreadsheet. 13. The method of claim 12 , wherein the at least one audio file is received by one of: recording the at least one audio file; pasting the at least one audio file into the cell; dropping the at least one audio file into the cell; retrieving the at least one audio file from storage; converting a range of values in the spreadsheet into the at least one audio file; converting a document into the at least one audio file; and converting a musical score into the at least one audio file. | 0.5 |
7,987,416 | 6 | 9 | 6. The method of claim 4 , the searchable items comprising text, and wherein the context operator receives an input annotation and a reference annotation and generates an output annotation comprising a plurality of text adjacent to the reference annotation. | 6. The method of claim 4 , the searchable items comprising text, and wherein the context operator receives an input annotation and a reference annotation and generates an output annotation comprising a plurality of text adjacent to the reference annotation. 9. The method of claim 6 wherein the context operator extracts text to the left of said reference annotation. | 0.509009 |
10,002,116 | 19 | 32 | 19. A computer-based system for processing one or more citations for inclusion in an electronic document, the system comprising: a processor; a memory communicatively coupled with the processor; computer executable code stored in the memory, the computer executable code comprising: a document rendering application; a citation editing code set comprising: citation interface code set when executed by the processor adapted to present a citation interface and receive a user input representing a set of citation terms related to a citation for inclusion in the electronic document; citation identifying code set adapted to identify a set of at least one citation record from at least one citation library based at least in part on the received set of citation terms; citation selection code set adapted to present a representation of one or more of the identified set of at least one citation record and to receive an electronic signal representing a user selection of a citation from the presented one or more of the set of at least one citation record; and citation insertion code set adapted to insert into the electronic document citation data from the corresponding citation record associated with the selected citation. | 19. A computer-based system for processing one or more citations for inclusion in an electronic document, the system comprising: a processor; a memory communicatively coupled with the processor; computer executable code stored in the memory, the computer executable code comprising: a document rendering application; a citation editing code set comprising: citation interface code set when executed by the processor adapted to present a citation interface and receive a user input representing a set of citation terms related to a citation for inclusion in the electronic document; citation identifying code set adapted to identify a set of at least one citation record from at least one citation library based at least in part on the received set of citation terms; citation selection code set adapted to present a representation of one or more of the identified set of at least one citation record and to receive an electronic signal representing a user selection of a citation from the presented one or more of the set of at least one citation record; and citation insertion code set adapted to insert into the electronic document citation data from the corresponding citation record associated with the selected citation. 32. The system of claim 19 further comprising a bibliography code set adapted to generate a bibliography comprising the citation data from the corresponding citation record associated with the selected citation. | 0.751765 |
8,396,586 | 47 | 48 | 47. The device of claim 43 , wherein the documents comprise currency bills and substitute currency media. | 47. The device of claim 43 , wherein the documents comprise currency bills and substitute currency media. 48. The device of claim 47 , wherein the at least one criterion comprises a document being a no call bill. | 0.735 |
8,090,885 | 12 | 15 | 12. A mobile computing device with a removable keypad and a default configuration, comprising: a memory; a data store; a processor coupled to the memory and the data store, wherein the processor is configured to execute program instructions for: detecting an identity of a newly connected removable keypad, the removable keypad including keying or index pins which transmit the identity of the keyboard to the processor; automatically retrieving customization parameters based on the detected identity, one of the retrieved customization parameters specifying a user interface language, one of the retrieved customization parameters specifying a currency and another of the other retrieved customization parameters specifying a communication customization, the customization parameter specifying the user interface language including the capability of supporting a customization for a Braille mapping of a user input to the removable keypad, a customization for a Mandarin dialect and a customization for an assignment of one or more statistical analysis functions to one more keys of the removable keypad, the communication customization providing a communication capability for the mobile computing device over a service provider network; automatically customizing the configuration of the computing device based on the retrieved customization parameters; providing computing device functionality based on the custom configuration; repeating the customization anytime one of: a new removable keypad and new removable keypad accessory is detected; and automatically adjusting two or more of the customization parameters of the computing device based on location information, one of the customization parameters being adjusted by the location information being the user interface language and another of the customization parameters being adjusted by the location information being the currency. | 12. A mobile computing device with a removable keypad and a default configuration, comprising: a memory; a data store; a processor coupled to the memory and the data store, wherein the processor is configured to execute program instructions for: detecting an identity of a newly connected removable keypad, the removable keypad including keying or index pins which transmit the identity of the keyboard to the processor; automatically retrieving customization parameters based on the detected identity, one of the retrieved customization parameters specifying a user interface language, one of the retrieved customization parameters specifying a currency and another of the other retrieved customization parameters specifying a communication customization, the customization parameter specifying the user interface language including the capability of supporting a customization for a Braille mapping of a user input to the removable keypad, a customization for a Mandarin dialect and a customization for an assignment of one or more statistical analysis functions to one more keys of the removable keypad, the communication customization providing a communication capability for the mobile computing device over a service provider network; automatically customizing the configuration of the computing device based on the retrieved customization parameters; providing computing device functionality based on the custom configuration; repeating the customization anytime one of: a new removable keypad and new removable keypad accessory is detected; and automatically adjusting two or more of the customization parameters of the computing device based on location information, one of the customization parameters being adjusted by the location information being the user interface language and another of the customization parameters being adjusted by the location information being the currency. 15. The system of claim 12 , wherein the mobile computing device is capable of communication through a plurality of communication modes, and wherein the customization of the configuration includes activation of at least one of the plurality of communication modes. | 0.5 |
9,015,597 | 1 | 2 | 1. A method for generating a social utility grid, the method comprising: gathering, by a computer comprising a processor, information regarding a first party, wherein the information comprises location information associated with the first party; determining, by the computer based on the information, a social relationship between the first party and a second party; determining, by the computer based on the information, a relationship type of the social relationship between the first party and the second party; correlating, by the computer, an address book entry associated with the second party with the location information associated with the first party to determine a proximity frequency between the first party and the second party; determining, by the computer based at least in part on the proximity frequency between the first party and the second party, a relationship strength of the social relationship between the first party and the second party; generating, by the computer, the social utility grid, the social utility grid identifying the social relationship, the relationship type of the social relationship, and the relationship strength of the social relationship; receiving, by the computer, a first communication rule identifying a date when a party associated with a first type of relationship is allowed to contact the first party and identifying a proximity to the first party required by the party associated with the first type of relationship to contact the first party; receiving, by the computer, a first request from the second party to contact the first party; determining, by the computer, whether a current date meets the date identified by the first communication rule, whether the second party is within the proximity to the first party identified by the first communication rule, and whether the relationship type of the social relationship between the first party and the second party meets the first type of relationship identified by the first communication rule; and in response to determining that the current date meets the date identified by the first communication rule, that the second party is within the proximity to the first party identified by the first communication rule, and that the relationship type of the social relationship between the first party and the second party meets the first type of relationship identified by the first communication rule, granting, by the computer, the first request from the second party to contact the first party. | 1. A method for generating a social utility grid, the method comprising: gathering, by a computer comprising a processor, information regarding a first party, wherein the information comprises location information associated with the first party; determining, by the computer based on the information, a social relationship between the first party and a second party; determining, by the computer based on the information, a relationship type of the social relationship between the first party and the second party; correlating, by the computer, an address book entry associated with the second party with the location information associated with the first party to determine a proximity frequency between the first party and the second party; determining, by the computer based at least in part on the proximity frequency between the first party and the second party, a relationship strength of the social relationship between the first party and the second party; generating, by the computer, the social utility grid, the social utility grid identifying the social relationship, the relationship type of the social relationship, and the relationship strength of the social relationship; receiving, by the computer, a first communication rule identifying a date when a party associated with a first type of relationship is allowed to contact the first party and identifying a proximity to the first party required by the party associated with the first type of relationship to contact the first party; receiving, by the computer, a first request from the second party to contact the first party; determining, by the computer, whether a current date meets the date identified by the first communication rule, whether the second party is within the proximity to the first party identified by the first communication rule, and whether the relationship type of the social relationship between the first party and the second party meets the first type of relationship identified by the first communication rule; and in response to determining that the current date meets the date identified by the first communication rule, that the second party is within the proximity to the first party identified by the first communication rule, and that the relationship type of the social relationship between the first party and the second party meets the first type of relationship identified by the first communication rule, granting, by the computer, the first request from the second party to contact the first party. 2. The method of claim 1 , further comprising: determining, based on the information, a relationship context between the first party and the second party, wherein the relationship context identifies a group in which the first party and the second party belong; and generating, by the computer, the social utility grid that identifies the social relationship, the relationship context of the first party and the second party, the relationship type of the social relationship, and the relationship strength of the social relationship. | 0.762075 |
8,879,854 | 1 | 2 | 1. A method for recognizing an emotion of an individual based on Action Units (AUs), the method comprising: receiving an input AU string including one or more AUs that represents a facial expression of an individual from an AU detector; comparing the input AU string with each of a plurality of AU strings, each of the plurality of AU strings includes a set of a same number of AUs that are most discriminative of a respective one of a plurality of emotions; identifying an AU string from the plurality of AU strings that best matches the input AU string; and outputting an emotion label corresponding to the best matching AU string that indicates the emotion of the individual. | 1. A method for recognizing an emotion of an individual based on Action Units (AUs), the method comprising: receiving an input AU string including one or more AUs that represents a facial expression of an individual from an AU detector; comparing the input AU string with each of a plurality of AU strings, each of the plurality of AU strings includes a set of a same number of AUs that are most discriminative of a respective one of a plurality of emotions; identifying an AU string from the plurality of AU strings that best matches the input AU string; and outputting an emotion label corresponding to the best matching AU string that indicates the emotion of the individual. 2. The method of claim 1 , wherein identifying the AU string from the plurality of AU strings that best matches the input AU string comprises: determining a common subsequence between the input AU string and each of the plurality of AU strings; and identifying a longest common subsequence from the determined common sub-sequences, wherein the longest common subsequence is indicative of a greatest amount of similarity between the input AU string and one of the plurality of AU strings. | 0.5 |
8,069,162 | 1 | 5 | 1. A method for crawling, comprising: retrieving a first electronic document from a location that includes a specific file name, wherein a web crawler is preconfigured to find information relating to searching at the specific file name; determining that an explicit enumeration of at least one document containing local content exists, wherein the enumeration of the at least one document containing local content is associated with the first document; detecting a tag associated with the enumeration of the at least one document containing local content, wherein the tag specifies that a second document at a specified location is indexable, wherein the second document is one of the at least one document containing local content; retrieving the second document from the specified location; and indexing the second document for searching. | 1. A method for crawling, comprising: retrieving a first electronic document from a location that includes a specific file name, wherein a web crawler is preconfigured to find information relating to searching at the specific file name; determining that an explicit enumeration of at least one document containing local content exists, wherein the enumeration of the at least one document containing local content is associated with the first document; detecting a tag associated with the enumeration of the at least one document containing local content, wherein the tag specifies that a second document at a specified location is indexable, wherein the second document is one of the at least one document containing local content; retrieving the second document from the specified location; and indexing the second document for searching. 5. The method of claim 1 , wherein indexing the second document for searching comprises generating search-related metadata relating to the second document, and adding the search-related metadata to a search index. | 0.5 |
7,552,116 | 12 | 16 | 12. A method for extracting semantic information about a plurality of electronic documents autonomously created by different sources and being accessible via a computer network, comprising: accessing an electronic document via the computer network; generating a set of tokens by a computer, the tokens indicative of document object model (DOM) nodes associated with visual information in a displayed document image of the electronic document; deriving a non-prescribed visual grammar from the set of tokens by the computer, to represent a hidden syntax convention of visual presentation in the displayed document image; and applying said derived visual grammar by the computer to construct multiple parse trees that represent semantic structure of the electronic document and interpret a maximum subset of the set of tokens, wherein said non-prescribed visual grammar is derived from autonomous or heterogeneous Web documents to represent the hidden syntax convention of the visual presentation, and said derived non-prescribed visual grammar is a five tuple <Σ, N, s, Pd, Pf> where Σ is a set of terminal symbols, N is a set of nonterminal symbols, sεN is a start symbol, Pd is a set of production rules that represent visual patterns and Pf is a set of preference rules that represent pattern precedence. | 12. A method for extracting semantic information about a plurality of electronic documents autonomously created by different sources and being accessible via a computer network, comprising: accessing an electronic document via the computer network; generating a set of tokens by a computer, the tokens indicative of document object model (DOM) nodes associated with visual information in a displayed document image of the electronic document; deriving a non-prescribed visual grammar from the set of tokens by the computer, to represent a hidden syntax convention of visual presentation in the displayed document image; and applying said derived visual grammar by the computer to construct multiple parse trees that represent semantic structure of the electronic document and interpret a maximum subset of the set of tokens, wherein said non-prescribed visual grammar is derived from autonomous or heterogeneous Web documents to represent the hidden syntax convention of the visual presentation, and said derived non-prescribed visual grammar is a five tuple <Σ, N, s, Pd, Pf> where Σ is a set of terminal symbols, N is a set of nonterminal symbols, sεN is a start symbol, Pd is a set of production rules that represent visual patterns and Pf is a set of preference rules that represent pattern precedence. 16. The method of claim 12 , wherein said production rules are a four tuple (H, M, C, F), where HεN is the head of the production, M ⊂ Σ∪N is a multiset of symbols, C is a boolean constraint defined on M and F is a constructor defined on M. | 0.69697 |
8,674,939 | 1 | 6 | 1. A device, comprising: a display; and logic configured to: receive, from a user, a selection of a first control action associated with an application stored in the device, provide, via the display, a plurality of choices associated with the first control action, receive, from the user, a word or a phrase to use as a voice command corresponding to the first control action, wherein the word or phrase is selected from the plurality of choices, associate the word or phrase with the first control action, receive voice input from the user, identify the voice input as corresponding to the word or phrase, and perform the first control action based on the identified voice input. | 1. A device, comprising: a display; and logic configured to: receive, from a user, a selection of a first control action associated with an application stored in the device, provide, via the display, a plurality of choices associated with the first control action, receive, from the user, a word or a phrase to use as a voice command corresponding to the first control action, wherein the word or phrase is selected from the plurality of choices, associate the word or phrase with the first control action, receive voice input from the user, identify the voice input as corresponding to the word or phrase, and perform the first control action based on the identified voice input. 6. The device of claim 1 , further comprising: a memory, and wherein the logic is further configured to: allow the user to provide at least one word corresponding to each of a plurality of control actions associated with the application, and store the at least one word corresponding to each of the plurality of respective control actions in the memory. | 0.555416 |
7,908,234 | 18 | 19 | 18. The computer-readable medium of claim 16 , the program code to configure the one or more processors to use the one or more features extracted from the given URL and the usefulness prediction model to generate the usefulness prediction in connection with the given URL further comprising program code to cause the one or more processors to: generate a positive usefulness prediction value in connection with the given URL using the one or more features extracted from the given URL and the usefulness prediction model; generate a negative usefulness prediction value in connection with the given URL using the one or more features extracted from the given URL and the usefulness prediction model; and compare the positive usefulness prediction value with the negative usefulness prediction value to generate the usefulness prediction in connection with the given URL. | 18. The computer-readable medium of claim 16 , the program code to configure the one or more processors to use the one or more features extracted from the given URL and the usefulness prediction model to generate the usefulness prediction in connection with the given URL further comprising program code to cause the one or more processors to: generate a positive usefulness prediction value in connection with the given URL using the one or more features extracted from the given URL and the usefulness prediction model; generate a negative usefulness prediction value in connection with the given URL using the one or more features extracted from the given URL and the usefulness prediction model; and compare the positive usefulness prediction value with the negative usefulness prediction value to generate the usefulness prediction in connection with the given URL. 19. The computer-readable medium of claim 18 , further comprising program code to configure the one or more processors to: identify a resource associated with the given URL as useful in a case that the positive usefulness prediction value is equal to or greater than the negative usefulness prediction value. | 0.893573 |
8,676,722 | 1 | 17 | 1. A computer implemented method for generating a semantic network, the method comprising: representing an information domain as a data set, the data set being defined by data entities and one or more relationships between the data entities; receiving a text query from a human user; and synthesizing, or facilitating the synthesizing of, by one or more computer processors, a semantic network in response to the text query, the synthesizing comprising: translating the text query from the human user into an active concept; including the active concept as a node in the semantic network; deriving relationships between the active concept and selected data entities from the information domain; and populating the semantic network at least in part with the selected data entities from the information domain and the derived relationships between the selected data entities and the active concept. | 1. A computer implemented method for generating a semantic network, the method comprising: representing an information domain as a data set, the data set being defined by data entities and one or more relationships between the data entities; receiving a text query from a human user; and synthesizing, or facilitating the synthesizing of, by one or more computer processors, a semantic network in response to the text query, the synthesizing comprising: translating the text query from the human user into an active concept; including the active concept as a node in the semantic network; deriving relationships between the active concept and selected data entities from the information domain; and populating the semantic network at least in part with the selected data entities from the information domain and the derived relationships between the selected data entities and the active concept. 17. The computer implemented method of claim 1 , further comprising storing the data set to a storage medium, wherein the data set includes means to create a semantic network. | 0.729938 |
7,738,635 | 1 | 8 | 1. A method for improving the recognition confidence of alphanumeric spoken input, suitable for use in a telephony application, said method comprising the steps of: determining an alphanumeric candidate from spoken input; determining present call data associated with a source of the spoken input; based on the alphanumeric candidate, creating a plurality of call data possibilities, wherein each of the plurality of call data possibilities is associated with historical call data; and comparing the present call data with the historical call data to select alphanumeric information to represent the spoken input. | 1. A method for improving the recognition confidence of alphanumeric spoken input, suitable for use in a telephony application, said method comprising the steps of: determining an alphanumeric candidate from spoken input; determining present call data associated with a source of the spoken input; based on the alphanumeric candidate, creating a plurality of call data possibilities, wherein each of the plurality of call data possibilities is associated with historical call data; and comparing the present call data with the historical call data to select alphanumeric information to represent the spoken input. 8. The method of claim 1 , wherein the present call data includes an Internet address. | 0.850694 |
8,412,531 | 15 | 16 | 15. The computer readable storage medium of claim 13 , wherein receiving the indication of the touch anywhere on the touch screen interface of the mobile computing device includes receiving the indication of the touch within the contextual area on the touch screen interface. | 15. The computer readable storage medium of claim 13 , wherein receiving the indication of the touch anywhere on the touch screen interface of the mobile computing device includes receiving the indication of the touch within the contextual area on the touch screen interface. 16. The computer readable storage medium of claim 15 , wherein receiving the indication of the touch within the contextual area on the touch screen interface includes receiving the touch on a portion of the touch screen interface where a given information is displayed by a software application operated via the mobile computing device. | 0.5 |
