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8,880,548 | 1 | 2 | 1. A system, comprising: a hardware processor; and hardware storage storing computer-readable instructions which, when executed by the hardware processor, cause the hardware processor to: obtain an input search query from a user, the input search query comprising an input search term, identify instances where other users have used the input search term in other search queries and added additional search terms to the other search queries to obtain refined search queries, wherein the refined search queries were previously entered by the other users, organize the refined search queries that were previously entered by the other users into at least multiple first refined search queries associated with a first topic and multiple second refined search queries associated with a second topic, estimate a first relative likelihood that an intent of the user matches the first topic, estimate a second relative likelihood that the intent of the user matches the second topic, the first relative likelihood being greater than the second relative likelihood, cause the multiple first related refined search queries that were previously entered by the other users to be displayed on a graphical user interface (GUI) concurrently with the multiple second refined search queries that were previously entered by the other users, the multiple first refined search queries being displayed relatively more prominently than the multiple second refined search queries to show that the first relative likelihood that the intent of the user matches the first topic is greater than the second relative likelihood that the intent of the user matches the second topic, and responsive to a scrolling action of a user input device, display additional first refined search queries associated with the first topic on the GUI while continuing to display the multiple second refined search queries associated with the second topic, wherein the additional first refined search queries were also previously entered by the other users by adding further additional search terms to the input search term. | 1. A system, comprising: a hardware processor; and hardware storage storing computer-readable instructions which, when executed by the hardware processor, cause the hardware processor to: obtain an input search query from a user, the input search query comprising an input search term, identify instances where other users have used the input search term in other search queries and added additional search terms to the other search queries to obtain refined search queries, wherein the refined search queries were previously entered by the other users, organize the refined search queries that were previously entered by the other users into at least multiple first refined search queries associated with a first topic and multiple second refined search queries associated with a second topic, estimate a first relative likelihood that an intent of the user matches the first topic, estimate a second relative likelihood that the intent of the user matches the second topic, the first relative likelihood being greater than the second relative likelihood, cause the multiple first related refined search queries that were previously entered by the other users to be displayed on a graphical user interface (GUI) concurrently with the multiple second refined search queries that were previously entered by the other users, the multiple first refined search queries being displayed relatively more prominently than the multiple second refined search queries to show that the first relative likelihood that the intent of the user matches the first topic is greater than the second relative likelihood that the intent of the user matches the second topic, and responsive to a scrolling action of a user input device, display additional first refined search queries associated with the first topic on the GUI while continuing to display the multiple second refined search queries associated with the second topic, wherein the additional first refined search queries were also previously entered by the other users by adding further additional search terms to the input search term. 2. The system of claim 1 , wherein the computer-readable instructions further cause the hardware processor to: refine one or more of the first relative likelihood or the second relative likelihood based upon subsequent user input so that the second relative likelihood is greater than the first relative likelihood, and dynamically update the GUI so that the multiple second refined search queries that were previously entered by the other users are displayed relatively more prominently than the multiple first refined search queries that were previously entered by the other users to show that the second relative likelihood is greater than the first relative likelihood. | 0.5 |
9,710,097 | 1 | 4 | 1. A method, comprising: using a computer to perform: detecting a touch on a surface of a tablet device; coincident with detecting the touch, detecting a stylus gesture performed via user manipulation of a stylus, the stylus gesture being one of a plurality of stylus gestures that each correspond to at least one of a plurality of actions in a graphics application, at least one of the plurality of stylus gestures configured to mimic a natural painting operation, and the stylus gesture includes moving the stylus in a fanning motion; responsive to detecting the stylus gesture and the touch, invoking a work mode that adjusts input parameters of a particular natural painting operation that corresponds to the stylus gesture, including invoking an arc drawing tool responsive to detecting the stylus moving in the fanning motion; and performing the particular natural painting operation in the graphics application, including drawing an arc in the graphics application with a focal point on the touch. | 1. A method, comprising: using a computer to perform: detecting a touch on a surface of a tablet device; coincident with detecting the touch, detecting a stylus gesture performed via user manipulation of a stylus, the stylus gesture being one of a plurality of stylus gestures that each correspond to at least one of a plurality of actions in a graphics application, at least one of the plurality of stylus gestures configured to mimic a natural painting operation, and the stylus gesture includes moving the stylus in a fanning motion; responsive to detecting the stylus gesture and the touch, invoking a work mode that adjusts input parameters of a particular natural painting operation that corresponds to the stylus gesture, including invoking an arc drawing tool responsive to detecting the stylus moving in the fanning motion; and performing the particular natural painting operation in the graphics application, including drawing an arc in the graphics application with a focal point on the touch. 4. The method of claim 1 , wherein said detecting a touch comprises detecting a touch on a pressure-sensitive tablet device. | 0.831063 |
6,070,007 | 5 | 6 | 5. The method of claim 1, further comprising: when the current selection is the selection of the computational construct representing the terminal operand in the left selection mode and the user inputs a second indicator, setting the current selection to a selection of a parent computational construct of the computational construct representing the terminal operand in the tree selection mode. | 5. The method of claim 1, further comprising: when the current selection is the selection of the computational construct representing the terminal operand in the left selection mode and the user inputs a second indicator, setting the current selection to a selection of a parent computational construct of the computational construct representing the terminal operand in the tree selection mode. 6. The method of claim 5 wherein the first indicator is a tab and the second indicator is a shift-tab. | 0.871859 |
8,781,102 | 17 | 19 | 17. A method for analyzing an electronic communication between a customer and a contact center, the method comprising: receiving a single electronic communication from a communicant; generating a text file from the electronic communication; analyzing the text file of the electronic communication by mining the text file generated from the electronic communication and applying a predetermined linguistic-based psychological behavioral model to the text file generated from the electronic communication; and generating behavioral assessment data including a personality type corresponding to the analyzed text file of the electronic communication. | 17. A method for analyzing an electronic communication between a customer and a contact center, the method comprising: receiving a single electronic communication from a communicant; generating a text file from the electronic communication; analyzing the text file of the electronic communication by mining the text file generated from the electronic communication and applying a predetermined linguistic-based psychological behavioral model to the text file generated from the electronic communication; and generating behavioral assessment data including a personality type corresponding to the analyzed text file of the electronic communication. 19. The method of claim 17 , which further comprises selecting the electronic communication to include at least one of a telephone call, facsimile transmission, e-mail, web interaction, voice over IP (“VoIP”) or video. | 0.867235 |
9,092,405 | 22 | 23 | 22. A computer-implemented method for presenting versions of network resources, the method comprising: under the control of a network computing component executing on one or more physical computing components of a network computing provider, the physical computing components configured to execute specific instructions, retrieving, from an electronic data store, a plurality of historical representations of a network resource as previously obtained from a content provider, the plurality of historical representations obtained from the content provider in response to a plurality of browse session requests for the network resource received from a particular client computing device at one or more previous times, wherein the content provider is separate from the network computing component; determining at least one difference between at least two of the plurality of historical representations; and generating a user interface comprising an interactive timeline and at least two objects, each object of the at least two objects comprising a visual representation of a corresponding one of the at least two historical representations, wherein a first object of the at least two objects comprises a visual indicator of the at least one difference, wherein a second object of the at least two objects is at least partially obscured by display of the first object, and wherein display of the first object is at least partially replaced by display of the second object in response to a user interaction with the interactive timeline. | 22. A computer-implemented method for presenting versions of network resources, the method comprising: under the control of a network computing component executing on one or more physical computing components of a network computing provider, the physical computing components configured to execute specific instructions, retrieving, from an electronic data store, a plurality of historical representations of a network resource as previously obtained from a content provider, the plurality of historical representations obtained from the content provider in response to a plurality of browse session requests for the network resource received from a particular client computing device at one or more previous times, wherein the content provider is separate from the network computing component; determining at least one difference between at least two of the plurality of historical representations; and generating a user interface comprising an interactive timeline and at least two objects, each object of the at least two objects comprising a visual representation of a corresponding one of the at least two historical representations, wherein a first object of the at least two objects comprises a visual indicator of the at least one difference, wherein a second object of the at least two objects is at least partially obscured by display of the first object, and wherein display of the first object is at least partially replaced by display of the second object in response to a user interaction with the interactive timeline. 23. The computer-implemented method of claim 22 , wherein the at least one difference comprises a difference in one of the text, layout, or structure of at least two of the plurality of historical representations. | 0.5 |
7,818,315 | 1 | 4 | 1. A system that facilitates re-ranking search results retrieved by a search engine, comprising one or more computers configured with: a query log component that, responsive to a target query, builds a language model specific to the target query based on queries stored in a query log of a search engine, the query log component comprising: a compilation module that determines absolute frequencies of search terms corresponding to the queries related lexically including a number of queries submitted by users that include the target query, and a session module that determines relative frequencies of the queries related temporally and submitted during a user session; a matching component that compares a lexical property of an initial search result for the target query and the language model and computes a match score that indicates a probability that two or more search results are related by utilizing a cosine similarity and a KL-divergence for comparing; a ranking component that re-ranks a subset of the initial search result based on the match score and a rank of the initial search result, wherein the re-ranking of the subset of the initial search result comprises processing a URL, one or more page titles, a plurality of page content and a plurality of snippets resulting from a document query pairing, and further wherein, the plurality of snippets are search-engine dependent; a user control selection component that allows a user to further re-rank the initial search result, wherein the user control selection component is configured to accept user input to manually control the further re-ranking of the initial search result; and a user control component that applies a selectable modification to at least one of the system components to change the re-ranking of the subset of the initial search result and the selectable modification is one of an increase in a diversity of top re-ranked search results and a decrease in the diversity of the top re-ranked search results. | 1. A system that facilitates re-ranking search results retrieved by a search engine, comprising one or more computers configured with: a query log component that, responsive to a target query, builds a language model specific to the target query based on queries stored in a query log of a search engine, the query log component comprising: a compilation module that determines absolute frequencies of search terms corresponding to the queries related lexically including a number of queries submitted by users that include the target query, and a session module that determines relative frequencies of the queries related temporally and submitted during a user session; a matching component that compares a lexical property of an initial search result for the target query and the language model and computes a match score that indicates a probability that two or more search results are related by utilizing a cosine similarity and a KL-divergence for comparing; a ranking component that re-ranks a subset of the initial search result based on the match score and a rank of the initial search result, wherein the re-ranking of the subset of the initial search result comprises processing a URL, one or more page titles, a plurality of page content and a plurality of snippets resulting from a document query pairing, and further wherein, the plurality of snippets are search-engine dependent; a user control selection component that allows a user to further re-rank the initial search result, wherein the user control selection component is configured to accept user input to manually control the further re-ranking of the initial search result; and a user control component that applies a selectable modification to at least one of the system components to change the re-ranking of the subset of the initial search result and the selectable modification is one of an increase in a diversity of top re-ranked search results and a decrease in the diversity of the top re-ranked search results. 4. The system of claim 1 , the query log component utilizes the absolute frequencies and the relative frequencies of the queries in the query log to derive the language model. | 0.752825 |
9,116,863 | 9 | 13 | 9. A processing device configured to: receive a document outline that includes at least a template link section to insert a link to a template and at least a mini-document link section to insert links to each of a plurality of mini-documents; receive, from a user, a request to insert a link to a template in the template link section; receive, from the user, a request to insert a link to each of a plurality of mini-documents in the mini-document link section; and upon receiving a request to assemble a final document: retrieve the template using the template link inserted in document outline at the request of the user; retrieve each of the plurality of mini-documents using the mini-document links inserted in the document outline at the request of the user; automatically assemble the final document, the assembled final document including the retrieved plurality of mini-documents in a format specified by the template; generate the final document; and output the final document to a database. | 9. A processing device configured to: receive a document outline that includes at least a template link section to insert a link to a template and at least a mini-document link section to insert links to each of a plurality of mini-documents; receive, from a user, a request to insert a link to a template in the template link section; receive, from the user, a request to insert a link to each of a plurality of mini-documents in the mini-document link section; and upon receiving a request to assemble a final document: retrieve the template using the template link inserted in document outline at the request of the user; retrieve each of the plurality of mini-documents using the mini-document links inserted in the document outline at the request of the user; automatically assemble the final document, the assembled final document including the retrieved plurality of mini-documents in a format specified by the template; generate the final document; and output the final document to a database. 13. A processing device in accordance with claim 9 , wherein said processing device is configured to receive a document outline that includes an order of the plurality of mini-documents, and wherein said processing device is configured to assemble the final document such that the mini-documents are incorporated into the final document in the order specified in the document outline. | 0.5 |
7,784,036 | 1 | 3 | 1. A method comprising: extracting, with a processing system, macroinstructions that are hard-coded into parser code of a command line interface (CLI) parser, wherein the macroinstructions define parse nodes utilized by the CLI parser to analyze whether one or more CLI commands input to a CLI prompt have a proper CLI syntax; generating a parse graph from the macroinstructions with the processing system, wherein the parse graph includes a representation of the parse nodes defined by the macroinstructions; hiding selected information within the parse nodes to create condensed parse nodes with the processing system, wherein the hiding prevents further processing of the selected information; simplifying selected complex patterns in the parse graph to create simplified parse graph patterns with the processing system; creating, with the processing system, branches on an AND/OR command tree from the parse nodes, the condensed parse nodes, and the simplified parse graph patterns; and creating an exportable representation of the AND/OR command tree with the processing system. | 1. A method comprising: extracting, with a processing system, macroinstructions that are hard-coded into parser code of a command line interface (CLI) parser, wherein the macroinstructions define parse nodes utilized by the CLI parser to analyze whether one or more CLI commands input to a CLI prompt have a proper CLI syntax; generating a parse graph from the macroinstructions with the processing system, wherein the parse graph includes a representation of the parse nodes defined by the macroinstructions; hiding selected information within the parse nodes to create condensed parse nodes with the processing system, wherein the hiding prevents further processing of the selected information; simplifying selected complex patterns in the parse graph to create simplified parse graph patterns with the processing system; creating, with the processing system, branches on an AND/OR command tree from the parse nodes, the condensed parse nodes, and the simplified parse graph patterns; and creating an exportable representation of the AND/OR command tree with the processing system. 3. The method of claim 1 further comprising merging selected cases in the AND/OR command tree that have common end of line terminations. | 0.613636 |
8,041,694 | 25 | 66 | 25. A system comprising: one or more computers, the one or more computers implementing: a dataset tool to identify each vector v in a dataset as a comparison vector, and a set of candidate vectors corresponding to each vector v; a similarity tool to determine, for each candidate vector w in each set of candidate vectors corresponding to v, a similarity estimate between the comparison vector v and the candidate vector w, and a similarity score between the comparison vector v and the candidate vector w if the similarity estimate meets a similarity threshold; and a results tool to include each pair of vectors comprising each comparison vector v and each respective candidate vector w in a list of similar pairs of vectors if the respective similarity score between the comparison vector v and the candidate vector w meets the similarity threshold. | 25. A system comprising: one or more computers, the one or more computers implementing: a dataset tool to identify each vector v in a dataset as a comparison vector, and a set of candidate vectors corresponding to each vector v; a similarity tool to determine, for each candidate vector w in each set of candidate vectors corresponding to v, a similarity estimate between the comparison vector v and the candidate vector w, and a similarity score between the comparison vector v and the candidate vector w if the similarity estimate meets a similarity threshold; and a results tool to include each pair of vectors comprising each comparison vector v and each respective candidate vector w in a list of similar pairs of vectors if the respective similarity score between the comparison vector v and the candidate vector w meets the similarity threshold. 66. The method of claim 25 , in which each vector represents a corresponding user in a community, and each feature of each vector represents the corresponding user's click-behavior with regard to a content item. | 0.633681 |
8,744,989 | 9 | 10 | 9. A non-transitory computer-readable medium having instructions stored thereon, the instructions, when executed by one or more processors, cause the processors to perform operations comprising: determining a current value of a respective approval metric for each of a plurality of content submissions; determining a statistical confidence interval for the respective approval metric of each content submission, wherein an upper bound and a lower bound of the statistical confidence interval each departs from the current value of the respective approval metric by a decreasing amount with an increasing sample size of favorability indications associated with each content submission, wherein each favorability indication indicates either positive or negative favorability; generating a priority ranking of the plurality of content submissions according to the upper bound of the statistical confidence interval calculated for each of the content submissions; and selecting first one or more content submissions in the priority ranking as featured submissions for presentation in order to elicit additional favorability indications according to respective ranks of the first one or more content submissions in the priority ranking. | 9. A non-transitory computer-readable medium having instructions stored thereon, the instructions, when executed by one or more processors, cause the processors to perform operations comprising: determining a current value of a respective approval metric for each of a plurality of content submissions; determining a statistical confidence interval for the respective approval metric of each content submission, wherein an upper bound and a lower bound of the statistical confidence interval each departs from the current value of the respective approval metric by a decreasing amount with an increasing sample size of favorability indications associated with each content submission, wherein each favorability indication indicates either positive or negative favorability; generating a priority ranking of the plurality of content submissions according to the upper bound of the statistical confidence interval calculated for each of the content submissions; and selecting first one or more content submissions in the priority ranking as featured submissions for presentation in order to elicit additional favorability indications according to respective ranks of the first one or more content submissions in the priority ranking. 10. The computer-readable medium of claim 9 , wherein the selecting the first one or more content submissions in the priority ranking for presentation in order to elicit additional favorability indications further comprises: randomizing the selection of the first one or more content submissions in the priority ranking according to a selection probability assigned to each of the first one or more content submissions in the priority ranking. | 0.578095 |
4,373,192 | 6 | 7 | 6. The device according to claim 5, wherein said display means comprises means for displaying data comprising a predetermined number of characters and means associated with said display means for shifting the displayed data when the data length of the second words and of the indicator of parts of speech exceeds the capacity of the display means, wherein the second words and the indicator of parts of speech are displayed on the display means for a given length of time prior to shifting. | 6. The device according to claim 5, wherein said display means comprises means for displaying data comprising a predetermined number of characters and means associated with said display means for shifting the displayed data when the data length of the second words and of the indicator of parts of speech exceeds the capacity of the display means, wherein the second words and the indicator of parts of speech are displayed on the display means for a given length of time prior to shifting. 7. The device according to claim 6, further comprising stopping means connected to the display means for stopping the shifting operation of the display means. | 0.662393 |
9,460,708 | 9 | 10 | 9. The method of claim 8 , wherein the second word includes a single additional letter that is not present in the first word and all other letters of the second word are identical to the first word. | 9. The method of claim 8 , wherein the second word includes a single additional letter that is not present in the first word and all other letters of the second word are identical to the first word. 10. The method of claim 9 , wherein the single additional letter is the last letter of the second word. | 0.5 |
