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7,831,582 | 1 | 17 | 1. A computer-implemented method, comprising: identifying a result set including one or more of a plurality of online content sources, wherein each of the included one or more online content sources satisfies a keyword query including one or more keywords; for a given one of the online content sources included in said result set, identifying and generating corresponding representations of one or more aggregate paths including said given online content source, wherein each of the one or more aggregate paths includes one or more navigation paths among said plurality of online content sources, wherein each of the one or more navigation paths is indicative of an access request by one or more users that originates from a corresponding originating one of said online content sources to access a corresponding destination one of said online content sources, wherein each such access request indicated by a navigation path occurs prior to generating representations of said one or more aggregate paths, wherein a representation of a given one of said one or more aggregate paths is indicative of multiple ones of said online content sources; detecting a selection of a particular online content source from one of said identified aggregate paths, wherein prior to said detecting, said particular online content source does not satisfy said keyword query; and in response to detecting said selection of said particular online content source, associating said one or more keywords included in said keyword query with said particular online content source, such that after said associating, said particular online content source satisfies said keyword query; wherein each of said identifying a result set, said identifying and generating corresponding representations, said detecting a selection, and said associating said one or more keywords is performed by one or more computer systems, each comprising at least a memory and a processor. | 1. A computer-implemented method, comprising: identifying a result set including one or more of a plurality of online content sources, wherein each of the included one or more online content sources satisfies a keyword query including one or more keywords; for a given one of the online content sources included in said result set, identifying and generating corresponding representations of one or more aggregate paths including said given online content source, wherein each of the one or more aggregate paths includes one or more navigation paths among said plurality of online content sources, wherein each of the one or more navigation paths is indicative of an access request by one or more users that originates from a corresponding originating one of said online content sources to access a corresponding destination one of said online content sources, wherein each such access request indicated by a navigation path occurs prior to generating representations of said one or more aggregate paths, wherein a representation of a given one of said one or more aggregate paths is indicative of multiple ones of said online content sources; detecting a selection of a particular online content source from one of said identified aggregate paths, wherein prior to said detecting, said particular online content source does not satisfy said keyword query; and in response to detecting said selection of said particular online content source, associating said one or more keywords included in said keyword query with said particular online content source, such that after said associating, said particular online content source satisfies said keyword query; wherein each of said identifying a result set, said identifying and generating corresponding representations, said detecting a selection, and said associating said one or more keywords is performed by one or more computer systems, each comprising at least a memory and a processor. 17. The method as recited in claim 1 , wherein a topology of said navigation paths among said plurality of online content sources differs from a topology of explicit navigation links included among said plurality of online content sources. | 0.657593 |
8,888,497 | 1 | 4 | 1. A method comprising: using one or more computers, obtaining and storing a first set of information comprising a set of emotional states with which online elements may be associated; using one or more computers, obtaining and storing a second set of information comprising information relating to a set of online elements; using one or more computers, based at least in part on the second set of information, assigning each of the set of online elements to at least one associated emotional state, of the set of emotional states; using one or more computers, obtaining and storing a third set of information comprising information relating to online activity of a user in association with at least one online element of the set of online elements, and comprising an emotional state to which the at least one online element of the set of online elements is assigned; using one or more computers, based at least in part on the third set of information, classifying the user into at least one emotional state of the set of emotional states; presenting the user with an online advertisement based at least in part on the at least one emotional state, of the set of emotional states, into which the user is classified; and based at least in part on at least one direct online activity of the user and at least one indirect online activity of the user, predicting an emotional state that the user is likely to be in at a particular time at which, or during a particular period of time during which, the online advertisement is anticipated to be served, wherein the at least one direct online activity of the user includes one or more of usage, usage frequency, extent of personalization, sharing of emoticons, and sharing of emoticlips, and wherein the at least one indirect online activity of the user includes one or more of visits to specific user post, user review or blog entry domains or Web sites, and engagement with specific user post, user review or blog entry domains or Web sites. | 1. A method comprising: using one or more computers, obtaining and storing a first set of information comprising a set of emotional states with which online elements may be associated; using one or more computers, obtaining and storing a second set of information comprising information relating to a set of online elements; using one or more computers, based at least in part on the second set of information, assigning each of the set of online elements to at least one associated emotional state, of the set of emotional states; using one or more computers, obtaining and storing a third set of information comprising information relating to online activity of a user in association with at least one online element of the set of online elements, and comprising an emotional state to which the at least one online element of the set of online elements is assigned; using one or more computers, based at least in part on the third set of information, classifying the user into at least one emotional state of the set of emotional states; presenting the user with an online advertisement based at least in part on the at least one emotional state, of the set of emotional states, into which the user is classified; and based at least in part on at least one direct online activity of the user and at least one indirect online activity of the user, predicting an emotional state that the user is likely to be in at a particular time at which, or during a particular period of time during which, the online advertisement is anticipated to be served, wherein the at least one direct online activity of the user includes one or more of usage, usage frequency, extent of personalization, sharing of emoticons, and sharing of emoticlips, and wherein the at least one indirect online activity of the user includes one or more of visits to specific user post, user review or blog entry domains or Web sites, and engagement with specific user post, user review or blog entry domains or Web sites. 4. The method of claim 1 , wherein comprising obtaining and storing a first set of information comprising a set of emotional states into which users may be classified comprises obtaining and storing a first set of information comprising a hierarchical network of nodes, including subnodes, in which the nodes represent emotional state categories, including subcategories. | 0.5 |
8,027,945 | 40 | 41 | 40. The article of claim 39 , wherein the operations further comprise conducting discourse transactions with the user, the conducting including: receiving signals from the user through at least one communication channel; and generating signals to the user through at least one communication channel; wherein said establishing an association between the linguistic or pragmatic item and the user goal comprises employing a model of discourse transactions. | 40. The article of claim 39 , wherein the operations further comprise conducting discourse transactions with the user, the conducting including: receiving signals from the user through at least one communication channel; and generating signals to the user through at least one communication channel; wherein said establishing an association between the linguistic or pragmatic item and the user goal comprises employing a model of discourse transactions. 41. The article of claim 40 , wherein the operations further comprise inferring at least one task, a parameter and the software component by inferring at least one item, the item comprising user goals, intentions, beliefs, assumptions, preferences, or changes of state, from a series of discourse transactions. | 0.788251 |
8,869,120 | 1 | 10 | 1. A method executed by a processor for performing static analysis on source code having a low level language source code embedded in a high level language source code, wherein the high level language source code is represented in a high level representation that the static analysis can be performed on, the method comprising the steps of: transforming the embedded low level language source code to a high level representation that does not retain the full semantics of the low level language source code and that the static analysis can be performed on, by identifying a set of instructions in the low level language source code that represents a single high level statement in the high level representation, and transforming the set of instructions to represent the single high level statement in the high level representation; and performing static analysis on the high level representation of the high level language source code and the high level representation of the low level source code. | 1. A method executed by a processor for performing static analysis on source code having a low level language source code embedded in a high level language source code, wherein the high level language source code is represented in a high level representation that the static analysis can be performed on, the method comprising the steps of: transforming the embedded low level language source code to a high level representation that does not retain the full semantics of the low level language source code and that the static analysis can be performed on, by identifying a set of instructions in the low level language source code that represents a single high level statement in the high level representation, and transforming the set of instructions to represent the single high level statement in the high level representation; and performing static analysis on the high level representation of the high level language source code and the high level representation of the low level source code. 10. The method according to claim 1 , wherein the source code forms part of an embedded system. | 0.926923 |
8,301,436 | 1 | 5 | 1. A computer-implemented method for interacting with a computer system, the method comprising: receiving input comprising at least one command executable by an application from a user and capturing the input for processing; performing recognition on the input to ascertain semantic information pertaining to a first portion of the input and outputting a semantic object comprising data including data for executing the at least one command in a format to be processed by a computer application and being in accordance with the input that has been recognized and semantic information for the first portion, wherein performing recognition and outputting the semantic object are performed using a language model comprising a combination of an N-gram language model and a context free grammar and while capturing continues for subsequent portions of the input, the language model storing information related to words and semantic information to be recognized; and rendering information to the user while the user is providing the input, at least some of the information being responsive to the data regarding the at least one command within said semantic object. | 1. A computer-implemented method for interacting with a computer system, the method comprising: receiving input comprising at least one command executable by an application from a user and capturing the input for processing; performing recognition on the input to ascertain semantic information pertaining to a first portion of the input and outputting a semantic object comprising data including data for executing the at least one command in a format to be processed by a computer application and being in accordance with the input that has been recognized and semantic information for the first portion, wherein performing recognition and outputting the semantic object are performed using a language model comprising a combination of an N-gram language model and a context free grammar and while capturing continues for subsequent portions of the input, the language model storing information related to words and semantic information to be recognized; and rendering information to the user while the user is providing the input, at least some of the information being responsive to the data regarding the at least one command within said semantic object. 5. The computer-implemented method of claim 1 wherein receiving and capturing input comprises receiving and capturing handwriting input from the user. | 0.637681 |