8,428,241 | 3 | 5 | 3. The method of claim 1 , further comprising the steps of: associating the recorded speech that represents the request by the caller for the destination with the recorded destination identifying information; and updating a destination map using the recorded speech that is associated with the recorded destination identifying information. | 3. The method of claim 1 , further comprising the steps of: associating the recorded speech that represents the request by the caller for the destination with the recorded destination identifying information; and updating a destination map using the recorded speech that is associated with the recorded destination identifying information. 5. The method of claim 3 , wherein the updated destination map is accessible to one or more call handling systems. | 0.685083 |
9,774,657 | 15 | 16 | 15. The non-transitory processor-readable medium of claim 14 , wherein the wrapper layer supports a one-to-one mapping of native objects and wrapper objects. | 15. The non-transitory processor-readable medium of claim 14 , wherein the wrapper layer supports a one-to-one mapping of native objects and wrapper objects. 16. The non-transitory processor-readable medium of claim 15 , wherein the wrapper layer supports callback functions and debug functions. | 0.5 |
9,672,524 | 1 | 7 | 1. A system for organizing, managing, and reporting data relating to a corporate entity, comprising: at least one database configured to store a document record relating to a corporate action, the document record further comprising a core record reflecting human-readable information for incorporation into the generated document and stored with the document record, the document record further comprising a set of tags stored with the document record; a business logic module, coupled to the at least one database; and at least one document template stored in the at least one database and comprising instructions for generating, at the business logic module, a document based on the core record with an initial set of tags, wherein a first tag of the set of tags is in a text format and associates a human-readable document type with the document record, and wherein the at least one database is configured to store the document record with a hierarchically delimited child tag by storing the document record in association with both the hierarchically delimited child tag and additional tags that are hierarchically related as parent tags of the hierarchically delimited child tag, without requiring a user to request storage of both the child tag and the additional parent tags, thereby providing namespaced tagging and retrieval of document records. | 1. A system for organizing, managing, and reporting data relating to a corporate entity, comprising: at least one database configured to store a document record relating to a corporate action, the document record further comprising a core record reflecting human-readable information for incorporation into the generated document and stored with the document record, the document record further comprising a set of tags stored with the document record; a business logic module, coupled to the at least one database; and at least one document template stored in the at least one database and comprising instructions for generating, at the business logic module, a document based on the core record with an initial set of tags, wherein a first tag of the set of tags is in a text format and associates a human-readable document type with the document record, and wherein the at least one database is configured to store the document record with a hierarchically delimited child tag by storing the document record in association with both the hierarchically delimited child tag and additional tags that are hierarchically related as parent tags of the hierarchically delimited child tag, without requiring a user to request storage of both the child tag and the additional parent tags, thereby providing namespaced tagging and retrieval of document records. 7. The system of claim 1 , further comprising a hierarchical schema of tags representing a set of document types stored in the system. | 0.838554 |
9,495,959 | 1 | 6 | 1. A system, comprising a computer, the computer comprising a processor and a memory, wherein the computer is configured to: register each of a plurality of application grammars, and further wherein registering an application grammar includes determining whether a command in the application grammar conflicts with a command in another grammar registered in the computer; receive input including at least one word; determine that the input is specified as a command in a plurality of the grammars, each of the grammars being associated with a respective instance of one application in a plurality of instances of the application each currently running on one of a plurality of devices; select one of the currently-running instances of the application to which to apply the command according a specified status of the selected instance of the application; and provide the command to the selected one of the applications. | 1. A system, comprising a computer, the computer comprising a processor and a memory, wherein the computer is configured to: register each of a plurality of application grammars, and further wherein registering an application grammar includes determining whether a command in the application grammar conflicts with a command in another grammar registered in the computer; receive input including at least one word; determine that the input is specified as a command in a plurality of the grammars, each of the grammars being associated with a respective instance of one application in a plurality of instances of the application each currently running on one of a plurality of devices; select one of the currently-running instances of the application to which to apply the command according a specified status of the selected instance of the application; and provide the command to the selected one of the applications. 6. The system of claim 1 , wherein the computer is further configured to reject a command included in an application grammar when the command in the application grammar is reserved by a system grammar. | 0.5 |
7,596,559 | 1 | 2 | 1. A system for automated data integration by querying multiple extensible markup language (XML) source schemas through a common XML target schema, said system comprising: a processor that: establishes XML mappings between said XML source schemas and said XML target schema, wherein said XML source schemas comprise data, and wherein said XML target schema comprise a set of constraints, said set of constraints comprising: data merging rules for integrating said data from multiple said XML source schemas comprising overlapping information; and nested equality-generating dependencies (NEGDs), said NEGDs comprising rules specifying that fields in two different data tuples must be merged; queries multiple XML source schemas through a common XML target schema; rewrites said target query in terms of said XML source schemas based on said XML mappings; integrates said data based on said set of constraints; and evaluates a union of a set of source queries, wherein an evaluation of said union of said set of source queries in said processor occurs at query run-time; and a display device adapted to display said target query and said data to a user. | 1. A system for automated data integration by querying multiple extensible markup language (XML) source schemas through a common XML target schema, said system comprising: a processor that: establishes XML mappings between said XML source schemas and said XML target schema, wherein said XML source schemas comprise data, and wherein said XML target schema comprise a set of constraints, said set of constraints comprising: data merging rules for integrating said data from multiple said XML source schemas comprising overlapping information; and nested equality-generating dependencies (NEGDs), said NEGDs comprising rules specifying that fields in two different data tuples must be merged; queries multiple XML source schemas through a common XML target schema; rewrites said target query in terms of said XML source schemas based on said XML mappings; integrates said data based on said set of constraints; and evaluates a union of a set of source queries, wherein an evaluation of said union of said set of source queries in said processor occurs at query run-time; and a display device adapted to display said target query and said data to a user. 2. The system according to claim 1 , wherein said processor rewrites said target query into a set of source queries comprising said source XML schemas. | 0.580556 |
7,783,617 | 15 | 17 | 15. A computer program product comprising program code associated with obtaining personals ads, the computer program product comprising code for a method comprising the steps of: performing a first search, using at least one computing device, of personal ads to identify a first personals ad; perform an affinity search, using the at least one computing device, based on the first personals ad identified in the first search to identify one or more second personals ads having an affinity to the first personals ad, wherein the affinity search comprises calculating an affinity score between the first personals ad and each of the second personals ads, wherein the affinity score of each of the second personals ads relates to the number of occurrences of an action taken by a plurality of users with respect to the first personals ad and the respective second personals ad, and wherein the affinity search does not identify personals ads having an affinity score with the first personal ad below a predefined threshold; and prioritizing, using the computing device, the identified second personals ads based upon its respective affinity score with the first personals ad. | 15. A computer program product comprising program code associated with obtaining personals ads, the computer program product comprising code for a method comprising the steps of: performing a first search, using at least one computing device, of personal ads to identify a first personals ad; perform an affinity search, using the at least one computing device, based on the first personals ad identified in the first search to identify one or more second personals ads having an affinity to the first personals ad, wherein the affinity search comprises calculating an affinity score between the first personals ad and each of the second personals ads, wherein the affinity score of each of the second personals ads relates to the number of occurrences of an action taken by a plurality of users with respect to the first personals ad and the respective second personals ad, and wherein the affinity search does not identify personals ads having an affinity score with the first personal ad below a predefined threshold; and prioritizing, using the computing device, the identified second personals ads based upon its respective affinity score with the first personals ad. 17. The computer program product of claim 15 , wherein the indicated interest in the first personal ad is performed through a website. | 0.752768 |
8,035,020 | 10 | 11 | 10. The system of claim 8 , wherein the set of music elements identify components of the music contribution at multiple time intervals within the music contribution. | 10. The system of claim 8 , wherein the set of music elements identify components of the music contribution at multiple time intervals within the music contribution. 11. The system of claim 10 , wherein the components of the music contribution are identified relative to a recorded performance. | 0.5 |
8,849,785 | 1 | 11 | 1. A computer-implemented method comprising: obtaining search results that are identified as responsive to an original search query; determining a frequency with which a particular term occurs in text associated with one or more of the search results; determining a frequency with which the particular term occurs in other text; determining that the frequency with which the particular term occurs in the text associated with the one or more search results differs, by a threshold extent, from the frequency with which the particular term occurs in the other text; in response to determining that the frequency with which the particular term occurs in a text associated with the one or more search results differs, by the threshold extent, from the frequency with which the term occurs in the other text, providing, for display, a representation of the particular term, a demote control to specify that the search results for the reformulated search query that include the particular term are to be demoted, and a promote control to specify that the search results for the formulated search query that include the particular term are to be promoted; receiving data indicative of a selection of the demote control or the promote control; and in response to receiving the data indicative of the selection of the demote control or the promote control, reformulating the original search query to promote or demote the particular term. | 1. A computer-implemented method comprising: obtaining search results that are identified as responsive to an original search query; determining a frequency with which a particular term occurs in text associated with one or more of the search results; determining a frequency with which the particular term occurs in other text; determining that the frequency with which the particular term occurs in the text associated with the one or more search results differs, by a threshold extent, from the frequency with which the particular term occurs in the other text; in response to determining that the frequency with which the particular term occurs in a text associated with the one or more search results differs, by the threshold extent, from the frequency with which the term occurs in the other text, providing, for display, a representation of the particular term, a demote control to specify that the search results for the reformulated search query that include the particular term are to be demoted, and a promote control to specify that the search results for the formulated search query that include the particular term are to be promoted; receiving data indicative of a selection of the demote control or the promote control; and in response to receiving the data indicative of the selection of the demote control or the promote control, reformulating the original search query to promote or demote the particular term. 11. The method of claim 1 , wherein the promotion criteria specifies that search results for the reformulated search query that include the particular term are to be promoted, or that the search results for the reformulated search query are required to include the particular term. | 0.631234 |
9,117,174 | 1 | 13 | 1. A non-transitory computer-readable storage medium having instructions stored thereon, the instructions executable to cause a data processing apparatus to perform operations including: accessing a representation of a document category; accessing a set of documents, wherein each of the documents of the set is associated with a label indicating whether or not the document is associated with the document category; compiling a list of terms, wherein the list of terms includes distinct terms found in the documents of the set; computing information gain of each of the terms, wherein information gain is computed with regard to the document category; sorting the list by ordering the terms based on respective information gain; creating multiple association rules, wherein at least one of the association rules includes a logical conjunction of at least two terms of the list and one or more of the association rules includes at least one of the distinct terms; and performing an association rule analysis that includes performing the following operations with respect to each of the association rules: obtaining categorization results by using the association rule in categorizing each of the documents of the set with regard to the document category; recording the categorization results; and evaluating a precision of the association rule based on the respective categorization results. | 1. A non-transitory computer-readable storage medium having instructions stored thereon, the instructions executable to cause a data processing apparatus to perform operations including: accessing a representation of a document category; accessing a set of documents, wherein each of the documents of the set is associated with a label indicating whether or not the document is associated with the document category; compiling a list of terms, wherein the list of terms includes distinct terms found in the documents of the set; computing information gain of each of the terms, wherein information gain is computed with regard to the document category; sorting the list by ordering the terms based on respective information gain; creating multiple association rules, wherein at least one of the association rules includes a logical conjunction of at least two terms of the list and one or more of the association rules includes at least one of the distinct terms; and performing an association rule analysis that includes performing the following operations with respect to each of the association rules: obtaining categorization results by using the association rule in categorizing each of the documents of the set with regard to the document category; recording the categorization results; and evaluating a precision of the association rule based on the respective categorization results. 13. The non-transitory computer-readable storage medium of claim 1 , wherein the document category is defined with respect to a topic such that documents in which the topic appears are associated with the document category and documents in which the topic does not appear are not associated with the document category. | 0.827736 |
7,496,516 | 35 | 39 | 35. The system of claim 34 , wherein the dialog engine is further for: interpreting the voice markup language script to produce an audio output. | 35. The system of claim 34 , wherein the dialog engine is further for: interpreting the voice markup language script to produce an audio output. 39. The method of claim 35 , wherein the dialog engine is further for: identifying text requiring conversion to speech; and placing a text-to-speech request in the output queue. | 0.5 |
9,569,729 | 1 | 13 | 1. A non-transient computer readable medium for causing a computer to perform the method of: allowing the computer to receive an at least one body of data associated with an organization, where the at least one body of data is comprised of being an at least one of the group of application data, databases, personnel records, survey and interview data, publicly and quasi-publicly available web data including logs, email communications, text communications, audio recordings, video recordings, images, electronic records, news articles, and records' contextual data; where the application of the analytics engine is further comprised of the step of labeling elements of the at least one body of data with tags and other types of metadata; determining with an analytics engine, for an author of data, an at least one property from the group of insider/outsider status, perspective, credibility score, sincerity, objectivity, actions, and demographic attributes; applying the analytics engine for assessing properties being an at least one from the group of credibility, sincerity, objectivity, and knowledgeableness of authors; determining with the analytics engine an at least one of the group of stability level, engagement levels, energy levels, organizational character, and culture properties; determining the informational value property of data items; applying the analytics engine for determining engagement and energy levels, where the determination of engagement and energy levels comprises assessing an at least one of the group of attitudes, beliefs, sentiment, tone, observations, and actionable suggestions; assessing organizational culture and character, assessing an at least one abstract trait; where the analytics engine further determines, for the organization, an assessment of an at least one from the group of future behavior, bases for past behavior, organizational character, organizational opportunities, benchmarking, and organizational adversities. | 1. A non-transient computer readable medium for causing a computer to perform the method of: allowing the computer to receive an at least one body of data associated with an organization, where the at least one body of data is comprised of being an at least one of the group of application data, databases, personnel records, survey and interview data, publicly and quasi-publicly available web data including logs, email communications, text communications, audio recordings, video recordings, images, electronic records, news articles, and records' contextual data; where the application of the analytics engine is further comprised of the step of labeling elements of the at least one body of data with tags and other types of metadata; determining with an analytics engine, for an author of data, an at least one property from the group of insider/outsider status, perspective, credibility score, sincerity, objectivity, actions, and demographic attributes; applying the analytics engine for assessing properties being an at least one from the group of credibility, sincerity, objectivity, and knowledgeableness of authors; determining with the analytics engine an at least one of the group of stability level, engagement levels, energy levels, organizational character, and culture properties; determining the informational value property of data items; applying the analytics engine for determining engagement and energy levels, where the determination of engagement and energy levels comprises assessing an at least one of the group of attitudes, beliefs, sentiment, tone, observations, and actionable suggestions; assessing organizational culture and character, assessing an at least one abstract trait; where the analytics engine further determines, for the organization, an assessment of an at least one from the group of future behavior, bases for past behavior, organizational character, organizational opportunities, benchmarking, and organizational adversities. 13. The computer readable medium of claim 1 further comprising producing results for an interface to receive a matrix display. | 0.903077 |
9,081,853 | 1 | 12 | 1. A non-transitory data processing system comprising: storage to store information files and associated metadata, the metadata indicating information about the associated information files, said metadata including typed-attributes usable in processing the information files; a database including a plurality of individually addressable user records, each including respective profile data structures, the profile data structures including therewithin a plurality of named interest nodes which are user-tunable channels, each named interest node data structure being logically connected with at least one target data structure, each target data structure being logically connected with at least one typed-attribute which can be logically connected by the user to any of said plural user-tunable channels at the user's option; logic executable to process the information files and associated meta data in response to a selected named interest node to produce a filtered set of information files using said at least one target in said selected named interest node, said meta data being produced by a person or an analyze engine performing analysis, extraction or classification on the associated information files; logic executable to compose and send executable documents via a network interface according to a communication protocol to a user terminal for rendition of a graphical user interface at a user terminal including display to the user of the interest node names, wherein the executable documents comprise data specifying a representation of the filtered set of information files and a representation of user selectable mark-up identifying typed-attributes represented by said metadata associated with the filtered set of information files; and logic to receive messages via a network interface according to a communication protocol indicating selection of particular mark-up in the graphical interface at the user terminal, and executable to modify the selected named interest node which is a user-tunable channel in response to said messages to add a target corresponding to the typed-attribute identified by the particular mark-up. | 1. A non-transitory data processing system comprising: storage to store information files and associated metadata, the metadata indicating information about the associated information files, said metadata including typed-attributes usable in processing the information files; a database including a plurality of individually addressable user records, each including respective profile data structures, the profile data structures including therewithin a plurality of named interest nodes which are user-tunable channels, each named interest node data structure being logically connected with at least one target data structure, each target data structure being logically connected with at least one typed-attribute which can be logically connected by the user to any of said plural user-tunable channels at the user's option; logic executable to process the information files and associated meta data in response to a selected named interest node to produce a filtered set of information files using said at least one target in said selected named interest node, said meta data being produced by a person or an analyze engine performing analysis, extraction or classification on the associated information files; logic executable to compose and send executable documents via a network interface according to a communication protocol to a user terminal for rendition of a graphical user interface at a user terminal including display to the user of the interest node names, wherein the executable documents comprise data specifying a representation of the filtered set of information files and a representation of user selectable mark-up identifying typed-attributes represented by said metadata associated with the filtered set of information files; and logic to receive messages via a network interface according to a communication protocol indicating selection of particular mark-up in the graphical interface at the user terminal, and executable to modify the selected named interest node which is a user-tunable channel in response to said messages to add a target corresponding to the typed-attribute identified by the particular mark-up. 12. The system of claim 1 , wherein the selected interest node includes delivery qualifiers, said delivery qualifiers being typed-attributes usable by logic executable to process information files using said delivery qualifiers to produce said filtered set of information files, said delivery qualifiers including time and location factors in the selected interest. | 0.610043 |