7,536,696 | 1 | 13 | 1. A computer readable storage medium encoded with a first data structure and a second data structure, comprising: a first parameter definition for a first input parameter, the first parameter definition to enable identification of an appropriate first input for the first input parameter, wherein the first parameter definition is a declared property of the first data structure; a second parameter definition for a second input parameter, the second parameter definition to enable identification of an appropriate second input for the second input parameter, wherein the second parameter definition is a declared property of the second data structure; and an instruction-based mechanism to use the first parameter definition to identify the appropriate first input for the first input parameter, and use the second parameter definition to identify the appropriate second input for the second input parameter, wherein the instruction-based mechanism is to further enable the first data structure to process the first input parameter based on the appropriate first input identified from an input source to output an object, and provide the object as an input for the second data structure to be processed by the second data structure by passing a reference of the object to the second data structure, and the instruction-based mechanism is to further enable the second data structure to process the second input parameter based on the appropriate second input identified from the input source, when the first and second data structures become instantiated into objects. | 1. A computer readable storage medium encoded with a first data structure and a second data structure, comprising: a first parameter definition for a first input parameter, the first parameter definition to enable identification of an appropriate first input for the first input parameter, wherein the first parameter definition is a declared property of the first data structure; a second parameter definition for a second input parameter, the second parameter definition to enable identification of an appropriate second input for the second input parameter, wherein the second parameter definition is a declared property of the second data structure; and an instruction-based mechanism to use the first parameter definition to identify the appropriate first input for the first input parameter, and use the second parameter definition to identify the appropriate second input for the second input parameter, wherein the instruction-based mechanism is to further enable the first data structure to process the first input parameter based on the appropriate first input identified from an input source to output an object, and provide the object as an input for the second data structure to be processed by the second data structure by passing a reference of the object to the second data structure, and the instruction-based mechanism is to further enable the second data structure to process the second input parameter based on the appropriate second input identified from the input source, when the first and second data structures become instantiated into objects. 13. The computer readable storage medium of claim 1 , further comprising a mapping mechanism that is operative to associate a mapped name with the first input parameter. | 0.540761 |
8,375,014 | 19 | 26 | 19. A method of constructing a query with which to retrieve information from a database, the method comprising: representing graphically a first dataset of a database as a first icon and a second dataset of the database as a second icon in a canvas presented to a user on a display of a computer system; receiving an indication to combine the first dataset with the second dataset, wherein the indication is received in response to the first icon being graphically associated with the second icon in the canvas; based on the received indication, presenting to the user on the display of the computer system options for combining elements of the first and second datasets; in response to the user's selection of one of the presented options, generating a third icon in the canvas representing a combination dataset of elements of the first and second datasets; representing user-defined relationships between the first and second datasets and the combination dataset as a connected graph of the first, second and third icons, wherein the connected graph presents a graphical representation of the query to the user; constructing, by at least one processor of the computer system, a machine-readable structured query based on the connected graph, wherein constructing the query includes processing the combination dataset, the second dataset and the first dataset, to identify columns of the first and second datasets referenced by one or more SQL substatements corresponding to an operation of the combination dataset and to add those columns to a SQL SELECT statement, and wherein the constructing further includes incorporating the SQL substatement into the machine-readable SQL query; generating natural language expressions describing the operations between the datasets in the graph; and presenting the natural language expressions describing the operations to the user; and returning data from the database, the returned data corresponding to an execution of the machine-readable structured query against the database. | 19. A method of constructing a query with which to retrieve information from a database, the method comprising: representing graphically a first dataset of a database as a first icon and a second dataset of the database as a second icon in a canvas presented to a user on a display of a computer system; receiving an indication to combine the first dataset with the second dataset, wherein the indication is received in response to the first icon being graphically associated with the second icon in the canvas; based on the received indication, presenting to the user on the display of the computer system options for combining elements of the first and second datasets; in response to the user's selection of one of the presented options, generating a third icon in the canvas representing a combination dataset of elements of the first and second datasets; representing user-defined relationships between the first and second datasets and the combination dataset as a connected graph of the first, second and third icons, wherein the connected graph presents a graphical representation of the query to the user; constructing, by at least one processor of the computer system, a machine-readable structured query based on the connected graph, wherein constructing the query includes processing the combination dataset, the second dataset and the first dataset, to identify columns of the first and second datasets referenced by one or more SQL substatements corresponding to an operation of the combination dataset and to add those columns to a SQL SELECT statement, and wherein the constructing further includes incorporating the SQL substatement into the machine-readable SQL query; generating natural language expressions describing the operations between the datasets in the graph; and presenting the natural language expressions describing the operations to the user; and returning data from the database, the returned data corresponding to an execution of the machine-readable structured query against the database. 26. The method of claim 19 , wherein the user's selection of one of the presented options includes a selection to create the combination dataset as an intersection of the first and second datasets, which includes only the records that are in both the first and second datasets. | 0.654613 |
8,291,213 | 14 | 17 | 14. A machine-implemented system, comprising: a machine having memory configured with executable instructions for a request search service that processes on the machine; and the machine or a different machine having memory configured with executable instructions for a process search service that processes on the machine or the different machine; wherein the request search service encrypts a search of a principal using a search service public key and encrypts search return instructions for delivering search results associated with the search service processing the search with a search return process public key, and wherein the encrypted search and search return instructions are delivered to the process search service, and wherein the process search service delivers the encrypted search to the search service along with a first public key for the principal and delivers the encrypted search return instructions to the search return process along with a second public key of the principal, and wherein the search is processed and the search results are encrypted by the search service using the first public key and delivered to the search return process, and the search return process encrypts the search results again with the second public key and then delivers the encrypted search results to the principal, and wherein a true identity of the principal is masked for the search processing. | 14. A machine-implemented system, comprising: a machine having memory configured with executable instructions for a request search service that processes on the machine; and the machine or a different machine having memory configured with executable instructions for a process search service that processes on the machine or the different machine; wherein the request search service encrypts a search of a principal using a search service public key and encrypts search return instructions for delivering search results associated with the search service processing the search with a search return process public key, and wherein the encrypted search and search return instructions are delivered to the process search service, and wherein the process search service delivers the encrypted search to the search service along with a first public key for the principal and delivers the encrypted search return instructions to the search return process along with a second public key of the principal, and wherein the search is processed and the search results are encrypted by the search service using the first public key and delivered to the search return process, and the search return process encrypts the search results again with the second public key and then delivers the encrypted search results to the principal, and wherein a true identity of the principal is masked for the search processing. 17. The machine-implemented system of claim 14 further comprising, the machine or the different machine having additional executable instructions for a receive result's service that processes on the machine or the different machine, and wherein the receive result's service receives the encrypted search results from the search return process on behalf of the principal and decrypts using multiple private keys associated with the principal to deliver the search results in accordance with the search return instructions. | 0.5 |
10,162,605 | 16 | 19 | 16. A method of completing a code snippet to define an object literal, the method comprising: providing a proxy object to a function that is included in code; performing global dynamic analysis by using a getter trap that is included in the proxy object to extract information regarding one or more properties of the object literal from the code, which includes the function; and generating a recommendation that recommends content for completion of the code snippet to define the object literal based at least in part on the information, the content identifying the one or more properties of the object literal. | 16. A method of completing a code snippet to define an object literal, the method comprising: providing a proxy object to a function that is included in code; performing global dynamic analysis by using a getter trap that is included in the proxy object to extract information regarding one or more properties of the object literal from the code, which includes the function; and generating a recommendation that recommends content for completion of the code snippet to define the object literal based at least in part on the information, the content identifying the one or more properties of the object literal. 19. The method of claim 16 , wherein performing the global dynamic analysis comprises: performing the global dynamic analysis by using the getter trap, which is included in the proxy object, to extract information regarding at least one property of the one or more properties of the object literal from one or more definitions of one or more dynamic properties that are included in the function, the one or more definitions of the one or more dynamic properties indicating the at least one property. | 0.521113 |
8,285,537 | 47 | 50 | 47. At least one computer readable medium as recited in claim 46 , wherein said predicting is based on a country associated with the proper noun. | 47. At least one computer readable medium as recited in claim 46 , wherein said predicting is based on a country associated with the proper noun. 50. At least one computer readable medium as recited in claim 47 , wherein the proper noun is a name and the country is derived from a telephone number associated with the name. | 0.5 |
8,572,062 | 6 | 7 | 6. A system, comprising: a computer processor; and storage coupled to the computer processor, wherein the storage stores a program, and wherein the computer processor executes the program to perform operations, wherein the operations comprise: obtaining a document with multiple subsets of pages that includes a different internal index set associated with each subset of pages from among the multiple subsets of pages, wherein each different internal index is located within a first area on a page within the associated subset of pages, is relevant to the page and subsequent pages in the associated subset of pages until one of another internal index set within the document is found and an end of the document is reached, and includes one or more name-value pairs, and wherein the first area is ignored by an application that processes a second area of the document; extracting the one or more name-value pairs from each different internal index set, wherein each of the one or more name-value pairs provides specific information about the document for use in identifying the document; and storing the extracted one or more-name value pairs in a table in a database to enable subsequent searching for the document, wherein, for a name-value pair, the name corresponds to a column name in the table, and the value corresponds to a value stored in a row for a column having the name. | 6. A system, comprising: a computer processor; and storage coupled to the computer processor, wherein the storage stores a program, and wherein the computer processor executes the program to perform operations, wherein the operations comprise: obtaining a document with multiple subsets of pages that includes a different internal index set associated with each subset of pages from among the multiple subsets of pages, wherein each different internal index is located within a first area on a page within the associated subset of pages, is relevant to the page and subsequent pages in the associated subset of pages until one of another internal index set within the document is found and an end of the document is reached, and includes one or more name-value pairs, and wherein the first area is ignored by an application that processes a second area of the document; extracting the one or more name-value pairs from each different internal index set, wherein each of the one or more name-value pairs provides specific information about the document for use in identifying the document; and storing the extracted one or more-name value pairs in a table in a database to enable subsequent searching for the document, wherein, for a name-value pair, the name corresponds to a column name in the table, and the value corresponds to a value stored in a row for a column having the name. 7. The system of claim 6 , wherein the operations for extracting further comprise using Application Programming Interfaces (APIs) to extract the one or more name-value pairs. | 0.792857 |
8,041,555 | 10 | 13 | 10. An information processing system for translating text within an image captured by a wireless device, the information processing system comprising: a memory; a processor communicatively coupled to the memory; a translation manager communicatively coupled to the memory and the processor, wherein the translation manager is configured to perform a method comprising: receiving at least one image from a wireless device; determining a location where the image was captured by the wireless device; identifying a set of text characters within the image; determining a language associated with the set of text characters based on at least the location that has been determined; determining a language context associated with the location that has been determined; identifying at least one word within a language dictionary associated with the language context; generating a prioritized language dictionary based on the at least one location and the language context that has been determined, wherein the generating comprises assigning a higher priority to the word associated with the language context than words in the language dictionary associated with other language contexts; and translating the set of text characters into a language that is different than language that has been determined based on the prioritized language dictionary that has been generated, wherein the word that has been assigned a higher priority is selected from the prioritized language dictionary to translate the set of text characters over other words in the prioritized language dictionary that have been assigned a lower priority. | 10. An information processing system for translating text within an image captured by a wireless device, the information processing system comprising: a memory; a processor communicatively coupled to the memory; a translation manager communicatively coupled to the memory and the processor, wherein the translation manager is configured to perform a method comprising: receiving at least one image from a wireless device; determining a location where the image was captured by the wireless device; identifying a set of text characters within the image; determining a language associated with the set of text characters based on at least the location that has been determined; determining a language context associated with the location that has been determined; identifying at least one word within a language dictionary associated with the language context; generating a prioritized language dictionary based on the at least one location and the language context that has been determined, wherein the generating comprises assigning a higher priority to the word associated with the language context than words in the language dictionary associated with other language contexts; and translating the set of text characters into a language that is different than language that has been determined based on the prioritized language dictionary that has been generated, wherein the word that has been assigned a higher priority is selected from the prioritized language dictionary to translate the set of text characters over other words in the prioritized language dictionary that have been assigned a lower priority. 13. The information processing system of claim 10 , wherein the determining the location further comprises: identifying location data associated with the image; querying a location database with the location data that has been identified; and receiving a location from the location database in response to the querying. | 0.5 |
9,626,360 | 1 | 2 | 1. A machine implemented method for providing statistics characterizing translation work in synchronizing content in different languages, comprising the steps of: activating a spider agent to crawl a website for content in a first language via a publicly accessible network path for synchronizing a translated version of the content in the first language previously generated in a second language; parsing the crawled content in the first language into a plurality of translatable components; accessing a database that stores translated components previously generated in the second language; identifying at least some of the plurality of translatable components in the first language that do not have a corresponding translated component in the second language in the database; generating statistics based on the identified translatable components to estimate the work load involved in language translation of the identified translatable components from the first language to the second language; and providing information including the generated statistics to characterize a service related to synchronizing the content in the first and second languages. | 1. A machine implemented method for providing statistics characterizing translation work in synchronizing content in different languages, comprising the steps of: activating a spider agent to crawl a website for content in a first language via a publicly accessible network path for synchronizing a translated version of the content in the first language previously generated in a second language; parsing the crawled content in the first language into a plurality of translatable components; accessing a database that stores translated components previously generated in the second language; identifying at least some of the plurality of translatable components in the first language that do not have a corresponding translated component in the second language in the database; generating statistics based on the identified translatable components to estimate the work load involved in language translation of the identified translatable components from the first language to the second language; and providing information including the generated statistics to characterize a service related to synchronizing the content in the first and second languages. 2. The method according to claim 1 , wherein the language translation includes human translating the identified translatable components. | 0.734375 |
9,672,063 | 8 | 13 | 8. A method according to claim 1 , wherein the expected number of interpreting tasks being based on the number of pages left in the document. | 8. A method according to claim 1 , wherein the expected number of interpreting tasks being based on the number of pages left in the document. 13. A method according to claim 8 , further comprising estimating the complexity of pages left in the document from a PDL source using at least one of the number of pages described by the PDL source, a number of objects on a page, and/or a number of z-bands of a page. | 0.5 |
9,507,874 | 1 | 2 | 1. A system comprising: a memory; and a processor configured to: receive a document instance as an input; for each element in the document instance: parse the element from the document instance; and perform a first validation of each parsed element using a second schema parse tree, wherein the second schema parse tree is generated from at least one schema that describes a document structure, schema elements parsed from the at least one schema are validated using a first schema parse tree that is static, the second schema parse tree is assembled to include nodes for respective types of document elements expected to be encountered and that correspond to the schema elements parsed from the at least one schema and validated using the first schema parse tree that is static, and at least one of the nodes is configured to call at least one user defined validation rule, external to the second schema parse tree, which validates the element of the document instance corresponding to the node; perform a second validation of at least one parsed element using the at least one user defined validation rule external to the second schema parse tree called from the node to which the parsed element corresponds; add the validated document elements to a validation report; and output the validation report. | 1. A system comprising: a memory; and a processor configured to: receive a document instance as an input; for each element in the document instance: parse the element from the document instance; and perform a first validation of each parsed element using a second schema parse tree, wherein the second schema parse tree is generated from at least one schema that describes a document structure, schema elements parsed from the at least one schema are validated using a first schema parse tree that is static, the second schema parse tree is assembled to include nodes for respective types of document elements expected to be encountered and that correspond to the schema elements parsed from the at least one schema and validated using the first schema parse tree that is static, and at least one of the nodes is configured to call at least one user defined validation rule, external to the second schema parse tree, which validates the element of the document instance corresponding to the node; perform a second validation of at least one parsed element using the at least one user defined validation rule external to the second schema parse tree called from the node to which the parsed element corresponds; add the validated document elements to a validation report; and output the validation report. 2. The system of claim 1 , wherein performing the second validation comprises identifying syntax in the second schema parse tree that calls the at least one user defined validation rule. | 0.5 |
9,501,295 | 4 | 5 | 4. The method of claim 2 , wherein said presenting process of said list of at least one language and locale combination option is performed during at least one of the following processes: changing user preferred locale and language settings, and creating a new user account and setting at least one user preferred locale and at least one user preferred language. | 4. The method of claim 2 , wherein said presenting process of said list of at least one language and locale combination option is performed during at least one of the following processes: changing user preferred locale and language settings, and creating a new user account and setting at least one user preferred locale and at least one user preferred language. 5. The method of claim 4 , wherein said changing process and said setting process of user preferred locale and language settings comprises: deriving a language code and a locale code from valid language and locale combinations selected by an user out of said list of language and locale combination options; storing said derived language code as user preferred language, and said derived locale code as user preferred locale; modifying said first composite values list of applicable locales and matching languages combinations and said second composite values list of applicable locales and matching languages combinations in case of performed changes of said service management system; and providing a new resulting composite values list of valid locales and languages combinations for further processing. | 0.5 |
8,250,080 | 1 | 10 | 1. A method executed by one or more computers, the method comprising: receiving a search query, the search query including a query label; identifying, from a data store, one or more uniform resource locator (URL) patterns, each of the one or more URL patterns including a component of a URL and at least one of a wildcard or a regular expression, and each of the one or more URL patterns being associated with a label that matches the query label; constructing a filter including: determining a filter size based on a length of the one or more URL patterns and a count of a number of URL patterns having each respective length; and constructing the filter having the filter size; and filtering one or more results of the search query using the filter. | 1. A method executed by one or more computers, the method comprising: receiving a search query, the search query including a query label; identifying, from a data store, one or more uniform resource locator (URL) patterns, each of the one or more URL patterns including a component of a URL and at least one of a wildcard or a regular expression, and each of the one or more URL patterns being associated with a label that matches the query label; constructing a filter including: determining a filter size based on a length of the one or more URL patterns and a count of a number of URL patterns having each respective length; and constructing the filter having the filter size; and filtering one or more results of the search query using the filter. 10. The method of claim 1 , wherein the label includes a term being associated with at token that indicates that the term is a label. | 0.87931 |