8,584,045 | 5 | 6 | 5. The method of claim 1 , further including moving the first graphical element from a first position to a second position on the display in response to a user input. | 5. The method of claim 1 , further including moving the first graphical element from a first position to a second position on the display in response to a user input. 6. The method of claim 5 , further including presenting the moving as an animation. | 0.876119 |
9,058,327 | 7 | 11 | 7. A non-transitory computer readable storage medium having instructions that, when executed by a processing device, cause the processing device to perform operations comprising: updating a set of training documents based on a selected portion of a training document of the set to obtain an updated set of training documents; searching content within other training documents in the updated set using a machine learning engine based on the selected portion and variations of the selected portion; determining a probability measure of the content; and classifying a second training document containing the content based on the probability measure. | 7. A non-transitory computer readable storage medium having instructions that, when executed by a processing device, cause the processing device to perform operations comprising: updating a set of training documents based on a selected portion of a training document of the set to obtain an updated set of training documents; searching content within other training documents in the updated set using a machine learning engine based on the selected portion and variations of the selected portion; determining a probability measure of the content; and classifying a second training document containing the content based on the probability measure. 11. The non-transitory computer readable storage medium of claim 7 , further comprising: identifying additional documents to add to the updated set; receiving input for the additional documents; and modifying the updated set of training documents to include at least one of the additional documents based on the received input associated with the additional documents. | 0.575058 |
9,727,925 | 25 | 26 | 25. A system, comprising: a processor; a memory comprising computer code executed using the processor, in which the computer code implements a process for, identifying an internal social network for an enterprise, collecting a set of messages from the internal social network, performing semantic filtering to the set of messages, the semantic filtering reducing an excess noise associated with data that is not relevant to the enterprise, performing semantic analysis upon the set of messages collected from the internal social network to determine a contextual significance of one or more terms in the set of messages, identifying themes in the set of messages, the themes pertaining to at least one of customer preferences, demographic information, industry trends, customer view points, as a result of the performed semantic analysis, clustering together messages that are similar to each other, categorizing the set of messages into a plurality of categories based at least in part on the contextual significance of the one or more terms in the set of messages, associating each category of the plurality of categories with one or more tags, associating respective tags to the set of messages that are categorized as a result of the semantic analysis to generate a set of tagged messages, determining associations between the one or more tags and one or more enterprise applications corresponding to the enterprise such that the set of tagged messages are sent to respective ones of the one or more enterprise applications based at least in part on the determined associations, and storing the tagged messages in an actionable social message store, the actionable message store storing in a table format, for each message, one or more of a source of the message, a topic parameter associated with the message, data associated with the message, and the one or more tags associated with the message, wherein the semantic analysis performed comprises latent semantic analysis (LSA), the LSA referring to a form of statistical language modeling that distinguishes two identical words based on a semantic significance of the word. | 25. A system, comprising: a processor; a memory comprising computer code executed using the processor, in which the computer code implements a process for, identifying an internal social network for an enterprise, collecting a set of messages from the internal social network, performing semantic filtering to the set of messages, the semantic filtering reducing an excess noise associated with data that is not relevant to the enterprise, performing semantic analysis upon the set of messages collected from the internal social network to determine a contextual significance of one or more terms in the set of messages, identifying themes in the set of messages, the themes pertaining to at least one of customer preferences, demographic information, industry trends, customer view points, as a result of the performed semantic analysis, clustering together messages that are similar to each other, categorizing the set of messages into a plurality of categories based at least in part on the contextual significance of the one or more terms in the set of messages, associating each category of the plurality of categories with one or more tags, associating respective tags to the set of messages that are categorized as a result of the semantic analysis to generate a set of tagged messages, determining associations between the one or more tags and one or more enterprise applications corresponding to the enterprise such that the set of tagged messages are sent to respective ones of the one or more enterprise applications based at least in part on the determined associations, and storing the tagged messages in an actionable social message store, the actionable message store storing in a table format, for each message, one or more of a source of the message, a topic parameter associated with the message, data associated with the message, and the one or more tags associated with the message, wherein the semantic analysis performed comprises latent semantic analysis (LSA), the LSA referring to a form of statistical language modeling that distinguishes two identical words based on a semantic significance of the word. 26. The system of claim 25 , further comprising a dashboard tool to visualize results from performing the semantic analysis. | 0.614907 |
10,146,883 | 1 | 11 | 1. A method comprising: determining, by one or more computing devices, a target geographic feature that does not initially have associated targeting information for providing targeted content associated with the target geographic feature, the target geographic feature defining a first geographic area; determining, by the one or more computing devices, one or more similar geographic features to the target geographic feature based at least in part on a comparison of query counts for each of a plurality of geographic features, the one or more similar geographic features defining one or more geographic areas distinct from the first geographic area, the one or more similar geographic features having associated targeting information; attributing, by the one or more computing devices, targeting information associated with at least one of the one or more similar geographic features to the target geographic feature; and providing, by the one or more computing devices, related targeted content responsive to one or more queries that relate to the target geographic feature, the related targeted content identified based at least in part on the attributed targeting information. | 1. A method comprising: determining, by one or more computing devices, a target geographic feature that does not initially have associated targeting information for providing targeted content associated with the target geographic feature, the target geographic feature defining a first geographic area; determining, by the one or more computing devices, one or more similar geographic features to the target geographic feature based at least in part on a comparison of query counts for each of a plurality of geographic features, the one or more similar geographic features defining one or more geographic areas distinct from the first geographic area, the one or more similar geographic features having associated targeting information; attributing, by the one or more computing devices, targeting information associated with at least one of the one or more similar geographic features to the target geographic feature; and providing, by the one or more computing devices, related targeted content responsive to one or more queries that relate to the target geographic feature, the related targeted content identified based at least in part on the attributed targeting information. 11. The method of claim 1 , wherein determining, by the one or more computing devices, one or more similar geographic features to the target geographic feature comprises comparing, by the one or more computing devices, a number of dissimilar excess queries for the target geographic feature and a candidate geographic feature to a dissimilarity threshold. | 0.512363 |
9,589,198 | 10 | 14 | 10. A computer system for text input and detection of a keyword within a printed page or a screen, the computer system comprising: a processor; and a memory with computer code instructions stored thereon, the processor and the memory, with the computer code instructions, being configured to cause the system to: detect a plurality of word blocks contained in a captured image of a printed page or a screen; determine candidate keyword blocks among the plurality of word blocks according to a keyword probability determination rule that results in a respective probability value for each word block, wherein the keyword probability determination rule is based at least in part on a spatial analysis of the word blocks relative to at least a portion of the captured image, the spatial analysis being relative to an indication of a user intention of a keyword to obtain for selection and where the candidate keyword blocks are identified among the plurality of word blocks based upon each respective probability value of the candidate keyword blocks being above a threshold; and upon selection of a candidate keyword block, forward content of the selected keyword block as text input to an application. | 10. A computer system for text input and detection of a keyword within a printed page or a screen, the computer system comprising: a processor; and a memory with computer code instructions stored thereon, the processor and the memory, with the computer code instructions, being configured to cause the system to: detect a plurality of word blocks contained in a captured image of a printed page or a screen; determine candidate keyword blocks among the plurality of word blocks according to a keyword probability determination rule that results in a respective probability value for each word block, wherein the keyword probability determination rule is based at least in part on a spatial analysis of the word blocks relative to at least a portion of the captured image, the spatial analysis being relative to an indication of a user intention of a keyword to obtain for selection and where the candidate keyword blocks are identified among the plurality of word blocks based upon each respective probability value of the candidate keyword blocks being above a threshold; and upon selection of a candidate keyword block, forward content of the selected keyword block as text input to an application. 14. The computer system of claim 10 wherein the keyword probability determination rule takes into account a database containing words with a low probability and words with a high probability. | 0.912385 |
7,752,035 | 5 | 9 | 5. A method according to claim 2 , further comprising the steps of: communicating the messages to the recipient application; and converting the messages to the data format of the recipient application. | 5. A method according to claim 2 , further comprising the steps of: communicating the messages to the recipient application; and converting the messages to the data format of the recipient application. 9. A method according to claim 5 , wherein the data format of either the originating application or the recipient application is selected from the group of: ICD, Docle, Read, ICPC, LOINC, or Snomed. | 0.62069 |