8,291,319 | 1 | 8 | 1. A computer implemented method for retrieving solutions that solve a problem experienced by a user, the computer implemented method comprising: generating, by a computer, a candidate solution document set for solving the problem, wherein a customized solution procedure for solving the problem is generated by the computer from a plurality of stored solution documents, and wherein a modified solution procedure with another set of instruction steps is generated by the computer for solving the problem based on the computer receiving an input rejecting one or more instruction steps included in the customized solution procedure; generating, by the computer, a document object model tree for the generated candidate solution document set; simplifying, by the computer, the generated document object model tree for the generated candidate solution document set by filtering out nodes in the generated document object model tree that do not have structural effects; generating, by the computer, a template based on the simplified document object model tree; calculating, by the computer, a structural similarity score for solution documents by comparing document object model trees of the solution documents with the generated template; determining, by the computer, whether the structural similarity score for the solution documents is greater than a predetermined threshold; responsive to the computer determining that the structural similarity score is greater than the predetermined threshold, storing, by the computer, the solution documents with structural similarity scores greater than the predetermined threshold; responsive to the computer receiving a query describing the problem, sending, by the computer, relevant candidate solutions to the problem, wherein the relevant candidate solutions include unstructured hypertext markup language solution documents found on a world wide web, and wherein the unstructured hypertext markup language solution documents include solution data found in web logs, instant messaging chat sessions, and online message boards; responsive to the computer receiving a selection of one relevant candidate solution from the relevant candidate solutions, analyzing, by the computer, instructions steps within the one relevant candidate solution selected; calculating, by the computer, an instruction step similarity between the instruction steps within the one relevant candidate solution selected and other instructions steps within the stored solution documents; and sending, by the computer, similar solutions containing similar instruction steps to the instruction steps contained within the one relevant candidate solution selected based on the calculated instruction step similarity. | 1. A computer implemented method for retrieving solutions that solve a problem experienced by a user, the computer implemented method comprising: generating, by a computer, a candidate solution document set for solving the problem, wherein a customized solution procedure for solving the problem is generated by the computer from a plurality of stored solution documents, and wherein a modified solution procedure with another set of instruction steps is generated by the computer for solving the problem based on the computer receiving an input rejecting one or more instruction steps included in the customized solution procedure; generating, by the computer, a document object model tree for the generated candidate solution document set; simplifying, by the computer, the generated document object model tree for the generated candidate solution document set by filtering out nodes in the generated document object model tree that do not have structural effects; generating, by the computer, a template based on the simplified document object model tree; calculating, by the computer, a structural similarity score for solution documents by comparing document object model trees of the solution documents with the generated template; determining, by the computer, whether the structural similarity score for the solution documents is greater than a predetermined threshold; responsive to the computer determining that the structural similarity score is greater than the predetermined threshold, storing, by the computer, the solution documents with structural similarity scores greater than the predetermined threshold; responsive to the computer receiving a query describing the problem, sending, by the computer, relevant candidate solutions to the problem, wherein the relevant candidate solutions include unstructured hypertext markup language solution documents found on a world wide web, and wherein the unstructured hypertext markup language solution documents include solution data found in web logs, instant messaging chat sessions, and online message boards; responsive to the computer receiving a selection of one relevant candidate solution from the relevant candidate solutions, analyzing, by the computer, instructions steps within the one relevant candidate solution selected; calculating, by the computer, an instruction step similarity between the instruction steps within the one relevant candidate solution selected and other instructions steps within the stored solution documents; and sending, by the computer, similar solutions containing similar instruction steps to the instruction steps contained within the one relevant candidate solution selected based on the calculated instruction step similarity. 8. The computer implemented method of claim 1 , wherein metadata is used to determine similarity between solutions. | 0.936464 |
9,171,066 | 1 | 2 | 1. A method comprising: configuring a client device to: process one or more natural language inputs with respect to data sources stored on the client device to determine a first set of interpretation candidates for the one or more natural language inputs; and to communicate, to a server, results from processing the one or more natural language inputs with respect to the data sources stored on the client device; determining, by the server and based on the results from processing the one or more natural language inputs with respect to the data sources stored on the client device, a list of possible interpretation candidates for the one or more natural language inputs, the list comprising a second set of interpretation candidates for the one or more natural language inputs; ranking, by the server, the list of possible interpretation candidates; pruning, by the server, the list of possible interpretation candidates; constraining, by the server and based on pseudo data corresponding to the data sources located on the client device, the pruning to prevent at least one interpretation candidate of the second set of interpretation candidates from being pruned from the list of possible interpretation candidates; and communicating, by the server and to the client device, the second set of interpretation candidates for the one or more natural language inputs, for a final output interpretation of the one or more natural language inputs by the client device that comprises ranking a plurality of interpretation candidates comprising the first set of interpretation candidates and the second set of interpretation candidates. | 1. A method comprising: configuring a client device to: process one or more natural language inputs with respect to data sources stored on the client device to determine a first set of interpretation candidates for the one or more natural language inputs; and to communicate, to a server, results from processing the one or more natural language inputs with respect to the data sources stored on the client device; determining, by the server and based on the results from processing the one or more natural language inputs with respect to the data sources stored on the client device, a list of possible interpretation candidates for the one or more natural language inputs, the list comprising a second set of interpretation candidates for the one or more natural language inputs; ranking, by the server, the list of possible interpretation candidates; pruning, by the server, the list of possible interpretation candidates; constraining, by the server and based on pseudo data corresponding to the data sources located on the client device, the pruning to prevent at least one interpretation candidate of the second set of interpretation candidates from being pruned from the list of possible interpretation candidates; and communicating, by the server and to the client device, the second set of interpretation candidates for the one or more natural language inputs, for a final output interpretation of the one or more natural language inputs by the client device that comprises ranking a plurality of interpretation candidates comprising the first set of interpretation candidates and the second set of interpretation candidates. 2. The method of claim 1 , wherein the results comprise semantic-classification results from processing the one or more natural language inputs with respect to the data sources stored on the client device. | 0.594862 |
8,311,792 | 15 | 16 | 15. A computer readable medium comprising computer readable program code embodied therein for causing a computer system to: rank a plurality of help postings to create an initial ranking, wherein the plurality of help postings in the initial ranking is ranked using initial parameter values; store user interactions with the plurality of help postings presented according to the initial ranking to obtain stored interactions; perform simulations using the stored interactions to generate revised parameter values, wherein performing the simulations comprises: calculating a plurality of relevance values from the stored interactions; creating a test posting; assigning, to the test posting, an initial score and a relevance value randomly selected from the plurality of relevance values to generate a test ranking; simulating user interactions with the plurality of help postings and the test posting in the test ranking to generate simulated rankings of the plurality of help postings and the test posting; and analyzing the simulated rankings based on the relevance value to obtain revised parameter values; rank, using the revised parameter values, the plurality of help postings to generate a revised ranking; and calculate, from the stored interactions, an average waiting time between postings to obtain a posting interval, wherein, during simulations, a new test posting is added to the simulated rankings at each posting interval. | 15. A computer readable medium comprising computer readable program code embodied therein for causing a computer system to: rank a plurality of help postings to create an initial ranking, wherein the plurality of help postings in the initial ranking is ranked using initial parameter values; store user interactions with the plurality of help postings presented according to the initial ranking to obtain stored interactions; perform simulations using the stored interactions to generate revised parameter values, wherein performing the simulations comprises: calculating a plurality of relevance values from the stored interactions; creating a test posting; assigning, to the test posting, an initial score and a relevance value randomly selected from the plurality of relevance values to generate a test ranking; simulating user interactions with the plurality of help postings and the test posting in the test ranking to generate simulated rankings of the plurality of help postings and the test posting; and analyzing the simulated rankings based on the relevance value to obtain revised parameter values; rank, using the revised parameter values, the plurality of help postings to generate a revised ranking; and calculate, from the stored interactions, an average waiting time between postings to obtain a posting interval, wherein, during simulations, a new test posting is added to the simulated rankings at each posting interval. 16. The computer readable medium of claim 15 , wherein the ranking the plurality of help postings and simulating user interactions uses the same ranking algorithm, wherein the ranking algorithm assigns a score to each of the plurality of help postings and test posting, and wherein the score is based on a popularity value and a seed value. | 0.5 |
8,074,184 | 16 | 17 | 16. A computer-implemented electronic document modification system having processor, memory, and data storage subsystems, the computer-implemented system, comprising: a processor programmed and adapted to: (a) maintain an electronic document, wherein at least a first portion of content in the electronic document includes content generated by a user via an input device and the content converted by a recognizer to recognized content as standard text, and (b) obtain data associated with the recognized content; and a text injector system to strip additional data from incoming input that is not supported in a data structure of the electronic document and move the stripped additional data to a supporting data structure, wherein: the data is stored in the data structure directly linked to the recognized content that includes information not included in the electronic document, the data structure including a plurality of different linked nodes, where each linked node stores additional information related to the data stored in the linked node including a location within the electronic document of the recognized content and a global unique identifier that identifies a source of the recognized content; the input device receives by the user, a selection of a segment that optionally includes the first portion of the content or the recognized content, the processor further is programmed and adapted to provide to the user, at least one selectable alternative for the first portion of the content or the recognized content selected by the user based at least in part on the data associated with the first portion where the user can modify the recognized content with the at least one selectable alternative; the data associated with the first portion of the content includes an expanded version of the electronic document separate from the electronic document; the expanded version is directly and exclusively linked to the electronic document, and the expanded version is saved and made available to the user upon subsequent access of the electronic document; the data associated with the first portion of the content includes properties associated with the first portion of the content that are not included in the electronic document; and the processor further is programmed and adapted to maintain synchronization between changes to either the content of the electronic document or the expanded version. | 16. A computer-implemented electronic document modification system having processor, memory, and data storage subsystems, the computer-implemented system, comprising: a processor programmed and adapted to: (a) maintain an electronic document, wherein at least a first portion of content in the electronic document includes content generated by a user via an input device and the content converted by a recognizer to recognized content as standard text, and (b) obtain data associated with the recognized content; and a text injector system to strip additional data from incoming input that is not supported in a data structure of the electronic document and move the stripped additional data to a supporting data structure, wherein: the data is stored in the data structure directly linked to the recognized content that includes information not included in the electronic document, the data structure including a plurality of different linked nodes, where each linked node stores additional information related to the data stored in the linked node including a location within the electronic document of the recognized content and a global unique identifier that identifies a source of the recognized content; the input device receives by the user, a selection of a segment that optionally includes the first portion of the content or the recognized content, the processor further is programmed and adapted to provide to the user, at least one selectable alternative for the first portion of the content or the recognized content selected by the user based at least in part on the data associated with the first portion where the user can modify the recognized content with the at least one selectable alternative; the data associated with the first portion of the content includes an expanded version of the electronic document separate from the electronic document; the expanded version is directly and exclusively linked to the electronic document, and the expanded version is saved and made available to the user upon subsequent access of the electronic document; the data associated with the first portion of the content includes properties associated with the first portion of the content that are not included in the electronic document; and the processor further is programmed and adapted to maintain synchronization between changes to either the content of the electronic document or the expanded version. 17. The computer-implemented system according to claim 16 , wherein the processor further is programmed and adapted to change the content of the electronic document when one of the selectable alternatives is selected. | 0.745902 |
8,099,313 | 46 | 48 | 46. The method of claim 1 , wherein a task suggestion is represented using a language structure organized as a set of terms to describe user tasks as abstractions of the obtained device function descriptions and task descriptions wherein the terms comprise ‘subjects’ and ‘modifiers’, wherein ‘modifiers’ modify ‘subjects’. | 46. The method of claim 1 , wherein a task suggestion is represented using a language structure organized as a set of terms to describe user tasks as abstractions of the obtained device function descriptions and task descriptions wherein the terms comprise ‘subjects’ and ‘modifiers’, wherein ‘modifiers’ modify ‘subjects’. 48. The method of claim 46 , wherein the modifiers describe a particular location to apply to the task suggestions. | 0.5 |
9,183,830 | 1 | 5 | 1. A method comprising: training an source hidden Markov model (HMM) based speech features generator implemented by one or more processors of a system using speech signals of a source speaker, wherein the source HMM based speech features generator comprises a configuration of source HMM state models, each of the source HMM state models having a set of generator-model functions; extracting speech features from speech signals of a target speaker to generate a target set of target-speaker vectors; for each given source HMM state model of the configuration, determining a particular target-speaker vector from among the target set that most closely matches parameters of the set of generator-model functions of the given source HMM; determining a fundamental frequency (F0) transform that speech-adapts F0 statistics of the source HMM based speech features generator to match F0 statistics of the speech of the target speaker; constructing a converted HMM based speech features generator implemented by one or more processors of the system to be the same as the source HMM based speech features generator, but wherein the parameters of the set of generator-model functions of each source HMM state model of the converted HMM based speech features generator are replaced with the determined particular most closely matching target-speaker vector from among the target set; and speech-adapting F0 statistics of the converted HMM based speech features generator using the F0 transform to thereby produce a speech-adapted converted HMM based speech features generator. | 1. A method comprising: training an source hidden Markov model (HMM) based speech features generator implemented by one or more processors of a system using speech signals of a source speaker, wherein the source HMM based speech features generator comprises a configuration of source HMM state models, each of the source HMM state models having a set of generator-model functions; extracting speech features from speech signals of a target speaker to generate a target set of target-speaker vectors; for each given source HMM state model of the configuration, determining a particular target-speaker vector from among the target set that most closely matches parameters of the set of generator-model functions of the given source HMM; determining a fundamental frequency (F0) transform that speech-adapts F0 statistics of the source HMM based speech features generator to match F0 statistics of the speech of the target speaker; constructing a converted HMM based speech features generator implemented by one or more processors of the system to be the same as the source HMM based speech features generator, but wherein the parameters of the set of generator-model functions of each source HMM state model of the converted HMM based speech features generator are replaced with the determined particular most closely matching target-speaker vector from among the target set; and speech-adapting F0 statistics of the converted HMM based speech features generator using the F0 transform to thereby produce a speech-adapted converted HMM based speech features generator. 5. The method of claim 1 , wherein the set of generator-model functions for each given source HMM state model comprises a multivariate spectral probability density function (PDF) for jointly modeling spectral envelope parameters of a phonetic unit modeled by a given source HMM state model, and a multivariate excitation PDF for jointly modeling excitation parameters of the phonetic unit, and wherein determining for each given source HMM state model the particular target-speaker vector from among the target set that most closely matches parameters of the set of generator-model functions of the given source HMM comprises: determining a target-speaker vector from among the target set that is computationally nearest to parameters of the multivariate spectral PDF of the given source HMM state model in terms of a distance criterion based on one of mean-squared-error (mse) or Kullback-Leibler distance; and determining a target-speaker vector from among the target set that is computationally nearest to the multivariate excitation PDF of the given source HMM state model in terms of a distance criterion based on one of mse or Kullback-Leibler distance. | 0.5 |
8,555,262 | 16 | 19 | 16. An engine configured to build messages, the engine having executable instructions stored in a non-transitory computer readable medium and further comprising: a plurality of handlers stored in a handler table, each handler being code for building at least one field of an output message using grammar for said at least one field, each of said handlers being separately compiled; a plurality of schemas stored in a schema table for different types of output messages, each schema in the schema table pointing to at least one of said handlers stored in the handler table and containing a grammar definition for one or more fields of the output message; and code for a common internal message format object, said handlers being configured to populate the output message with fields from the common internal message format object. | 16. An engine configured to build messages, the engine having executable instructions stored in a non-transitory computer readable medium and further comprising: a plurality of handlers stored in a handler table, each handler being code for building at least one field of an output message using grammar for said at least one field, each of said handlers being separately compiled; a plurality of schemas stored in a schema table for different types of output messages, each schema in the schema table pointing to at least one of said handlers stored in the handler table and containing a grammar definition for one or more fields of the output message; and code for a common internal message format object, said handlers being configured to populate the output message with fields from the common internal message format object. 19. The engine of claim 16 , wherein the output message format is determined by a destination for the output message. | 0.693717 |
8,578,334 | 8 | 9 | 8. A computer system comprising: a processing unit coupled to a memory, the memory storing computer-executable instructions for causing the processing unit to: provide a browser-based integrated development environment that is implemented at least in part using a dynamic language; receive input from a user in a form of at least a portion of a code command, the code command implemented at least in part using a dynamic language; use introspection of the browser-based integrated development environment that is implemented at least in part using a dynamic language to dynamically generate a list of one or more possible code command completion suggestions that are suitable completions for the input received from the user, wherein at least a portion of the code command completion suggestions are generated from searching a plurality of available commands for one or more suitable completions to the code command input by the user, wherein introspection is performed against one or more available classes and global variables implemented using the dynamic language in the integrated development environment; and provide at least a portion of the list of possible code command completion suggestions. | 8. A computer system comprising: a processing unit coupled to a memory, the memory storing computer-executable instructions for causing the processing unit to: provide a browser-based integrated development environment that is implemented at least in part using a dynamic language; receive input from a user in a form of at least a portion of a code command, the code command implemented at least in part using a dynamic language; use introspection of the browser-based integrated development environment that is implemented at least in part using a dynamic language to dynamically generate a list of one or more possible code command completion suggestions that are suitable completions for the input received from the user, wherein at least a portion of the code command completion suggestions are generated from searching a plurality of available commands for one or more suitable completions to the code command input by the user, wherein introspection is performed against one or more available classes and global variables implemented using the dynamic language in the integrated development environment; and provide at least a portion of the list of possible code command completion suggestions. 9. The computer system of claim 8 , wherein the dynamic language is JavaScript. | 0.637615 |
8,090,538 | 1 | 4 | 1. A computer-implemented method of interpreting well log data indicative of physical attributes of a portion of a subterranean formation comprising: partitioning, via computer, the well log data into i segments, each ith segment representing a respective contiguous portion of the extent of the logged well; defining, via computer, a membership function for each segment, the membership function defining a degree to which each segment belongs to a defined set; and determining, via computer, based on attribute values derived from previously interpreted depositional units, and the membership functions, a depositional type for each segment, converting, via computer, the well log data to shale volume data, wherein the shale volume data are represented as a series of numerical values V sh =s 1 , s 2 , . . . , s i , where s i is an average of the shale volume data within the ith segment. | 1. A computer-implemented method of interpreting well log data indicative of physical attributes of a portion of a subterranean formation comprising: partitioning, via computer, the well log data into i segments, each ith segment representing a respective contiguous portion of the extent of the logged well; defining, via computer, a membership function for each segment, the membership function defining a degree to which each segment belongs to a defined set; and determining, via computer, based on attribute values derived from previously interpreted depositional units, and the membership functions, a depositional type for each segment, converting, via computer, the well log data to shale volume data, wherein the shale volume data are represented as a series of numerical values V sh =s 1 , s 2 , . . . , s i , where s i is an average of the shale volume data within the ith segment. 4. The computer-implemented method as in claim 1 , wherein the previously interpreted depositional units are previously interpreted by a human log data interpreter. | 0.770308 |