8,537,401 | 13 | 14 | 13. The system of claim 9 , wherein receiving input identifying a printing option to be performed in response to locating the first text item in the electronic file comprises receiving an input identifying a finishing operation. | 13. The system of claim 9 , wherein receiving input identifying a printing option to be performed in response to locating the first text item in the electronic file comprises receiving an input identifying a finishing operation. 14. The system of claim 13 , wherein the finishing operation comprises performing one or more of the following: stapling; punching holes; folding; and creating a booklet. | 0.5 |
10,044,862 | 11 | 12 | 11. The computer program product of claim 8 , wherein the conversation model is learned by a Markov Decision Process based on at least one of a finite set of states, a finite set of actions, and a discount factor. | 11. The computer program product of claim 8 , wherein the conversation model is learned by a Markov Decision Process based on at least one of a finite set of states, a finite set of actions, and a discount factor. 12. The computer program product of claim 11 , wherein the finite set of states includes an emotion, a talking time, a historical transaction, a contacted frequency, a demographic information, and a customer category, and wherein the finite set of actions includes conversation topics. | 0.5 |
7,844,464 | 8 | 10 | 8. The method of claim 6 , further comprising: (E) using a text-to-speech engine to play the emphasis-adjusted audio stream. | 8. The method of claim 6 , further comprising: (E) using a text-to-speech engine to play the emphasis-adjusted audio stream. 10. The method of claim 8 , wherein the step (C) comprises a step of deriving, from the likelihood and the measure of relevance, a signal power adjustment factor that adjusts a signal power of the audio stream. | 0.5 |
7,849,437 | 1 | 10 | 1. A computer-readable storage device having computer-executable components executable by a processing device to perform a method, the processing device accessing a local memory, comprising: (a) receiving a web-page including elements defined to access a plurality of resources in a JavaScript framework provided to the processing device in local memory, the resources including methods interpreted by a browser to create resource objects; (b) parsing the web-page to define instances of objects based on bindings to the resources defined by elements in the web-page; (c) downloading a base set of resources in a core framework library of the JavaScript framework to the local memory; (d) determining if additional resources not included in the local memory are needed based on the bindings declared in the web-page and if so, downloading the additional resources to a resource cache in local memory; (e) managing instances of the objects by maintaining a global list of binding declarations maintained by the JavaScript framework; and (f) displaying the web-page in the browser using one or more of the objects. | 1. A computer-readable storage device having computer-executable components executable by a processing device to perform a method, the processing device accessing a local memory, comprising: (a) receiving a web-page including elements defined to access a plurality of resources in a JavaScript framework provided to the processing device in local memory, the resources including methods interpreted by a browser to create resource objects; (b) parsing the web-page to define instances of objects based on bindings to the resources defined by elements in the web-page; (c) downloading a base set of resources in a core framework library of the JavaScript framework to the local memory; (d) determining if additional resources not included in the local memory are needed based on the bindings declared in the web-page and if so, downloading the additional resources to a resource cache in local memory; (e) managing instances of the objects by maintaining a global list of binding declarations maintained by the JavaScript framework; and (f) displaying the web-page in the browser using one or more of the objects. 10. The computer-readable storage device of claim 1 further including destroying the bindings on unloading of the web-page. | 0.762548 |
9,466,289 | 16 | 18 | 16. A non-transitory computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which, when executed by an electronic device with one or more processors, cause the device to: train an acoustic model with an International Phonetic Alphabet (IPA) phoneme mapping collection and a plurality of audio samples in a plurality of different languages, wherein the acoustic model includes: a foreground model configured to match a phoneme in an input audio signal to a corresponding keyword, wherein the foreground model is trained by (i) obtaining a phoneme collection for each of the plurality of different languages, (ii) generating a plurality of triphones by linking phonemes in the phoneme collection corresponding to the language, and (iii) performing Gaussian splitting training on the triphones that are clustered with a decision tree corresponding to the language; and a background model configured to match a phoneme in the input audio signal to a corresponding non-keyword; after training the acoustic model, generate a phone decoder based on the trained acoustic model; obtain a keyword phoneme sequence for a respective keyword in a respective language of the plurality of different languages, wherein the obtaining includes: collecting a set of keyword audio samples for the respective keyword in the respective language; decoding the set of keyword audio samples with the phone decoder to generate a set of phoneme sequence candidates for the respective keyword, each phoneme sequence candidate corresponding to a respective keyword audio sample; and selecting the keyword phoneme sequence for the respective keyword from the set of phoneme sequence candidates by choosing a phoneme of a highest confidence measure from one of the set of phoneme sequence candidates at each location in the corresponding sequence and assembling the chosen phonemes into the keyword phoneme sequence according to their locations in the corresponding sequence; after obtaining the keyword phoneme sequence, detect one or more keywords in the input audio signal with the trained acoustic model, wherein the detecting includes: matching one or more phonemic keyword portions of the input audio signal with one or more phonemes in the keyword phoneme sequence with the foreground model; and filtering out one or more phonemic non-keyword portions of the input audio signal with the background model. | 16. A non-transitory computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which, when executed by an electronic device with one or more processors, cause the device to: train an acoustic model with an International Phonetic Alphabet (IPA) phoneme mapping collection and a plurality of audio samples in a plurality of different languages, wherein the acoustic model includes: a foreground model configured to match a phoneme in an input audio signal to a corresponding keyword, wherein the foreground model is trained by (i) obtaining a phoneme collection for each of the plurality of different languages, (ii) generating a plurality of triphones by linking phonemes in the phoneme collection corresponding to the language, and (iii) performing Gaussian splitting training on the triphones that are clustered with a decision tree corresponding to the language; and a background model configured to match a phoneme in the input audio signal to a corresponding non-keyword; after training the acoustic model, generate a phone decoder based on the trained acoustic model; obtain a keyword phoneme sequence for a respective keyword in a respective language of the plurality of different languages, wherein the obtaining includes: collecting a set of keyword audio samples for the respective keyword in the respective language; decoding the set of keyword audio samples with the phone decoder to generate a set of phoneme sequence candidates for the respective keyword, each phoneme sequence candidate corresponding to a respective keyword audio sample; and selecting the keyword phoneme sequence for the respective keyword from the set of phoneme sequence candidates by choosing a phoneme of a highest confidence measure from one of the set of phoneme sequence candidates at each location in the corresponding sequence and assembling the chosen phonemes into the keyword phoneme sequence according to their locations in the corresponding sequence; after obtaining the keyword phoneme sequence, detect one or more keywords in the input audio signal with the trained acoustic model, wherein the detecting includes: matching one or more phonemic keyword portions of the input audio signal with one or more phonemes in the keyword phoneme sequence with the foreground model; and filtering out one or more phonemic non-keyword portions of the input audio signal with the background model. 18. The non-transitory computer readable storage medium of claim 16 , wherein the one or more programs comprising instructions, which further cause the device to: collect the plurality of audio samples in the plurality of different languages and labeled data for the plurality of audio samples; obtain a phoneme collection for each of the plurality of different languages; map phonemes from each phoneme collection to phonemes in the IPA so as to generate the IPA phoneme mapping collection; and wherein the acoustic model is trained based on the collected plurality of audio samples in the plurality of different languages, the collected labeled data for the plurality of audio samples, and the generated IPA phoneme mapping collection. | 0.606303 |
9,390,183 | 13 | 15 | 13. A system, comprising: one or more computers; and memory having instructions stored thereon, the instructions, when performed by the one or more computers, cause the one or more computers to perform operations comprising: identifying queries that each include (i) one or more first terms that are associated with a particular topic and (ii) one or more second terms, different than the one or more first terms, that are associated with a particular author; identifying web resources for which the particular author has been identified as an author; determining a quantity of selections of search results that (i) are generated in response to one or more of the queries and (ii) reference one or more of the web resources for which the particular author has been identified as an author; associating the particular author with the particular topic, as a topic-to-author association, when the quantity of selections satisfies a threshold that is associated with more than one selection; and using the topic-to-author association in ranking a search result, which references one or more of the web resources, that is generated in response to one or more subsequently received queries that includes one or more of the first terms that are associated with the particular topic. | 13. A system, comprising: one or more computers; and memory having instructions stored thereon, the instructions, when performed by the one or more computers, cause the one or more computers to perform operations comprising: identifying queries that each include (i) one or more first terms that are associated with a particular topic and (ii) one or more second terms, different than the one or more first terms, that are associated with a particular author; identifying web resources for which the particular author has been identified as an author; determining a quantity of selections of search results that (i) are generated in response to one or more of the queries and (ii) reference one or more of the web resources for which the particular author has been identified as an author; associating the particular author with the particular topic, as a topic-to-author association, when the quantity of selections satisfies a threshold that is associated with more than one selection; and using the topic-to-author association in ranking a search result, which references one or more of the web resources, that is generated in response to one or more subsequently received queries that includes one or more of the first terms that are associated with the particular topic. 15. The system of claim 13 , wherein the web resources are websites or webpages having respective associated web addresses. | 0.887156 |
9,063,975 | 8 | 11 | 8. A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the data processing system to: receive an input question directed to a previous execution of a question answering (QA) system with regard to a previous input question; process the input question to generate at least one query for application to a corpus of information, wherein the corpus of information comprises information about the QA system and the previous execution of the QA system on the previous input question; apply the at least one query to the corpus of information to generate candidate answers to the input question; and output a final answer for the input question based on the candidate answers; wherein the input question is directed to identifying differences between the previous execution of the QA system and another previous execution of the QA system, and wherein the candidate answers identify possible sources of differences between the previous execution of the QA system and the another previous execution of the QA system. | 8. A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the data processing system to: receive an input question directed to a previous execution of a question answering (QA) system with regard to a previous input question; process the input question to generate at least one query for application to a corpus of information, wherein the corpus of information comprises information about the QA system and the previous execution of the QA system on the previous input question; apply the at least one query to the corpus of information to generate candidate answers to the input question; and output a final answer for the input question based on the candidate answers; wherein the input question is directed to identifying differences between the previous execution of the QA system and another previous execution of the QA system, and wherein the candidate answers identify possible sources of differences between the previous execution of the QA system and the another previous execution of the QA system. 11. The computer program product of claim 8 , wherein the graphical output is based on confidence values calculated for each of the candidate answers. | 0.807198 |
8,560,549 | 8 | 10 | 8. A system comprising: one or more computers configured to perform operations comprising: receiving a content access history of a user, the content access history including one or more user actions, each user action being associated with a content item upon which the user action is performed; identifying one or more action trails from the content access history, each action trail including a sequence of user actions corresponding to content items relating to a topic, wherein identifying a particular action trail includes: clustering the user actions into a series of segments using temporal proximity of the user actions; calculating semantic similarities between the content items, wherein the semantic similarities change as a function of the series of segments; and determining whether to add a segment of the series of segments to the action trail based on the semantic similarities between content items corresponding to the user actions in the segment and content items corresponding to the user actions in another segment; and providing the action trails for display on a display device. | 8. A system comprising: one or more computers configured to perform operations comprising: receiving a content access history of a user, the content access history including one or more user actions, each user action being associated with a content item upon which the user action is performed; identifying one or more action trails from the content access history, each action trail including a sequence of user actions corresponding to content items relating to a topic, wherein identifying a particular action trail includes: clustering the user actions into a series of segments using temporal proximity of the user actions; calculating semantic similarities between the content items, wherein the semantic similarities change as a function of the series of segments; and determining whether to add a segment of the series of segments to the action trail based on the semantic similarities between content items corresponding to the user actions in the segment and content items corresponding to the user actions in another segment; and providing the action trails for display on a display device. 10. The system of claim 8 , further comprising: splitting a segment of the series of segments into two or more segments when a topical coherence value of the segment satisfies a threshold coherence value. | 0.816876 |
5,412,756 | 25 | 31 | 25. A software shell as claimed in claim 24 wherein the at least one knowledge source module includes: a rule-based knowledge source module having a forward-chaining belief propagation scheme including rules in if-then-else form with associated levels of belief; and a case-based knowledge source module having a data comparison scheme including predefined patterns and conditions, whereupon execution of the case-based knowledge source, the conditions are inferred to be true if a certain level of closeness is found between received data and the patterns. | 25. A software shell as claimed in claim 24 wherein the at least one knowledge source module includes: a rule-based knowledge source module having a forward-chaining belief propagation scheme including rules in if-then-else form with associated levels of belief; and a case-based knowledge source module having a data comparison scheme including predefined patterns and conditions, whereupon execution of the case-based knowledge source, the conditions are inferred to be true if a certain level of closeness is found between received data and the patterns. 31. A software shell as claimed in claim 25 wherein the means for determining includes means for receiving input data and determining when knowledge source execution preconditions are met. | 0.855607 |
8,380,507 | 49 | 51 | 49. An electronic device having at least one processor and memory storing at least one program for execution by the at least one processor, the at least one program including instructions for: identifying a plurality of text strings; assigning a rank to each of the plurality of text strings; detecting that a language of a lower rank text string and a higher rank text string are different; determining that the language of the lower rank text string is speakable in the language of the higher rank text string; and generating speech content for at least the lower rank text string and the higher rank text string using the language of the higher rank text string. | 49. An electronic device having at least one processor and memory storing at least one program for execution by the at least one processor, the at least one program including instructions for: identifying a plurality of text strings; assigning a rank to each of the plurality of text strings; detecting that a language of a lower rank text string and a higher rank text string are different; determining that the language of the lower rank text string is speakable in the language of the higher rank text string; and generating speech content for at least the lower rank text string and the higher rank text string using the language of the higher rank text string. 51. The electronic device of claim 49 , the at least one program further comprising instructions for: identifying a default language associated with a personal electronic device providing the speech content; determining that the languages of the lower rank text string and the higher rank text string are speakable in the default language; and generating speech content for the lower rank text string and the higher rank text string using the default language. | 0.5 |
7,694,311 | 6 | 7 | 6. The article of manufacture of claim 1 , wherein each task is associated with at least one attribute associated with at least one of the following entities: user, user's computer, user's account, subject matter of the task, and application. | 6. The article of manufacture of claim 1 , wherein each task is associated with at least one attribute associated with at least one of the following entities: user, user's computer, user's account, subject matter of the task, and application. 7. The article of manufacture of claim 6 , wherein information associated with a user is retrieved from a database. | 0.676966 |
8,694,505 | 1 | 5 | 1. One or more memory devices having computer-executable instructions embodied thereon that, when executed, perform a method for providing a user interface for exploring facets relevant to a user search query term, wherein a state of the user search query is maintained, the method comprising: receiving at least one search query term from a user; sending the at least one search query term to a back-end server; receiving relevant facets for the at least one search query term, wherein each of the relevant facets categorizes a set of topics relevant to the at least one search query term and allows the user to refine the one or more search query terms; displaying the relevant facets for the at least one search query term; receiving a selection of one of the relevant facets; sending the selected relevant facet to the back-end server receiving a set of search results based on the at least one search query term and the selected relevant facet input as a refinement query, wherein the refinement query is executed to generate the set of search results; displaying: (1) the relevant facets for the at least one search query term, wherein the relevant facets are generated by the back-end server using the at least one search query term recalled by the back-end server; and (2) the received set of search results. | 1. One or more memory devices having computer-executable instructions embodied thereon that, when executed, perform a method for providing a user interface for exploring facets relevant to a user search query term, wherein a state of the user search query is maintained, the method comprising: receiving at least one search query term from a user; sending the at least one search query term to a back-end server; receiving relevant facets for the at least one search query term, wherein each of the relevant facets categorizes a set of topics relevant to the at least one search query term and allows the user to refine the one or more search query terms; displaying the relevant facets for the at least one search query term; receiving a selection of one of the relevant facets; sending the selected relevant facet to the back-end server receiving a set of search results based on the at least one search query term and the selected relevant facet input as a refinement query, wherein the refinement query is executed to generate the set of search results; displaying: (1) the relevant facets for the at least one search query term, wherein the relevant facets are generated by the back-end server using the at least one search query term recalled by the back-end server; and (2) the received set of search results. 5. The method of claim 1 , the method further comprising displaying at least one static facet, wherein the static facet allows the user to refine the at least one search query term. | 0.5 |
9,549,085 | 1 | 9 | 1. A computer system for proactively creating an image product, comprising: a computer memory configured to store a library of specification terms for image products; and a computer processing system configured to receive a command comprising a text from a user, conduct lexical analysis of the text in the command, tokenize the command into a plurality of tokens, and match one or more of the tokens to the specification terms in the library to determine specification parameters for an image product, wherein the computer processing system is configured to automatically identify images based on the specification parameters by the computer system, and automatically create a design for the image product that incorporates at least some of the images identified based on the specification parameters. | 1. A computer system for proactively creating an image product, comprising: a computer memory configured to store a library of specification terms for image products; and a computer processing system configured to receive a command comprising a text from a user, conduct lexical analysis of the text in the command, tokenize the command into a plurality of tokens, and match one or more of the tokens to the specification terms in the library to determine specification parameters for an image product, wherein the computer processing system is configured to automatically identify images based on the specification parameters by the computer system, and automatically create a design for the image product that incorporates at least some of the images identified based on the specification parameters. 9. The computer system of claim 1 , further comprising: a printer configured to produce a physical manifestation of the image product based on the design for the image product. | 0.796767 |
9,286,370 | 1 | 8 | 1. A computer-implemented method to interleave dimensional and relational query constructs in a single, dimensional query, based on a report specification and a predetermined sequence and without introducing semantic inconsistencies, the computer-implemented method comprising: receiving user indication of a plurality of query constructs to include in a the report specification to retrieve a set of query results from a dimensional data model, wherein the report specification is expressed in a predefined reporting language of a higher level of abstraction than both a relational query language and a dimensional query language, wherein the dimensional data model includes a cube having a plurality of dimensions, at least one dimension including a hierarchy of members, wherein the plurality of query constructs includes the dimensional and relational query constructs; generating the single, dimensional query from the report specification by operation of the one or more computer processors and based on the predetermined sequence of applying the plurality of query constructs in the single, dimensional query and based further on a plurality of mapping rules specifying how to map between the dimensional data model and a corresponding relational data model, in order to prevent one or more semantic inconsistencies in the set of query results when interleaving the dimensional and relational query constructs in the single, dimensional query; wherein the predetermined sequence specifies to arrange the plurality of query constructs in an order of application of: a dimensional slicer, a dimensional pre-aggregation detail filter, a relational post-aggregation detail filter, a dimensional set filtering operator, a dimensional suppression, a relational summary filter, a relational sort, and a relational summary operator; wherein the plurality of mapping rules includes a model mapping rule, a level mapping rule, a leaf mapping rule, a cell mapping rule, a ragged mapping rule, a fact mapping rule, and a child mapping rule; wherein the single, dimensional query is executed in order to generate the set of query results; and outputting the set of query results responsive to the report specification. | 1. A computer-implemented method to interleave dimensional and relational query constructs in a single, dimensional query, based on a report specification and a predetermined sequence and without introducing semantic inconsistencies, the computer-implemented method comprising: receiving user indication of a plurality of query constructs to include in a the report specification to retrieve a set of query results from a dimensional data model, wherein the report specification is expressed in a predefined reporting language of a higher level of abstraction than both a relational query language and a dimensional query language, wherein the dimensional data model includes a cube having a plurality of dimensions, at least one dimension including a hierarchy of members, wherein the plurality of query constructs includes the dimensional and relational query constructs; generating the single, dimensional query from the report specification by operation of the one or more computer processors and based on the predetermined sequence of applying the plurality of query constructs in the single, dimensional query and based further on a plurality of mapping rules specifying how to map between the dimensional data model and a corresponding relational data model, in order to prevent one or more semantic inconsistencies in the set of query results when interleaving the dimensional and relational query constructs in the single, dimensional query; wherein the predetermined sequence specifies to arrange the plurality of query constructs in an order of application of: a dimensional slicer, a dimensional pre-aggregation detail filter, a relational post-aggregation detail filter, a dimensional set filtering operator, a dimensional suppression, a relational summary filter, a relational sort, and a relational summary operator; wherein the plurality of mapping rules includes a model mapping rule, a level mapping rule, a leaf mapping rule, a cell mapping rule, a ragged mapping rule, a fact mapping rule, and a child mapping rule; wherein the single, dimensional query is executed in order to generate the set of query results; and outputting the set of query results responsive to the report specification. 8. The computer-implemented method of claim 1 , wherein the ragged mapping rule specifies that any ragged and unbalanced hierarchy maps to a column having a null value. | 0.947203 |