10,142,708 | 25 | 37 | 25. A method of operation in a content delivery platform including at least one processor-based component having at least one nontransitory processor-readable medium communicatively coupled to the processor and which stores at least one of processor-executable instructions or data, the method comprising: for each of a plurality of media content consumers: providing access, via at least one processor-based component, to one or more narrative segments of a narrative presentation while limiting access by the respective media content consumer to at least one of a number of controlled access narrative segments of the narrative presentation or a piece of bonus media content until an access condition is met; and determining, via at least one processor-based component, whether the access condition is met; and providing access, via the at least one processor-based component, by the respective media content consumer to the at least one of the controlled narrative segment of the narrative presentation or the piece of bonus media content in response to the access condition being met. | 25. A method of operation in a content delivery platform including at least one processor-based component having at least one nontransitory processor-readable medium communicatively coupled to the processor and which stores at least one of processor-executable instructions or data, the method comprising: for each of a plurality of media content consumers: providing access, via at least one processor-based component, to one or more narrative segments of a narrative presentation while limiting access by the respective media content consumer to at least one of a number of controlled access narrative segments of the narrative presentation or a piece of bonus media content until an access condition is met; and determining, via at least one processor-based component, whether the access condition is met; and providing access, via the at least one processor-based component, by the respective media content consumer to the at least one of the controlled narrative segment of the narrative presentation or the piece of bonus media content in response to the access condition being met. 37. The method of claim 25 wherein determining whether the access condition is met includes assessing a performance in a contest of a team media content consumers which includes the respective media content consumer. | 0.845934 |
7,519,579 | 7 | 11 | 7. A computer-readable storage medium storing computer-executable instructions for updating a summary page of a text file, comprising: performing a query to identify data in the text file stored in accordance with a software application of a client device, wherein the data is identified by metadata stored with the text file, wherein the software application is at least one member of a group comprising: a word processing application and a notes application; in response to performing the query, generating, on the software application of the client device, a dynamic container for the query, wherein the dynamic container facilitates a synchronous relationship between the text file and the summary page; obtaining a result of the query on the software application of the client device, wherein the result includes information within the text file, wherein the result is determined by the query; storing the result in the dynamic container; displaying the query result on the summary page, wherein modification to the query of the text file results in a new query result being displayed on the summary page, wherein the summary page includes a plurality of links, wherein each link indicates a portion of the text file that is displayed on the summary page, wherein selection of one of the links causes navigation to the portion of the text file that is displayed on the summary page and is indicate by the selected link; and upon receiving an input that modifies the data of the text file in relation to one of the categories identified in the summary page, automatically updating the dynamic container with the modification to cause the dynamic container to automatically update the summary page with the modification. | 7. A computer-readable storage medium storing computer-executable instructions for updating a summary page of a text file, comprising: performing a query to identify data in the text file stored in accordance with a software application of a client device, wherein the data is identified by metadata stored with the text file, wherein the software application is at least one member of a group comprising: a word processing application and a notes application; in response to performing the query, generating, on the software application of the client device, a dynamic container for the query, wherein the dynamic container facilitates a synchronous relationship between the text file and the summary page; obtaining a result of the query on the software application of the client device, wherein the result includes information within the text file, wherein the result is determined by the query; storing the result in the dynamic container; displaying the query result on the summary page, wherein modification to the query of the text file results in a new query result being displayed on the summary page, wherein the summary page includes a plurality of links, wherein each link indicates a portion of the text file that is displayed on the summary page, wherein selection of one of the links causes navigation to the portion of the text file that is displayed on the summary page and is indicate by the selected link; and upon receiving an input that modifies the data of the text file in relation to one of the categories identified in the summary page, automatically updating the dynamic container with the modification to cause the dynamic container to automatically update the summary page with the modification. 11. The computer-readable storage medium of claim 7 , wherein the metadata is embedded in the text file. | 0.8753 |
10,095,869 | 11 | 17 | 11. A non-transitory, computer-readable storage medium embodying computer program code for generating a security analysis effort, cost and process scope estimates within a security intelligence environment, the security intelligence environment comprising a plurality of data sources and a security intelligence platform, the security intelligence platform comprising a security analysis estimation module, the security analysis estimation module executing on a hardware processor of a computer system, the computer program code comprising computer executable instructions configured for: analyzing a software system, the analyzing the software system utilizing information received from at least one of the plurality of data sources; identifying a complexity level of a security analysis, the complexity level of the security analysis comprising identification of an effort level for the security analysis, the complexity level of the security analysis comprising identification of an effort level for the security analysis, the identifying comprising performing a security analysis estimation operation, the security analysis estimation operation comprising a dynamic analysis performed via a dynamic analysis scanning subsystem and a static analysis performed via a static analysis tool; and, generating the security analysis effort estimate, the security analysis effort estimate comprising an estimate of an effort expenditure to perform a security analysis on the software system at the identified complexity level, the security analysis estimation module providing a quantitative machine learning based analytics driven determination, the quantitative machine learning based analytics driven determination providing an estimation of parameters and correlation of coefficients which can drive price and cost factors for the software security vulnerabilities identification operation. | 11. A non-transitory, computer-readable storage medium embodying computer program code for generating a security analysis effort, cost and process scope estimates within a security intelligence environment, the security intelligence environment comprising a plurality of data sources and a security intelligence platform, the security intelligence platform comprising a security analysis estimation module, the security analysis estimation module executing on a hardware processor of a computer system, the computer program code comprising computer executable instructions configured for: analyzing a software system, the analyzing the software system utilizing information received from at least one of the plurality of data sources; identifying a complexity level of a security analysis, the complexity level of the security analysis comprising identification of an effort level for the security analysis, the complexity level of the security analysis comprising identification of an effort level for the security analysis, the identifying comprising performing a security analysis estimation operation, the security analysis estimation operation comprising a dynamic analysis performed via a dynamic analysis scanning subsystem and a static analysis performed via a static analysis tool; and, generating the security analysis effort estimate, the security analysis effort estimate comprising an estimate of an effort expenditure to perform a security analysis on the software system at the identified complexity level, the security analysis estimation module providing a quantitative machine learning based analytics driven determination, the quantitative machine learning based analytics driven determination providing an estimation of parameters and correlation of coefficients which can drive price and cost factors for the software security vulnerabilities identification operation. 17. The non-transitory, computer-readable storage medium of claim 11 , wherein the computer executable instructions are provided by a service provider to a user on an on-demand basis. | 0.734012 |
7,818,340 | 1 | 4 | 1. A computer-implemented method comprising: interfacing with a search engine, the search engine to produce search results from one or more content datastores by matching a search query with content items stored in the one or more content datastores; receiving a sponsored concept from a sponsoring company at a server computer via a data network; receiving a first search query corresponding to a search by a first user, the first search query being used by the search engine to match with content items stored in the one or more content datastores, the first search query also being used to determine if the sponsored concept and the first search query fit within match criteria; determining, by use of a data processor, if the sponsored concept and the first search query fit within match criteria; generating for the first user, by use of the data processor, if the sponsored concept and the first search query fit within match criteria, a first link enabling the first user to initiate a conversation between the first user and an agent of the sponsoring company, the first link being a user interface element that can be activated by the first user; initiating a conversation between the first user and the agent of the sponsoring company upon activation of the first link; receiving a second search query corresponding to a search by a second user, the second search query being used by the search engine to match with content items stored in the one or more content datastores; determining if the first search query and the second search query fit within match criteria; generating for the first user, if the first search query and the second search query fit within match criteria, a second link enabling the first user to initiate a conversation between the first user and the second user, the second link being a user interface element that can be activated by the first user, the second link including at least a portion of the second search query from the second user; and initiating a conversation between the first user and the second user upon activation of the second link. | 1. A computer-implemented method comprising: interfacing with a search engine, the search engine to produce search results from one or more content datastores by matching a search query with content items stored in the one or more content datastores; receiving a sponsored concept from a sponsoring company at a server computer via a data network; receiving a first search query corresponding to a search by a first user, the first search query being used by the search engine to match with content items stored in the one or more content datastores, the first search query also being used to determine if the sponsored concept and the first search query fit within match criteria; determining, by use of a data processor, if the sponsored concept and the first search query fit within match criteria; generating for the first user, by use of the data processor, if the sponsored concept and the first search query fit within match criteria, a first link enabling the first user to initiate a conversation between the first user and an agent of the sponsoring company, the first link being a user interface element that can be activated by the first user; initiating a conversation between the first user and the agent of the sponsoring company upon activation of the first link; receiving a second search query corresponding to a search by a second user, the second search query being used by the search engine to match with content items stored in the one or more content datastores; determining if the first search query and the second search query fit within match criteria; generating for the first user, if the first search query and the second search query fit within match criteria, a second link enabling the first user to initiate a conversation between the first user and the second user, the second link being a user interface element that can be activated by the first user, the second link including at least a portion of the second search query from the second user; and initiating a conversation between the first user and the second user upon activation of the second link. 4. The computer-implemented method as claimed in claim 1 wherein the conversation initiated upon activation of the first link is initiated using HTTP communication. | 0.726667 |