4,713,777 | 1 | 2 | 1. In a speech recognition apparatus wherein speech units are each characterized by a sequence of template patterns, and having means for processing a speech input signal for repetitively deriving therefrom, at a frame repetition rate, a plurality of speech recognition acoustic parameters, and means responsive to said acoustic parameters for generating likelihood costs between said acoustic parameters and said speech template patterns, and for processing said likelihood costs for determining the speech units in said speech input signal, a method for inhibiting a response to nonvocabulary utterances in a speech input for which template patterns have not been created, comprising the steps of repeatedly, at a frame repetition rate, generating acoustic parameters representing said speech input, generating likelihood costs at each frame time for said acoustic parameters and said template patterns, said template patterns including a pattern representing silence, beginning a normal speech recognition process whenever said cost for an active template pattern is better than a predetermined threshold value, and reverting to a non-speech recognition process whenever said cost of said template patterns, including silence, is worse than said predetermined threshold value. | 1. In a speech recognition apparatus wherein speech units are each characterized by a sequence of template patterns, and having means for processing a speech input signal for repetitively deriving therefrom, at a frame repetition rate, a plurality of speech recognition acoustic parameters, and means responsive to said acoustic parameters for generating likelihood costs between said acoustic parameters and said speech template patterns, and for processing said likelihood costs for determining the speech units in said speech input signal, a method for inhibiting a response to nonvocabulary utterances in a speech input for which template patterns have not been created, comprising the steps of repeatedly, at a frame repetition rate, generating acoustic parameters representing said speech input, generating likelihood costs at each frame time for said acoustic parameters and said template patterns, said template patterns including a pattern representing silence, beginning a normal speech recognition process whenever said cost for an active template pattern is better than a predetermined threshold value, and reverting to a non-speech recognition process whenever said cost of said template patterns, including silence, is worse than said predetermined threshold value. 2. The method of claim 1 further comprising the steps of setting a second threshold value and remaining in said non-speech recognition process until a likelihood cost of said silence template in better than said second threshold. | 0.5 |
8,713,028 | 11 | 15 | 11. A non-transitory computer-readable storage medium storing a computer program, the computer program comprising: program instructions for defining a plurality of candidates based on a seed s; program instructions for calculating, for each candidate d, scores for relevance, novelty, connection clarity, and transition smoothness, wherein the score for connection clarity is based on a relevance score of an intersection between the seed and each candidate, wherein the score for transition smoothness measures an interest in reading each candidate when transitioning from the seed to each candidate; program instructions for calculating, for each candidate, a relatedness score based on the calculated scores for relevance, novelty, connection clarity, and transition smoothness; and program instructions for selecting at least one candidate from the plurality of candidates based on the relatedness scores. | 11. A non-transitory computer-readable storage medium storing a computer program, the computer program comprising: program instructions for defining a plurality of candidates based on a seed s; program instructions for calculating, for each candidate d, scores for relevance, novelty, connection clarity, and transition smoothness, wherein the score for connection clarity is based on a relevance score of an intersection between the seed and each candidate, wherein the score for transition smoothness measures an interest in reading each candidate when transitioning from the seed to each candidate; program instructions for calculating, for each candidate, a relatedness score based on the calculated scores for relevance, novelty, connection clarity, and transition smoothness; and program instructions for selecting at least one candidate from the plurality of candidates based on the relatedness scores. 15. The non-transitory computer-readable storage medium as recited in claim 11 , wherein the score for connection clarity is further based on topic distributions of the seed and each candidate. | 0.756313 |
7,945,553 | 15 | 17 | 15. The method of claim 1 , further comprising altering the criteria for subsequent correlations of received search arguments to the second database. | 15. The method of claim 1 , further comprising altering the criteria for subsequent correlations of received search arguments to the second database. 17. The method of claim 15 , further comprising extracting a toll from a seller associated with the particular advertisement based upon the recorded selection. | 0.5 |
9,767,255 | 30 | 33 | 30. The method of claim 24 , wherein the displaying comprises: assigning a respective rank to each of the plurality of predefined suggestions according to one or more predetermined criteria; and displaying the plurality of predefined suggestions according to their respective ranks. | 30. The method of claim 24 , wherein the displaying comprises: assigning a respective rank to each of the plurality of predefined suggestions according to one or more predetermined criteria; and displaying the plurality of predefined suggestions according to their respective ranks. 33. The method of claim 30 , wherein the predetermined criteria include proximity of plurality of medical devices to the portable electronic device, and wherein the data entry is directed toward the medical devices. | 0.540598 |
9,142,137 | 26 | 27 | 26. Apparatus of claim 25 , wherein analyzing each sentence comprises: selecting a longest sentence of the plurality of sentences, to be included in the summary; and generating the summary to include the sentences and the participial and prepositional phrase linked to the verbs within the sentences. | 26. Apparatus of claim 25 , wherein analyzing each sentence comprises: selecting a longest sentence of the plurality of sentences, to be included in the summary; and generating the summary to include the sentences and the participial and prepositional phrase linked to the verbs within the sentences. 27. Apparatus of claim 26 , further comprises: traversing the selected sentences to be included in the summary back into similar sentences from the textual information and including the similar sentences in the summary. | 0.5 |
8,996,371 | 1 | 3 | 1. A method for adapting a language model to a specific environment, the method comprising: receiving, by a processor, a plurality of interactions captured in the specific environment; retrieving, by the processor, a plurality of external documents based on a plurality of query expressions, wherein query expressions are generated by applying a topic detection algorithm on textual transcripts in order to detect the different topics discussed in the plurality of interactions; generating, by the processor, an external corpus by selecting documents from the documents retrieved; detecting in the external corpus terms related to the environment that are not included in an initial language model; and adapting, by the processor, the initial language model to include a plurality of the terms detected. | 1. A method for adapting a language model to a specific environment, the method comprising: receiving, by a processor, a plurality of interactions captured in the specific environment; retrieving, by the processor, a plurality of external documents based on a plurality of query expressions, wherein query expressions are generated by applying a topic detection algorithm on textual transcripts in order to detect the different topics discussed in the plurality of interactions; generating, by the processor, an external corpus by selecting documents from the documents retrieved; detecting in the external corpus terms related to the environment that are not included in an initial language model; and adapting, by the processor, the initial language model to include a plurality of the terms detected. 3. The method of claim 1 , wherein generating the external corpus comprises selecting a plurality of documents from the documents retrieved based on semantic similarity between the documents retrieved and the plurality of interactions. | 0.623397 |
4,069,393 | 7 | 8 | 7. A method for receiving spoken input training words and a subsequent spoken input command word and generating a correlation function that is indicative of the resemblance of the command word to each training word, comprising the steps of: a. extracting features from received input words and generating digital feature output signals on particular ones of a number of feature output lines, the particular ones being dependent upon the features present in an input word; b. storing, as a time dependent matrix, the presence or absence status of the feature signals which occur during each training word; c. storing, as a time dependent matrix, the presence or absence status of the feature signals which occur during the command word; and d. comparing, member by member, the command word matrix with each training word matrix and generating a correlation figure which reflects each matrix comparison, the member comparison taking account only of the presence of features in the matrices being compared. | 7. A method for receiving spoken input training words and a subsequent spoken input command word and generating a correlation function that is indicative of the resemblance of the command word to each training word, comprising the steps of: a. extracting features from received input words and generating digital feature output signals on particular ones of a number of feature output lines, the particular ones being dependent upon the features present in an input word; b. storing, as a time dependent matrix, the presence or absence status of the feature signals which occur during each training word; c. storing, as a time dependent matrix, the presence or absence status of the feature signals which occur during the command word; and d. comparing, member by member, the command word matrix with each training word matrix and generating a correlation figure which reflects each matrix comparison, the member comparison taking account only of the presence of features in the matrices being compared. 8. The method as defined by claim 7 further comprising a step of time normalizing the training word matrices and the command word matrix before the comparisons thereof. | 0.648536 |
9,997,158 | 1 | 7 | 1. A system comprising a user device and one or more storage devices on which are stored instructions that are operable, when executed by the user device, to cause the user device to perform operations comprising: receiving, by a computer-implemented agent specific to the user device, a digital representation of speech encoding an utterance; in response to processing the digital representation of the speech to determine the words included in the utterance, determining, using the utterance, to request input identifying a sentiment of a user who spoke the utterance; in response to determining to request the input identifying a sentiment of the user who spoke the utterance, presenting a request for user input identifying a sentiment; in response to presenting the request, receiving user input identifying a sentiment of a speaker of the utterance; determining, by the computer-implemented agent, that the utterance specifies a request for another computer-implemented agent that is a different computer-implemented agent than the computer-implemented agent by: determining, by the computer-implemented agent using the utterance, that the utterance is not specific to a particular second computer-implemented agent; in response to determining that the utterance is not specific to a particular second computer-implemented agent, determining, by the computer-implemented agent using the utterance, two or more second computer-implemented agents each of which can respond to the utterance; in response to determining the two or more second computer-implemented agents each of which can respond to the utterance, automatically selecting, without receiving user input, one of the second computer-implemented agents each of which can respond to the utterance; and in response to automatically selecting, without receiving user input, one of the second computer-implemented agents each of which can respond to the utterance, determining, by the computer-implemented agent, that the utterance specifies a request for the selected one of the second computer-implemented agents; providing, by the computer-implemented agent to the other computer-implemented agent, a representation of the utterance and data identifying the sentiment and user profile data for a user who spoke the utterance to enable the other computer-implemented agent to automatically complete a transaction using the user profile data without requesting, from the user, data that is included in the user profile data; receiving, by the user device, a response to the utterance for the other computer-implemented agent that was determined using the sentiment; and in response to receiving identification of the response, presenting, by the user device, the response to the utterance received from the other computer-implemented agent that was determined using the sentiment. | 1. A system comprising a user device and one or more storage devices on which are stored instructions that are operable, when executed by the user device, to cause the user device to perform operations comprising: receiving, by a computer-implemented agent specific to the user device, a digital representation of speech encoding an utterance; in response to processing the digital representation of the speech to determine the words included in the utterance, determining, using the utterance, to request input identifying a sentiment of a user who spoke the utterance; in response to determining to request the input identifying a sentiment of the user who spoke the utterance, presenting a request for user input identifying a sentiment; in response to presenting the request, receiving user input identifying a sentiment of a speaker of the utterance; determining, by the computer-implemented agent, that the utterance specifies a request for another computer-implemented agent that is a different computer-implemented agent than the computer-implemented agent by: determining, by the computer-implemented agent using the utterance, that the utterance is not specific to a particular second computer-implemented agent; in response to determining that the utterance is not specific to a particular second computer-implemented agent, determining, by the computer-implemented agent using the utterance, two or more second computer-implemented agents each of which can respond to the utterance; in response to determining the two or more second computer-implemented agents each of which can respond to the utterance, automatically selecting, without receiving user input, one of the second computer-implemented agents each of which can respond to the utterance; and in response to automatically selecting, without receiving user input, one of the second computer-implemented agents each of which can respond to the utterance, determining, by the computer-implemented agent, that the utterance specifies a request for the selected one of the second computer-implemented agents; providing, by the computer-implemented agent to the other computer-implemented agent, a representation of the utterance and data identifying the sentiment and user profile data for a user who spoke the utterance to enable the other computer-implemented agent to automatically complete a transaction using the user profile data without requesting, from the user, data that is included in the user profile data; receiving, by the user device, a response to the utterance for the other computer-implemented agent that was determined using the sentiment; and in response to receiving identification of the response, presenting, by the user device, the response to the utterance received from the other computer-implemented agent that was determined using the sentiment. 7. The system of claim 1 comprising: data, stored on the user device, that represents the other computer-implemented agent and includes text-to-speech parameters for responses provided by the other computer-implemented agent, the operations comprising: determining, by the other computer-implemented agent implemented on the user device using the sentiment and the data that represents the other computer-implemented agent, the response, wherein: presenting, by the user device, the response to the utterance received from the other computer-implemented agent that was determined using the sentiment comprises presenting, by the user device, the response using the text-to-speech parameters. | 0.5 |
8,706,715 | 1 | 2 | 1. A computer-implemented method of improving a query, the method comprising: receiving at a network interface of a server in a multi-tenant database system an original query transmitted to the multi-tenant database system by a user associated with a tenant, wherein the original query is associated with data accessible only by the tenant, and wherein the multi-tenant database system includes at least a first index and a second index, wherein the first index is a standard index and wherein the second index is a custom index to provide a private sharing paradigm within the multi-tenant database system that allows groups defined within one or more particular tenants to share information only among members of that group; retrieving, using a processor of the server, metadata associated with the data accessible only by the tenant in the multi-tenant database system, wherein at least a portion of the data accessible only by the tenant is stored in a common table within the multi-tenant database system; scanning a first index column to identify a first set of rows, wherein the first index column is selected based on the original query; scanning a second index column to identify a second set of rows, wherein the second index column is based on the original query; determining a third set of rows corresponding to an intersection of the first set of rows and the second set of rows; determining, using the processor, a tenant-selective query syntax, wherein determining comprises analyzing at least one of metadata generated from information about the tenant or metadata generated from the data accessible by the tenant; and generating, using the processor, an improved query using the query syntax, wherein the improved query is based upon the original query and the third set of rows. | 1. A computer-implemented method of improving a query, the method comprising: receiving at a network interface of a server in a multi-tenant database system an original query transmitted to the multi-tenant database system by a user associated with a tenant, wherein the original query is associated with data accessible only by the tenant, and wherein the multi-tenant database system includes at least a first index and a second index, wherein the first index is a standard index and wherein the second index is a custom index to provide a private sharing paradigm within the multi-tenant database system that allows groups defined within one or more particular tenants to share information only among members of that group; retrieving, using a processor of the server, metadata associated with the data accessible only by the tenant in the multi-tenant database system, wherein at least a portion of the data accessible only by the tenant is stored in a common table within the multi-tenant database system; scanning a first index column to identify a first set of rows, wherein the first index column is selected based on the original query; scanning a second index column to identify a second set of rows, wherein the second index column is based on the original query; determining a third set of rows corresponding to an intersection of the first set of rows and the second set of rows; determining, using the processor, a tenant-selective query syntax, wherein determining comprises analyzing at least one of metadata generated from information about the tenant or metadata generated from the data accessible by the tenant; and generating, using the processor, an improved query using the query syntax, wherein the improved query is based upon the original query and the third set of rows. 2. The method of claim 1 , further comprising: receiving information identifying the user; retrieving, using the processor, metadata about the user; and wherein determining, using the processor, comprises analyzing at least one of the group consisting of metadata generated from information about the user, metadata generated from information about the tenant, and metadata generated from the data accessible by the tenant. | 0.5 |
8,594,468 | 1 | 3 | 1. A method of annotating a personal image comprising: compiling visual features and textual information from a plurality of images; hashing the visual features; clustering the plurality of images based at least in part on a hash value, the clustering creating clustered images; building one or more statistical language models based at least in part on the clustered images; and annotating the personal image by selecting words with a maximum joint probability between the personal image and the clustered images. | 1. A method of annotating a personal image comprising: compiling visual features and textual information from a plurality of images; hashing the visual features; clustering the plurality of images based at least in part on a hash value, the clustering creating clustered images; building one or more statistical language models based at least in part on the clustered images; and annotating the personal image by selecting words with a maximum joint probability between the personal image and the clustered images. 3. A method as recited in claim 1 , wherein the plurality of images with a same hash value are grouped into the clustered images. | 0.806306 |
8,234,117 | 1 | 2 | 1. A speech-synthesis device, comprising: a speech-synthesis unit configured to perform read-aloud processing; a user dictionary provided to register read-aloud information corresponding to a specific phrase for the speech-synthesis unit according to a user instruction, wherein the user dictionary is configured to be used commonly by a plurality of communication partner selection functions that can register name information corresponding to a name of a communication partner; and a determination unit configured to determine whether the user dictionary is to be used in a case where any one of a plurality of functions using the read-aloud processing by the speech-synthesis unit is selected, wherein the determination unit determines that the user dictionary is to be used in a case where a communication partner selection function is selected and determines that the user dictionary is not to be used in a case where a predetermined function other than the communication partner selection function is selected, and wherein, in a case where any one of the plurality of communication partner selection functions is executed and the read-aloud processing corresponding to the name information is performed by the speech-synthesis unit, whatever communication partner selection function is executed from among the plurality of communication partner selection functions, the speech-synthesis unit performs the read-aloud processing corresponding to the name information by using the user dictionary when the name of the communication partner is read-aloud. | 1. A speech-synthesis device, comprising: a speech-synthesis unit configured to perform read-aloud processing; a user dictionary provided to register read-aloud information corresponding to a specific phrase for the speech-synthesis unit according to a user instruction, wherein the user dictionary is configured to be used commonly by a plurality of communication partner selection functions that can register name information corresponding to a name of a communication partner; and a determination unit configured to determine whether the user dictionary is to be used in a case where any one of a plurality of functions using the read-aloud processing by the speech-synthesis unit is selected, wherein the determination unit determines that the user dictionary is to be used in a case where a communication partner selection function is selected and determines that the user dictionary is not to be used in a case where a predetermined function other than the communication partner selection function is selected, and wherein, in a case where any one of the plurality of communication partner selection functions is executed and the read-aloud processing corresponding to the name information is performed by the speech-synthesis unit, whatever communication partner selection function is executed from among the plurality of communication partner selection functions, the speech-synthesis unit performs the read-aloud processing corresponding to the name information by using the user dictionary when the name of the communication partner is read-aloud. 2. The speech-synthesis device according to claim 1 , wherein the speech-synthesis unit has a mode of operating by using a combination of at least two dictionaries, and wherein the mode can be selected from at least one speech-synthesis function of calling up speech-synthesis processing. | 0.761983 |
6,144,997 | 1 | 15 | 1. A system for communicating with a portable electronic document reference transport device, the portable electronic document reference transport device having a memory for storing document references, said system comprising: a wire-based network for providing a communications link between devices coupled thereto; a transceiver coupled to said wire-based network for establishing a wireless communications link with the portable electronic document reference transport device and devices coupled to said wire-based network; a subsystem coupled to said wire-based network and communicating with said transceiver; said subsystem being adapted to operate a database that associates electronic document references with electronic documents; each electronic document reference identifying a location of a single electronic document stored in a memory on a device operating on said wire-based network; said subsystem receiving, from said transceiver, a communication originating from the portable electronic document reference transport device; the communication including an electronic document reference that is associated with a selected electronic document and a request for a service provided by a device coupled to the wire-based network that is to be performed on the selected electronic document. | 1. A system for communicating with a portable electronic document reference transport device, the portable electronic document reference transport device having a memory for storing document references, said system comprising: a wire-based network for providing a communications link between devices coupled thereto; a transceiver coupled to said wire-based network for establishing a wireless communications link with the portable electronic document reference transport device and devices coupled to said wire-based network; a subsystem coupled to said wire-based network and communicating with said transceiver; said subsystem being adapted to operate a database that associates electronic document references with electronic documents; each electronic document reference identifying a location of a single electronic document stored in a memory on a device operating on said wire-based network; said subsystem receiving, from said transceiver, a communication originating from the portable electronic document reference transport device; the communication including an electronic document reference that is associated with a selected electronic document and a request for a service provided by a device coupled to the wire-based network that is to be performed on the selected electronic document. 15. The system according to claim 1, wherein said transceiver communicates with the portable electronic document reference transport device over a public telephone network. | 0.679104 |