8,234,174 | 1 | 4 | 1. A method for managing inventory sales advertisement information by a host company over a network, comprising the steps of: a. forming an inventory advertisement based information network having at least two tiers of access with at least one host server and at least one remote company user I/O device; b. configuring the host server with an interactive inventory listing builder for generating inventory listings; c. configuring the host server to manage remote company user access to the inventory listings; and d. enabling remote access to the at least one host server for the remote company user to create and manage inventory listings. | 1. A method for managing inventory sales advertisement information by a host company over a network, comprising the steps of: a. forming an inventory advertisement based information network having at least two tiers of access with at least one host server and at least one remote company user I/O device; b. configuring the host server with an interactive inventory listing builder for generating inventory listings; c. configuring the host server to manage remote company user access to the inventory listings; and d. enabling remote access to the at least one host server for the remote company user to create and manage inventory listings. 4. The method of claim 1 , wherein: a. the host company further distributes print advertisements from the inventory listings generated. | 0.752747 |
8,214,359 | 12 | 16 | 12. The system of claim 11 , wherein determining whether the first document is the query-specific duplicate of the second document further comprises comparing one or more first query-relevant parts of the first document and one or more second query-relevant parts of the second document, where each first query-relevant part and each second query-relevant part includes at least one of the one or more keywords. | 12. The system of claim 11 , wherein determining whether the first document is the query-specific duplicate of the second document further comprises comparing one or more first query-relevant parts of the first document and one or more second query-relevant parts of the second document, where each first query-relevant part and each second query-relevant part includes at least one of the one or more keywords. 16. The system of claim 12 , wherein determining whether the first document is the query-specific duplicate of the second document further comprises extracting one or more query-relevant parts from the first document and extracting one or more query-relevant parts from the second document. | 0.5 |
7,962,462 | 19 | 21 | 19. A computer-readable memory device containing instructions for controlling at least one processor to perform a method, the method comprising: receiving a same search query from a plurality of users; identifying search result documents based on the search query; providing information regarding the search result documents to the users; receiving information regarding which of the search result documents were selected by the users; determining whether a majority of the selections were selections for a particular one of the search result documents; assigning points to the particular search result document when a majority of the selections were selections for the particular search result document, where a quantity of the points assigned to the particular search result document is restricted over a given period of time; assigning no points to the particular search result document when there is no majority of selections for the particular search result document; and using the assigned points as one factor of a plurality of factors to determine a measure of quality for the particular search result document in subsequently received search queries. | 19. A computer-readable memory device containing instructions for controlling at least one processor to perform a method, the method comprising: receiving a same search query from a plurality of users; identifying search result documents based on the search query; providing information regarding the search result documents to the users; receiving information regarding which of the search result documents were selected by the users; determining whether a majority of the selections were selections for a particular one of the search result documents; assigning points to the particular search result document when a majority of the selections were selections for the particular search result document, where a quantity of the points assigned to the particular search result document is restricted over a given period of time; assigning no points to the particular search result document when there is no majority of selections for the particular search result document; and using the assigned points as one factor of a plurality of factors to determine a measure of quality for the particular search result document in subsequently received search queries. 21. The computer-readable memory device of claim 19 , where a quantity of the points assigned to the particular search result document is limited. | 0.713725 |
9,292,797 | 14 | 16 | 14. A system for providing a semi-supervised data integration model for named entity classification from a first repository of entity information in view of an auxiliary repository of classification assistance data, the system comprising: memory having computer readable computer instructions; and a processor for executing the computer readable instructions to perform a method comprising: comparing training data to named entity candidates taken from the first repository, thereby forming a positive training seed set in view of identified commonality between the training data and the named entity candidates; in view of the positive training seed set, populating a decision tree; in view of populating the decision tree, creating classification rules for classifying the named entity candidates; sampling a number of entities from the named entity candidates; in view of the classification rules, labeling the sampled entities as positive examples and/or negative examples; in view of the positive examples and the auxiliary repository, updating the positive training seed set to include identified commonality between the positive examples and the auxiliary repository; in view of the negative examples and the auxiliary repository, updating a negative training seed set to include negative examples which lack commonality with the auxiliary repository; and in view of both the updated positive and negative training seed sets, updating the decision tree and the classification rules. | 14. A system for providing a semi-supervised data integration model for named entity classification from a first repository of entity information in view of an auxiliary repository of classification assistance data, the system comprising: memory having computer readable computer instructions; and a processor for executing the computer readable instructions to perform a method comprising: comparing training data to named entity candidates taken from the first repository, thereby forming a positive training seed set in view of identified commonality between the training data and the named entity candidates; in view of the positive training seed set, populating a decision tree; in view of populating the decision tree, creating classification rules for classifying the named entity candidates; sampling a number of entities from the named entity candidates; in view of the classification rules, labeling the sampled entities as positive examples and/or negative examples; in view of the positive examples and the auxiliary repository, updating the positive training seed set to include identified commonality between the positive examples and the auxiliary repository; in view of the negative examples and the auxiliary repository, updating a negative training seed set to include negative examples which lack commonality with the auxiliary repository; and in view of both the updated positive and negative training seed sets, updating the decision tree and the classification rules. 16. The system of claim 14 , comprising: performing the method for each of a plurality of named entity types to determine the classification rules for each of the named entity types, wherein the training data comprise a plurality of data sources comprising only positive examples associated with each of the plurality of named entity types. | 0.721768 |
8,669,888 | 12 | 13 | 12. The Hangeul input method of claim 11 , wherein the basic consonants assigned to the selected consonant key are sequentially and circularly selected by repeatedly operating the selected consonant key, the basic vowels assigned to the selected vowel key are sequentially and circularly selected by repeatedly operating the selected consonant key, and the extended consonants to be converted from the corresponding basic convertible consonants are sequentially and circularly selected by repeatedly operating the shift key. | 12. The Hangeul input method of claim 11 , wherein the basic consonants assigned to the selected consonant key are sequentially and circularly selected by repeatedly operating the selected consonant key, the basic vowels assigned to the selected vowel key are sequentially and circularly selected by repeatedly operating the selected consonant key, and the extended consonants to be converted from the corresponding basic convertible consonants are sequentially and circularly selected by repeatedly operating the shift key. 13. The Hangeul input method of claim 12 , wherein the extended consonants are circularly selected in the sequence of an aspirate of the corresponding basic convertible consonant and fortis of the corresponding basic convertible consonant by repeatedly operating the shift key. | 0.831509 |
9,188,456 | 12 | 14 | 12. A computer based method of fixing user input errors in an in-vehicle computing system, the method comprising: interpreting an entire first user input received by the in-vehicle computing system, the entire first user input including a full string destination address and contextual information corresponding to the full string destination address, the contextual information comprising a commercial category associated with a particular point of interest of a plurality of points of interest, the commercial category further specifying a type of the particular point of interest; presenting the interpreted input to the user; receiving a second user input indicating an error in the entire first user input; identifying, by the in-vehicle computing system, a first portion of the received entire first user input most likely to be incorrect using the contextual information, wherein the input portions include the first portion and a second portion; providing a new query requesting the user to re-enter the first portion received by the in-vehicle computing system; receiving a spoken third user input of the first portion responsive to the new query; and identifying the full string destination address corresponding to the entire first user input based on the entire first user input, the spoken third user input, and the contextual information. | 12. A computer based method of fixing user input errors in an in-vehicle computing system, the method comprising: interpreting an entire first user input received by the in-vehicle computing system, the entire first user input including a full string destination address and contextual information corresponding to the full string destination address, the contextual information comprising a commercial category associated with a particular point of interest of a plurality of points of interest, the commercial category further specifying a type of the particular point of interest; presenting the interpreted input to the user; receiving a second user input indicating an error in the entire first user input; identifying, by the in-vehicle computing system, a first portion of the received entire first user input most likely to be incorrect using the contextual information, wherein the input portions include the first portion and a second portion; providing a new query requesting the user to re-enter the first portion received by the in-vehicle computing system; receiving a spoken third user input of the first portion responsive to the new query; and identifying the full string destination address corresponding to the entire first user input based on the entire first user input, the spoken third user input, and the contextual information. 14. The method of claim 12 , further comprising: receiving a fourth user input indicating an error in the identified first portion of the entire first user input; and providing a new query requesting a user to input another portion of the entire first user input received by the in-vehicle system. | 0.5 |
9,661,105 | 22 | 25 | 22. A server device, comprising: a storage system configured to store semantic atoms which can be shared between a plurality of external services, the storage system further including a library of commands to perform functions at the plurality of external services; a plurality of plugins, each plugin corresponding to a respective one of the plurality of external services, each plugin being configured to translate between semantic atoms and a respective proprietary language of the corresponding external service; and a platform configured to share semantic atoms between the plurality of external services by using the proprietary language translated at the respective plugins and to share semantic atoms with a mobile device in which commands to perform a function are initially input and transmitted to the platform as sematic atoms; the server device configured to generate a second semantic atom comprising a generated language command in response to receiving a semantic atom from the mobile device, and to transmit the second semantic atom back to the mobile device to enable the mobile device to directly control the one or more external services using a wireless connection. | 22. A server device, comprising: a storage system configured to store semantic atoms which can be shared between a plurality of external services, the storage system further including a library of commands to perform functions at the plurality of external services; a plurality of plugins, each plugin corresponding to a respective one of the plurality of external services, each plugin being configured to translate between semantic atoms and a respective proprietary language of the corresponding external service; and a platform configured to share semantic atoms between the plurality of external services by using the proprietary language translated at the respective plugins and to share semantic atoms with a mobile device in which commands to perform a function are initially input and transmitted to the platform as sematic atoms; the server device configured to generate a second semantic atom comprising a generated language command in response to receiving a semantic atom from the mobile device, and to transmit the second semantic atom back to the mobile device to enable the mobile device to directly control the one or more external services using a wireless connection. 25. The server device of claim 22 , wherein the platform is configured to wirelessly connect to a plurality of mobile devices to share information therebetween and to receive input commands therefrom, the information being shared and input commands being received being provided as semantic atoms. | 0.5 |
8,396,714 | 25 | 31 | 25. A system, comprising: one or more processors; and memory, the memory storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors, cause the one or more processors to perform operations comprising: obtaining phonemes for a text string, the text string comprising at least a preceding word and a succeeding word to be concatenated; identifying a last letter of the preceding word to be concatenated, and identifying a first letter of the succeeding word to be concatenated; selecting a connector term and a connector term type based on the identified last letter and the identified first letter; and creating a modified text string for speech synthesis including the selected connector term and the selected connector type. | 25. A system, comprising: one or more processors; and memory, the memory storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors, cause the one or more processors to perform operations comprising: obtaining phonemes for a text string, the text string comprising at least a preceding word and a succeeding word to be concatenated; identifying a last letter of the preceding word to be concatenated, and identifying a first letter of the succeeding word to be concatenated; selecting a connector term and a connector term type based on the identified last letter and the identified first letter; and creating a modified text string for speech synthesis including the selected connector term and the selected connector type. 31. The system of claim 25 , wherein the operations further comprise: synthesizing a speech segment based on the modified text string; and combining the media asset with the synthesized speech segment into a single file. | 0.565217 |
10,068,178 | 9 | 12 | 9. A method comprising: receiving, at a computing system, an item of content from a user, the item of content comprising one or more first annotations, wherein the one or more first annotations comprise at least one designated tag; identifying a probability distribution of the one or more first annotations over two or more geographic locations based, at least in part, on a language model; smoothing the probability distribution based, at least in part, on annotation-specific smoothing with the item of content based, at least in part on a smoothing coefficient, wherein the smoothing coefficient for a particular annotation is proportional to a spatial ambiguity of the annotation; receiving one or more second annotations; and wherein in response to receiving the one or more second annotations, determining one or more of the geographical locations corresponding to the one or more second annotations based, at least in part, on the smoothed probability distribution. | 9. A method comprising: receiving, at a computing system, an item of content from a user, the item of content comprising one or more first annotations, wherein the one or more first annotations comprise at least one designated tag; identifying a probability distribution of the one or more first annotations over two or more geographic locations based, at least in part, on a language model; smoothing the probability distribution based, at least in part, on annotation-specific smoothing with the item of content based, at least in part on a smoothing coefficient, wherein the smoothing coefficient for a particular annotation is proportional to a spatial ambiguity of the annotation; receiving one or more second annotations; and wherein in response to receiving the one or more second annotations, determining one or more of the geographical locations corresponding to the one or more second annotations based, at least in part, on the smoothed probability distribution. 12. The method of claim 9 , wherein the one or more first or second annotations include at least one of: a tag, a title, a description, a keyword of a query, or a comment. | 0.648148 |
7,823,058 | 1 | 4 | 1. An interactive, electronic method of authoring annotated traversals through visual data, the method comprising: displaying the visual data, wherein the visual data comprises motion video; interactively defining a traversal of the displayed visual data by positioning a resizable overlay window relative to the displayed visual data, wherein said resizable overlay window is resizable while the visual data is being displayed, said traversal comprising a subset of motion video that specifies a time-based sequence of frames of said motion video, each of said frames comprising the visual data delineated by the overlay window, wherein the displaying said visual data comprises displaying the visual data in a cylindrical layout, and wherein said positioning of the overlay window is defined by a field of view of a virtual camera located centrally to said cylindrical layout; annotating the traversal; storing a persistent record of the annotated traversal; and using an integrated graphical user interface to perform said method, and wherein said graphical user interface comprises a plurality of computer display regions including: an overview region displaying the visual data; a detail region displaying current data within the overlay window; and a worksheet region displaying a list of a plurality of stored annotated traversal records. | 1. An interactive, electronic method of authoring annotated traversals through visual data, the method comprising: displaying the visual data, wherein the visual data comprises motion video; interactively defining a traversal of the displayed visual data by positioning a resizable overlay window relative to the displayed visual data, wherein said resizable overlay window is resizable while the visual data is being displayed, said traversal comprising a subset of motion video that specifies a time-based sequence of frames of said motion video, each of said frames comprising the visual data delineated by the overlay window, wherein the displaying said visual data comprises displaying the visual data in a cylindrical layout, and wherein said positioning of the overlay window is defined by a field of view of a virtual camera located centrally to said cylindrical layout; annotating the traversal; storing a persistent record of the annotated traversal; and using an integrated graphical user interface to perform said method, and wherein said graphical user interface comprises a plurality of computer display regions including: an overview region displaying the visual data; a detail region displaying current data within the overlay window; and a worksheet region displaying a list of a plurality of stored annotated traversal records. 4. The method of claim 1 , wherein the motion video includes panoramic video imagery captured using a plurality of cameras facing inward from a perimeter of a scene, and wherein said traversal reflects a user-adjusted 3D-perspective. | 0.618033 |
9,412,392 | 63 | 64 | 63. The method of claim 47 , wherein the contextual information comprises information associated with a telephone call that occurred prior to or while recording the at least a portion of a voice command. | 63. The method of claim 47 , wherein the contextual information comprises information associated with a telephone call that occurred prior to or while recording the at least a portion of a voice command. 64. The method of claim 63 , wherein the information associated with the telephone call comprises a telephone number or contact information associated with the telephone call. | 0.5 |
8,326,818 | 1 | 12 | 1. A method of managing websites registered in a search engine in a search engine administration system, the method comprising: receiving, by an interface module, website information of a website; sorting, by a website registration module, the website information according to an information field; recording the sorted website information in a database; maintaining an adult keyword database that stores adult keywords; extracting a hypertext markup language (HTML) file of a web page of the website; extracting a redirection tag included in the HTML file by analyzing the extracted HTML file, the redirection tag comprising a target universal resource locator (URL); analyzing the target URL or a target HTML file corresponding to the target URL; extracting a character string within the analyzed target URL or target HTML file; searching the adult keyword database for an adult keyword corresponding to the extracted character string; and controlling a process for the website in response to an adult keyword corresponding to the extracted character string being found, wherein controlling a process for the website comprises taking measures against the website that has been determined to be an adult site when the website is not registered as an adult site. | 1. A method of managing websites registered in a search engine in a search engine administration system, the method comprising: receiving, by an interface module, website information of a website; sorting, by a website registration module, the website information according to an information field; recording the sorted website information in a database; maintaining an adult keyword database that stores adult keywords; extracting a hypertext markup language (HTML) file of a web page of the website; extracting a redirection tag included in the HTML file by analyzing the extracted HTML file, the redirection tag comprising a target universal resource locator (URL); analyzing the target URL or a target HTML file corresponding to the target URL; extracting a character string within the analyzed target URL or target HTML file; searching the adult keyword database for an adult keyword corresponding to the extracted character string; and controlling a process for the website in response to an adult keyword corresponding to the extracted character string being found, wherein controlling a process for the website comprises taking measures against the website that has been determined to be an adult site when the website is not registered as an adult site. 12. The method of claim 1 , wherein controlling the process for the website comprises: in response to detecting an adult keyword in the web page, determining a upper-level web page thereof as an adult web page. | 0.778481 |
8,924,844 | 1 | 12 | 1. A method, comprising: selecting, by a computing device, an object rendered on a display, wherein the object is associated with an application that includes a first call to a program, wherein the first call is configured to cause the program to render the object on the display; receiving, by the computing device, a plurality of annotations associated with the selected object; determining, by the computing device, the verbosity level of each annotation of the plurality of annotations, wherein each of the plurality of annotations includes a different verbosity level, and wherein for any two of the annotations associated with the selected object and having different verbosity levels, a first annotation associated with a first verbosity level represents information in a textually-shortened form from information represented in a second of the two annotations with a second verbosity level higher than the first verbosity level, and wherein the first annotation associated with the lower verbosity level comprises an icon; and during execution of the application, and prior to execution of the first call, replacing, by the computing device, the first call with a second call to be executed in place of the first call, wherein the second call is different from the first call and is itself configured to both render the plurality of annotations on the display and to cause the program to render the object without requiring execution of the first call, wherein the second call is configured to render the annotations individually based on a requested verbosity level. | 1. A method, comprising: selecting, by a computing device, an object rendered on a display, wherein the object is associated with an application that includes a first call to a program, wherein the first call is configured to cause the program to render the object on the display; receiving, by the computing device, a plurality of annotations associated with the selected object; determining, by the computing device, the verbosity level of each annotation of the plurality of annotations, wherein each of the plurality of annotations includes a different verbosity level, and wherein for any two of the annotations associated with the selected object and having different verbosity levels, a first annotation associated with a first verbosity level represents information in a textually-shortened form from information represented in a second of the two annotations with a second verbosity level higher than the first verbosity level, and wherein the first annotation associated with the lower verbosity level comprises an icon; and during execution of the application, and prior to execution of the first call, replacing, by the computing device, the first call with a second call to be executed in place of the first call, wherein the second call is different from the first call and is itself configured to both render the plurality of annotations on the display and to cause the program to render the object without requiring execution of the first call, wherein the second call is configured to render the annotations individually based on a requested verbosity level. 12. The method of claim 1 , wherein selecting comprises selecting, by the computing device, graphical-based content rendered on the display. | 0.811828 |