10,013,729 | 1 | 6 | 1. A computer implemented method comprising: storing a plurality of events and user interactions performed by users of a social networking system with the plurality of events; associating a set of events of the plurality of events with a category; selecting a set of users associated with the set of events, comprising, for each event from the set of events: identifying users performing user interactions with the event, for each identified user, determining a measure of user interactions of the user with the event, wherein the measure of user interactions of the user with the event is based on a frequency of interaction of the user with the event, and including the user in the set of users, responsive to the measure of user interactions of the user with the event exceeding a threshold; selecting a set of candidate events associated with the set of users, based on user interactions of users from the set of users with the candidate events, wherein each candidate event associated with a selected user is selected responsive to interactions of the selected user with the candidate event exceeding a second threshold; determining whether each candidate event is associated with the category based on keyword occurrences in content associated with each candidate event; and providing information describing a particular event to a user for performing an action, the information provided based on the category. | 1. A computer implemented method comprising: storing a plurality of events and user interactions performed by users of a social networking system with the plurality of events; associating a set of events of the plurality of events with a category; selecting a set of users associated with the set of events, comprising, for each event from the set of events: identifying users performing user interactions with the event, for each identified user, determining a measure of user interactions of the user with the event, wherein the measure of user interactions of the user with the event is based on a frequency of interaction of the user with the event, and including the user in the set of users, responsive to the measure of user interactions of the user with the event exceeding a threshold; selecting a set of candidate events associated with the set of users, based on user interactions of users from the set of users with the candidate events, wherein each candidate event associated with a selected user is selected responsive to interactions of the selected user with the candidate event exceeding a second threshold; determining whether each candidate event is associated with the category based on keyword occurrences in content associated with each candidate event; and providing information describing a particular event to a user for performing an action, the information provided based on the category. 6. The computer implemented method of claim 1 , further comprising: responsive to determining that a candidate event is associated with the category, adding the candidate event to the set of events. | 0.743523 |
8,024,193 | 95 | 96 | 95. A text-to-speech synthesis system comprising a voice table, wherein the voice table is pruned from an original voice table according to a machine-implemented method comprising: identifying instances in the original voice table; creating feature vectors derived from a machine perception transformation of time-domain samples corresponding to the instances of speech segments in the original voice table onto a feature space, wherein the machine perception transformation is correlated with human perception by using the time-domain samples retaining both amplitude and phase information of the speech segments; clustering the feature vectors using a similarity measure in the feature space; and replacing the clustered instances corresponding to the clustered feature vectors within a radius by a single instance. | 95. A text-to-speech synthesis system comprising a voice table, wherein the voice table is pruned from an original voice table according to a machine-implemented method comprising: identifying instances in the original voice table; creating feature vectors derived from a machine perception transformation of time-domain samples corresponding to the instances of speech segments in the original voice table onto a feature space, wherein the machine perception transformation is correlated with human perception by using the time-domain samples retaining both amplitude and phase information of the speech segments; clustering the feature vectors using a similarity measure in the feature space; and replacing the clustered instances corresponding to the clustered feature vectors within a radius by a single instance. 96. The text-to-speech synthesis system of claim 95 wherein the instances are the instances of a phoneme, a diphone, a syllable, a word, or a sequence unit. | 0.933786 |
8,510,321 | 14 | 15 | 14. A method of accessing and managing a relational database comprising: executing a program of instructions with a machine, the program of instructions being configured to: query a relational database; and access semantically relevant query results from the relational database, wherein to access query results from the relational database further comprises; accessing at least one ontology; extracting domain knowledge from at least one ontology and; employing the domain knowledge in obtaining the semantically relevant query results from the relational database; wherein said semantically relevant query results comprise direct results obtained directly from relational database tables, inferred results inferred utilizing information explicitly listed in the relational database and the at least one ontology, and related results obtained utilizing data in the relational database tables and one or more definitions of similarity of concepts and individuals based on the at least one ontology; and wherein to access semantically relevant query results comprises: applying a query generalization strategy, the query generalization strategy comprising applying strategies to an original query to obtain a generalized level of queries comprising one or more general queries and repeatedly applying the strategies to the generalized level of queries until a prespecified number of results is obtained; and ranking results obtained through the query generalization strategy based on a number generalizations performed. | 14. A method of accessing and managing a relational database comprising: executing a program of instructions with a machine, the program of instructions being configured to: query a relational database; and access semantically relevant query results from the relational database, wherein to access query results from the relational database further comprises; accessing at least one ontology; extracting domain knowledge from at least one ontology and; employing the domain knowledge in obtaining the semantically relevant query results from the relational database; wherein said semantically relevant query results comprise direct results obtained directly from relational database tables, inferred results inferred utilizing information explicitly listed in the relational database and the at least one ontology, and related results obtained utilizing data in the relational database tables and one or more definitions of similarity of concepts and individuals based on the at least one ontology; and wherein to access semantically relevant query results comprises: applying a query generalization strategy, the query generalization strategy comprising applying strategies to an original query to obtain a generalized level of queries comprising one or more general queries and repeatedly applying the strategies to the generalized level of queries until a prespecified number of results is obtained; and ranking results obtained through the query generalization strategy based on a number generalizations performed. 15. The method according to claim 14 , wherein: to query comprises querying the relational database using SPARQL; and to access semantically relevant query results comprises interfacing with the relational database in a manner to obtain SPARQL querying results. | 0.67375 |
8,447,772 | 7 | 11 | 7. A computer-readable storage medium containing a program which, when executed, performs an operation, comprising: determining a plurality of execution plans for executing a query, wherein each of the plurality of execution plans represents an alternative way of executing the query relative to the other execution plans in the plurality of execution plans, and wherein each of the plurality of execution plans includes a plurality of operations to be performed in executing the query and a schedule in which to perform operations in the plurality of operations; for each of the plurality of execution plans: determining a power consumption value for the execution plan, wherein the power consumption value approximates an amount of power that would be consumed by executing the query using the respective execution plan; and determining a monetary cost for executing the query according to the execution plan, based on one or more pricing values and the determined power consumption value for the execution plan; selecting a first execution plan from the plurality of execution plans to use in executing the query, wherein the first execution plan is selected based on the determined monetary cost for the first execution plan; and executing the query according to the first execution plan, wherein an entirety of the query is executed once the first execution plan is completed. | 7. A computer-readable storage medium containing a program which, when executed, performs an operation, comprising: determining a plurality of execution plans for executing a query, wherein each of the plurality of execution plans represents an alternative way of executing the query relative to the other execution plans in the plurality of execution plans, and wherein each of the plurality of execution plans includes a plurality of operations to be performed in executing the query and a schedule in which to perform operations in the plurality of operations; for each of the plurality of execution plans: determining a power consumption value for the execution plan, wherein the power consumption value approximates an amount of power that would be consumed by executing the query using the respective execution plan; and determining a monetary cost for executing the query according to the execution plan, based on one or more pricing values and the determined power consumption value for the execution plan; selecting a first execution plan from the plurality of execution plans to use in executing the query, wherein the first execution plan is selected based on the determined monetary cost for the first execution plan; and executing the query according to the first execution plan, wherein an entirety of the query is executed once the first execution plan is completed. 11. The computer-readable storage medium of claim 7 , wherein the one or more pricing values specify power usage prices at one or more different times. | 0.763323 |
8,108,392 | 12 | 14 | 12. One or more non-transitory computer-readable tangible media encoding software operable when executed to: access a record to perform a clustering analysis, the record recording a plurality of affinities, an affinity calculated using a directional affinity between a first word and a second word describing a quantitative relationship between the first word and the second word, the directional affinity indicating the probability of the occurrence of the second word given the occurrence of the first word; identify a plurality of clusters of the plurality of words according to a plurality of affinities, a cluster comprising two or more words that are sufficiently affine with each other, a first word sufficiently affine with a second word if the affinity between the first word and the second word satisfies one or more affinity criteria comprising a forward affinity with a current word criterion and a backward affinity with a seed word criterion, the identifying comprising building a cluster by: placing a seed word in the cluster; and repeating the following for each word added to the cluster: sorting the plurality of words according a forward affinity between the each word and a current word, identifying one or more candidate words satisfying the one or more affinity criteria, and placing the one or more candidate words in the cluster; perform the clustering analysis using the clusters to yield a result; and report the result of the clustering analysis. | 12. One or more non-transitory computer-readable tangible media encoding software operable when executed to: access a record to perform a clustering analysis, the record recording a plurality of affinities, an affinity calculated using a directional affinity between a first word and a second word describing a quantitative relationship between the first word and the second word, the directional affinity indicating the probability of the occurrence of the second word given the occurrence of the first word; identify a plurality of clusters of the plurality of words according to a plurality of affinities, a cluster comprising two or more words that are sufficiently affine with each other, a first word sufficiently affine with a second word if the affinity between the first word and the second word satisfies one or more affinity criteria comprising a forward affinity with a current word criterion and a backward affinity with a seed word criterion, the identifying comprising building a cluster by: placing a seed word in the cluster; and repeating the following for each word added to the cluster: sorting the plurality of words according a forward affinity between the each word and a current word, identifying one or more candidate words satisfying the one or more affinity criteria, and placing the one or more candidate words in the cluster; perform the clustering analysis using the clusters to yield a result; and report the result of the clustering analysis. 14. The non-transitory computer-readable tangible media of claim 12 , the software further operable to identify the plurality of clusters of the plurality of words according to the plurality of affinities by sorting the words into the clusters by repeating the following for each word of the plurality of words: if the affinity between the each word and a first word of a cluster satisfies an affinity threshold, placing the each word in the cluster; and otherwise, placing the each word in an empty cluster. | 0.643258 |
8,113,825 | 5 | 6 | 5. The device according to claim 1 , and further comprising cross-bars that extend between lateral sides of the frame, the cross-bars having holes through each of which one of the pixel tubes projects. | 5. The device according to claim 1 , and further comprising cross-bars that extend between lateral sides of the frame, the cross-bars having holes through each of which one of the pixel tubes projects. 6. The device according to claim 5 , wherein spacing between the cross-bars and spacing between the pixel tubes is equal. | 0.5 |