9,760,624 | 11 | 19 | 11. The system of claim 2 , wherein the operations further comprise: loading the online resource in the web browser; and determining whether contents of the online resource have textual information that exceeds at least one text threshold, wherein the accessing, the calculating, and the selecting are performed when the contents of the online resource have textual information that exceeds at least one text threshold. | 11. The system of claim 2 , wherein the operations further comprise: loading the online resource in the web browser; and determining whether contents of the online resource have textual information that exceeds at least one text threshold, wherein the accessing, the calculating, and the selecting are performed when the contents of the online resource have textual information that exceeds at least one text threshold. 19. The system of claim 11 , wherein the at least one text threshold comprises a number of characters in a particular language. | 0.853009 |
9,811,171 | 1 | 3 | 1. A method of multimodal text input in a mobile device, the method comprising: using an original communication interface between an original keyboard module of the mobile device and a third party application to enable communication between a multimodal input module, that replaces the original keyboard module, and the third party application by: executing the multimodal input module by: steadily running the multimodal input module in the background of the mobile device and constantly monitoring in the background of the mobile device to detect when a text input field of the third party application is activated; and responding to detecting that the text input field of the third party application is activated by: activating a keyboard mode; displaying an A-Z-keyboard in a first field of a display for text input; automatically activating a camera mode when the keyboard mode is activated; capturing an image of written text having characters different from characters of the A-Z-keyboard, reducing a size of the A-Z-keyboard, displaying the A-Z-keyboard reduced in a reduced first field, and displaying the captured image with the written text in a second field of the display of the mobile device, the reduced first field and the second field together occupying a same field size as the first field; converting the captured image to character text by optical character recognition (OCR) and displaying the recognized character text on the display; and outputting a selected part of the recognized character text as the input text to the third party application receiving the input text upon a selection of the part of the recognized character text, wherein the outputting to the third party application from the multimodal input module is via the original communication interface to the third party application as between the original keyboard module and the third party application, and wherein the multimodal input module is configured to enable the respective selection to take place by a single keypress or control command, or by a single gesture. | 1. A method of multimodal text input in a mobile device, the method comprising: using an original communication interface between an original keyboard module of the mobile device and a third party application to enable communication between a multimodal input module, that replaces the original keyboard module, and the third party application by: executing the multimodal input module by: steadily running the multimodal input module in the background of the mobile device and constantly monitoring in the background of the mobile device to detect when a text input field of the third party application is activated; and responding to detecting that the text input field of the third party application is activated by: activating a keyboard mode; displaying an A-Z-keyboard in a first field of a display for text input; automatically activating a camera mode when the keyboard mode is activated; capturing an image of written text having characters different from characters of the A-Z-keyboard, reducing a size of the A-Z-keyboard, displaying the A-Z-keyboard reduced in a reduced first field, and displaying the captured image with the written text in a second field of the display of the mobile device, the reduced first field and the second field together occupying a same field size as the first field; converting the captured image to character text by optical character recognition (OCR) and displaying the recognized character text on the display; and outputting a selected part of the recognized character text as the input text to the third party application receiving the input text upon a selection of the part of the recognized character text, wherein the outputting to the third party application from the multimodal input module is via the original communication interface to the third party application as between the original keyboard module and the third party application, and wherein the multimodal input module is configured to enable the respective selection to take place by a single keypress or control command, or by a single gesture. 3. The method according to claim 1 , wherein converting the captured image to character text by optical character recognition and displaying the recognized character text on the display comprises: determining one or more suggestion candidates using an algorithm in connection with a database; and displaying the one or more suggestion candidates in one or more third fields or as an overlay within the second field, wherein one or more of the one or more candidates are selectable by a keypress event. | 0.693015 |
7,664,730 | 5 | 6 | 5. The profile of claim 1 , wherein the tuning data further comprises: optimization information for the query statement; and an action to set a parameter of an optimizer based on the optimization information. | 5. The profile of claim 1 , wherein the tuning data further comprises: optimization information for the query statement; and an action to set a parameter of an optimizer based on the optimization information. 6. The profile of claim 5 , wherein the optimization information is related to an execution history of the query statement. | 0.5 |
6,118,451 | 4 | 5 | 4. The operating system of claim 1, wherein the plurality of dialog boxes are logically associated in a hierarchical relationship based on the location of the dialog launch display element used to launch each dialog box, said hierarchical relationship defining parent-child relationships between said plurality of dialog boxes, and further wherein said dialog launch modalities comprise: a modal dialog launch modality wherein only the selected dialog box, as an active dialog box, and its parent dialog boxes are open simultaneously, and wherein the user can only interact with said active dialog box until said active dialog box is closed; a modeless dialog launch modality wherein any number of dialog boxes may be open simultaneously, and wherein the user can interact with any aspect of the graphical user interface other than the active dialog box while the active dialog box is open; and a semi-modeless dialog launch modality wherein only the selected dialog box, as an active dialog box, and parent dialog boxes of the selected dialog box are open simultaneously, and wherein a user can interact with selected aspects of the graphical user interface other than the active dialog box while the active dialog box is open. | 4. The operating system of claim 1, wherein the plurality of dialog boxes are logically associated in a hierarchical relationship based on the location of the dialog launch display element used to launch each dialog box, said hierarchical relationship defining parent-child relationships between said plurality of dialog boxes, and further wherein said dialog launch modalities comprise: a modal dialog launch modality wherein only the selected dialog box, as an active dialog box, and its parent dialog boxes are open simultaneously, and wherein the user can only interact with said active dialog box until said active dialog box is closed; a modeless dialog launch modality wherein any number of dialog boxes may be open simultaneously, and wherein the user can interact with any aspect of the graphical user interface other than the active dialog box while the active dialog box is open; and a semi-modeless dialog launch modality wherein only the selected dialog box, as an active dialog box, and parent dialog boxes of the selected dialog box are open simultaneously, and wherein a user can interact with selected aspects of the graphical user interface other than the active dialog box while the active dialog box is open. 5. The operating system of claim 4, wherein said selected aspects of the graphical user interface comprises: one or more open parent dialog boxes of said selected dialog box. | 0.5 |
9,886,525 | 6 | 8 | 6. The system of claim 5 , wherein the map display further includes an area of interest. | 6. The system of claim 5 , wherein the map display further includes an area of interest. 8. The system of claim 6 , wherein the area of interest comprises a predicted weather track. | 0.800866 |
8,321,446 | 1 | 10 | 1. A computer-implemented method, comprising: receiving a search query for a particular application from an input field of a user interface; performing a keyword search based on the search query to generate keyword search results; performing a natural language search of a frequently-asked question database based on the search query to generate frequently-asked question search results; and outputting a first display page, wherein the first display page categorizes the keyword search results and the frequently-asked question search results into a plurality of categories, wherein each category of the first display page is associated with a category title, a first display region, and a second display region that is separate from the first display region, wherein the first display region of a particular category includes one or more keyword search results associated with the particular category and does not include any of the frequently-asked question search results, and wherein the second display region associated with the particular category includes one or more frequently-asked question search results associated with the particular category and does not include any of the keyword search results; wherein a statistical cluster analysis is performed to determine the plurality of categories for the particular application based on group assignments and name assignments. | 1. A computer-implemented method, comprising: receiving a search query for a particular application from an input field of a user interface; performing a keyword search based on the search query to generate keyword search results; performing a natural language search of a frequently-asked question database based on the search query to generate frequently-asked question search results; and outputting a first display page, wherein the first display page categorizes the keyword search results and the frequently-asked question search results into a plurality of categories, wherein each category of the first display page is associated with a category title, a first display region, and a second display region that is separate from the first display region, wherein the first display region of a particular category includes one or more keyword search results associated with the particular category and does not include any of the frequently-asked question search results, and wherein the second display region associated with the particular category includes one or more frequently-asked question search results associated with the particular category and does not include any of the keyword search results; wherein a statistical cluster analysis is performed to determine the plurality of categories for the particular application based on group assignments and name assignments. 10. The computer-implemented method of claim 1 , wherein the first display region associated with the particular category is a first column and wherein the second display region associated with the particular category is a second column adjacent to the first column. | 0.595745 |
9,576,262 | 10 | 12 | 10. The system of claim 8 , the decision component predicts a risk of fraud for an input transaction. | 10. The system of claim 8 , the decision component predicts a risk of fraud for an input transaction. 12. The system of claim 10 , the multiple levels of granularity include industry, merchant, and product. | 0.5 |
9,940,381 | 1 | 8 | 1. A method, comprising: obtaining a collection of clusters of immutable observations about entities, at least a plurality of the clusters each: corresponding to a respective entity, identifying immutable observations determined to describe the respective entity, and having summary attribute-value pairs that summarize the identified observations and collectively describe an inferred current state of the respective entity; receiving a new observation about a given entity; selecting, with one or more processors, a cluster among the collection of clusters based on correspondence of the selected cluster to the given entity; summarizing, with one or more processors, the selected cluster by updating at least some of the summary attribute-value pairs of the selected cluster based on both the new observation and the observations identified by the selected cluster; storing the updated attribute-value pairs in memory in association with the selected cluster; and updating a user interface at a user device using the new observation. | 1. A method, comprising: obtaining a collection of clusters of immutable observations about entities, at least a plurality of the clusters each: corresponding to a respective entity, identifying immutable observations determined to describe the respective entity, and having summary attribute-value pairs that summarize the identified observations and collectively describe an inferred current state of the respective entity; receiving a new observation about a given entity; selecting, with one or more processors, a cluster among the collection of clusters based on correspondence of the selected cluster to the given entity; summarizing, with one or more processors, the selected cluster by updating at least some of the summary attribute-value pairs of the selected cluster based on both the new observation and the observations identified by the selected cluster; storing the updated attribute-value pairs in memory in association with the selected cluster; and updating a user interface at a user device using the new observation. 8. The method of claim 1 , wherein: the new observation includes a user-generated value of an attribute of the given entity and a context that includes the values of one or more other attributes of the given entity; and selecting a cluster among the collection of clusters as corresponding to the given entity comprises: composing at least part of a query based on one more of the values of attributes included in the context; identifying candidate clusters responsive to the query based on the candidate clusters having summary attribute-value pairs that match the attribute values upon which the query is based; calculating a respective score for each of the candidate clusters; and associating the new observation with a candidate cluster selected based on the calculated scores. | 0.679508 |
9,501,551 | 1 | 5 | 1. A system for categorizing items, the system comprising: a data store that stores item information related to a first plurality of items offered for sale using a network-based service; and a computing device having one or more processors, wherein the computing device is configured to operate the network-based service and is in communication with the data store, and wherein the network-based service is operative to: receive a request for a category recommendation for an item of the first plurality of items, the request including first item information associated with the item; and submit the first item information to a categorization service, wherein the categorization service is operative to: generate an item vector from the first item information according to a vector space model; compare the item vector to at least one category vector, wherein the at least one category vector comprises a representation of at least a portion of a textual description of an item category according to the vector space model, wherein the item category is maintained by the network-based service and associated with a second plurality of items; determine the category recommendation to be the item category when a deviation of an angle between the item vector and the at least one category vector is less than a threshold value; and update the first item information to include the item category. | 1. A system for categorizing items, the system comprising: a data store that stores item information related to a first plurality of items offered for sale using a network-based service; and a computing device having one or more processors, wherein the computing device is configured to operate the network-based service and is in communication with the data store, and wherein the network-based service is operative to: receive a request for a category recommendation for an item of the first plurality of items, the request including first item information associated with the item; and submit the first item information to a categorization service, wherein the categorization service is operative to: generate an item vector from the first item information according to a vector space model; compare the item vector to at least one category vector, wherein the at least one category vector comprises a representation of at least a portion of a textual description of an item category according to the vector space model, wherein the item category is maintained by the network-based service and associated with a second plurality of items; determine the category recommendation to be the item category when a deviation of an angle between the item vector and the at least one category vector is less than a threshold value; and update the first item information to include the item category. 5. The system of claim 1 , wherein a value of a term in the item vector is determined based on a frequency with which the term occurs in the first item information and a term frequency-inverse document frequency of the term. | 0.5 |
8,429,188 | 1 | 20 | 1. A computer implemented user-interface method of selecting and presenting a collection of content items in which the presentation is ordered at least in part based on analyzing user selections of content items to learn the content preferences of the user according to a context within which the user selected the content and using the learned content preferences to select and order subsequent user content search results when the user is within the same context, the method comprising: providing access to a set of content items, each content item having at least one associated descriptive term to describe the content item, the content items of the set being organized into categories of related content items; receiving incremental input entered by the user for incrementally identifying desired content items of the set, wherein the incremental input includes at least one character in a series of characters; in response to each character of the incremental input entered by the user, presenting a subset of content items of the set to the user; receiving actions from the user selecting content items of the subset; analyzing the date, day, and time of day of the user selection actions and analyzing the descriptive terms of the selected content items to learn content preferences of the user and to learn a plurality of periodicities of user selections of similar content items, wherein similarity is determined by comparing the descriptive terms of the selected content item with the previously selected content item, and wherein each periodicity indicates an amount of time between user selections of similar content items relative to a reference point; associating each learned periodicity with the respective similar content items and the respective descriptive terms of the similar content items; determining the context in which the user performed the selection actions, the context including a geographic location of the user at the time of the selection actions and at least one of: date, day, and time of day; associating the determined contexts of the user selection actions, including the geographic location of the user at the time of the selection actions, with the user content preferences learned from the corresponding user selections; in response to receiving subsequent incremental input entered by the user, determining a context of said subsequent incremental input and selecting and ordering a collection of content items from the set based on a comparison of the descriptive terms of the content items of the collection with the learned content preferences of the user associated with the determined context in which the user entered the subsequent incremental input and further based on (i) promoting the ranking of those content items associated with descriptive terms further associated with periodicities similar to the date, day, and time of day of the subsequent incremental input, and (ii) promoting the ranking of those content items associated with a first periodicity similar to the date, day, and time of day of the subsequent incremental input relative to the ranking of those content items associated with a second periodicity similar to the date, day, and time of day of the subsequent incremental input; and presenting said collection of content items to the user on a display screen. | 1. A computer implemented user-interface method of selecting and presenting a collection of content items in which the presentation is ordered at least in part based on analyzing user selections of content items to learn the content preferences of the user according to a context within which the user selected the content and using the learned content preferences to select and order subsequent user content search results when the user is within the same context, the method comprising: providing access to a set of content items, each content item having at least one associated descriptive term to describe the content item, the content items of the set being organized into categories of related content items; receiving incremental input entered by the user for incrementally identifying desired content items of the set, wherein the incremental input includes at least one character in a series of characters; in response to each character of the incremental input entered by the user, presenting a subset of content items of the set to the user; receiving actions from the user selecting content items of the subset; analyzing the date, day, and time of day of the user selection actions and analyzing the descriptive terms of the selected content items to learn content preferences of the user and to learn a plurality of periodicities of user selections of similar content items, wherein similarity is determined by comparing the descriptive terms of the selected content item with the previously selected content item, and wherein each periodicity indicates an amount of time between user selections of similar content items relative to a reference point; associating each learned periodicity with the respective similar content items and the respective descriptive terms of the similar content items; determining the context in which the user performed the selection actions, the context including a geographic location of the user at the time of the selection actions and at least one of: date, day, and time of day; associating the determined contexts of the user selection actions, including the geographic location of the user at the time of the selection actions, with the user content preferences learned from the corresponding user selections; in response to receiving subsequent incremental input entered by the user, determining a context of said subsequent incremental input and selecting and ordering a collection of content items from the set based on a comparison of the descriptive terms of the content items of the collection with the learned content preferences of the user associated with the determined context in which the user entered the subsequent incremental input and further based on (i) promoting the ranking of those content items associated with descriptive terms further associated with periodicities similar to the date, day, and time of day of the subsequent incremental input, and (ii) promoting the ranking of those content items associated with a first periodicity similar to the date, day, and time of day of the subsequent incremental input relative to the ranking of those content items associated with a second periodicity similar to the date, day, and time of day of the subsequent incremental input; and presenting said collection of content items to the user on a display screen. 20. The method of claim 1 , wherein at least one of receiving incremental input, presenting the subset of content items, receiving actions from the user, analyzing the descriptive terms, determining the context in which the user performed the selection actions, associating the determined contexts, determining a context of the subsequent incremental input, and selecting and ordering the collection of content items is performed on a server system remote from the user. | 0.654919 |
9,009,192 | 28 | 31 | 28. The system of claim 23 , wherein selecting a web resource referenced by a particular search result of the obtained search results as relevant additional content for the first web resource comprises: generating a first score for each of one or more of the identified central entities based at least in part on weights of outgoing edges of respective nodes corresponding to the identified central entities; determining that the particular search result was identified as being responsive to a search query generated for a central entity having a highest first score among the one or more identified central entities; and selecting a web resource referenced by the particular search result. | 28. The system of claim 23 , wherein selecting a web resource referenced by a particular search result of the obtained search results as relevant additional content for the first web resource comprises: generating a first score for each of one or more of the identified central entities based at least in part on weights of outgoing edges of respective nodes corresponding to the identified central entities; determining that the particular search result was identified as being responsive to a search query generated for a central entity having a highest first score among the one or more identified central entities; and selecting a web resource referenced by the particular search result. 31. The system of claim 28 , wherein the first score for the particular identified central entity is based at least in part on a frequency of occurrence of the identified central entity in the collection of resources used to generate the first entity graph. | 0.5 |