9,424,277 | 1 | 3 | 1. A method comprising: identifying, by one or more computers, an image in a first storage system; determining, by the one or more computers, whether a second storage system includes an entry for the image identifying a region of interest in the image; when the second storage system does not include an entry for the image, identifying, by the one or more computers, a region of interest for the image; performing, by the one or more computers, one or more transforms on the identified region of interest in order to identify one or more aspects of the identified region of interest. | 1. A method comprising: identifying, by one or more computers, an image in a first storage system; determining, by the one or more computers, whether a second storage system includes an entry for the image identifying a region of interest in the image; when the second storage system does not include an entry for the image, identifying, by the one or more computers, a region of interest for the image; performing, by the one or more computers, one or more transforms on the identified region of interest in order to identify one or more aspects of the identified region of interest. 3. The method of claim 1 , further comprising: for each given aspect of the one or more aspects of the identified region of interest, generating a physical vector representing that given aspect; and generating a new entry in the second storage system for the image based on the identified region of interest and any generated physical vector. | 0.72508 |
8,504,489 | 19 | 25 | 19. A computer program product comprising: a non-transitory computer-readable medium comprising: a first set of codes for causing a computer to receive a plurality of document coding inputs that each assign a review code to one of a plurality of first documents, which are associated with a case within an electronic discovery system and have been previously collected from one of a plurality of custodians associated with the case, wherein the review code indicates a level of relevancy or importance in relation to the case and at least one document coding input codes a first document as privileged; a second set of codes for causing a computer to (1), in response to receiving each of the plurality of document coding inputs, determine if one or more second documents, which are associated with the case, have been previously collected from the plurality of custodians and are pending review, are similar to or same as the first document and (2) in response to receiving the at least one document coding input that codes the first document as privileged determine if one or more third documents which are associated with other cases in the electronic discovery system, have been collected from the plurality of custodians and are pending review are same as the first document; third set of codes for causing a computer to (1), in response to determining that the second documents are same as the first document, automatically assign a review code assigned to the first document to the one or more second documents that are the same as the first document and (2), in response to determining that the third documents are same as the first document, automatically assign the privilege code to the one or more third documents that are the same as the first document; a fourth set of codes for causing a computer to remove the one or more second documents from a plurality of pending review documents based on the assignment of the review code, wherein the pending review documents are included in a document review assignment currently being reviewed by a reviewer; and a fifth set of codes for causing a computer to present, on a computing device display, the one or more second documents that are similar to the first document and a confidence indicator that indicates a level of similarity between the first document and a presented second document, wherein the reviewer makes a determination based on the confidence indicator as to whether the presented second document reaches a level of similarity to the first document to justify assigning the review code to the presented document. | 19. A computer program product comprising: a non-transitory computer-readable medium comprising: a first set of codes for causing a computer to receive a plurality of document coding inputs that each assign a review code to one of a plurality of first documents, which are associated with a case within an electronic discovery system and have been previously collected from one of a plurality of custodians associated with the case, wherein the review code indicates a level of relevancy or importance in relation to the case and at least one document coding input codes a first document as privileged; a second set of codes for causing a computer to (1), in response to receiving each of the plurality of document coding inputs, determine if one or more second documents, which are associated with the case, have been previously collected from the plurality of custodians and are pending review, are similar to or same as the first document and (2) in response to receiving the at least one document coding input that codes the first document as privileged determine if one or more third documents which are associated with other cases in the electronic discovery system, have been collected from the plurality of custodians and are pending review are same as the first document; third set of codes for causing a computer to (1), in response to determining that the second documents are same as the first document, automatically assign a review code assigned to the first document to the one or more second documents that are the same as the first document and (2), in response to determining that the third documents are same as the first document, automatically assign the privilege code to the one or more third documents that are the same as the first document; a fourth set of codes for causing a computer to remove the one or more second documents from a plurality of pending review documents based on the assignment of the review code, wherein the pending review documents are included in a document review assignment currently being reviewed by a reviewer; and a fifth set of codes for causing a computer to present, on a computing device display, the one or more second documents that are similar to the first document and a confidence indicator that indicates a level of similarity between the first document and a presented second document, wherein the reviewer makes a determination based on the confidence indicator as to whether the presented second document reaches a level of similarity to the first document to justify assigning the review code to the presented document. 25. The computer program product of claim 19 , wherein the second set of codes is further configured to cause the computer to determine if the one or more second documents include a predetermined threshold of concepts included in the first document. | 0.68401 |
7,490,092 | 18 | 19 | 18. A method of indexing and searching timed media files, as recited in claim 1 , wherein said data extraction includes extracting meta-data about text visible on-screen within the timed media file. | 18. A method of indexing and searching timed media files, as recited in claim 1 , wherein said data extraction includes extracting meta-data about text visible on-screen within the timed media file. 19. A method of indexing and searching timed media files, as recited in claim 18 , wherein said meta-data includes the display position of said text visible on-screen within the timed media file. | 0.5 |
5,471,610 | 32 | 35 | 32. A text search method for deciding en bloc whether or not a plurality of user-designated search terms exist in a text composed of characters expressed in the form of character codes, characterized in that character codes are sequentially fetched from said text to thereby make a decision as to whether or not n, where n represents an integer not smaller than 2, character codes as fetched are included in said search terms as a concatenate character string and output by extracting said n concatenate character codes, referred to as concatenate filtering, only when they are included in said search terms, and that matching is performed en bloc for deciding whether or not said search terms exist in a compound character string constituted by a chain of character strings each composed of the n concatenate character codes as outputted, and synchronizing between said filtering step and said matching step for buffering differences in processing speed while transferring data from said filtering step to said matching step. | 32. A text search method for deciding en bloc whether or not a plurality of user-designated search terms exist in a text composed of characters expressed in the form of character codes, characterized in that character codes are sequentially fetched from said text to thereby make a decision as to whether or not n, where n represents an integer not smaller than 2, character codes as fetched are included in said search terms as a concatenate character string and output by extracting said n concatenate character codes, referred to as concatenate filtering, only when they are included in said search terms, and that matching is performed en bloc for deciding whether or not said search terms exist in a compound character string constituted by a chain of character strings each composed of the n concatenate character codes as outputted, and synchronizing between said filtering step and said matching step for buffering differences in processing speed while transferring data from said filtering step to said matching step. 35. A text search method set forth in claim 32, characterized in that in said concatenate filtering is effected by using a pointer table storing order numbers, referred to as serial numbers, allotted in correspondence to the character codes included in the search term designated previously in slots indicated by said character codes, and a concatenate filtering table storing flags indicating "ON" in slots accessed by using as addresses therefore codes each represented by a string of the serial numbers corresponding to n concatenate character codes, respectively, which are included in the designated search term while storing "OFF" in the other slots. | 0.806032 |
7,853,622 | 5 | 6 | 5. The method of claim 4 , wherein outputting a value of a label of the selected label type for a particular user node comprises determining a value for a particular video label associated with a particular user represented by the particular user node. | 5. The method of claim 4 , wherein outputting a value of a label of the selected label type for a particular user node comprises determining a value for a particular video label associated with a particular user represented by the particular user node. 6. The method of claim 5 , further comprising determining one or more videos to recommend to the particular user by identifying one or more videos labels having values for the particular user node that satisfy a threshold requirement. | 0.5 |
8,064,736 | 1 | 2 | 1. A method for providing a document comprising: processing, by one or more processors, a graphical document to identify one or more ideas associated with the graphical document including processing documents that are linked to the graphical document to derive at least one of the one or more ideas; receiving, by the one or more processors, a request for a document where the document is associated with a concept; comparing, by the one or more processors, the one or more ideas with the concept; and responsive to the request, delivering, by the one or more processors, the graphical document based on the comparison if the one or more ideas match the concept. | 1. A method for providing a document comprising: processing, by one or more processors, a graphical document to identify one or more ideas associated with the graphical document including processing documents that are linked to the graphical document to derive at least one of the one or more ideas; receiving, by the one or more processors, a request for a document where the document is associated with a concept; comparing, by the one or more processors, the one or more ideas with the concept; and responsive to the request, delivering, by the one or more processors, the graphical document based on the comparison if the one or more ideas match the concept. 2. The method of claim 1 , wherein processing the graphical document to identify one or more ideas associated with the graphical document comprises: identifying one or more documents similar to the graphical document; and identifying that the one or more ideas are associated with the one or more similar documents. | 0.561281 |
8,443,278 | 2 | 3 | 2. The non-transitory machine readable medium of claim 1 , wherein the program further comprises a set of instructions for defining a structured document comprising a plurality of structural elements, the plurality of structural elements including the tabular structural element. | 2. The non-transitory machine readable medium of claim 1 , wherein the program further comprises a set of instructions for defining a structured document comprising a plurality of structural elements, the plurality of structural elements including the tabular structural element. 3. The non-transitory machine readable medium of claim 2 , wherein the set of instructions for defining the structured document comprises a set of instructions for defining a hierarchical model of the document with the plurality of structural elements comprising nodes in the hierarchical model. | 0.5 |
9,558,733 | 4 | 8 | 4. A computing system comprising: one or more processors; and a memory including instructions operable to be executed by the one or more processors to perform a set of actions to configure the system to: identify a textual indicator of secondary content in a string of text, the string of text including a first string prior to the textual indicator and a second string subsequent to the textual indicator, wherein the secondary content is at least one of data relating to formatting of the string of text or additional data associated with the string of text; and output first audio including an audio tone preceded by first speech and followed by second speech, the first speech corresponding to the first string, the second speech corresponding to the second string, the audio tone associated with the textual indicator and based on a type of the secondary content. | 4. A computing system comprising: one or more processors; and a memory including instructions operable to be executed by the one or more processors to perform a set of actions to configure the system to: identify a textual indicator of secondary content in a string of text, the string of text including a first string prior to the textual indicator and a second string subsequent to the textual indicator, wherein the secondary content is at least one of data relating to formatting of the string of text or additional data associated with the string of text; and output first audio including an audio tone preceded by first speech and followed by second speech, the first speech corresponding to the first string, the second speech corresponding to the second string, the audio tone associated with the textual indicator and based on a type of the secondary content. 8. The system of claim 4 , wherein a moment of the audio tone in the first audio corresponds to a location of the textual indicator in the string of text. | 0.792453 |
7,610,546 | 1 | 10 | 1. A document processing apparatus comprising: an input device; a display device; a processor; a memory device which stores a plurality of instructions, which when executed by the processor, cause the processor to operate with the display device and the input device to: (a) detect video data designation information attached to electronic document data, the electronic document data including: (i) a first element having a first central activation value used to generate an index; (ii) a second element having a second central activation value used to generate said index; and (iii) read out audio attribute information; (b) generate a summary of said electronic document data, wherein said generation of said summary includes spreading said first central activation value to said second central activation value; (c) select video data in accordance with said detected video data designation information; (d) store a categorization model, the categorization model including a plurality of data categories; (e) create an automatic categorization based on any one of said video data and electronic document data in accordance with the categorization model; (f) update the categorization model with the automatic categorization; (g) control an output of said summary of said electronic document data such that said summary of said electronic data being output is automatically progressed based on at least one of a size of a display area and a length of time displayed; (h) control an output of said selected video data in correspondence with the output of said summary of said electronic document data such that said selected video data being output is output in synchronization with said progress of the said operation of outputting said summary of said electronic data; (i) control an output of a read out audio based on read out audio attribute information in said electronic document to synthesize said read out audio; and (j) automatically terminate the output of said video data upon completion of the outputting of said summary of said electronic document data regardless of whether an end of the video data has been reached. | 1. A document processing apparatus comprising: an input device; a display device; a processor; a memory device which stores a plurality of instructions, which when executed by the processor, cause the processor to operate with the display device and the input device to: (a) detect video data designation information attached to electronic document data, the electronic document data including: (i) a first element having a first central activation value used to generate an index; (ii) a second element having a second central activation value used to generate said index; and (iii) read out audio attribute information; (b) generate a summary of said electronic document data, wherein said generation of said summary includes spreading said first central activation value to said second central activation value; (c) select video data in accordance with said detected video data designation information; (d) store a categorization model, the categorization model including a plurality of data categories; (e) create an automatic categorization based on any one of said video data and electronic document data in accordance with the categorization model; (f) update the categorization model with the automatic categorization; (g) control an output of said summary of said electronic document data such that said summary of said electronic data being output is automatically progressed based on at least one of a size of a display area and a length of time displayed; (h) control an output of said selected video data in correspondence with the output of said summary of said electronic document data such that said selected video data being output is output in synchronization with said progress of the said operation of outputting said summary of said electronic data; (i) control an output of a read out audio based on read out audio attribute information in said electronic document to synthesize said read out audio; and (j) automatically terminate the output of said video data upon completion of the outputting of said summary of said electronic document data regardless of whether an end of the video data has been reached. 10. The document processing apparatus of claim 1 , wherein the categorization model is created on the basis of the categorization that has been manually performed by the user. | 0.721338 |
8,453,128 | 7 | 9 | 7. A computer system for implementing a just-in-time compiler, comprising: a virtual machine configured to execute instructions in an intermediate language; an optimizing static compiler adapted for runtime use with the virtual machine; a plurality of high-level code templates in a high-level programming language, wherein the high-level programming language is designed for compilation to the intermediate language, and wherein each high-level code template selected from the plurality of high-level code templates represents an instruction in the intermediate language; and a software development environment configured to: compile the plurality of high-level code templates to native code to obtain a plurality of optimized native code templates prior to runtime; mark a constant in an optimized native code template selected from the plurality of optimized native code templates with an annotation, wherein the annotation indicates that the constant requires modification by the just-in-time compiler at runtime; and implement the just-in-time compiler using the plurality of optimized native code templates, wherein the just-in-time compiler is configured to substitute a copy of an optimized native code template selected from the plurality of optimized native code templates when a corresponding instruction in the intermediate language is encountered at runtime. | 7. A computer system for implementing a just-in-time compiler, comprising: a virtual machine configured to execute instructions in an intermediate language; an optimizing static compiler adapted for runtime use with the virtual machine; a plurality of high-level code templates in a high-level programming language, wherein the high-level programming language is designed for compilation to the intermediate language, and wherein each high-level code template selected from the plurality of high-level code templates represents an instruction in the intermediate language; and a software development environment configured to: compile the plurality of high-level code templates to native code to obtain a plurality of optimized native code templates prior to runtime; mark a constant in an optimized native code template selected from the plurality of optimized native code templates with an annotation, wherein the annotation indicates that the constant requires modification by the just-in-time compiler at runtime; and implement the just-in-time compiler using the plurality of optimized native code templates, wherein the just-in-time compiler is configured to substitute a copy of an optimized native code template selected from the plurality of optimized native code templates when a corresponding instruction in the intermediate language is encountered at runtime. 9. The computer system of claim 7 , wherein the annotation is associated with an instruction editor, and wherein the just-in-time compiler is further configured to modify the constant, using the instruction editor, at runtime. | 0.785579 |
7,516,125 | 15 | 17 | 15. An apparatus for indexing a corpus of documents, wherein the words in the corpus of documents include a set of words having a characteristic to be subject of queries, comprising: a data processor arranged to parse documents in the corpus of documents to identify words found in the documents and locations of the words in the documents, and to create an index structure including entries representing words found in the corpus of documents mapping entries in the index structure to locations of the words in documents in the corpus memory storing the index structure writable and readable by the data processor; wherein the data processor includes an indexing processor which indentifies words in a set of words having a characteristic represent d by a mark in a set of marks, and add entries in the index structure representing marks for the identified the set mapping the marks to the locations of the identified words, and wherein one or more entries representing marks include fewer, if any, than all of the characters of the corresponding identified words; wherein entries in the index structure representing the marks comprise tokens coalesced with prefixes of respective marked words, the prefixes comprising one or more leading characters of the respective marked words. | 15. An apparatus for indexing a corpus of documents, wherein the words in the corpus of documents include a set of words having a characteristic to be subject of queries, comprising: a data processor arranged to parse documents in the corpus of documents to identify words found in the documents and locations of the words in the documents, and to create an index structure including entries representing words found in the corpus of documents mapping entries in the index structure to locations of the words in documents in the corpus memory storing the index structure writable and readable by the data processor; wherein the data processor includes an indexing processor which indentifies words in a set of words having a characteristic represent d by a mark in a set of marks, and add entries in the index structure representing marks for the identified the set mapping the marks to the locations of the identified words, and wherein one or more entries representing marks include fewer, if any, than all of the characters of the corresponding identified words; wherein entries in the index structure representing the marks comprise tokens coalesced with prefixes of respective marked words, the prefixes comprising one or more leading characters of the respective marked words. 17. The apparatus of claim 15 , wherein the prefix comprises N leading characters of the marked word, and N is 1 . | 0.873614 |