8,266,520 | 1 | 5 | 1. A system including a computer readable memory, comprising: program code stored on said computer readable memory for processing comment contents associated with a parent document by providing a parent document user interface including a display object enabling a user to enter comment contents, inputting said comment contents, creating a comment document associated with said parent document and storing said comment contents in said comment document, obtaining said comment contents from said comment document and displaying said comment contents within a region of said parent document user interface, detecting a change of state in said parent document from a state in which comments can be entered to a state in which comments cannot be added, copying, in response to said detecting said change of state of said parent document from said state in which comments can be added to said state in which comments cannot be added, said comment contents from said comment document into said parent document and deleting said comment document, and presenting said comment contents to said user when said parent document is in said state in which comments cannot be added as part of a user interface to said parent document after said deleting of said comment document. | 1. A system including a computer readable memory, comprising: program code stored on said computer readable memory for processing comment contents associated with a parent document by providing a parent document user interface including a display object enabling a user to enter comment contents, inputting said comment contents, creating a comment document associated with said parent document and storing said comment contents in said comment document, obtaining said comment contents from said comment document and displaying said comment contents within a region of said parent document user interface, detecting a change of state in said parent document from a state in which comments can be entered to a state in which comments cannot be added, copying, in response to said detecting said change of state of said parent document from said state in which comments can be added to said state in which comments cannot be added, said comment contents from said comment document into said parent document and deleting said comment document, and presenting said comment contents to said user when said parent document is in said state in which comments cannot be added as part of a user interface to said parent document after said deleting of said comment document. 5. The system of claim 1 , wherein said obtaining of said comment contents from said comment document is responsive to determining that said state of said parent document comprises said state in which comments can be added. | 0.680516 |
8,180,627 | 1 | 5 | 1. An apparatus for clustering process models each consisting of model elements comprising a text phrase which describes in a natural language a process activity according to a process modeling language grammar and a natural language grammar, wherein said apparatus comprises: (a) a process object ontology memory for storing a process object ontology; (b) a distance calculation unit for calculating a distance matrix employing said processing modeling language grammar and said natural language grammar, wherein said distance matrix consists of distances each indicating a dissimilarity of a pair of said process models; and (c) a clustering unit which partitions said process models into a set of clusters based on said calculated distance matrix. | 1. An apparatus for clustering process models each consisting of model elements comprising a text phrase which describes in a natural language a process activity according to a process modeling language grammar and a natural language grammar, wherein said apparatus comprises: (a) a process object ontology memory for storing a process object ontology; (b) a distance calculation unit for calculating a distance matrix employing said processing modeling language grammar and said natural language grammar, wherein said distance matrix consists of distances each indicating a dissimilarity of a pair of said process models; and (c) a clustering unit which partitions said process models into a set of clusters based on said calculated distance matrix. 5. The apparatus according to claim 1 , wherein said model elements are labeled by text phrases each comprising at least one term. | 0.770318 |
7,627,588 | 10 | 13 | 10. The computer-readable storage medium of claim 1 , wherein the set of objects is a first set of objects, and the performing multi-dimensional analysis includes one or more of: determining a number of objects within the first subset of objects; determining a frequency of occurrence of the concept within the first set of objects; determining a frequency of occurrence of the concept within the first subset of objects; determining a frequency of occurrence of the concept within a second set of objects different from the first set of objects; determining a normalized frequency of occurrence of the concept within the first set of objects; determining a normalized frequency of occurrence of the concept within the first subset of objects; determining a normalized frequency of occurrence of the concept within the second set of objects; determining an electronic path to the location of the first subset of objects; determining a characteristic of the first subset of objects; determining a concept type for the concept; determining a definition for the first concept; and if the first subset of objects contains the concept, determining a position of the concept within the first subset. | 10. The computer-readable storage medium of claim 1 , wherein the set of objects is a first set of objects, and the performing multi-dimensional analysis includes one or more of: determining a number of objects within the first subset of objects; determining a frequency of occurrence of the concept within the first set of objects; determining a frequency of occurrence of the concept within the first subset of objects; determining a frequency of occurrence of the concept within a second set of objects different from the first set of objects; determining a normalized frequency of occurrence of the concept within the first set of objects; determining a normalized frequency of occurrence of the concept within the first subset of objects; determining a normalized frequency of occurrence of the concept within the second set of objects; determining an electronic path to the location of the first subset of objects; determining a characteristic of the first subset of objects; determining a concept type for the concept; determining a definition for the first concept; and if the first subset of objects contains the concept, determining a position of the concept within the first subset. 13. The computer-readable storage medium of claim 10 , wherein the performing multi-dimensional analysis includes slicing-and-dicing across at least one dimension of the set of objects. | 0.5 |
10,095,688 | 10 | 11 | 10. A method for dynamically adapting and displaying at least one query on a recipient device within a network of networked devices, the method comprising: mapping, by a query processor device in communication with the recipient device, (1) a recipient profile against a set of known profiles; and (2) the relevancy of the at least one query to the recipient profile associated with the recipient device; dynamically selecting, by the query processor, a query from the at least one query based on the recipient profile and the relevancy mapping to transmit to the recipient device; generating, by the query processor, a query message including a plurality of query data including a plurality of attributes on the at least one query, a suggested query format, the suggested recipient profile, and a suggested user interface template; transmitting, by the query processor device, the query message to the recipient device; analyzing, by an adaptive processor on the recipient device, the query message and identifying the plurality of attributes; comparing, by the adaptive processor, the plurality of attributes associated with the suggested recipient profile against a set of known profiles on the recipient device; selecting, by the adaptive processor, a real-time recipient profile; determining, by the adaptive processor, a final user interface template based on the suggested user interface template and the real-time recipient profile, the determining including selecting an initial user interface template from a plurality of templates on the recipient device; and generating a user interface for display on the recipient device comprised of the final user interface template, a final set of query data from the plurality of query data and selecting the location of each element of the final set of query data within the final user interface template. | 10. A method for dynamically adapting and displaying at least one query on a recipient device within a network of networked devices, the method comprising: mapping, by a query processor device in communication with the recipient device, (1) a recipient profile against a set of known profiles; and (2) the relevancy of the at least one query to the recipient profile associated with the recipient device; dynamically selecting, by the query processor, a query from the at least one query based on the recipient profile and the relevancy mapping to transmit to the recipient device; generating, by the query processor, a query message including a plurality of query data including a plurality of attributes on the at least one query, a suggested query format, the suggested recipient profile, and a suggested user interface template; transmitting, by the query processor device, the query message to the recipient device; analyzing, by an adaptive processor on the recipient device, the query message and identifying the plurality of attributes; comparing, by the adaptive processor, the plurality of attributes associated with the suggested recipient profile against a set of known profiles on the recipient device; selecting, by the adaptive processor, a real-time recipient profile; determining, by the adaptive processor, a final user interface template based on the suggested user interface template and the real-time recipient profile, the determining including selecting an initial user interface template from a plurality of templates on the recipient device; and generating a user interface for display on the recipient device comprised of the final user interface template, a final set of query data from the plurality of query data and selecting the location of each element of the final set of query data within the final user interface template. 11. The method of claim 10 , the method further including inserting within the query message a file. | 0.781659 |
8,239,425 | 10 | 19 | 10. A system comprising processing circuitry and at least one form of storage media, the processing circuitry executing instructions stored on the storage media to: parse content-rich pages to obtain a language-independent representation comprising a hierarchical structure of one or more nodes, the content-rich pages being parts of a live website, a static harvest of a website, or both provided by the storage media, communications circuitry connected with the system, or both; generate a node representation for each node, the node representation comprising an expression stored in the storage media; generate a feature vector for each node of the language independent representation; assign a label to each node representation according to a schema by executing a trained classification algorithm on the feature vectors of the content-rich pages, the schema comprising one or more schema elements, each schema element corresponding to a label; gather, for each schema element, all node representations having matching labels; elect from among the node representations having matching labels one node representation to assign to a schema element field in a template; and apply the template to extract the desired content, metadata, or both according to the schema from all of the content-rich pages. | 10. A system comprising processing circuitry and at least one form of storage media, the processing circuitry executing instructions stored on the storage media to: parse content-rich pages to obtain a language-independent representation comprising a hierarchical structure of one or more nodes, the content-rich pages being parts of a live website, a static harvest of a website, or both provided by the storage media, communications circuitry connected with the system, or both; generate a node representation for each node, the node representation comprising an expression stored in the storage media; generate a feature vector for each node of the language independent representation; assign a label to each node representation according to a schema by executing a trained classification algorithm on the feature vectors of the content-rich pages, the schema comprising one or more schema elements, each schema element corresponding to a label; gather, for each schema element, all node representations having matching labels; elect from among the node representations having matching labels one node representation to assign to a schema element field in a template; and apply the template to extract the desired content, metadata, or both according to the schema from all of the content-rich pages. 19. The system of claim 10 , wherein the language-independent representation comprises a document object model (DOM) structure. | 0.775618 |