8,335,679 | 15 | 19 | 15. A system for local computer-aided translation using remotely generated translation predictions comprising: a remote translation server comprising: a receiver receiving a request for a translation of a document from a first one of a plurality of local machines; a remote translation memory providing access to a stored translation; a means for determining that the stored translation is useful in translating a first portion of a document; a means for identifying a modification of the translation of the first portion of the document as useful in the translation of a second portion of the document prior to receiving a request to translate a second portion of the document and generating a translation of the second portion of the document using the modification of the translation of the first portion of the document, responsive to the identification of the utility of the modification of the translation of the first portion of the document in the translation of the second portion of the document; and a means for transmitting the stored translation; and the first one local machine comprising: a means for receiving a translation of the first portion of the document, a means for storing the translation of the first portion of the document, a means for receiving from a user the modification of the translation of the first portion of the document, a means for transmitting to the remote translation server the modification of the translation of the first portion of the document; a means for receiving the translation of a second portion of the document, the translation of the second portion of the document generated using the modification of the translation of the first portion of the document; and a transmitter transmitting the request to the remote translation server; and transmitting the modification of the translation of the first portion of the document to the remote translation server. | 15. A system for local computer-aided translation using remotely generated translation predictions comprising: a remote translation server comprising: a receiver receiving a request for a translation of a document from a first one of a plurality of local machines; a remote translation memory providing access to a stored translation; a means for determining that the stored translation is useful in translating a first portion of a document; a means for identifying a modification of the translation of the first portion of the document as useful in the translation of a second portion of the document prior to receiving a request to translate a second portion of the document and generating a translation of the second portion of the document using the modification of the translation of the first portion of the document, responsive to the identification of the utility of the modification of the translation of the first portion of the document in the translation of the second portion of the document; and a means for transmitting the stored translation; and the first one local machine comprising: a means for receiving a translation of the first portion of the document, a means for storing the translation of the first portion of the document, a means for receiving from a user the modification of the translation of the first portion of the document, a means for transmitting to the remote translation server the modification of the translation of the first portion of the document; a means for receiving the translation of a second portion of the document, the translation of the second portion of the document generated using the modification of the translation of the first portion of the document; and a transmitter transmitting the request to the remote translation server; and transmitting the modification of the translation of the first portion of the document to the remote translation server. 19. The system of claim 15 , wherein the means for determining that the stored translation is useful further comprises a means for determining whether an updated version of a translation makes a received translation obsolete. | 0.521277 |
9,848,068 | 12 | 13 | 12. The method of claim 4 , further comprising updating the plurality of counters in response to one of an addition of a new rule to the table and a removal of a rule from the table. | 12. The method of claim 4 , further comprising updating the plurality of counters in response to one of an addition of a new rule to the table and a removal of a rule from the table. 13. The method of claim 12 , wherein updating the plurality of counters in response to an addition of the new rule comprises incrementing each of the plurality of counters associated with the match fields that require extraction based on the new rule. | 0.5 |
8,538,808 | 13 | 14 | 13. The method of claim 12 , wherein the creative vector comprises a web page, wherein inspecting a technical attribute comprises: detecting an interaction between the candidate creative and the browser. | 13. The method of claim 12 , wherein the creative vector comprises a web page, wherein inspecting a technical attribute comprises: detecting an interaction between the candidate creative and the browser. 14. The method of claim 13 , wherein detecting an interaction comprises detecting an attempt to alter a state of a window associated with the browser. | 0.5 |
7,698,080 | 1 | 8 | 1. A method, comprising: measuring spectral information for a sample with a measurement system and determining, based on the measured spectral information, probabilities that the sample corresponds to each of a plurality of candidates in a library of reference information; determining, based on the probabilities, whether sample identity information can be obtained by comparing the measured spectral information to the reference information; when the sample identity information cannot be obtained, adjusting a characteristic of the measurement system to increase a magnitude of one of the probabilities relative to the other probabilities, and repeating the steps of measuring spectral information, determining the probabilities, and determining whether the sample identity information can be obtained, until the sample identity information can be obtained; and using an electronic processor to compare the measured spectral information to the reference information to determine the sample identity information. | 1. A method, comprising: measuring spectral information for a sample with a measurement system and determining, based on the measured spectral information, probabilities that the sample corresponds to each of a plurality of candidates in a library of reference information; determining, based on the probabilities, whether sample identity information can be obtained by comparing the measured spectral information to the reference information; when the sample identity information cannot be obtained, adjusting a characteristic of the measurement system to increase a magnitude of one of the probabilities relative to the other probabilities, and repeating the steps of measuring spectral information, determining the probabilities, and determining whether the sample identity information can be obtained, until the sample identity information can be obtained; and using an electronic processor to compare the measured spectral information to the reference information to determine the sample identity information. 8. The method of claim 1 , wherein adjusting the characteristic of the measurement system comprises adjusting an intensity of a radiation source configured to illuminate the sample with incident radiation. | 0.816308 |
9,535,983 | 3 | 4 | 3. The method in accordance with claim 1 , further comprising: an act of performing a search on the plurality of text sample entry groups. | 3. The method in accordance with claim 1 , further comprising: an act of performing a search on the plurality of text sample entry groups. 4. The method in accordance with claim 3 , wherein the act of performing a search comprises an act of performing a search for a sequence of text components, the method comprising: an act of identifying a plurality of text components in the sequence of text components to be searched for; an act of scanning through the text sample identifiers of the plurality of text sample entry groups in search of a text component identifier associated with a first text component in the sequence of text components; whenever upon finding a text component identifier associated with the first text component during the act of scanning, performing the following: an act of confirming whether or not the found text component identifier in association with one or more text component identifiers that follow within the same text sample entry group collectively identify the sequence of text components to be searched for; and if the act of confirming confirms that the found text component identifier in association with the one or more text component identifiers that follow within the same text sample entry group do collectively identify the sequence of text component to be searched for, an act of using the corresponding text sample identifier to identify the text sample that includes the sequence to be searched for. | 0.5 |
6,047,255 | 6 | 8 | 6. The system of claim 5, further comprising an input device interconnected to said processor, said input device adapted to receive and provide commands to said processor to produce speech signals at said output device. | 6. The system of claim 5, further comprising an input device interconnected to said processor, said input device adapted to receive and provide commands to said processor to produce speech signals at said output device. 8. The system of claim 6, wherein said output device comprises a digital to analog converter. | 0.5 |
7,925,735 | 11 | 16 | 11. A computer-implemented method that facilitates late network binding comprising: invoking a generic application type interface for a request from a client to an application gateway of a network; analyzing historical data to generate a context for the request; publishing a new event via an indicating mechanism associated with an application, after an internal behavior of the application is altered; and inferring a late network application binding for the request based in-part on the context, wherein the late network application binding is specific to a web service of the network, and the late network application binding is overridable by a choice of the client. | 11. A computer-implemented method that facilitates late network binding comprising: invoking a generic application type interface for a request from a client to an application gateway of a network; analyzing historical data to generate a context for the request; publishing a new event via an indicating mechanism associated with an application, after an internal behavior of the application is altered; and inferring a late network application binding for the request based in-part on the context, wherein the late network application binding is specific to a web service of the network, and the late network application binding is overridable by a choice of the client. 16. The method of claim 11 , further comprising applying a profile associated with the application to determine the context. | 0.711628 |
7,962,578 | 1 | 16 | 1. A computer-implemented method for managing a conversational system on a server, the method comprising: presenting on a desktop of a computer of a first party, at least one sales chatbot as part of at least one messaging window at a website of a second party, the chatbot adapted to sell at least one of a good and a service; displaying a message to the first party through the messaging window; and reviewing a response from the first party using a combination of scripting and artificial intelligence; wherein the scripting, the messaging window and the artificial intelligence are all managed via a web-based management console of a third party, the web-based management console includes user selectable components for managing a chat window of the chatbot, separate from a window of the website of the second party for display on the computer of the first party, the user selectable components including at least one event/response pair trigger to launch the chatbot; and at least one sales pitch to be presented by the chatbot. | 1. A computer-implemented method for managing a conversational system on a server, the method comprising: presenting on a desktop of a computer of a first party, at least one sales chatbot as part of at least one messaging window at a website of a second party, the chatbot adapted to sell at least one of a good and a service; displaying a message to the first party through the messaging window; and reviewing a response from the first party using a combination of scripting and artificial intelligence; wherein the scripting, the messaging window and the artificial intelligence are all managed via a web-based management console of a third party, the web-based management console includes user selectable components for managing a chat window of the chatbot, separate from a window of the website of the second party for display on the computer of the first party, the user selectable components including at least one event/response pair trigger to launch the chatbot; and at least one sales pitch to be presented by the chatbot. 16. The method of claim 1 , wherein the web-based management console further includes selectable components for managing the chat window of the chatbot, separate from the window of the website of the second party for display on the computer of the first party, the user selectable components includes at least one uniform resource locator link back to the website of the second party. | 0.604124 |
8,606,583 | 7 | 8 | 7. A speech synthesis method, comprising: accepting text information representing text, by a client device; transmitting a speech element request for requesting speech element information representing respective speech elements composing speech corresponding to the text represented by the accepted text information, to a server device, by the client device; receiving the speech element request transmitted by the client device and, in response to the received speech element request, transmitting speech element information, which is information stored in a storing device of the server device and is information representing respective speech elements included in speech uttered by a speech registering user, to the client device so that the speech element information is received by the client device in a different order from an order of arrangement of the speech elements in the speech corresponding to the text, by the server device; and receiving the speech element information transmitted by the server device, rearranging the received speech element information so that speech elements represented by the received speech element information are arranged in a same order as the order of arrangement of the speech elements in the speech corresponding to the text, and generating speech information obtained by converting the text into speech based on the rearranged speech element information, by the client device. | 7. A speech synthesis method, comprising: accepting text information representing text, by a client device; transmitting a speech element request for requesting speech element information representing respective speech elements composing speech corresponding to the text represented by the accepted text information, to a server device, by the client device; receiving the speech element request transmitted by the client device and, in response to the received speech element request, transmitting speech element information, which is information stored in a storing device of the server device and is information representing respective speech elements included in speech uttered by a speech registering user, to the client device so that the speech element information is received by the client device in a different order from an order of arrangement of the speech elements in the speech corresponding to the text, by the server device; and receiving the speech element information transmitted by the server device, rearranging the received speech element information so that speech elements represented by the received speech element information are arranged in a same order as the order of arrangement of the speech elements in the speech corresponding to the text, and generating speech information obtained by converting the text into speech based on the rearranged speech element information, by the client device. 8. The speech synthesis method according to claim 7 , comprising: rearranging the speech element information in accordance with a rule represented by rearrangement-order information, which is information stored in the storing device of the server device and is information representing a rule for rearranging the speech element information, and transmitting the rearranged speech element information to the client device, by the server device; and rearranging the received speech element information based on the rearrangement-order information stored in the storing device of the client device, by the client device. | 0.505609 |
7,865,364 | 17 | 19 | 17. A method for performing recognition of speech in reply to a prompt, the method comprising: storing a data set, in a dialog manager module, the data set including a plurality of information items and a corresponding set of one or more improbable values for each of the plurality of information items, the one or more improbable values for each of the plurality of information items comprising one or more values that are not valid for the information item based on a context of the prompt; receiving a plurality of recognized ordered interpretations from an automatic speech recognition (ASR) engine, the plurality of recognized ordered interpretations each including a plurality of received information items; and comparing a value of one of the plurality of received information items for each of the plurality of recognized ordered interpretations to the data set to determine if the value of the one of the plurality of received information items matches any of the set of one or more improbable values for the information item for each of the plurality of recognized ordered interpretations. | 17. A method for performing recognition of speech in reply to a prompt, the method comprising: storing a data set, in a dialog manager module, the data set including a plurality of information items and a corresponding set of one or more improbable values for each of the plurality of information items, the one or more improbable values for each of the plurality of information items comprising one or more values that are not valid for the information item based on a context of the prompt; receiving a plurality of recognized ordered interpretations from an automatic speech recognition (ASR) engine, the plurality of recognized ordered interpretations each including a plurality of received information items; and comparing a value of one of the plurality of received information items for each of the plurality of recognized ordered interpretations to the data set to determine if the value of the one of the plurality of received information items matches any of the set of one or more improbable values for the information item for each of the plurality of recognized ordered interpretations. 19. The method of claim 17 , further including deleting all of the plurality of recognized ordered interpretations if one of the one or more values of the one of the plurality of received information items in one of the recognized ordered interpretations matches the set of one or more improbable values for the corresponding information item in the data set. | 0.5 |
10,049,109 | 1 | 5 | 1. A computer-implemented method, comprising: determining, by one or more computers, that a web page in a source language has been opted in to a translation feature that enables one or more other users to translate the web page to a different target language; obtaining, by the one or more computers and from the one or more other users, translations of at least a portion of the web page from the source language to the target language; generating, by the one or more computers, a translated web page based on the obtained translations, wherein the translated web page is a translation of the web page into the target language; detecting, by the one or more computers, a request for the web page from a computing device associated with the target language; and outputting, from the one or more computers and to the computing device, the translated web page. | 1. A computer-implemented method, comprising: determining, by one or more computers, that a web page in a source language has been opted in to a translation feature that enables one or more other users to translate the web page to a different target language; obtaining, by the one or more computers and from the one or more other users, translations of at least a portion of the web page from the source language to the target language; generating, by the one or more computers, a translated web page based on the obtained translations, wherein the translated web page is a translation of the web page into the target language; detecting, by the one or more computers, a request for the web page from a computing device associated with the target language; and outputting, from the one or more computers and to the computing device, the translated web page. 5. The computer-implemented method of claim 1 , wherein the translations of at least a portion of the web page are provided by the one or more users via web browsers while viewing the web page. | 0.795117 |
7,657,515 | 25 | 26 | 25. A computer-implemented document search method performed by a computing system configured with computer executable instructions, the method comprising: executing sub-queries for searching a data store such that at least two search sub-queries are executed in parallel, wherein the search sub-queries are formed by logically combining each of a plurality of query terms from a first list with each of a plurality of query terms from a second list; and obtaining results from each sub-query execution, the results including information that identifies the location of each of the query terms in document data stored in the data store. | 25. A computer-implemented document search method performed by a computing system configured with computer executable instructions, the method comprising: executing sub-queries for searching a data store such that at least two search sub-queries are executed in parallel, wherein the search sub-queries are formed by logically combining each of a plurality of query terms from a first list with each of a plurality of query terms from a second list; and obtaining results from each sub-query execution, the results including information that identifies the location of each of the query terms in document data stored in the data store. 26. The method of claim 25 further comprising: combining the results from all sub-queries to create a single query result; and returning the single query result. | 0.5 |
8,661,046 | 1 | 21 | 1. A method for inferring activity-related context information from a message, comprising: scanning, by a computer, for a keyword in the message, wherein the keyword is associated with an activity category in a content database; determining that the keyword indicates the associated activity category; inferring message-related context information from the keyword in the message; and recommending an activity in the activity category based on the inferred message-related context information, wherein recommending the activity involves: identifying one or more content items associated with the keyword, wherein each content item corresponds to a recommendable activity; generating a combined model based on the message-related context information, wherein the combined model includes an activity model for one or more activities associated with the message-related context information, and includes a user preference model for the user; scoring the one or more content items using at least the activity model and the user preference model of the combined model; and returning a content item associated with a top score. | 1. A method for inferring activity-related context information from a message, comprising: scanning, by a computer, for a keyword in the message, wherein the keyword is associated with an activity category in a content database; determining that the keyword indicates the associated activity category; inferring message-related context information from the keyword in the message; and recommending an activity in the activity category based on the inferred message-related context information, wherein recommending the activity involves: identifying one or more content items associated with the keyword, wherein each content item corresponds to a recommendable activity; generating a combined model based on the message-related context information, wherein the combined model includes an activity model for one or more activities associated with the message-related context information, and includes a user preference model for the user; scoring the one or more content items using at least the activity model and the user preference model of the combined model; and returning a content item associated with a top score. 21. The method of claim 1 , further comprising adjusting the weight associated with the keyword based on phrasal or positional information, which involves assigning the keyword a higher weight when the keyword is followed by a respective phrase than when the keyword is preceded by the phrase. | 0.527419 |
9,524,081 | 4 | 7 | 4. The method of claim 2 , further comprising: identifying a reading object within a field of view of the mobile device, the first utterance corresponds with a first sentence from the reading object. | 4. The method of claim 2 , further comprising: identifying a reading object within a field of view of the mobile device, the first utterance corresponds with a first sentence from the reading object. 7. The method of claim 4 , further comprising: detecting a page of the reading object within the field of view of the mobile device; identifying the virtual object based on the page of the reading object; and the displaying the sequence of images includes displaying the sequence of images such that the virtual object appears to be attached to the reading object. | 0.5 |
8,838,992 | 1 | 4 | 1. A computer-implemented method of identifying normal scripts, the method comprising: receiving a machine learning model and a feature set in a client computer, the machine learning model being trained using sample scripts that are known to be normal and sample scripts that are known to be potentially malicious and takes into account lexical and semantic characteristics of the sample scripts that are known to be normal and the sample scripts that are known to be potentially malicious; receiving a target script along with a web page in the client computer, the target script and the web page being received from a server computer over a computer network; extracting from the target script features that are included in the feature set; inputting the extracted features of the target script into the machine learning model to receive a classification of the target script from the machine learning model; and detecting that the target script is a normal script and not a potentially malicious script based on the classification of the target script. | 1. A computer-implemented method of identifying normal scripts, the method comprising: receiving a machine learning model and a feature set in a client computer, the machine learning model being trained using sample scripts that are known to be normal and sample scripts that are known to be potentially malicious and takes into account lexical and semantic characteristics of the sample scripts that are known to be normal and the sample scripts that are known to be potentially malicious; receiving a target script along with a web page in the client computer, the target script and the web page being received from a server computer over a computer network; extracting from the target script features that are included in the feature set; inputting the extracted features of the target script into the machine learning model to receive a classification of the target script from the machine learning model; and detecting that the target script is a normal script and not a potentially malicious script based on the classification of the target script. 4. The method of claim 1 further comprising: in response to detecting that the target script is a normal script and not a potentially malicious script, allowing a web browser in the client computer to use the target script without having an anti-malware in the client computer first evaluate the target script. | 0.662309 |
8,200,486 | 9 | 10 | 9. The method of claim 2 , wherein said computer is further programmed to execute, and does execute, the following actions: selecting each of said matrix cells to be rectangularity shaped. | 9. The method of claim 2 , wherein said computer is further programmed to execute, and does execute, the following actions: selecting each of said matrix cells to be rectangularity shaped. 10. The method of claim 9 , wherein said computer is further programmed to execute, and does execute, the following actions: selecting at least two of said matrix cells to have different sizes. | 0.5 |