7,634,131 | 12 | 13 | 12. An image recognition method comprising: a step of capturing an image of a standard object having a standard conformation among object to be recognized and extracting a characteristic value from a teacher image of the standard object through image processing; a step of making a user input an already-known knowledge of a range of fluctuation of objects and other objects and entering the knowledge in a knowledge base; a step of generating the context data in which various attributes associated with the objects are described together with their semantics in accordance with a characteristic value extracted from the teacher image of the standard object and the knowledge entered in the knowledge base and updating the context data in the knowledge base based on comparison between the presently generated context data and the context data stored in the knowledge base; a step of extracting an attribute corresponding to the type of a characteristic value used for recognition processing from the context data entered in the knowledge base and generating the teaching data based on the extracted attribute for the recognition processing; a teaching stage for storing the generated teaching data in a teaching data storage device; a step of capturing the image of a work and extracting a characteristic value from the image of the work through image processing; and an image recognition stage for executing recognition processing in accordance with the teaching data read from the teaching data storage device and the extracted characteristic value and determining whether the work is an object; wherein the teaching data include, at least, parameters related to a recognition logic, a threshold value, and a region recognized as the object applied to the recognition process; and wherein the already-known knowledge includes a parameter related to tolerance. | 12. An image recognition method comprising: a step of capturing an image of a standard object having a standard conformation among object to be recognized and extracting a characteristic value from a teacher image of the standard object through image processing; a step of making a user input an already-known knowledge of a range of fluctuation of objects and other objects and entering the knowledge in a knowledge base; a step of generating the context data in which various attributes associated with the objects are described together with their semantics in accordance with a characteristic value extracted from the teacher image of the standard object and the knowledge entered in the knowledge base and updating the context data in the knowledge base based on comparison between the presently generated context data and the context data stored in the knowledge base; a step of extracting an attribute corresponding to the type of a characteristic value used for recognition processing from the context data entered in the knowledge base and generating the teaching data based on the extracted attribute for the recognition processing; a teaching stage for storing the generated teaching data in a teaching data storage device; a step of capturing the image of a work and extracting a characteristic value from the image of the work through image processing; and an image recognition stage for executing recognition processing in accordance with the teaching data read from the teaching data storage device and the extracted characteristic value and determining whether the work is an object; wherein the teaching data include, at least, parameters related to a recognition logic, a threshold value, and a region recognized as the object applied to the recognition process; and wherein the already-known knowledge includes a parameter related to tolerance. 13. The image recognition method according to claim 12 , wherein when failing in recognition at an image recognition stage, work context data for defining various attributes of the work is generated from the characteristic value of the extracted work, image processing is applied to the image of the work in accordance with the difference between attributes of the generated context data and the context data for an object entered in a knowledge base, and recognition processing is executed in accordance with an image-processed work image. | 0.653846 |
9,098,533 | 1 | 10 | 1. A method of searching, comprising: receiving a voice directed query related to visual content rendered on a display, wherein the visual content is one of a frame from a video stream, a two-dimensional image, or a three-dimensional image; causing a processor to detect an object from the visual content based on a search word from the voice directed query; selecting an edge detection algorithm from a set of edge detection algorithms based on at least one of the visual content, the search word from the voice directed query, or contextual information; extracting an image of the object from the visual content based on the search word from the voice directed query, the image of the object being extracted from the visual content using the edge detection algorithm selected from the set of edge detection algorithms, wherein the image of the object is a portion of the visual content and is extracted from a remainder of the visual content; using the image of the object extracted from the visual content as an input for a reverse visual search, wherein the reverse visual search is executed based upon the image of the object extracted from the visual content, and wherein the reverse visual search returns a result; and rendering the result of the reverse visual search on the display. | 1. A method of searching, comprising: receiving a voice directed query related to visual content rendered on a display, wherein the visual content is one of a frame from a video stream, a two-dimensional image, or a three-dimensional image; causing a processor to detect an object from the visual content based on a search word from the voice directed query; selecting an edge detection algorithm from a set of edge detection algorithms based on at least one of the visual content, the search word from the voice directed query, or contextual information; extracting an image of the object from the visual content based on the search word from the voice directed query, the image of the object being extracted from the visual content using the edge detection algorithm selected from the set of edge detection algorithms, wherein the image of the object is a portion of the visual content and is extracted from a remainder of the visual content; using the image of the object extracted from the visual content as an input for a reverse visual search, wherein the reverse visual search is executed based upon the image of the object extracted from the visual content, and wherein the reverse visual search returns a result; and rendering the result of the reverse visual search on the display. 10. The method of claim 1 , wherein an edge of the image of the object is not delineated in the visual content prior to the detecting of the object. | 0.869489 |
9,015,097 | 1 | 10 | 1. A method comprising: retrieving, from a memory, a global structure and a plurality of candidate answers therein; computing a first probability of a candidate answer based on a local structure of the candidate answer within the global structure; computing a second probability of the candidate answer based on content of the candidate answer given content of a query; computing a third probability of the candidate answer based on context of the candidate answer given the content of the query; and providing a combined probability of the candidate answer as a function of the first probability, second probability, and third probability. | 1. A method comprising: retrieving, from a memory, a global structure and a plurality of candidate answers therein; computing a first probability of a candidate answer based on a local structure of the candidate answer within the global structure; computing a second probability of the candidate answer based on content of the candidate answer given content of a query; computing a third probability of the candidate answer based on context of the candidate answer given the content of the query; and providing a combined probability of the candidate answer as a function of the first probability, second probability, and third probability. 10. The method of claim 1 , further comprising returning a candidate answer of the plurality of candidate answers with a highest combined probability to provide to a device that submitted the query. | 0.763723 |
9,026,529 | 9 | 10 | 9. The method of claim 1 , wherein the steps of determining a member-level demographic characteristic and determining a first demographic characteristic further comprise: assigning each member, of the first result-base, a confidence distribution, wherein a plurality of values are assigned, each with a confidence level; and combining the confidence distributions. | 9. The method of claim 1 , wherein the steps of determining a member-level demographic characteristic and determining a first demographic characteristic further comprise: assigning each member, of the first result-base, a confidence distribution, wherein a plurality of values are assigned, each with a confidence level; and combining the confidence distributions. 10. The method of claim 9 , wherein each confidence distribution represents a demographic characteristic with two values. | 0.5 |
7,882,045 | 1 | 11 | 1. A computerized method comprising: accessing at least one data store that provides information regarding prior ad presentation opportunities and information regarding actions taken by users in response to the prior ad presentation opportunities; providing at least one ad opportunity signature to a plurality of different content providers; receiving ad candidates from the plurality of different content providers at least partly in response to providing the at least one ad opportunity signature; generating a model based on information accessed from said at least one data store using a machine learning approach, wherein the model maps ad presentation opportunities to ad candidates received from the plurality of different content providers that are assessed as appropriate for the respective ad presentation opportunities; using the model to map a new ad presentation opportunity to one or more ad candidates; and filtering the one or more ad candidates, wherein the filtering comprises reducing or eliminating ad selections which do not exhibit a preferred degree of variation. | 1. A computerized method comprising: accessing at least one data store that provides information regarding prior ad presentation opportunities and information regarding actions taken by users in response to the prior ad presentation opportunities; providing at least one ad opportunity signature to a plurality of different content providers; receiving ad candidates from the plurality of different content providers at least partly in response to providing the at least one ad opportunity signature; generating a model based on information accessed from said at least one data store using a machine learning approach, wherein the model maps ad presentation opportunities to ad candidates received from the plurality of different content providers that are assessed as appropriate for the respective ad presentation opportunities; using the model to map a new ad presentation opportunity to one or more ad candidates; and filtering the one or more ad candidates, wherein the filtering comprises reducing or eliminating ad selections which do not exhibit a preferred degree of variation. 11. The computerized method of claim 1 , wherein the ad candidates are delivered to the users in the context of content provided by a plurality of affiliate modules. | 0.650424 |
9,361,386 | 1 | 6 | 1. A method, in a data processing system comprising a processor and a memory, for clarifying an input question, the method comprising: receiving, in the data processing system from a computing device, the input question for generation of an answer to the input question; generating, in the data processing system, a set of candidate answers for the input question based on an analysis of a corpus of information, wherein each candidate answer in the set of candidate answers corresponds to an evidence passage supporting the candidate answer as answering the input question; determining, in the data processing system, based on the set of candidate answers, whether clarification of the input question is required; identifying, in response to a determination that clarification of the input question is required, a differentiating factor in evidence passages of at least two candidate answers in the set of candidate answers; sending, by the data processing system, in response to a determination that clarification of the input question is required, a request for user input to clarify the input question, wherein the request for user input is generated based on the identified differentiating factor; receiving, in the data processing system, user input from the computing device in response to the request; and selecting, by the data processing system, at least one candidate answer in the set of candidate answers as an answer for the input question based on the user input, wherein identifying a differentiating factor in evidence passages of at least two candidate answers in the set of candidate answers comprises: identifying a plurality of differentiating factors between evidence passages of the at least two candidate answers; and selecting a subset of differentiating factors from the plurality of differentiating factors based on an evaluation of which differentiating factors in the plurality of differentiating factors clarify the input question. | 1. A method, in a data processing system comprising a processor and a memory, for clarifying an input question, the method comprising: receiving, in the data processing system from a computing device, the input question for generation of an answer to the input question; generating, in the data processing system, a set of candidate answers for the input question based on an analysis of a corpus of information, wherein each candidate answer in the set of candidate answers corresponds to an evidence passage supporting the candidate answer as answering the input question; determining, in the data processing system, based on the set of candidate answers, whether clarification of the input question is required; identifying, in response to a determination that clarification of the input question is required, a differentiating factor in evidence passages of at least two candidate answers in the set of candidate answers; sending, by the data processing system, in response to a determination that clarification of the input question is required, a request for user input to clarify the input question, wherein the request for user input is generated based on the identified differentiating factor; receiving, in the data processing system, user input from the computing device in response to the request; and selecting, by the data processing system, at least one candidate answer in the set of candidate answers as an answer for the input question based on the user input, wherein identifying a differentiating factor in evidence passages of at least two candidate answers in the set of candidate answers comprises: identifying a plurality of differentiating factors between evidence passages of the at least two candidate answers; and selecting a subset of differentiating factors from the plurality of differentiating factors based on an evaluation of which differentiating factors in the plurality of differentiating factors clarify the input question. 6. The method of claim 1 , wherein selecting at least one candidate answer in the set of candidate answers as an answer for the input question based on the user input comprises: updating the set of candidate answers based on the user input; and selecting the at least one candidate answer from the updated set of candidate answers. | 0.621281 |
6,122,361 | 8 | 10 | 8. An automated directory assistance system as defined in claim 7, wherein said directory assistance system includes a plurality of data structures, each data structure being associated with an identifier of geographical location from which a user may input the spoken utterance. | 8. An automated directory assistance system as defined in claim 7, wherein said directory assistance system includes a plurality of data structures, each data structure being associated with an identifier of geographical location from which a user may input the spoken utterance. 10. An automated directory assistance system as defined in claim 8, comprising: a) an input for receiving data indicative of at least a portion of a telephone number of a terminal at which the user is inputting the spoken utterance, b) an identification unit for identifying a data structure associated with the data indicative of at least a portion of a telephone number of a terminal at which the user is inputting the spoken utterance, c) a search unit for searching the data structure identified at paragraph b to extract therefrom probability data corresponding to at least one candidate. | 0.5 |
6,128,634 | 13 | 26 | 13. An apparatus for facilitating skimming of a document by a user, the document having a plurality of terms, the apparatus comprising: a processing system that determines a term-score for each of the plurality of terms and that maps the term-score of each of the plurality of terms onto one of at least three values of at least one variable emphasis attribute usable to present the document; and a presentation system that presents each of the plurality of terms of the document using the corresponding mapped values of the at least one variable emphasis attribute. | 13. An apparatus for facilitating skimming of a document by a user, the document having a plurality of terms, the apparatus comprising: a processing system that determines a term-score for each of the plurality of terms and that maps the term-score of each of the plurality of terms onto one of at least three values of at least one variable emphasis attribute usable to present the document; and a presentation system that presents each of the plurality of terms of the document using the corresponding mapped values of the at least one variable emphasis attribute. 26. The apparatus of claim 13, wherein the term-score is biased. | 0.849057 |
8,825,828 | 1 | 7 | 1. A method for implementing notifications, the method comprising: storing data defining notification operations in a memory, the data defining notification operations comprising a hierarchy of Uniform Resource Identifiers (URIs) and Extensible Markup Language (XML) document schema defining XML documents; using a processor to: receive a notification command comprising an URI identifying a notification resource and a Hypertext Transfer Protocol (HTTP) GET method; and determine a notification operation based on the data defining notification operations stored in the memory and the notification command received; obtaining a list of notifications indicated in the notification; and returning a NotificationList XML document. | 1. A method for implementing notifications, the method comprising: storing data defining notification operations in a memory, the data defining notification operations comprising a hierarchy of Uniform Resource Identifiers (URIs) and Extensible Markup Language (XML) document schema defining XML documents; using a processor to: receive a notification command comprising an URI identifying a notification resource and a Hypertext Transfer Protocol (HTTP) GET method; and determine a notification operation based on the data defining notification operations stored in the memory and the notification command received; obtaining a list of notifications indicated in the notification; and returning a NotificationList XML document. 7. The method according to claim 1 , further comprising: receiving a second notification command comprising an HTTP GET method and an URI identifying a notification resource associated with a notification ID; and executing a second notification operation comprising obtaining properties of a notification described in the URI and returning a Notification XML document. | 0.568075 |
9,263,046 | 9 | 11 | 9. A method of distributed dictation and transcription performed using at least one processor associated with a dictation manager, the method comprising the steps of: receiving an audio signal from a user operating a client station; identifying a user profile stored in a memory of the dictation manager associated with the user of the received audio signal; determining whether the identified user profile of the user is stored in at least one of a plurality of servers coupled to the dictation manager; if it is determined that the user profile is stored in at least one of the plurality of servers, then selecting the one server having the user profiled stored to transcribe the received audio signal; and causing the received audio signal to be transmitted to the one server wherein the received audio signal is converted into a textual data signal. | 9. A method of distributed dictation and transcription performed using at least one processor associated with a dictation manager, the method comprising the steps of: receiving an audio signal from a user operating a client station; identifying a user profile stored in a memory of the dictation manager associated with the user of the received audio signal; determining whether the identified user profile of the user is stored in at least one of a plurality of servers coupled to the dictation manager; if it is determined that the user profile is stored in at least one of the plurality of servers, then selecting the one server having the user profiled stored to transcribe the received audio signal; and causing the received audio signal to be transmitted to the one server wherein the received audio signal is converted into a textual data signal. 11. The method of claim 9 wherein the step of determining whether the identified user profile has been previously provided comprises determining at least two servers of the plurality of servers have been previously provided with the identified user profile and the step of selecting the dictation server further comprises selecting one of the at least two dictation servers to balance a load between the at least two servers. | 0.566327 |
9,219,746 | 1 | 3 | 1. A method, comprising: receiving, by a risk identification system including at least one computing device having at least one processor and at least one memory coupled to the processor, a string of terms; determining, by the at least one computing device of the risk identification system, whether the string of terms includes at least one word matching a keyword of a keyword listing; responsive to determining that the string of terms includes at least one word matching the keyword, identifying, by the risk identification system, at least a noun and a verb in the string of terms; identifying, by the at least one computing device of the risk identification system, a category of risk associated with the identified noun and a category of risk associated with the identified verb; determining, by the at least one computing device of the risk identification system, whether the identified category of risk associated with the identified noun and the identified category of risk associated with the identified verb are a same category of risk; responsive to determining that the identified category of risk associated with the identified noun and the identified category of risk associated with the identified verb are the same category, determining, by the risk identification system, a first risk rating of the received string of terms including the identified noun and the identified verb, the first risk rating being based on the identified noun, the identified verb and the same category; and responsive to determining that the identified category of risk associated with the identified noun and the identified category of risk associated with the identified verb are different categories, determining, by the risk identification system, a second risk rating of the received string of terms including the identified noun and the identified verb, the second risk rating being based on the identified noun, the identified verb, the identified category of risk associated with the noun and the identified category of risk associated with the verb, the second risk rating being different from the first risk rating. | 1. A method, comprising: receiving, by a risk identification system including at least one computing device having at least one processor and at least one memory coupled to the processor, a string of terms; determining, by the at least one computing device of the risk identification system, whether the string of terms includes at least one word matching a keyword of a keyword listing; responsive to determining that the string of terms includes at least one word matching the keyword, identifying, by the risk identification system, at least a noun and a verb in the string of terms; identifying, by the at least one computing device of the risk identification system, a category of risk associated with the identified noun and a category of risk associated with the identified verb; determining, by the at least one computing device of the risk identification system, whether the identified category of risk associated with the identified noun and the identified category of risk associated with the identified verb are a same category of risk; responsive to determining that the identified category of risk associated with the identified noun and the identified category of risk associated with the identified verb are the same category, determining, by the risk identification system, a first risk rating of the received string of terms including the identified noun and the identified verb, the first risk rating being based on the identified noun, the identified verb and the same category; and responsive to determining that the identified category of risk associated with the identified noun and the identified category of risk associated with the identified verb are different categories, determining, by the risk identification system, a second risk rating of the received string of terms including the identified noun and the identified verb, the second risk rating being based on the identified noun, the identified verb, the identified category of risk associated with the noun and the identified category of risk associated with the verb, the second risk rating being different from the first risk rating. 3. The method of claim 1 , wherein the categories of risk include at least one of: behavior, information technology sabotage, unauthorized access, and intellectual property theft. | 0.841312 |
9,348,873 | 5 | 7 | 5. The method as defined in claim 1 , wherein the calculating of the times between the adjacent ones of the inbound links includes: determining posting times of the posts that are relevant to the topic; comparing a first list including the posting times to a second list including the inbound links to determine a third list including posting times of the inbound links; and determining the times between the adjacent ones of the inbound links in the third list. | 5. The method as defined in claim 1 , wherein the calculating of the times between the adjacent ones of the inbound links includes: determining posting times of the posts that are relevant to the topic; comparing a first list including the posting times to a second list including the inbound links to determine a third list including posting times of the inbound links; and determining the times between the adjacent ones of the inbound links in the third list. 7. The method as defined in claim 5 , further including comparing the first list including the posting times to the second list including the inbound links by determining matches between first post identifiers corresponding to the posting times and second post identifiers corresponding to the inbound links. | 0.5 |
8,909,810 | 17 | 32 | 17. A multimedia content sharing system, comprising A. a shared content server storing a plurality of items of content, where the stored items of content are any of still images, moving images and audio, B. a plurality of nodes, each in communication coupling with the shared content server via one or more networks, C. the shared content server transmitting, via the one or more networks, one or more of the plurality of items of content stored on the server to each node in a first set of said nodes without a request by any user of that node for such item, wherein each node in the first set of said nodes stores the one or more of the plurality of items in a local store associated therewith D. at least one said node (“first peer node”) in the first set of nodes, (i) presenting any of visually and/or aurally the content of at least one item of content received from the shared content server, (ii) accepting user feedback with respect to that item of content, the user feedback reflecting user input regarding the item of content, and (iii) transmitting that user feedback to the shared content server, E. the shared content server transmitting the user feedback to at least one other node (“second peer node”) that is in the first set of nodes without retransmission of the article of content with respect to which the user feedback was accepted, which second peer node alters a presentation on that node of that item of content as stored on the local store associated therewith based on that user feedback. | 17. A multimedia content sharing system, comprising A. a shared content server storing a plurality of items of content, where the stored items of content are any of still images, moving images and audio, B. a plurality of nodes, each in communication coupling with the shared content server via one or more networks, C. the shared content server transmitting, via the one or more networks, one or more of the plurality of items of content stored on the server to each node in a first set of said nodes without a request by any user of that node for such item, wherein each node in the first set of said nodes stores the one or more of the plurality of items in a local store associated therewith D. at least one said node (“first peer node”) in the first set of nodes, (i) presenting any of visually and/or aurally the content of at least one item of content received from the shared content server, (ii) accepting user feedback with respect to that item of content, the user feedback reflecting user input regarding the item of content, and (iii) transmitting that user feedback to the shared content server, E. the shared content server transmitting the user feedback to at least one other node (“second peer node”) that is in the first set of nodes without retransmission of the article of content with respect to which the user feedback was accepted, which second peer node alters a presentation on that node of that item of content as stored on the local store associated therewith based on that user feedback. 32. The system of claim 17 , wherein the one or more networks include (i) a cellular network, and (ii) zero, or more Internets, metropolitan area networks (MANs), wide area networks (WANs), local area networks, personal area networks (PANs). | 0.713095 |