8,463,648 | 1 | 4 | 1. A computer-implemented method of automatically extracting previously unknown topics from questions submitted to an online consultation system, each question having been posted by a user to one of a variety of subject matter segments to be answered by a subject matter expert, the computer-implemented method comprising using at least one processor to: for each posted question in a database of questions posted to the online consultation system: perform linguistic analysis to break down the question into component parts and extracts candidate topics, wherein the candidate topics are words and phrases that include semantic meaning, and wherein each posted question is relates to a subject matter segment; and for a desired subject matter segment: count the frequency of occurrence of each candidate topic within the subject matter segment; select the candidate topics whose frequency of occurrence within the subject matter segment is above a first and second popularity threshold; score each selected candidate topic with an affinity score, wherein the affinity score quantifies the affinity of each candidate topic to the subject matter segment; and identify the selected candidate topics with an affinity score above a third threshold as the best topics for the subject matter segment. | 1. A computer-implemented method of automatically extracting previously unknown topics from questions submitted to an online consultation system, each question having been posted by a user to one of a variety of subject matter segments to be answered by a subject matter expert, the computer-implemented method comprising using at least one processor to: for each posted question in a database of questions posted to the online consultation system: perform linguistic analysis to break down the question into component parts and extracts candidate topics, wherein the candidate topics are words and phrases that include semantic meaning, and wherein each posted question is relates to a subject matter segment; and for a desired subject matter segment: count the frequency of occurrence of each candidate topic within the subject matter segment; select the candidate topics whose frequency of occurrence within the subject matter segment is above a first and second popularity threshold; score each selected candidate topic with an affinity score, wherein the affinity score quantifies the affinity of each candidate topic to the subject matter segment; and identify the selected candidate topics with an affinity score above a third threshold as the best topics for the subject matter segment. 4. The method of claim 1 wherein the second threshold is based on the frequency of occurrence of the candidate topic among all posted questions expressed as a an absolute number. | 0.660305 |
8,793,124 | 2 | 9 | 2. The method of claim 1 , wherein said codebook stores, for the plural number of predetermined speech parameter vectors, respective codes representing the respective predetermined speech parameter vectors, and said step (a) further includes a step of quantizing each set of speech parameters obtained from respective one of the plurality of the frames in the portion of the input speech by using said codebook to obtain the code. | 2. The method of claim 1 , wherein said codebook stores, for the plural number of predetermined speech parameter vectors, respective codes representing the respective predetermined speech parameter vectors, and said step (a) further includes a step of quantizing each set of speech parameters obtained from respective one of the plurality of the frames in the portion of the input speech by using said codebook to obtain the code. 9. The method of claim 2 , wherein said step (b) includes a step of calculating a conditional probability of emphasized-state by linear interpolation of said independent emphasized-state appearance probability and said conditional emphasized-state appearance probabilities. | 0.5125 |
9,081,852 | 15 | 17 | 15. A computer-implemented method comprising: receiving a set of target search terms for a search; using a microprocessor of a computer, selecting a plurality of candidate terms, each candidate term selected to reduce an ontology space of the document due to the candidate term having a higher affinity with a target tag and a lower affinity with the other candidate terms; sending the candidate terms to a user of the computer via a user interface of the computer to recommend the candidate terms as search terms; receiving, via the user interface of the computer, a selection by the user of one or more terms of the candidate terms and identifying one or more terms of the candidate terms that were not selected by the user; using the microprocessor of the computer, determining a plurality of next terms that have both (i) an affinity with the one or more terms selected by the user that is above a first affinity threshold and (ii) an affinity with the one or more terms that were not selected by the user that is below a second affinity threshold; and sending the next terms to the user via the user interface of the computer to recommend the next terms as search terms. | 15. A computer-implemented method comprising: receiving a set of target search terms for a search; using a microprocessor of a computer, selecting a plurality of candidate terms, each candidate term selected to reduce an ontology space of the document due to the candidate term having a higher affinity with a target tag and a lower affinity with the other candidate terms; sending the candidate terms to a user of the computer via a user interface of the computer to recommend the candidate terms as search terms; receiving, via the user interface of the computer, a selection by the user of one or more terms of the candidate terms and identifying one or more terms of the candidate terms that were not selected by the user; using the microprocessor of the computer, determining a plurality of next terms that have both (i) an affinity with the one or more terms selected by the user that is above a first affinity threshold and (ii) an affinity with the one or more terms that were not selected by the user that is below a second affinity threshold; and sending the next terms to the user via the user interface of the computer to recommend the next terms as search terms. 17. The method of claim 15 , further comprising: establishing a source of each target search term of the set of target search terms; retrieving a plurality of search results, a search result associated with a target search term; and ranking the search results according to the sources of the target search terms. | 0.501597 |
7,730,059 | 3 | 5 | 3. The method of claim 2 , wherein constructing a facet hierarchy includes constructing a facet hierarchy based on the metadata describing the hierarchical relationship associated with the documents that satisfy the query. | 3. The method of claim 2 , wherein constructing a facet hierarchy includes constructing a facet hierarchy based on the metadata describing the hierarchical relationship associated with the documents that satisfy the query. 5. The method of claim 3 , wherein: the facet hierarchy includes a plurality of facets; and creating the cube structure comprises creating dimensions for the cube structure based on the plurality of facets. | 0.5 |
8,527,509 | 15 | 19 | 15. The search system according to claim 14 , wherein the search service subsystem further comprises instructions that, when executed by the processor, cause the processor to extract the user interest model from user personalized data according to the search request, so as to select the member engine according to the meta index of each member engine, the search request, and the user interest model. | 15. The search system according to claim 14 , wherein the search service subsystem further comprises instructions that, when executed by the processor, cause the processor to extract the user interest model from user personalized data according to the search request, so as to select the member engine according to the meta index of each member engine, the search request, and the user interest model. 19. The search system according to claim 15 , wherein the search service subsystem comprises a search server, an application server, and a user database, the user database is configured to store or provide user personalized data; the application server is configured to receive the search request sent from a client, extract the user interest model from the user personalized data according to the search request; and send the search request and the user interest model to the search server; and the search server is configured to receive the search request and the user interest model sent from the application server, receive the meta index reported by each member engine, and select the member engine according to the meta index of each member engine, the search request, and the user interest model; and send the search request to the selected member engine. | 0.560652 |
8,799,265 | 1 | 12 | 1. A text index, comprising a data structure stored in a memory for access by at least one application program being executed on a data processing system, for use in the preparation of semantically associated text searches of electronic documents in a search engine, said text index comprising: one or more content records corresponding to an electronic document, the content records identifying one or more text terms present within the electronic document; one or more term association records associating a text term present within the electronic document identified by a content record with a pre-determined semantic definition of the text term; one or more content association records linking a term association record of the one or more term association records with a content record of the one or more content records; wherein a user is enabled to provide one or more user-defined text search terms desired to be located within the text index; wherein, for user-defined text search terms having term association records within the search index, the user is iteratively presented a list of one or more pre-determined semantic definitions associated with each of the one or more user-defined text search terms, enabling the user to select a pre-determined semantic definition of those one or more user-defined text search terms; wherein the one or more content association records contained within the text index are searched using the user-defined text search terms to locate electronic documents previously catalogued as containing the one or more user-defined text search terms in association with the selected pre-determined semantic definitions, to form a semantically relevant results set; wherein new term association records are updated or created during the use of the text index by enabling a user to select pre-determined semantic definitions for association with user-defined text search terms; and wherein new content association records are updated or created during the use of the text index in a search, by associating a particular electronic document with the use-defined text search terms, and selected pre-determined semantic definitions of the user-defined text search terms, used to locate the particular electronic document. | 1. A text index, comprising a data structure stored in a memory for access by at least one application program being executed on a data processing system, for use in the preparation of semantically associated text searches of electronic documents in a search engine, said text index comprising: one or more content records corresponding to an electronic document, the content records identifying one or more text terms present within the electronic document; one or more term association records associating a text term present within the electronic document identified by a content record with a pre-determined semantic definition of the text term; one or more content association records linking a term association record of the one or more term association records with a content record of the one or more content records; wherein a user is enabled to provide one or more user-defined text search terms desired to be located within the text index; wherein, for user-defined text search terms having term association records within the search index, the user is iteratively presented a list of one or more pre-determined semantic definitions associated with each of the one or more user-defined text search terms, enabling the user to select a pre-determined semantic definition of those one or more user-defined text search terms; wherein the one or more content association records contained within the text index are searched using the user-defined text search terms to locate electronic documents previously catalogued as containing the one or more user-defined text search terms in association with the selected pre-determined semantic definitions, to form a semantically relevant results set; wherein new term association records are updated or created during the use of the text index by enabling a user to select pre-determined semantic definitions for association with user-defined text search terms; and wherein new content association records are updated or created during the use of the text index in a search, by associating a particular electronic document with the use-defined text search terms, and selected pre-determined semantic definitions of the user-defined text search terms, used to locate the particular electronic document. 12. The text index of claim 1 , wherein the pre-determined semantic definitions selected can be associated with the user-defined text search term in a new term association record within the text index. | 0.781046 |
8,484,297 | 11 | 23 | 11. The computer-readable medium of claim 8 , wherein the web browsing behavior further comprises tagging web documents. | 11. The computer-readable medium of claim 8 , wherein the web browsing behavior further comprises tagging web documents. 23. The computer-readable medium of claim 11 , wherein the degrees of similarities of online behavior are determined based on the overlap of frequently used tags between the first user and the friends. | 0.5 |