9,437,027 | 1 | 2 | 1. In a computing environment, a method performed at least in part on at least one processor, comprising: generating, by an image understanding mechanism implemented on the at least one processor, a scene representation for a query image, including: searching scenes corresponding to a set of sample images to identify one or more objects from one or more of the set of sample images for comparing with a scene expressed by the query image; and transforming the query image and the one or more objects into semantic segments comprising at least a portion of the scene representation, wherein the semantic segments provide a set of relationships between the one or more objects and between the one or more objects and the scene expressed by the query image, including relative depth between the one or more objects and a depth layer or position at which at least one object is inserted into the scene expressed by the query image, wherein transforming the query image and the one or more objects further comprises: generating a synthesized image using the one or more objects, the depth layer of the at least one inserted object in the synthesized image determined by using the semantic segments and relative depth information to match a depth distribution of objects in layers above and below the at least one inserted object in a corresponding sample image to the depth distribution of objects in layers above and below the at least one inserted object in the synthesized image. | 1. In a computing environment, a method performed at least in part on at least one processor, comprising: generating, by an image understanding mechanism implemented on the at least one processor, a scene representation for a query image, including: searching scenes corresponding to a set of sample images to identify one or more objects from one or more of the set of sample images for comparing with a scene expressed by the query image; and transforming the query image and the one or more objects into semantic segments comprising at least a portion of the scene representation, wherein the semantic segments provide a set of relationships between the one or more objects and between the one or more objects and the scene expressed by the query image, including relative depth between the one or more objects and a depth layer or position at which at least one object is inserted into the scene expressed by the query image, wherein transforming the query image and the one or more objects further comprises: generating a synthesized image using the one or more objects, the depth layer of the at least one inserted object in the synthesized image determined by using the semantic segments and relative depth information to match a depth distribution of objects in layers above and below the at least one inserted object in a corresponding sample image to the depth distribution of objects in layers above and below the at least one inserted object in the synthesized image. 2. The method of claim 1 , wherein transforming the query image and the one or more objects further comprises estimating support constraints between the one or more objects, and assigning a depth layer to individual objects of the one or more objects. | 0.573129 |
8,234,561 | 54 | 68 | 54. A system comprising one or more processors: an input/output system; an auto-fill engine providing proposed values and corresponding likelihood assessments generated based on values entered in observed form fields using the input/output system, the likelihood assessments indicating relative probability of the proposed values being entered in one or more current form field objects in a current form instance; and a form presentation component displaying the current form instance using the input/output system such that one or more predicted values are displayed in connection with the one or more current form field objects, the one or more predicted values being selected from the proposed values based on the likelihood assessments, wherein the auto-fill engine provides the proposed values and the corresponding likelihood assessments based on a determination of semantic similarity among the one or more current form field objects and the observed form fields. | 54. A system comprising one or more processors: an input/output system; an auto-fill engine providing proposed values and corresponding likelihood assessments generated based on values entered in observed form fields using the input/output system, the likelihood assessments indicating relative probability of the proposed values being entered in one or more current form field objects in a current form instance; and a form presentation component displaying the current form instance using the input/output system such that one or more predicted values are displayed in connection with the one or more current form field objects, the one or more predicted values being selected from the proposed values based on the likelihood assessments, wherein the auto-fill engine provides the proposed values and the corresponding likelihood assessments based on a determination of semantic similarity among the one or more current form field objects and the observed form fields. 68. The system of claim 54 , wherein the determination of semantic similarity comprises a comparison of the observed values with each other. | 0.852941 |
9,564,120 | 12 | 14 | 12. A speech synthesis system, comprising: a first source of text having content that replies to a user request; a second source of text having content that replies to the user request; a first speech database including pre-recorded speech from a first speaker; a second speech database including pre-recorded speech from a second speaker; a pre-processor to convert text into synthesizable output; a processor to convert first and second text inputs from the first and second sources of text into respective first and second speech outputs corresponding to the pre-recorded speech respectively from the first and second speakers, wherein the content of the first text input and the second text input collectively forms a reply to the user request; a post-processor to adapt the second speech output of the second speaker to sound like the first speech output of the first speaker; an acoustic interface to convert speech output into audio signals; and a speaker to convert the audio signals to audible speech, wherein the speaker outputs the first speech output of the first speaker, and outputs the adapted second speech output of the second speaker wherein the first and second speech outputs include different content and are presented sequentially to a user of the text-to-speech system. | 12. A speech synthesis system, comprising: a first source of text having content that replies to a user request; a second source of text having content that replies to the user request; a first speech database including pre-recorded speech from a first speaker; a second speech database including pre-recorded speech from a second speaker; a pre-processor to convert text into synthesizable output; a processor to convert first and second text inputs from the first and second sources of text into respective first and second speech outputs corresponding to the pre-recorded speech respectively from the first and second speakers, wherein the content of the first text input and the second text input collectively forms a reply to the user request; a post-processor to adapt the second speech output of the second speaker to sound like the first speech output of the first speaker; an acoustic interface to convert speech output into audio signals; and a speaker to convert the audio signals to audible speech, wherein the speaker outputs the first speech output of the first speaker, and outputs the adapted second speech output of the second speaker wherein the first and second speech outputs include different content and are presented sequentially to a user of the text-to-speech system. 14. The system of claim 12 , wherein the post-processor analyzes acoustic features of the first speech output for at least one speaker specific characteristic of the first speaker, adjusts an acoustic feature filter used to filter acoustic features from the second speech output, based on the at least one speaker specific characteristic of the first speaker, and filters acoustic features from the second speech output using the adjust filter. | 0.5 |
8,265,939 | 20 | 26 | 20. An apparatus comprising: at least one processor programmed to determine an intended action specified via a spoken input of a user of a computing system environment comprising a voice system by: obtaining a decoding of the spoken input of the user, wherein the voice system has a precise machine-based grammar to allow the user to invoke the intended action by speaking one or more predetermined voice commands and wherein the spoken input is a free form voice instruction that is different than the precise machine-based grammar; and extracting the intended action from the decoding of the spoken input using an iterative hierarchical extraction process comprising analyzing the decoding of the spoken input in multiple hierarchically dependent semantic stages, comprising: determining a first level of classification of the intended action from the decoding of the spoken input during a first semantic stage of the iterative hierarchical extraction process, the first level of classification having a plurality of sub-classifications associated with the first level of classification; and determining, from among the plurality of sub-classifications associated with the first level of classification, a second level of classification of the intended action from the same decoding of the spoken input during a second semantic stage of the iterative hierarchical extraction process. | 20. An apparatus comprising: at least one processor programmed to determine an intended action specified via a spoken input of a user of a computing system environment comprising a voice system by: obtaining a decoding of the spoken input of the user, wherein the voice system has a precise machine-based grammar to allow the user to invoke the intended action by speaking one or more predetermined voice commands and wherein the spoken input is a free form voice instruction that is different than the precise machine-based grammar; and extracting the intended action from the decoding of the spoken input using an iterative hierarchical extraction process comprising analyzing the decoding of the spoken input in multiple hierarchically dependent semantic stages, comprising: determining a first level of classification of the intended action from the decoding of the spoken input during a first semantic stage of the iterative hierarchical extraction process, the first level of classification having a plurality of sub-classifications associated with the first level of classification; and determining, from among the plurality of sub-classifications associated with the first level of classification, a second level of classification of the intended action from the same decoding of the spoken input during a second semantic stage of the iterative hierarchical extraction process. 26. The apparatus of claim 20 , wherein the at least one processor is further programmed to generate one or more questions of the user and to use answers to the one or more questions to facilitate determining the intended action. | 0.833576 |
8,396,820 | 16 | 20 | 16. A computer-implemented method for using a sentiment thesaurus stored according to the computer-readable storage medium of claim 1 to retrieve sentiment words for use with online content, comprising: receiving an indication of an adjective; determining from the sentiment thesaurus one or more related sentiment adjective word-senses that relate to the indicated adjective, by looking up the indicated adjective and retrieving related sentiment adjective word-senses; and returning indications to the retrieved related sentiment adjective word-senses. | 16. A computer-implemented method for using a sentiment thesaurus stored according to the computer-readable storage medium of claim 1 to retrieve sentiment words for use with online content, comprising: receiving an indication of an adjective; determining from the sentiment thesaurus one or more related sentiment adjective word-senses that relate to the indicated adjective, by looking up the indicated adjective and retrieving related sentiment adjective word-senses; and returning indications to the retrieved related sentiment adjective word-senses. 20. The method of claim 16 wherein the looking up the indicated adjective and retrieving related sentiment adjective word-senses further comprises: determining a set of related sentiment adjective word-senses that correspond to the indicated adjective based upon an indicated connection type or a range of connection types; and returning the determined related sentiment adjective word-senses. | 0.5 |
7,711,732 | 1 | 10 | 1. A computer-implemented method comprising: receiving first query terms from a user; automatically selecting, based at least in part on the first query terms, and from among a set of documents, first documents; automatically generating second query terms based at least in part on anchor text that is contained within links that occur within the first documents; automatically selecting, based at least in part on the second query terms, and from among the set of documents, second documents; wherein the first documents differ from the second documents; automatically generating third query terms based at least in part on anchor text that is contained within links that occur within the second documents, and not based on any input from said user; and automatically selecting, based at least in part on the third query terms, and from among the set of documents, third documents; wherein the third documents differ from the first documents and the second documents; determining a first number that is a number of documents in an intersection of (a) a set consisting of the first documents and (b) a set consisting of the second documents; determining a second number that is the first number divided by a number of documents in the set consisting of the second documents; determining whether the second number is at least as great as a specified percentage; and in response to determining that the second number is not at least as great as the specified percentage, automatically generating the third query terms based at least in part on anchor text that is contained within links that occur within the second documents; wherein the step of generating the third query terms is performed by one or more computing devices; and wherein said first and second documents are not displayed to said user. | 1. A computer-implemented method comprising: receiving first query terms from a user; automatically selecting, based at least in part on the first query terms, and from among a set of documents, first documents; automatically generating second query terms based at least in part on anchor text that is contained within links that occur within the first documents; automatically selecting, based at least in part on the second query terms, and from among the set of documents, second documents; wherein the first documents differ from the second documents; automatically generating third query terms based at least in part on anchor text that is contained within links that occur within the second documents, and not based on any input from said user; and automatically selecting, based at least in part on the third query terms, and from among the set of documents, third documents; wherein the third documents differ from the first documents and the second documents; determining a first number that is a number of documents in an intersection of (a) a set consisting of the first documents and (b) a set consisting of the second documents; determining a second number that is the first number divided by a number of documents in the set consisting of the second documents; determining whether the second number is at least as great as a specified percentage; and in response to determining that the second number is not at least as great as the specified percentage, automatically generating the third query terms based at least in part on anchor text that is contained within links that occur within the second documents; wherein the step of generating the third query terms is performed by one or more computing devices; and wherein said first and second documents are not displayed to said user. 10. The method of claim 1 , further comprising: associating, with each particular link in the links that occur within the first documents, a weight that is based on a number of times that text of that particular link occurs within the links that occur within the first documents. | 0.626005 |
9,106,979 | 1 | 8 | 1. A method for associating a second media content item with a first media content item, wherein the first media content item comprises a first audio/video content and the second media content item comprises a second audio/video content, the method comprising: receiving, by a recommender system, a first map of a first delimited segment of the first media content item, the first map comprising a first sentiment-state keyword associated with the first segment and a first temporal delimitation of the first segment; receiving, by the recommender system, a second map of a second delimited segment of the second media content item, the second map comprising a second sentiment-state keyword associated with the second segment and a second temporal delimitation of the second segment; comparing, by the recommender system, the first and second maps; and if a result of the comparison is a favorable result for a temporal extent associated with the first and second temporal delimitations, then associating, by the recommender system, the second media content item with the first media content item. | 1. A method for associating a second media content item with a first media content item, wherein the first media content item comprises a first audio/video content and the second media content item comprises a second audio/video content, the method comprising: receiving, by a recommender system, a first map of a first delimited segment of the first media content item, the first map comprising a first sentiment-state keyword associated with the first segment and a first temporal delimitation of the first segment; receiving, by the recommender system, a second map of a second delimited segment of the second media content item, the second map comprising a second sentiment-state keyword associated with the second segment and a second temporal delimitation of the second segment; comparing, by the recommender system, the first and second maps; and if a result of the comparison is a favorable result for a temporal extent associated with the first and second temporal delimitations, then associating, by the recommender system, the second media content item with the first media content item. 8. The method of claim 1 wherein the first map comprises an amplitude value associated with the first sentiment-state keyword. | 0.894472 |
8,380,530 | 25 | 27 | 25. The system of claim 24 , wherein the aggregator processes each PHR of the plurality of PHRs by further identifying a second event-concept pair associated with the desired event-concept pair, and by finding a relationship corresponding to the desired event-concept pair and the second event-concept pair, and wherein the aggregator finds an aggregate result by aggregating the plurality of relationships corresponding to the plurality of PHRs. | 25. The system of claim 24 , wherein the aggregator processes each PHR of the plurality of PHRs by further identifying a second event-concept pair associated with the desired event-concept pair, and by finding a relationship corresponding to the desired event-concept pair and the second event-concept pair, and wherein the aggregator finds an aggregate result by aggregating the plurality of relationships corresponding to the plurality of PHRs. 27. The system of claim 25 , wherein the relationship is to determine whether the second event-concept satisfies a medical criteria. | 0.609467 |
8,356,044 | 1 | 4 | 1. A computer-implemented method for providing default hierarchical training for social indexing, comprising: maintaining articles of digital information for social indexing; specifying a hierarchically-structured tree of topics, which each comprise a label comprising one or more words; identifying hard constraints based on the labels comprised in the topic tree and the topic tree's hierarchical structure, and defining the hard constraints to include immutable rules comprising at least one of: requiring that a topic model comprises a single term comprised from a label that is duplicated within the topic tree; requiring that a topic model includes no term from the label for the topic to which the topic model belongs; and when the label is duplicated within the topic tree, requiring that a topic model includes no term from the label of a parent topic for the topic to which the topic model belongs; for each topic in the topic tree, creating a topic model subject to the hard constraints, the topic model comprising a finite state pattern that comprises a pattern evaluable against the articles; evaluating the topic models for the topic tree against the hard constraints and disfavoring those topic models that violate one or more of the immutable rules; and identifying for each topic, the topic model, which best satisfies the constraints. | 1. A computer-implemented method for providing default hierarchical training for social indexing, comprising: maintaining articles of digital information for social indexing; specifying a hierarchically-structured tree of topics, which each comprise a label comprising one or more words; identifying hard constraints based on the labels comprised in the topic tree and the topic tree's hierarchical structure, and defining the hard constraints to include immutable rules comprising at least one of: requiring that a topic model comprises a single term comprised from a label that is duplicated within the topic tree; requiring that a topic model includes no term from the label for the topic to which the topic model belongs; and when the label is duplicated within the topic tree, requiring that a topic model includes no term from the label of a parent topic for the topic to which the topic model belongs; for each topic in the topic tree, creating a topic model subject to the hard constraints, the topic model comprising a finite state pattern that comprises a pattern evaluable against the articles; evaluating the topic models for the topic tree against the hard constraints and disfavoring those topic models that violate one or more of the immutable rules; and identifying for each topic, the topic model, which best satisfies the constraints. 4. A method according to claim 1 , further comprising: counting the articles that are matched by each of the topic models; and favoring those topic models proportionately matching a percentage of the articles closest to an ideal percentage. | 0.742489 |
9,418,655 | 1 | 3 | 1. A method to model and transfer the prosody of tag questions across languages, the method comprising: receiving speech of a first person speaking in a first language; analyzing the speech in the first language using automatic speech recognition; extracting prosodic parameters of the speech in the first language and outputting text in the first language corresponding to the speech in the first language based on the analyzing; searching the speech in the first language for a tag question in the first language; translating the text in the first language to text in a second language; outputting translated speech in the second language that is translated from the speech in the first language based on the translated text in the second language; analyzing the speech in the first language to find speech segments that correspond to the tag question in the first language; extracting a fundamental frequency from the speech segments that correspond to the tag question in the first language based on the extracted prosodic parameters of the speech in the first language; fitting a stylized smooth contour to the fundamental frequency; mapping the stylized smooth contour into a corresponding part of pitch range of the speech in the second language; stretching or contracting the stylized smooth contour over time; aligning the stylized smooth contour with corresponding speech segments in the second language that correspond to the tag question; and applying the smooth contour to the speech in the second language. | 1. A method to model and transfer the prosody of tag questions across languages, the method comprising: receiving speech of a first person speaking in a first language; analyzing the speech in the first language using automatic speech recognition; extracting prosodic parameters of the speech in the first language and outputting text in the first language corresponding to the speech in the first language based on the analyzing; searching the speech in the first language for a tag question in the first language; translating the text in the first language to text in a second language; outputting translated speech in the second language that is translated from the speech in the first language based on the translated text in the second language; analyzing the speech in the first language to find speech segments that correspond to the tag question in the first language; extracting a fundamental frequency from the speech segments that correspond to the tag question in the first language based on the extracted prosodic parameters of the speech in the first language; fitting a stylized smooth contour to the fundamental frequency; mapping the stylized smooth contour into a corresponding part of pitch range of the speech in the second language; stretching or contracting the stylized smooth contour over time; aligning the stylized smooth contour with corresponding speech segments in the second language that correspond to the tag question; and applying the smooth contour to the speech in the second language. 3. The method of claim 1 , wherein the first language is English. | 0.829843 |
7,668,812 | 6 | 7 | 6. A system comprising: one or more computers; a computer-readable medium coupled to the one or more computers having instructions stored thereon which, when executed by the one or more computers, causes the one or more computers to perform operations comprising: receiving a search query over a network, the search query comprising a query term and a categorical identifier; selecting a domain name associated with the categorical identifier; generating, based on selecting a first result set of resources responsive to the query term, a second result set of resources in which a respective domain name for each resource matches the determined domain name associated with the categorical identifier; selecting a resource identifier associated with the categorical identifier, the selected resource identifier comprising the determined domain name and a path; enhancing a respective relevance score of each resource in the second result set whose respective resource identifier includes the selected resource identifier, a magnitude of enhancement being based on a weight associated with the categorical identifier, the respective relevance score indicating a degree of relevance between the resource and the query term; ranking each of the resources in the second result set based on the respective relevance scores; annotating, with the categorical identifier, one or more indicia identifying the resources of the ranked second result set whose respective resource identifier matches the selected resource identifier associated with the categorical identifier; and providing the annotated indicia over the network as a search engine result of the search query. | 6. A system comprising: one or more computers; a computer-readable medium coupled to the one or more computers having instructions stored thereon which, when executed by the one or more computers, causes the one or more computers to perform operations comprising: receiving a search query over a network, the search query comprising a query term and a categorical identifier; selecting a domain name associated with the categorical identifier; generating, based on selecting a first result set of resources responsive to the query term, a second result set of resources in which a respective domain name for each resource matches the determined domain name associated with the categorical identifier; selecting a resource identifier associated with the categorical identifier, the selected resource identifier comprising the determined domain name and a path; enhancing a respective relevance score of each resource in the second result set whose respective resource identifier includes the selected resource identifier, a magnitude of enhancement being based on a weight associated with the categorical identifier, the respective relevance score indicating a degree of relevance between the resource and the query term; ranking each of the resources in the second result set based on the respective relevance scores; annotating, with the categorical identifier, one or more indicia identifying the resources of the ranked second result set whose respective resource identifier matches the selected resource identifier associated with the categorical identifier; and providing the annotated indicia over the network as a search engine result of the search query. 7. The system of claim 6 , wherein the operations further comprise: selecting other labels associated with the second result set of resources; and providing the selected other labels with the annotated indicia. | 0.5 |