8,645,312 | 15 | 16 | 15. A computer-implemented system, comprising: an expertise inferencing function executed on a processor-based computing device, wherein the expertise inferencing function generates a first expertise vector associated with a first user in accordance with an inference of a level of expertise from a plurality of behaviors; and a computer-implemented expertise discovery function that generates a recommendation of a person for delivery to a user in accordance with the first expertise vector. | 15. A computer-implemented system, comprising: an expertise inferencing function executed on a processor-based computing device, wherein the expertise inferencing function generates a first expertise vector associated with a first user in accordance with an inference of a level of expertise from a plurality of behaviors; and a computer-implemented expertise discovery function that generates a recommendation of a person for delivery to a user in accordance with the first expertise vector. 16. The system of claim 15 , further comprising: the computer-implemented expertise discovery function that generates the recommendation, wherein the recommendation is generated in accordance with a comparison of the first expertise vector with a second expertise vector. | 0.5 |
8,515,160 | 1 | 6 | 1. A method of actionable intelligence for detecting anomalous entities, comprising an act of initializing one or more processors to perform operations of: receiving an input signal; selecting a class of entities to be recognized; recognizing a set of entities of the selected class in the input signal using an Adaptive Resonance Theory (ART)-based neural network; selecting a set of threshold criteria by which a set of anomalous entities can be detected within the set of recognized entities; detecting the set of anomalous entities by comparing the set of recognized entities against the set of threshold criteria; alerting an operator to the presence of the set of anomalous entities, whereby anomalous entities are detected; prompting the operator to assign new labels to the set of anomalous entities; discovering underlying hierarchical relationships between the new labels assigned by the operator; and updating a knowledge database with the new labels and hierarchical relationships, whereby anomalous entities are classified and hierarchically related. | 1. A method of actionable intelligence for detecting anomalous entities, comprising an act of initializing one or more processors to perform operations of: receiving an input signal; selecting a class of entities to be recognized; recognizing a set of entities of the selected class in the input signal using an Adaptive Resonance Theory (ART)-based neural network; selecting a set of threshold criteria by which a set of anomalous entities can be detected within the set of recognized entities; detecting the set of anomalous entities by comparing the set of recognized entities against the set of threshold criteria; alerting an operator to the presence of the set of anomalous entities, whereby anomalous entities are detected; prompting the operator to assign new labels to the set of anomalous entities; discovering underlying hierarchical relationships between the new labels assigned by the operator; and updating a knowledge database with the new labels and hierarchical relationships, whereby anomalous entities are classified and hierarchically related. 6. The method of claim 1 , wherein in recognizing the set of entities, the one or more processors further perform operations of: using a Scene Recognition Engine (SCE) to recognize spatial relationships between objects that compose a particular scene category in static imagery as captured from the input signal, where the input signal is a video signal; and using an Event Recognition Engine (ERE) to recognize spatio-temporal sequences and, thereby, recognize events and behaviors in the video signal by maintaining and recognizing ordered spatio-temporal sequences. | 0.5 |
7,593,911 | 13 | 19 | 13. A system for screening incoming data of a network, comprising: an analyzing module to analyze the relationships between a plurality of existing rules in a rule set of a firewall used to screen the incoming data of the network, wherein the relationships include cause interactions and effect interactions among the existing rules; a representation module to create a representation of the relationships including the cause interactions and effect interactions; a receiving module to receive a new rule to be inserted into the rule set; a new rule analyzing module to analyze if a conflict is created by insertion of the new rule in the rule set by inserting further relationships between the new rule and the existing rules to create a modified representation and determining if a conflict is created based on the modified representation; and a display to display the representation and the modified representation to a user of the system. | 13. A system for screening incoming data of a network, comprising: an analyzing module to analyze the relationships between a plurality of existing rules in a rule set of a firewall used to screen the incoming data of the network, wherein the relationships include cause interactions and effect interactions among the existing rules; a representation module to create a representation of the relationships including the cause interactions and effect interactions; a receiving module to receive a new rule to be inserted into the rule set; a new rule analyzing module to analyze if a conflict is created by insertion of the new rule in the rule set by inserting further relationships between the new rule and the existing rules to create a modified representation and determining if a conflict is created based on the modified representation; and a display to display the representation and the modified representation to a user of the system. 19. The system of claim 13 , wherein the new rule analyzing module suggests, when the conflict is identified, a new placement for the new rule within the rule set. | 0.617371 |
7,689,540 | 6 | 10 | 6. A computer-readable storage medium encoded with a computer program comprising instructions that, when executed, operate to cause a computer to perform operations comprising: receiving multiple series of queries; determining that each of the multiple series of queries were submitted by a user when seeking information that satisfies a single information need; determining that a starting point of each of the multiple series of queries is the same, the starting point being at least one query submitted chronologically before other queries included in the multiple series of queries; determining that an end point of at least some of the multiple series of queries differs, the end point being at least one query submitted chronologically after the at least one query representing the starting point; in response to the determination that the starting point of each of the multiple series of queries is the same and the determination that the ending point of at least some of the multiple series of queries differs: identifying a subset of end points of the multiple series of queries that excludes at least the end point found in a minimum number of the multiple series of queries; and generating a query reformulation path that associates the starting point with the subset of end points; and storing the generated query reformulation path to enable presentation of the subset of end points in response to receipt of the at least one query representing the starting point. | 6. A computer-readable storage medium encoded with a computer program comprising instructions that, when executed, operate to cause a computer to perform operations comprising: receiving multiple series of queries; determining that each of the multiple series of queries were submitted by a user when seeking information that satisfies a single information need; determining that a starting point of each of the multiple series of queries is the same, the starting point being at least one query submitted chronologically before other queries included in the multiple series of queries; determining that an end point of at least some of the multiple series of queries differs, the end point being at least one query submitted chronologically after the at least one query representing the starting point; in response to the determination that the starting point of each of the multiple series of queries is the same and the determination that the ending point of at least some of the multiple series of queries differs: identifying a subset of end points of the multiple series of queries that excludes at least the end point found in a minimum number of the multiple series of queries; and generating a query reformulation path that associates the starting point with the subset of end points; and storing the generated query reformulation path to enable presentation of the subset of end points in response to receipt of the at least one query representing the starting point. 10. The computer-readable storage medium of claim 6 wherein: identifying the subset of end points of the multiple series of queries that excludes at least the end point found in the minimum number of the multiple series of queries comprises identifying the end point found in a maximum number of the multiple series of queries; generating the query reformulation path that associates the starting point with the subset of end points comprises generating the query reformulation path that associates the starting point with the end point found in the maximum number of the multiple series of queries; and storing the generated query reformulation path to enable presentation of the subset of end points in response to receipt of the at least one query representing the starting point comprises storing the generated query reformulation path to enable presentation of the end point found in the maximum number of the multiple series of queries in response to receipt of the at least one query representing the starting point. | 0.5 |
9,092,119 | 1 | 2 | 1. A method comprising: a) displaying a predetermined portion of an application in execution upon a processor within an application display window to a user, the predetermined portion of the application containing a menu or toolbar option; b) tracking a cursor location within the application display window controlled by the user; c) identifying a first function within the menu or toolbar option by the location being within a predetermined range for a predetermined period of time over a position within the application display window associated with the first function; d) automatically inserting code relating to the first function into a first memory medium storing code for subsequent execution by the processor in relation to the application; e) automatically executing the code relating to the first function; and f) updating the application display window in response to the execution of the code relating to the first function. | 1. A method comprising: a) displaying a predetermined portion of an application in execution upon a processor within an application display window to a user, the predetermined portion of the application containing a menu or toolbar option; b) tracking a cursor location within the application display window controlled by the user; c) identifying a first function within the menu or toolbar option by the location being within a predetermined range for a predetermined period of time over a position within the application display window associated with the first function; d) automatically inserting code relating to the first function into a first memory medium storing code for subsequent execution by the processor in relation to the application; e) automatically executing the code relating to the first function; and f) updating the application display window in response to the execution of the code relating to the first function. 2. The method according to claim 1 further comprising; pushing the code relating to the first function to an undo stack when the first function selected is capable of being reversed. | 0.749311 |
8,776,118 | 2 | 3 | 2. The method of claim 1 wherein automatically locating includes identifying a variable associated with a particular type of hypertext element. | 2. The method of claim 1 wherein automatically locating includes identifying a variable associated with a particular type of hypertext element. 3. The method of claim 2 including automatically locating the hypertext element in response to an indication provided on a screen display of the hypertext document. | 0.5 |
8,359,309 | 17 | 23 | 17. A storage device encoded with a program product, the program product which, when executed by data processing apparatus, cause the data processing apparatus to perform operations comprising: determining, for a plurality of search results responsive to a query, a respective count of times search results in the plurality of search results that refer to documents in a base corpus have been presented, and a respective count of times search results in the plurality of search results that refer to documents in the base corpus have been selected, wherein the respective counts for presentations and selections of search results that refer to documents in the base corpus are for searches initiated by users in a plurality of different countries who employ a specific language; determining, for the plurality of search results responsive to the query, a respective count of times search results in the plurality of search results that refer to documents in a second corpus have been presented, and a respective count of times search results in the plurality of search results that refer to documents in the second corpus have been selected, wherein the respective counts for presentations and selections of search results that refer to documents in the second corpus are for searches initiated by users in the plurality of different countries who employ the specific language; calculating a click through rate of the base corpus for the query based at least in part on the respective counts for the base corpus; calculating a click through rate of the second corpus for the query based at least in part on the respective counts for the second corpus; calculating a measure of relative relevance based at least in part on a ratio of the second corpus click through rate to the base corpus click through rate; and providing the measure of relative relevance to a ranking engine for ranking of search results for a search corresponding to the query; and wherein fewer search results of the plurality refer to documents in the second corpus than to documents in the base corpus. | 17. A storage device encoded with a program product, the program product which, when executed by data processing apparatus, cause the data processing apparatus to perform operations comprising: determining, for a plurality of search results responsive to a query, a respective count of times search results in the plurality of search results that refer to documents in a base corpus have been presented, and a respective count of times search results in the plurality of search results that refer to documents in the base corpus have been selected, wherein the respective counts for presentations and selections of search results that refer to documents in the base corpus are for searches initiated by users in a plurality of different countries who employ a specific language; determining, for the plurality of search results responsive to the query, a respective count of times search results in the plurality of search results that refer to documents in a second corpus have been presented, and a respective count of times search results in the plurality of search results that refer to documents in the second corpus have been selected, wherein the respective counts for presentations and selections of search results that refer to documents in the second corpus are for searches initiated by users in the plurality of different countries who employ the specific language; calculating a click through rate of the base corpus for the query based at least in part on the respective counts for the base corpus; calculating a click through rate of the second corpus for the query based at least in part on the respective counts for the second corpus; calculating a measure of relative relevance based at least in part on a ratio of the second corpus click through rate to the base corpus click through rate; and providing the measure of relative relevance to a ranking engine for ranking of search results for a search corresponding to the query; and wherein fewer search results of the plurality refer to documents in the second corpus than to documents in the base corpus. 23. The storage device of claim 17 wherein the respective counts for the base and second corpora are derived from language-specific data and country-specific data, and wherein calculating a click through rate for one of the corpora is based on a weighted combination of the language-specific data and the country-specific data. | 0.575325 |
8,260,331 | 22 | 23 | 22. The method according to claim 1 further comprising limiting a number of said linking mechanisms in said message according to a predetermined maximum threshold. | 22. The method according to claim 1 further comprising limiting a number of said linking mechanisms in said message according to a predetermined maximum threshold. 23. The method according to claim 22 wherein said limiting comprises discarding lower priority matches. | 0.5 |
9,396,185 | 11 | 15 | 11. A non-transitory computer readable medium containing program instructions, which when executed, perform a method for providing a contextual description of an object, the computer readable medium comprising program instructions for: receiving a first object having a first object type representing a person, the first object associated with a first user and including a first attribute associated with the respective person; identifying a second object having a second attribute related to the first attribute, the second object having a second object type representing an event; retrieving, from a data store, a first pre-defined phrase template corresponding to the first object type and a second pre-defined phrase template corresponding to the second object type; determining automatically a temporal phrase template including a temporal expression based on a time related to the event, the temporal phrase template selected from a plurality of phrase templates based on an interval of the time related to the event such that different phrase templates are associated with different intervals; dynamically combining the first pre-defined phrase template with the second pre-defined phrase template and with the temporal phrase template to form a linguistic prompt related to the person representing the first object, wherein the linguistic prompt comprises the first pre-defined phrase template, the second pre-defined phrase template, and the temporal phrase template; and presenting the linguistic prompt. | 11. A non-transitory computer readable medium containing program instructions, which when executed, perform a method for providing a contextual description of an object, the computer readable medium comprising program instructions for: receiving a first object having a first object type representing a person, the first object associated with a first user and including a first attribute associated with the respective person; identifying a second object having a second attribute related to the first attribute, the second object having a second object type representing an event; retrieving, from a data store, a first pre-defined phrase template corresponding to the first object type and a second pre-defined phrase template corresponding to the second object type; determining automatically a temporal phrase template including a temporal expression based on a time related to the event, the temporal phrase template selected from a plurality of phrase templates based on an interval of the time related to the event such that different phrase templates are associated with different intervals; dynamically combining the first pre-defined phrase template with the second pre-defined phrase template and with the temporal phrase template to form a linguistic prompt related to the person representing the first object, wherein the linguistic prompt comprises the first pre-defined phrase template, the second pre-defined phrase template, and the temporal phrase template; and presenting the linguistic prompt. 15. The computer readable medium of claim 11 wherein prior to combining the first pre-defined phrase template with the second pre-defined phrase template, the computer readable medium comprises instructions for: providing a set of first object phrase templates associated with the first object type; providing a set of second object phrase templates associated with the second object type; selecting from the set of first object phrase templates at least one first object phrase template that refers to the second object type; and selecting from the set of second phrase templates at least one second object phrase template that refers to the first object type. | 0.537762 |
9,786,283 | 1 | 3 | 1. A speech media transcription system comprising a playback device arranged to play back speech delimited in segments, the system being programmed: to play back an entire segment for a transcriber to transcribe; to estimate a limit of a working memory of the transcriber; to provide, for a segment being transcribed, an adaptive estimate of the proportion of the segment that has not been transcribed by the transcriber; and subsequently to play back again said proportion of the segment that has not been transcribed, immediately when the adaptive estimate indicates that a current playback position is exceeding the limit of the working memory of the transcriber. | 1. A speech media transcription system comprising a playback device arranged to play back speech delimited in segments, the system being programmed: to play back an entire segment for a transcriber to transcribe; to estimate a limit of a working memory of the transcriber; to provide, for a segment being transcribed, an adaptive estimate of the proportion of the segment that has not been transcribed by the transcriber; and subsequently to play back again said proportion of the segment that has not been transcribed, immediately when the adaptive estimate indicates that a current playback position is exceeding the limit of the working memory of the transcriber. 3. A system according to claim 1 , wherein where analysis of a segment indicates that it contains no speech, the segment is omitted from playback or played at high speed. | 0.743202 |
8,411,086 | 17 | 21 | 17. A computer-implemented method for defining a model, comprising: at a computer system having one or more processors and memory storing one or more programs that when executed by the one or more processors cause the computer system to perform the method, the method comprising: analyzing a plurality of images of a space in a static configuration, at least one of the images including a plurality of distinctive visual features, wherein the plurality of distinctive visual features include: one or more distinctive visual features corresponding to a physical object in the space in the static configuration; and a predefined marker associated with respective semantic information specifying respective dynamic behavior; and defining a model in accordance with the plurality of distinctive visual features and the respective semantic information, wherein: the model includes a model aspect corresponding to the physical object; and the model aspect is configured to be manipulated in accordance with the respective dynamic behavior specified by the predefined marker. | 17. A computer-implemented method for defining a model, comprising: at a computer system having one or more processors and memory storing one or more programs that when executed by the one or more processors cause the computer system to perform the method, the method comprising: analyzing a plurality of images of a space in a static configuration, at least one of the images including a plurality of distinctive visual features, wherein the plurality of distinctive visual features include: one or more distinctive visual features corresponding to a physical object in the space in the static configuration; and a predefined marker associated with respective semantic information specifying respective dynamic behavior; and defining a model in accordance with the plurality of distinctive visual features and the respective semantic information, wherein: the model includes a model aspect corresponding to the physical object; and the model aspect is configured to be manipulated in accordance with the respective dynamic behavior specified by the predefined marker. 21. The method of claim 17 , further comprising: in the model, modeling the dynamic behavior of the aspect using a rigid body physics modeler. | 0.791176 |
7,526,501 | 1 | 7 | 1. A system that facilitates handling a change associated with a database, comprising at least a processor executing the following components: an interface that receives data associated with a change to an object graph that is a cached view of the database; and a state transition logic component that maintains the change related to the object graph utilizing a context and a respective set of rules, wherein a rules component enforces the following set of rules to the object graph: 1) a detached object cannot be related to a non-detached object; and 2) a deleted object cannot be related to a non-deleted object, the context employs metadata to view the object graph with an abstraction of at least one of an entity or a relationship. | 1. A system that facilitates handling a change associated with a database, comprising at least a processor executing the following components: an interface that receives data associated with a change to an object graph that is a cached view of the database; and a state transition logic component that maintains the change related to the object graph utilizing a context and a respective set of rules, wherein a rules component enforces the following set of rules to the object graph: 1) a detached object cannot be related to a non-detached object; and 2) a deleted object cannot be related to a non-deleted object, the context employs metadata to view the object graph with an abstraction of at least one of an entity or a relationship. 7. The system of claim 1 , further comprising the rules component employs at least one of the following operations: 1) add an object; 2) delete object; 3) accept change; or 4) reject change. | 0.690554 |