9,613,004 | 4 | 6 | 4. The method of claim 1 wherein the disambiguating which entities are being referred to in the indicated text segment by determining of the one or more mostly likely entities which are referred to in the text segment by comparing, using both linguistic and contextual information, the entity profiles generated for each potential entity name with attributes of one or more candidate entities further comprises: searching a knowledge repository for a set of candidate entities that have similar characteristics to the properties of one or more of the generated entity profiles; ranking the candidate entities in the set of candidate entities to determine a set of mostly likely entities which are referred to in the text segment; and providing the determined set of mostly likely entities. | 4. The method of claim 1 wherein the disambiguating which entities are being referred to in the indicated text segment by determining of the one or more mostly likely entities which are referred to in the text segment by comparing, using both linguistic and contextual information, the entity profiles generated for each potential entity name with attributes of one or more candidate entities further comprises: searching a knowledge repository for a set of candidate entities that have similar characteristics to the properties of one or more of the generated entity profiles; ranking the candidate entities in the set of candidate entities to determine a set of mostly likely entities which are referred to in the text segment; and providing the determined set of mostly likely entities. 6. The method of claim 4 wherein the ranking the candidate entities weights the candidate entities according to preference information. | 0.68894 |
8,873,856 | 2 | 3 | 2. The method of claim 1 , wherein the at least one image segment includes a plurality of image segments, wherein the image segment is a first image segment, wherein the confidence score is a first confidence score, wherein the region of the image is a first region of the image and wherein the method further comprises: determining a second segmentation score for a second image segment included in the plurality of image segments based on a comparison of the second image segment and a second region of the image, the second region of the image associated with the second image segment; and determining a second confidence score for the second image segment based on the second segmentation score and a second classification score, the second classification score indicative of a similarity between the second image segment and the at least one class. | 2. The method of claim 1 , wherein the at least one image segment includes a plurality of image segments, wherein the image segment is a first image segment, wherein the confidence score is a first confidence score, wherein the region of the image is a first region of the image and wherein the method further comprises: determining a second segmentation score for a second image segment included in the plurality of image segments based on a comparison of the second image segment and a second region of the image, the second region of the image associated with the second image segment; and determining a second confidence score for the second image segment based on the second segmentation score and a second classification score, the second classification score indicative of a similarity between the second image segment and the at least one class. 3. The method of claim 2 , wherein the determining the class associated with the image further comprises determining the class associated with the image based on the first confidence score and the second confidence score. | 0.513216 |
9,213,707 | 24 | 25 | 24. The computer implemented system of claim 16 , wherein said interrelated data access component is further configured to execute one of said one or more predetermined subprograms configured to process said instances of a current one of said records of a current one of said source data files, and access a subsequent one of said source data files using a parent position number attached to said current one of said records of said current one of said source data files. | 24. The computer implemented system of claim 16 , wherein said interrelated data access component is further configured to execute one of said one or more predetermined subprograms configured to process said instances of a current one of said records of a current one of said source data files, and access a subsequent one of said source data files using a parent position number attached to said current one of said records of said current one of said source data files. 25. The computer implemented system of claim 24 , wherein said interrelated data access component is further configured to access said subsequent one of said source data files by: determining a subsequent one of said records to be accessed from one or more of said current one of said source data files, a corresponding parent file, and one of child files contained in said corresponding parent file, based on one or more of a position number and a parent position number attached to each of said records in said current one of said source data files; and executing another of said one or more predetermined subprograms on one of an occurrence of a transition from a parent file to a corresponding one of said child files and an occurrence of a transition from a child file to a corresponding parent file. | 0.5 |
7,660,705 | 9 | 10 | 9. The method of claim 8 , the Bayesian score of each model being computed as a sum of the scores at the leaves of each respective model. | 9. The method of claim 8 , the Bayesian score of each model being computed as a sum of the scores at the leaves of each respective model. 10. The method of claim 9 , the score at a respective leaf being computed as a function of a structure prior and a marginal likelihood of the data that falls to the respective leaf. | 0.5 |
9,471,642 | 7 | 10 | 7. A computer system comprising: a processor; and a non-transitory computer readable medium having stored thereon one or more programs, which when executed by the processor, causes the processor to: provide a usage tracking engine in an in-memory database and in communication with usage data of a data object of the in-memory database; cause the usage tracking engine to track access to the data object by a database user; cause the usage tracking engine to receive a ranking of the data object from a heuristic learning module in the in-memory database that considers: a geographic location of the database user, a time stamp of access to the data object by the database user, and prior manual placement of the data object in a shelf by the database user; cause the usage tracking engine to check a personalization setting reflecting a number of data objects to be located on the shelf in an application layer accessible to the database user, the number based upon a storage capacity available to the database user; and cause the usage tracking engine to displace the data object from the shelf based upon the ranking and the personalization setting. | 7. A computer system comprising: a processor; and a non-transitory computer readable medium having stored thereon one or more programs, which when executed by the processor, causes the processor to: provide a usage tracking engine in an in-memory database and in communication with usage data of a data object of the in-memory database; cause the usage tracking engine to track access to the data object by a database user; cause the usage tracking engine to receive a ranking of the data object from a heuristic learning module in the in-memory database that considers: a geographic location of the database user, a time stamp of access to the data object by the database user, and prior manual placement of the data object in a shelf by the database user; cause the usage tracking engine to check a personalization setting reflecting a number of data objects to be located on the shelf in an application layer accessible to the database user, the number based upon a storage capacity available to the database user; and cause the usage tracking engine to displace the data object from the shelf based upon the ranking and the personalization setting. 10. The computer system of claim 7 wherein the usage information is stored in the in-memory database. | 0.7475 |
8,914,395 | 11 | 20 | 11. A non-transitory computer-readable storage medium having stored thereon a set of instructions, executable by a processor, for translation of a medical database query from a first language into a second language, the instructions comprising: instructions for receiving a query for a medical database, wherein the query is in the first language; instructions for transmitting the received query to a plurality of translation engines; instructions for receiving a plurality of translations for the query from the first language into the second language, wherein a respective translation is received from each translation engine in the plurality of translation engines; instructions for determining a respective ranking score for each received translation of the plurality of received translations; and instructions for selecting, based on the determined ranking scores, a translation from the plurality of translations; and instruction for performing one or both of: (i) transmitting the selected translation to the user, and (ii) utilizing the selected translation to search the medical database to obtain search results for the query, and transmitting the obtained search results to the user, wherein instructions for determining the respective ranking score for each translation comprise instructions for determining the respective ranking score based on (i) a literature criterion, the literature criterion indicating a frequency of appearance of the translation in a body of literature contained in the medical database, (ii) a search log criterion, the search log criterion indicating a frequency of appearance of the translation in a record of queries previously entered into the medical database, or (iii) both a literature criterion, the literature criterion indicating a frequency of appearance of the translation in a body of literature contained in the medical database, and a search log criterion, the search log criterion indicating a frequency of appearance of the translation in a record of queries previously entered into the medical database. | 11. A non-transitory computer-readable storage medium having stored thereon a set of instructions, executable by a processor, for translation of a medical database query from a first language into a second language, the instructions comprising: instructions for receiving a query for a medical database, wherein the query is in the first language; instructions for transmitting the received query to a plurality of translation engines; instructions for receiving a plurality of translations for the query from the first language into the second language, wherein a respective translation is received from each translation engine in the plurality of translation engines; instructions for determining a respective ranking score for each received translation of the plurality of received translations; and instructions for selecting, based on the determined ranking scores, a translation from the plurality of translations; and instruction for performing one or both of: (i) transmitting the selected translation to the user, and (ii) utilizing the selected translation to search the medical database to obtain search results for the query, and transmitting the obtained search results to the user, wherein instructions for determining the respective ranking score for each translation comprise instructions for determining the respective ranking score based on (i) a literature criterion, the literature criterion indicating a frequency of appearance of the translation in a body of literature contained in the medical database, (ii) a search log criterion, the search log criterion indicating a frequency of appearance of the translation in a record of queries previously entered into the medical database, or (iii) both a literature criterion, the literature criterion indicating a frequency of appearance of the translation in a body of literature contained in the medical database, and a search log criterion, the search log criterion indicating a frequency of appearance of the translation in a record of queries previously entered into the medical database. 20. The non-transitory computer-readable storage medium of claim 11 , wherein instructions for determining the respective ranking score, when the respective score is determined based on both the literature criterion and the search criterion, further comprise: instructions for determining a respective literature factor that indicates frequency of appearance of the translation in the body of literature; instructions for determining a respective search factor that indicates frequency of appearance of the translation in the record of queries previously entered into the medical database; instructions for determining a respective weighting factor to be applied to (i) the literature factor and (ii) the search log factor; instructions for applying corresponding respective weighting factors to the respective literature factor and the respective search factor to generate a respective weighted literature factor and a respective weighted search log factor; instructions for determining a respective combined factor by performing a mathematical summation of the respective weighted literature factor and the respective weighted search factor; instructions for normalizing the respective combined factor with respect to combined factors determined for the other translations to produce a normalized combined factor for the translation; and instructions for utilizing the normalized combined factor for the translation as the ranking score for the translation. | 0.5 |