9,547,854 | 13 | 16 | 13. A method of facilitating an electronic transaction on a user device, comprising: receiving, by an interface module of a user device from a payee identity module, a list of suggested payees for a user; generating, by the interface module, a main page that includes images of the suggested payees; displaying, by the interface module, in response to a detected first user gesture input, a selected payee on a first page of user interface; receiving, by the interface module from a funding source module, available funding sources for the user; displaying, by the interface module, in response to a detected second user gesture input, a selected funding source on a second page of the user interface; detecting, by the interface module, a speed and length of a third user gesture input, wherein the speed of the third user gesture input indicates how fast to increase or decrease a payment amount and the length of the third user gesture input indicates a size of the payment amount; and detecting, by the interface module, a fourth user gesture input that indicates that the user would like to send the payment amount to the selected payee. | 13. A method of facilitating an electronic transaction on a user device, comprising: receiving, by an interface module of a user device from a payee identity module, a list of suggested payees for a user; generating, by the interface module, a main page that includes images of the suggested payees; displaying, by the interface module, in response to a detected first user gesture input, a selected payee on a first page of user interface; receiving, by the interface module from a funding source module, available funding sources for the user; displaying, by the interface module, in response to a detected second user gesture input, a selected funding source on a second page of the user interface; detecting, by the interface module, a speed and length of a third user gesture input, wherein the speed of the third user gesture input indicates how fast to increase or decrease a payment amount and the length of the third user gesture input indicates a size of the payment amount; and detecting, by the interface module, a fourth user gesture input that indicates that the user would like to send the payment amount to the selected payee. 16. The method of claim 13 , wherein the method further comprises automatically closing, by the interface module, a third page comprising a checkmark on an image of a selected payee and returning to the main page. | 0.638983 |
6,085,206 | 1 | 3 | 1. In an electronic word processing system for creating and editing a document, the document comprising a plurality of sentences, a method for verifying the accuracy of spelling and grammatical composition of the plurality of sentences in the document, the method comprising the steps of: performing a first sequence comprising the steps of: extracting one of the plurality of sentences from the document, the sentence comprising a plurality of words; checking the spelling of each word in the sentence for a misspelled word in a spell checker program module; displaying the sentence and each misspelled word within a first instance of a combined spelling and grammar dialog box; displaying a plurality of common command buttons operative for correcting the spelling errors; and performing a second sequence, subsequent to the first sequence, comprising the steps of: checking the grammatical composition of the sentence in a grammar checker program module; displaying the sentence and the grammatical errors within a second instance of the combined spelling and grammar dialog box; and displaying the plurality of common command buttons operative for correcting the grammatical errors. | 1. In an electronic word processing system for creating and editing a document, the document comprising a plurality of sentences, a method for verifying the accuracy of spelling and grammatical composition of the plurality of sentences in the document, the method comprising the steps of: performing a first sequence comprising the steps of: extracting one of the plurality of sentences from the document, the sentence comprising a plurality of words; checking the spelling of each word in the sentence for a misspelled word in a spell checker program module; displaying the sentence and each misspelled word within a first instance of a combined spelling and grammar dialog box; displaying a plurality of common command buttons operative for correcting the spelling errors; and performing a second sequence, subsequent to the first sequence, comprising the steps of: checking the grammatical composition of the sentence in a grammar checker program module; displaying the sentence and the grammatical errors within a second instance of the combined spelling and grammar dialog box; and displaying the plurality of common command buttons operative for correcting the grammatical errors. 3. The method recited in claim 1, wherein the step of extracting one of the plurality of sentences from the document comprises the step of sentence-breaking to determine a beginning and an end of the sentence of the plurality of sentences. | 0.914825 |
8,874,565 | 20 | 21 | 20. The system of claim 17 , where the at least one processor is further to: identify one or more of the identified clusters that include only one or more of the first documents, and identify the one or more of the first documents, in the identified one or more of the identified clusters, as documents of a third type that is different than the first type and the second type. | 20. The system of claim 17 , where the at least one processor is further to: identify one or more of the identified clusters that include only one or more of the first documents, and identify the one or more of the first documents, in the identified one or more of the identified clusters, as documents of a third type that is different than the first type and the second type. 21. The system of claim 20 , where when generating the proxy pad score, the at least one processor is to: aggregate information regarding the one or more identified documents of the first type as aggregated first information, aggregate information regarding the one or more identified documents of the second type as aggregated second information, aggregate information regarding the one or more identified documents of the third type as aggregated third information, and compute the proxy pad score based on the aggregated first information, the aggregated second information, and the aggregated third information. | 0.5 |
7,657,519 | 8 | 14 | 8. A computer-readable medium having stored thereon computer-executable instructions for performing a method of analyzing a plurality of search sessions to identify intent-based clusters therein, each session comprising at least one received query from a user and a corresponding set of returned search results, each set of search results including or referring to at least one piece of content, each cluster representing a group of similar search sessions that are perceived as representing a common intent of a plurality of different users and that can be mapped to a common set of search results, the method comprising: identifying for each search session each received query thereof, the corresponding set of search results, and whether any particular piece of content of the search results was acceptable to the user as responsive to the corresponding search session; and grouping search sessions into clusters based on the commonality of judgments of a plurality of different users about a search result that is common to the user's respective search sessions, wherein each of said clusters includes search queries and search results, such grouping comprising: constructing a table with a plurality of entries therein, each entry representing a unique pair of sessions such that each session is paired with every other session a single time in the table; judging, for each entry of the table, a strength of commonality of the pair of sessions thereof; reordering the entries in the table according to decreasing strength; and reviewing each entry in the table as reordered to decide based on the judged strength thereof whether to assign each session thereof to an intent-based cluster. | 8. A computer-readable medium having stored thereon computer-executable instructions for performing a method of analyzing a plurality of search sessions to identify intent-based clusters therein, each session comprising at least one received query from a user and a corresponding set of returned search results, each set of search results including or referring to at least one piece of content, each cluster representing a group of similar search sessions that are perceived as representing a common intent of a plurality of different users and that can be mapped to a common set of search results, the method comprising: identifying for each search session each received query thereof, the corresponding set of search results, and whether any particular piece of content of the search results was acceptable to the user as responsive to the corresponding search session; and grouping search sessions into clusters based on the commonality of judgments of a plurality of different users about a search result that is common to the user's respective search sessions, wherein each of said clusters includes search queries and search results, such grouping comprising: constructing a table with a plurality of entries therein, each entry representing a unique pair of sessions such that each session is paired with every other session a single time in the table; judging, for each entry of the table, a strength of commonality of the pair of sessions thereof; reordering the entries in the table according to decreasing strength; and reviewing each entry in the table as reordered to decide based on the judged strength thereof whether to assign each session thereof to an intent-based cluster. 14. The medium of claim 8 wherein the method further comprises mapping each cluster to a common set of search results that is believed to satisfy the common purpose of such cluster so that all queries with the same common purpose map correctly based on such cluster. | 0.813725 |
9,990,432 | 1 | 11 | 1. A method, comprising the steps of: receiving, by a server computer communicatively coupled to a network and comprising at least one processor executing specific computer-executable instructions within a memory, via a first graphical user interface (GUI) for a multi-lingual domain name search engine displayed on a client computer, a domain name search string; tokenizing, by the server computer, the domain name search string; identifying, by the server computer, a search string token within the domain name search string as a concept seed; executing, by the server computer, a first database command to create a data record storing the search string token in association with a concept id; executing, by the server computer, a crawl of: a domain name search log, or at least one domain name system (DNS) zone file; tokenizing, by the server computer, at least one domain name text string within the domain name search log or the at least one DNS zone file; identifying, by the server computer, within the at least one domain name text string, at least one synonym or translation of the search string token; executing, by the server computer, a second database command to create at least one data record storing: the at least one synonym or translation of the search string token; the concept id; and at least one language associated with the at least one synonym or translation; identifying, by the server computer, based on the search string token in the domain name search string, at least one concept comprising a collection of the at least one data record sharing the concept id; generating, by the server computer, a second GUI including a displayed list recommending at least one available domain name comprising the at least one concept in the at least one language, the displayed list being ordered according to a frequency of use of the at least one concept; and transmitting, by the server computer, the second GUI to the client computer for display. | 1. A method, comprising the steps of: receiving, by a server computer communicatively coupled to a network and comprising at least one processor executing specific computer-executable instructions within a memory, via a first graphical user interface (GUI) for a multi-lingual domain name search engine displayed on a client computer, a domain name search string; tokenizing, by the server computer, the domain name search string; identifying, by the server computer, a search string token within the domain name search string as a concept seed; executing, by the server computer, a first database command to create a data record storing the search string token in association with a concept id; executing, by the server computer, a crawl of: a domain name search log, or at least one domain name system (DNS) zone file; tokenizing, by the server computer, at least one domain name text string within the domain name search log or the at least one DNS zone file; identifying, by the server computer, within the at least one domain name text string, at least one synonym or translation of the search string token; executing, by the server computer, a second database command to create at least one data record storing: the at least one synonym or translation of the search string token; the concept id; and at least one language associated with the at least one synonym or translation; identifying, by the server computer, based on the search string token in the domain name search string, at least one concept comprising a collection of the at least one data record sharing the concept id; generating, by the server computer, a second GUI including a displayed list recommending at least one available domain name comprising the at least one concept in the at least one language, the displayed list being ordered according to a frequency of use of the at least one concept; and transmitting, by the server computer, the second GUI to the client computer for display. 11. The method of claim 1 , further comprising the step of identifying at least one word in the at least one language corresponding to a concept unit stored in the database. | 0.802059 |
8,224,523 | 3 | 4 | 3. The method of claim 1 , further comprising: establishing communication between a vehicle computing system and a nomadic device; sending a periodic signal to the nomadic device to transfer a language designation known by the nomadic device to the vehicle computing system. | 3. The method of claim 1 , further comprising: establishing communication between a vehicle computing system and a nomadic device; sending a periodic signal to the nomadic device to transfer a language designation known by the nomadic device to the vehicle computing system. 4. The method of claim 3 , wherein the periodic signal is at least sent when a predetermined geographic border is crossed by a vehicle. | 0.5 |
9,479,911 | 17 | 19 | 17. A receiver-side terminal for supporting a translation-based communication service, the receiver-side terminal comprising: a communication unit configured to receive a text in a first language with voice-related characteristic information, wherein the voice-related characteristic information is used to generate a translation voice signal in a second language with a pitch and a tone similar to a voice signal in the first language; a storage unit configured to store a translation database for conversion of the received text in the first language to a translation text of the second language; a display unit configured to output the translation text in the second language; and a controller configured to perform a control to convert the text in the first language to the translation text in the second language and then output the converted translation text. | 17. A receiver-side terminal for supporting a translation-based communication service, the receiver-side terminal comprising: a communication unit configured to receive a text in a first language with voice-related characteristic information, wherein the voice-related characteristic information is used to generate a translation voice signal in a second language with a pitch and a tone similar to a voice signal in the first language; a storage unit configured to store a translation database for conversion of the received text in the first language to a translation text of the second language; a display unit configured to output the translation text in the second language; and a controller configured to perform a control to convert the text in the first language to the translation text in the second language and then output the converted translation text. 19. The receiver-side terminal of claim 17 , wherein the storage unit comprises a voice-for-TTS database for conversion of the translation text in the second language to the translation voice signal in the second language, and the controller is configured to control to use the voice-for-TTS database to generate the translation voice signal in the second language corresponding to the translation text in the second language. | 0.634021 |
8,949,241 | 9 | 10 | 9. A computer, comprising: (a) a memory; and (b) a program resident in the memory and configured to facilitate disambiguation of entities within a user-created electronic document in which the user has entered a plurality of terms by (i) presenting to a user as the user is entering the plurality of terms in the electronic document, for a particular term entered by the user in the electronic document, a list of entities, wherein each entity on the list corresponds to a different instance of the particular term, wherein each entity on the list has a unique semantic interpretation of the particular term, and wherein each entity on the list does not appear on the list more than one time, (ii) ranking each entity in the list of entities, wherein the ranking is based on (1) the plurality of terms in the electronic document; (2) one or more entities previously selected by the user in the electronic document and how recently those one or more entities were selected by the user; (3) saved preferences of the user; (4) terms in previous electronic documents created by the user; and (5) one or more entities previously selected in electronic documents created by the user; (iii) enabling the user to select one of the entities from the list; (iv) receiving a selection of one of the entities from the list by the user, and including in the electronic document an identifier corresponding to the entity selected by the user, wherein the identifier comprises a link to additional information about the entity; and (v) storing the entity selected by the user in both a first entity database that contains entities selected by the user and a second entity database that contains entities that have been selected by all users. | 9. A computer, comprising: (a) a memory; and (b) a program resident in the memory and configured to facilitate disambiguation of entities within a user-created electronic document in which the user has entered a plurality of terms by (i) presenting to a user as the user is entering the plurality of terms in the electronic document, for a particular term entered by the user in the electronic document, a list of entities, wherein each entity on the list corresponds to a different instance of the particular term, wherein each entity on the list has a unique semantic interpretation of the particular term, and wherein each entity on the list does not appear on the list more than one time, (ii) ranking each entity in the list of entities, wherein the ranking is based on (1) the plurality of terms in the electronic document; (2) one or more entities previously selected by the user in the electronic document and how recently those one or more entities were selected by the user; (3) saved preferences of the user; (4) terms in previous electronic documents created by the user; and (5) one or more entities previously selected in electronic documents created by the user; (iii) enabling the user to select one of the entities from the list; (iv) receiving a selection of one of the entities from the list by the user, and including in the electronic document an identifier corresponding to the entity selected by the user, wherein the identifier comprises a link to additional information about the entity; and (v) storing the entity selected by the user in both a first entity database that contains entities selected by the user and a second entity database that contains entities that have been selected by all users. 10. The computer according to claim 9 , wherein the program is configured to incorporate metadata pertaining to the user-selected entity into the electronic document. | 0.5 |
5,493,185 | 1 | 11 | 1. An animation method comprising the steps of: (a) providing a figure to be animated, said figure having at least one drive unit for driving a part or segment of the figure; (b) providing a control system and manually causing the control system to emit control signals for controlling said at least one drive unit; (c) digitizing the control signals; (d) feeding the digitized control signals to a processor and processing them individually or in sets; (e) storing the processed control signals in a memory; (f) transmitting the stored control signals to said at least one drive unit after converting said signals to analog form in order to animate the figure; (g) modifying said control signals by superimposing sub-control signals on them during animation of the figure, wherein life-like and spontaneous movements of the figure can be obtained under direct control of an animator even as the stored control signals control basic animation functions to relieve the animator of the need to control said basic animation functions. | 1. An animation method comprising the steps of: (a) providing a figure to be animated, said figure having at least one drive unit for driving a part or segment of the figure; (b) providing a control system and manually causing the control system to emit control signals for controlling said at least one drive unit; (c) digitizing the control signals; (d) feeding the digitized control signals to a processor and processing them individually or in sets; (e) storing the processed control signals in a memory; (f) transmitting the stored control signals to said at least one drive unit after converting said signals to analog form in order to animate the figure; (g) modifying said control signals by superimposing sub-control signals on them during animation of the figure, wherein life-like and spontaneous movements of the figure can be obtained under direct control of an animator even as the stored control signals control basic animation functions to relieve the animator of the need to control said basic animation functions. 11. A method as claimed in claim 1, further comprising the step of arranging a totality of said control signals by individual channels which can be expressed as computer graphics or represented on a screen for processing by changing the graphics, and causing said processor to form control sequences based on the changes in the graphics. | 0.549465 |
9,507,767 | 12 | 15 | 12. 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: computing, by said computer processor, a term frequency-inverse document frequency (tf-idf) associated with n-grams of an n-gram model of a domain; determining, by said computer processor based on a computed term frequency-inverse document frequency (tf-idf) associated with n-grams of an n-gram model of a domain, a frequently occurring group of n-grams of said n-grams; generating, by said computer processor executing a deep parser component of said computing system with respect to said frequently occurring group of n-grams, a deep parse output comprising results of said executing said deep parser component with respect to said frequently occurring group of n-grams; indexing, by said computer processor executing said frequently occurring group of n-grams stored in a database cache storing said said deep parse output, said deep parse output; and verifying, by said computer processor, if a pre-computed specified text word sequence of said deep parse output is available in said database cache, wherein said verifying comprises: retrieving from said deep parse output, a plurality of tokens of said deep parser output, wherein said plurality of tokens are associated with a portion of said pre-computed specified text word sequence, wherein said plurality of tokens comprise suffixes associated with structures of said deep parser output, and wherein said plurality of tokens comprise a version token; and determining based on said plurality of tokens, variations associated with said pre-computed specified text word sequence. | 12. 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: computing, by said computer processor, a term frequency-inverse document frequency (tf-idf) associated with n-grams of an n-gram model of a domain; determining, by said computer processor based on a computed term frequency-inverse document frequency (tf-idf) associated with n-grams of an n-gram model of a domain, a frequently occurring group of n-grams of said n-grams; generating, by said computer processor executing a deep parser component of said computing system with respect to said frequently occurring group of n-grams, a deep parse output comprising results of said executing said deep parser component with respect to said frequently occurring group of n-grams; indexing, by said computer processor executing said frequently occurring group of n-grams stored in a database cache storing said said deep parse output, said deep parse output; and verifying, by said computer processor, if a pre-computed specified text word sequence of said deep parse output is available in said database cache, wherein said verifying comprises: retrieving from said deep parse output, a plurality of tokens of said deep parser output, wherein said plurality of tokens are associated with a portion of said pre-computed specified text word sequence, wherein said plurality of tokens comprise suffixes associated with structures of said deep parser output, and wherein said plurality of tokens comprise a version token; and determining based on said plurality of tokens, variations associated with said pre-computed specified text word sequence. 15. The computer system of claim 12 , wherein each n-gram of said frequently occurring group of n-grams comprises a cache key. | 0.839286 |
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