7,711,560 | 1 | 9 | 1. A speech recognition apparatus for calculating a degree of likelihood of a non-language speech from an unidentified input speech, said speech recognition apparatus calculating, per path, a cumulative score of a language score, a word acoustic score and a garbage acoustic score and outputting a word string with a highest cumulative score as a recognition result of the unidentified input speech including a non-language speech, said apparatus comprising: a garbage acoustic model storage unit operable to store, in advance, a garbage acoustic model which is an acoustic model learned from a collection of a plurality of unnecessary words; a feature value calculation unit operable to calculate a feature parameter necessary for recognition by acoustically analyzing the unidentified input speech per frame which is a unit for speech analysis; a garbage acoustic score calculation unit operable to calculate the garbage acoustic score by comparing the feature parameter and the garbage acoustic model per frame; an estimate value calculation unit operable to calculate, per frame, an estimate value which indicates a degree of likelihood to be a non-language speech of one of the plurality of unnecessary words; a garbage acoustic score correction unit operable to correct the garbage acoustic score calculated by said garbage acoustic score calculation unit so as to raise the score only in the frame where the non-language speech is inputted by adding the estimate value to the garbage acoustic score, the estimate value being calculated by said estimate value calculation unit, and the garbage acoustic score being calculated by said garbage acoustic score calculation unit; and a recognition result output unit operable to output the word string as the recognition result of the unidentified input speech, the word string having the highest cumulative score of the language score, the word acoustic score, and the garbage acoustic score corrected by said garbage acoustic score correction unit. | 1. A speech recognition apparatus for calculating a degree of likelihood of a non-language speech from an unidentified input speech, said speech recognition apparatus calculating, per path, a cumulative score of a language score, a word acoustic score and a garbage acoustic score and outputting a word string with a highest cumulative score as a recognition result of the unidentified input speech including a non-language speech, said apparatus comprising: a garbage acoustic model storage unit operable to store, in advance, a garbage acoustic model which is an acoustic model learned from a collection of a plurality of unnecessary words; a feature value calculation unit operable to calculate a feature parameter necessary for recognition by acoustically analyzing the unidentified input speech per frame which is a unit for speech analysis; a garbage acoustic score calculation unit operable to calculate the garbage acoustic score by comparing the feature parameter and the garbage acoustic model per frame; an estimate value calculation unit operable to calculate, per frame, an estimate value which indicates a degree of likelihood to be a non-language speech of one of the plurality of unnecessary words; a garbage acoustic score correction unit operable to correct the garbage acoustic score calculated by said garbage acoustic score calculation unit so as to raise the score only in the frame where the non-language speech is inputted by adding the estimate value to the garbage acoustic score, the estimate value being calculated by said estimate value calculation unit, and the garbage acoustic score being calculated by said garbage acoustic score calculation unit; and a recognition result output unit operable to output the word string as the recognition result of the unidentified input speech, the word string having the highest cumulative score of the language score, the word acoustic score, and the garbage acoustic score corrected by said garbage acoustic score correction unit. 9. The speech recognition apparatus according to claim 1 , wherein said estimate value calculation unit includes a non-language phenomenon estimation unit operable to calculate an estimate value of a non-language phenomenon which is related to the non-language speech based on user's information interlocking the non-language speech of one of the plurality of necessary words, and said garbage acoustic score correction unit corrects the garbage acoustic score so as to raise the score using the estimate value in the frame where the non-language phenomenon which is calculated by said non-language phenomenon estimation unit is inputted. | 0.5 |
9,928,875 | 4 | 5 | 4. The computer-implemented method of claim 1 , wherein said generating step generates the estimated box annotations of the given target in the suggested frames to include a current estimated box annotation and at least one alternate estimated box annotation which are subsequently displayed to a user in said displaying step for user selection. | 4. The computer-implemented method of claim 1 , wherein said generating step generates the estimated box annotations of the given target in the suggested frames to include a current estimated box annotation and at least one alternate estimated box annotation which are subsequently displayed to a user in said displaying step for user selection. 5. The computer-implemented method of claim 4 , wherein each of the current estimated box annotation and the at least one alternate estimated box annotation are displayed to be visually distinct from each other. | 0.601887 |
8,776,094 | 5 | 6 | 5. The computer-implemented method of claim 1 , wherein the binary contract includes one or more function signatures. | 5. The computer-implemented method of claim 1 , wherein the binary contract includes one or more function signatures. 6. The computer-implemented method of claim 5 , wherein the binary contract includes one or more function names. | 0.5 |
9,646,056 | 15 | 16 | 15. A computer program product for rank-ordering and cognitive saliency schema-based selection, the computer program product comprising computer-readable instructions stored on a non-transitory computer-readable medium that are executable by a computer having a processor for causing the processor to perform operations of: receiving a set of unnormalized probabilities corresponding to a set of objects competing for attentional selection in a current environment, wherein each unnormalized probability in the set of unnormalized probabilities is based on a likelihood estimation of encountering the corresponding object in the current environment; ranking the set of objects based on a set of cognitive saliency values corresponding to the set of objects to generate a rank-ordered list of cognitive saliency values, wherein the set of cognitive saliency values is proportional to the set of unnormalized probabilities; analyzing the rank-ordered list of cognitive saliency values to detect a schema of the current environment by which the set of objects is ranked; learning and storing the schema along with a reward measure of the schema's utility; and selecting a maximum saliency object in the set of objects based on the rank-ordered list of cognitive saliency values. | 15. A computer program product for rank-ordering and cognitive saliency schema-based selection, the computer program product comprising computer-readable instructions stored on a non-transitory computer-readable medium that are executable by a computer having a processor for causing the processor to perform operations of: receiving a set of unnormalized probabilities corresponding to a set of objects competing for attentional selection in a current environment, wherein each unnormalized probability in the set of unnormalized probabilities is based on a likelihood estimation of encountering the corresponding object in the current environment; ranking the set of objects based on a set of cognitive saliency values corresponding to the set of objects to generate a rank-ordered list of cognitive saliency values, wherein the set of cognitive saliency values is proportional to the set of unnormalized probabilities; analyzing the rank-ordered list of cognitive saliency values to detect a schema of the current environment by which the set of objects is ranked; learning and storing the schema along with a reward measure of the schema's utility; and selecting a maximum saliency object in the set of objects based on the rank-ordered list of cognitive saliency values. 16. The computer program product as set forth in claim 15 , further comprising instructions for causing the processor to perform operations of: recalling the stored schema and the reward measure when presented with a new environment; and appending a set of processing strategies onto the rank-ordered list of cognitive saliency values based on the recall of the stored schema and the reward measure, thereby generating a processed rank-ordered list of cognitive saliency values. | 0.5 |
10,110,533 | 8 | 13 | 8. A computer program product comprising computer-readable program code to be executed by one or more processors when retrieved from a non-transitory computer-readable medium, the program code including instructions to: create a plurality of scores for each token, in a sequence of tokens from an email signature block, based on a corresponding independent probability distribution that has been previously trained for a plurality of entity types, wherein each token comprises one of a word, a punctuation symbol, and an end-of-line character, an entity being a part of one of a person name, a job title, an enterprise name, a telephone number, an email address, and a uniform resource locator, and being associated with at least one of an entity type, an entity sequence, and a set of entities; identify each entity sequence that has a total number of entities that is identical to a total number of tokens in the sequence of tokens; determine, for each of the identified entity sequences, an entity sequence score by combining corresponding scores for each token, in the sequence of tokens, that corresponds to an entity type in an identified entity sequence; identify an entity sequence from the identified entity sequences with a highest entity sequence score; and output the sequence of tokens as an identified set of entities, in the email signature block, based on the entity sequence with the highest score. | 8. A computer program product comprising computer-readable program code to be executed by one or more processors when retrieved from a non-transitory computer-readable medium, the program code including instructions to: create a plurality of scores for each token, in a sequence of tokens from an email signature block, based on a corresponding independent probability distribution that has been previously trained for a plurality of entity types, wherein each token comprises one of a word, a punctuation symbol, and an end-of-line character, an entity being a part of one of a person name, a job title, an enterprise name, a telephone number, an email address, and a uniform resource locator, and being associated with at least one of an entity type, an entity sequence, and a set of entities; identify each entity sequence that has a total number of entities that is identical to a total number of tokens in the sequence of tokens; determine, for each of the identified entity sequences, an entity sequence score by combining corresponding scores for each token, in the sequence of tokens, that corresponds to an entity type in an identified entity sequence; identify an entity sequence from the identified entity sequences with a highest entity sequence score; and output the sequence of tokens as an identified set of entities, in the email signature block, based on the entity sequence with the highest score. 13. The computer program product of claim 8 , wherein combining corresponding scores for each token, in the sequence of tokens, that corresponds to an entity type in an to identified entity sequence comprises multiplying scores for each token, in the sequence of tokens, that corresponds to each entity type, for each identified entity sequence. | 0.615813 |
8,082,264 | 9 | 11 | 9. The article of manufacture according to claim 7 , wherein the operations further comprise: identifying at least one question class that includes at least one concept; identifying words in the current user query that are associated with the concept; modifying the identified question class by replacing the concept with the identified words in the current user query; and responding to the current user query with the modified question class. | 9. The article of manufacture according to claim 7 , wherein the operations further comprise: identifying at least one question class that includes at least one concept; identifying words in the current user query that are associated with the concept; modifying the identified question class by replacing the concept with the identified words in the current user query; and responding to the current user query with the modified question class. 11. The article of manufacture according to claim 9 , wherein the question classes include associated probability values that indicate a probability that the question classes correctly respond to the current user query and the question classes are sent or not sent in response to the current user query according to the associated probability values. | 0.5 |
9,448,975 | 6 | 7 | 6. An information processing apparatus, comprising a memory storing executable instructions; and a processor connected to the memory and configured to execute the instructions, execution of the instructions causes the processor to: detect glyph variant information from an input character data string; convert detected glyph variant information to extended expression data including basic character data and the detected glyph variant information, the basic character data being associated with the detected glyph variant information in the input character data string, wherein the extended expression data can be converted to the basic character data by specific bit arithmetic processing; deliver the extended expression data obtained by the converting unit to a processing unit to perform processing of the extended expression data; obtain the extended expression data that the processing of the processing unit has been performed; and convert the obtained extended expression data to a character data string of a standard expression including glyph variant information and basic character data associated with the glyph variant information. | 6. An information processing apparatus, comprising a memory storing executable instructions; and a processor connected to the memory and configured to execute the instructions, execution of the instructions causes the processor to: detect glyph variant information from an input character data string; convert detected glyph variant information to extended expression data including basic character data and the detected glyph variant information, the basic character data being associated with the detected glyph variant information in the input character data string, wherein the extended expression data can be converted to the basic character data by specific bit arithmetic processing; deliver the extended expression data obtained by the converting unit to a processing unit to perform processing of the extended expression data; obtain the extended expression data that the processing of the processing unit has been performed; and convert the obtained extended expression data to a character data string of a standard expression including glyph variant information and basic character data associated with the glyph variant information. 7. The information processing apparatus according to claim 6 , wherein the extended expression data includes a value obtained by shifting of predetermined bits of a variant identification code value included in the glyph variant information. | 0.5 |
8,429,179 | 2 | 3 | 2. The system of claim 1 , further comprising instructions for constructing a survey based on a survey ontology, including adding concepts to the survey ontology, wherein the added concepts are mapped to the domain ontology. | 2. The system of claim 1 , further comprising instructions for constructing a survey based on a survey ontology, including adding concepts to the survey ontology, wherein the added concepts are mapped to the domain ontology. 3. The system of claim 2 , wherein the survey is a graph representation of a set of questions mapped to the survey ontology. | 0.5 |
9,760,559 | 34 | 37 | 34. A system comprising: one or more processors; memory; one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for: receiving a text input, the text input associated with an input context; determining, using a first language model, a first frequency of occurrence of an m-gram with respect to a first subset of a corpus, wherein the first subset is associated with a first context, and wherein the m-gram includes at least one word in the text input; determining, based on a degree of similarity between the input context and the first context, a first weighting factor, wherein a weighted first frequency of occurrence of the m-gram is obtained by applying the first weighting factor to the first frequency of occurrence of the m-gram; determining, using the first language model, a second frequency of occurrence of the m-gram with respect to a second subset of the corpus, wherein the second subset is associated with a second context; and determining, based on a degree of similarity between the input context and the second context, a second weighting factor, wherein a weighted second frequency of occurrence of the m-gram is obtained by applying the second weighting factor to the second frequency of occurrence of the m-gram; determining, based on the weighted first frequency of occurrence of the m-gram and the weighted second frequency of occurrence of the m-gram, a first weighted probability of a first predicted text given the text input, wherein the m-gram includes at least one word in the first predicted text. | 34. A system comprising: one or more processors; memory; one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for: receiving a text input, the text input associated with an input context; determining, using a first language model, a first frequency of occurrence of an m-gram with respect to a first subset of a corpus, wherein the first subset is associated with a first context, and wherein the m-gram includes at least one word in the text input; determining, based on a degree of similarity between the input context and the first context, a first weighting factor, wherein a weighted first frequency of occurrence of the m-gram is obtained by applying the first weighting factor to the first frequency of occurrence of the m-gram; determining, using the first language model, a second frequency of occurrence of the m-gram with respect to a second subset of the corpus, wherein the second subset is associated with a second context; and determining, based on a degree of similarity between the input context and the second context, a second weighting factor, wherein a weighted second frequency of occurrence of the m-gram is obtained by applying the second weighting factor to the second frequency of occurrence of the m-gram; determining, based on the weighted first frequency of occurrence of the m-gram and the weighted second frequency of occurrence of the m-gram, a first weighted probability of a first predicted text given the text input, wherein the m-gram includes at least one word in the first predicted text. 37. The system of claim 34 , wherein the first language model includes a plurality of sub-models arranged in a hierarchical context tree, and wherein each sub-model is associated with a specific context. | 0.694277 |
7,996,589 | 1 | 3 | 1. A computer-implemented method for receiving user input comprising: receiving handwritten input via a user entering information in a handwriting receiving panel in a form of electronic ink, the electronic ink is a sequence or a set of strokes with properties, the handwriting receiving panel attempts to recognize the handwritten input and forwards recognition results of the handwritten input to an auto-complete provider as text; integrating said handwritten input via the auto-complete provider that provides an auto-suggest list comprising a list of suggested completions based on said text, wherein the auto-suggest list varies by application, such that the auto-suggest list updates after each character of the text is recognized or after a segment of the text is recognized, the auto-suggest list updates after a subsequent character or subsequent segment of text is recognized, and wherein the user adds additional information to a user selection and has newly recognized information forwarded to the auto-complete provider; registering the auto-complete provider to an auto-complete client, wherein the auto-complete client identifies and stores a pointer to the auto-complete provider, and wherein the auto-complete client exchanges data with the auto-complete provider; forwarding the user selection back to the handwriting receiving panel for additional information to be appended to the user selection; and notifying the auto-complete provider that the user has made a choice. | 1. A computer-implemented method for receiving user input comprising: receiving handwritten input via a user entering information in a handwriting receiving panel in a form of electronic ink, the electronic ink is a sequence or a set of strokes with properties, the handwriting receiving panel attempts to recognize the handwritten input and forwards recognition results of the handwritten input to an auto-complete provider as text; integrating said handwritten input via the auto-complete provider that provides an auto-suggest list comprising a list of suggested completions based on said text, wherein the auto-suggest list varies by application, such that the auto-suggest list updates after each character of the text is recognized or after a segment of the text is recognized, the auto-suggest list updates after a subsequent character or subsequent segment of text is recognized, and wherein the user adds additional information to a user selection and has newly recognized information forwarded to the auto-complete provider; registering the auto-complete provider to an auto-complete client, wherein the auto-complete client identifies and stores a pointer to the auto-complete provider, and wherein the auto-complete client exchanges data with the auto-complete provider; forwarding the user selection back to the handwriting receiving panel for additional information to be appended to the user selection; and notifying the auto-complete provider that the user has made a choice. 3. The computer-implemented method according to claim 1 , wherein said autosuggest list is part of an operating system. | 0.85199 |
9,253,306 | 10 | 13 | 10. An apparatus, comprising: a mashup stimulus module that comprises a processor to determine at least one of a role, context, presence, or location of a user, wherein at least one of the role or context is determined based on a telephone call and wherein a specified mashup is determined based on an Instant Message session and the telephone call occurring simultaneously; and a mashup management module that comprises the processor, wherein the mashup management module identifies that the Instant Message session and the telephone call are on different computational devices, in response to identifying that the Instant Message session and the telephone call are on different computational devices, transfers the Instant Message session and the telephone call to a common computational device for displaying the telephone call and the Instant Message session by using the specified mashup for a user interface of the common computational device. | 10. An apparatus, comprising: a mashup stimulus module that comprises a processor to determine at least one of a role, context, presence, or location of a user, wherein at least one of the role or context is determined based on a telephone call and wherein a specified mashup is determined based on an Instant Message session and the telephone call occurring simultaneously; and a mashup management module that comprises the processor, wherein the mashup management module identifies that the Instant Message session and the telephone call are on different computational devices, in response to identifying that the Instant Message session and the telephone call are on different computational devices, transfers the Instant Message session and the telephone call to a common computational device for displaying the telephone call and the Instant Message session by using the specified mashup for a user interface of the common computational device. 13. The apparatus of claim 10 , wherein the at least one of the role, context, presence, or location is the presence state and wherein the presence state is a function of a status indicator of the user's presence. | 0.879388 |
8,234,274 | 1 | 6 | 1. A method for characterizing a corpus of documents each having one or more links, comprising: forming a Bayesian network using the documents; determining a Bayesian network structure using the one or more links; generating a content link model where the model is a generative probabilistic model of the corpus along with citation information among documents, each document represented as a mixture over latent topics, and each relationship among documents is modeled by another generative process with a topic distribution of each document being a mixture of distributions associated with related documents; using a citation-topic (CT) model with a generative process for each word w in the document d in the corpus, with document probabilities Ξ, topic distribution matrix Θ and word probabilities matrix Ψ, including: choosing a related document c from p (c|d,Ξ), a multinomial probability conditioned on the document d; choosing a topic z from the topic distribution of the document c, p(z|c,Θ); choosing a word w which follows the multinomial distribution p(w|z,Ψ) conditioned on the topic z; and determining one or more topics in the corpus and topic distribution for each document wherein the content link model captures direct and indirect relationships represented by the links. | 1. A method for characterizing a corpus of documents each having one or more links, comprising: forming a Bayesian network using the documents; determining a Bayesian network structure using the one or more links; generating a content link model where the model is a generative probabilistic model of the corpus along with citation information among documents, each document represented as a mixture over latent topics, and each relationship among documents is modeled by another generative process with a topic distribution of each document being a mixture of distributions associated with related documents; using a citation-topic (CT) model with a generative process for each word w in the document d in the corpus, with document probabilities Ξ, topic distribution matrix Θ and word probabilities matrix Ψ, including: choosing a related document c from p (c|d,Ξ), a multinomial probability conditioned on the document d; choosing a topic z from the topic distribution of the document c, p(z|c,Θ); choosing a word w which follows the multinomial distribution p(w|z,Ψ) conditioned on the topic z; and determining one or more topics in the corpus and topic distribution for each document wherein the content link model captures direct and indirect relationships represented by the links. 6. The method of claim 1 , wherein the Bayesian network encodes direct and indirect relations, and wherein relationships are derived explicitly from links or implicitly from similarities among documents. | 0.902498 |
9,251,474 | 2 | 7 | 2. The method of claim 1 , wherein reconfiguring the ranker array based on results of the determined correctness comprises: generating a ranker array reward value for the ranker array based on the correctness of the corresponding ranked listing of candidate answers for each of the rankers in the ranker array; comparing the ranker array reward value to a reward value threshold value; and reconfiguring the ranker array in response to the ranker array reward value not satisfying a predetermined relationship with regard to the reward value threshold value. | 2. The method of claim 1 , wherein reconfiguring the ranker array based on results of the determined correctness comprises: generating a ranker array reward value for the ranker array based on the correctness of the corresponding ranked listing of candidate answers for each of the rankers in the ranker array; comparing the ranker array reward value to a reward value threshold value; and reconfiguring the ranker array in response to the ranker array reward value not satisfying a predetermined relationship with regard to the reward value threshold value. 7. The method of claim 2 , wherein reconfiguring the ranker array comprises at least one of modifying a statistical classification function used by one or more of the rankers in the ranker array, or modifying a sampling of a corpus of information upon which one or more of the rankers in the ranker array operate. | 0.758488 |
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