10,055,416 | 11 | 16 | 11. A computer program product, comprising: a computer readable storage medium having program code embodied therewith, the program code executable by a computer to cause the computer to: initiate, by the computer, a new document notification process in response to receipt of a new document, the new document notification process comprising: evaluating enhanced metadata of the new document via a relationship analyzing process to produce a priority list defining a likelihood of possible access, wherein the enhanced metadata includes a set of keywords and/or references from the new document; and store, by the computer, the new document in a storage tier of a hierarchical storage environment according to the priority list; receive a request to access the new document; accessing the new document; triggering and/or implementing a second document access notification process in response to the request, the second document access notification process comprising: determining documents that are related to the new document based on the enhanced metadata and the priority list; and placing the related documents in a highest level storage tier of the hierarchical storage environment. | 11. A computer program product, comprising: a computer readable storage medium having program code embodied therewith, the program code executable by a computer to cause the computer to: initiate, by the computer, a new document notification process in response to receipt of a new document, the new document notification process comprising: evaluating enhanced metadata of the new document via a relationship analyzing process to produce a priority list defining a likelihood of possible access, wherein the enhanced metadata includes a set of keywords and/or references from the new document; and store, by the computer, the new document in a storage tier of a hierarchical storage environment according to the priority list; receive a request to access the new document; accessing the new document; triggering and/or implementing a second document access notification process in response to the request, the second document access notification process comprising: determining documents that are related to the new document based on the enhanced metadata and the priority list; and placing the related documents in a highest level storage tier of the hierarchical storage environment. 16. The computer program product according to claim 11 , wherein the priority list comprises at least one items selected from the group consisting of: a first sub list including relations and/or references between different objects, a second sub list including frequently and/or recently accessed objects, and a third sub list including user based relations between different objects. | 0.556582 |
8,214,349 | 31 | 41 | 31. A computer implemented method for processing database content, the method comprising the steps of: receiving one or more data objects associated with a database at one or more remote computers; parsing one or more source documents to identify at least one term based on one or more predetermined rules; identifying content for the at least one term; linking the content with the at least one term; and automatically associating the at least one term in the one or more source documents with at least one link; wherein the at least one link denotes an association between the at least one term and the linked content; wherein the one or more data objects associated with the database provide a representation of at least a portion of the database at the one or more remote computers; and wherein the linked content is displayed on a user interface based upon a user interaction with at least a portion of the one or more source documents. | 31. A computer implemented method for processing database content, the method comprising the steps of: receiving one or more data objects associated with a database at one or more remote computers; parsing one or more source documents to identify at least one term based on one or more predetermined rules; identifying content for the at least one term; linking the content with the at least one term; and automatically associating the at least one term in the one or more source documents with at least one link; wherein the at least one link denotes an association between the at least one term and the linked content; wherein the one or more data objects associated with the database provide a representation of at least a portion of the database at the one or more remote computers; and wherein the linked content is displayed on a user interface based upon a user interaction with at least a portion of the one or more source documents. 41. The method of claim 31 , wherein one or more of the predetermined rules are based on presence or lack thereof of an entry in the database. | 0.5 |
7,644,095 | 12 | 13 | 12. The system of claim 11 , wherein the domain-specific schema is composed using the XML markup language. | 12. The system of claim 11 , wherein the domain-specific schema is composed using the XML markup language. 13. The system of claim 12 , wherein transforming the domain-specific compound document into a generic compound document comprises applying an XSLT transform to the domain-specific compound document. | 0.5 |
8,909,697 | 9 | 11 | 9. An apparatus according to claim 8 , wherein the Javascript in the HTTP response includes a function call to a first random number function that uses the first random number algorithm. | 9. An apparatus according to claim 8 , wherein the Javascript in the HTTP response includes a function call to a first random number function that uses the first random number algorithm. 11. An apparatus according to claim 9 , wherein the algorithm different from the first random number algorithm is selected such that a sequence of values output by the algorithm different from the first random number algorithm when executed by one apparatus and a sequence of values output by the algorithm different from the first random number algorithm when executed by another apparatus are the same. | 0.574737 |
7,844,502 | 19 | 34 | 19. An automated shopping method for automated management of a process in which a user places an order for at least one provider, a degree of matching between each order-provider pairing is computed, and a score is reported to at least the user and optionally to at least one provider, the automated shopping method comprising: providing (a) at least one processor to receive and process information including at least one of (i) user information from at least one user including information that specifies provider criteria and order information that specifies order criteria for that particular order, (ii) provider information, and (iii) third party information; (b) at least one data storage device that communicates with the at least one processor, that includes at least one database, and that receives and stores the information in the at least one database; and (c) a knowledge base that is stored in a data storage device which may be said at least one data storage device, and that contains information on which to base requests for information by the automated shopping method; receiving information (a) from the user including information that specifies provider criteria and order information that specifies order criteria for that particular order, and (b) from the at least one provider; creating at least one virtual provider with program code within the at least one processor by pairing provider information of a particular provider with order information of a particular order to create an informational pair, and storing the at least one virtual provider within the at least one database; determining a score that reflects a degree of matching for each respective informational pair using a scoring system that resides in program code within the at least one processor, and that compares the provider information of a particular provider and the order information of a particular order within each respective informational pair in at least one step; and tracking each order-provider pairing through multiple steps using a management system that resides in program code within the at least one processor, and that uses sequencing to specify contents of each step of the multiple steps, the contents at least including instructions to at least one of (a) the user regarding the input of user information, (b) the provider regarding the input of provider information, and (c) third parties regarding the input of information. | 19. An automated shopping method for automated management of a process in which a user places an order for at least one provider, a degree of matching between each order-provider pairing is computed, and a score is reported to at least the user and optionally to at least one provider, the automated shopping method comprising: providing (a) at least one processor to receive and process information including at least one of (i) user information from at least one user including information that specifies provider criteria and order information that specifies order criteria for that particular order, (ii) provider information, and (iii) third party information; (b) at least one data storage device that communicates with the at least one processor, that includes at least one database, and that receives and stores the information in the at least one database; and (c) a knowledge base that is stored in a data storage device which may be said at least one data storage device, and that contains information on which to base requests for information by the automated shopping method; receiving information (a) from the user including information that specifies provider criteria and order information that specifies order criteria for that particular order, and (b) from the at least one provider; creating at least one virtual provider with program code within the at least one processor by pairing provider information of a particular provider with order information of a particular order to create an informational pair, and storing the at least one virtual provider within the at least one database; determining a score that reflects a degree of matching for each respective informational pair using a scoring system that resides in program code within the at least one processor, and that compares the provider information of a particular provider and the order information of a particular order within each respective informational pair in at least one step; and tracking each order-provider pairing through multiple steps using a management system that resides in program code within the at least one processor, and that uses sequencing to specify contents of each step of the multiple steps, the contents at least including instructions to at least one of (a) the user regarding the input of user information, (b) the provider regarding the input of provider information, and (c) third parties regarding the input of information. 34. The automated shopping method of claim 19 , wherein requests for additional information continue until sufficient information is received. | 0.843956 |
9,229,977 | 11 | 13 | 11. The apparatus of claim 10 , in which the at least one processor is further configured: to analyze the extracted data; and to receive feedback from the user based at least in part on the established communication channel. | 11. The apparatus of claim 10 , in which the at least one processor is further configured: to analyze the extracted data; and to receive feedback from the user based at least in part on the established communication channel. 13. The apparatus of claim 11 , in which the at least one processor is further configured to expand the search terms to include at least misspelling, synonyms, sub-topics, antonyms, or a combination thereof. | 0.590909 |
8,949,232 | 1 | 2 | 1. A method, comprising: creating a graph of nodes and node relationships, the graph comprising content nodes of content recommended by members of a social network, entity nodes of the recommending members, and links between the entity nodes according to social links between associated members in the social network, each content node being configured so that the content node (i) does not link to another content node and (ii) links to only a single entity node, the single entity node being the entity node of the recommending member who has recommended the content of the content node; converting each node to a feature set of auxiliary information; sampling some sets of the nodes, the nodes of the sampled sets being seed nodes, each feature set including (i) information about seed nodes closest to the node from which the feature set was converted and (ii) distances to those seed nodes, a distance between a pair of feature sets approximating a social distance between the nodes from which the feature sets were converted; indexing the node relationships for keyword searches that return recommended content of the content nodes; identifying a searching user as associated with the social network; and processing a query against the index using keywords. | 1. A method, comprising: creating a graph of nodes and node relationships, the graph comprising content nodes of content recommended by members of a social network, entity nodes of the recommending members, and links between the entity nodes according to social links between associated members in the social network, each content node being configured so that the content node (i) does not link to another content node and (ii) links to only a single entity node, the single entity node being the entity node of the recommending member who has recommended the content of the content node; converting each node to a feature set of auxiliary information; sampling some sets of the nodes, the nodes of the sampled sets being seed nodes, each feature set including (i) information about seed nodes closest to the node from which the feature set was converted and (ii) distances to those seed nodes, a distance between a pair of feature sets approximating a social distance between the nodes from which the feature sets were converted; indexing the node relationships for keyword searches that return recommended content of the content nodes; identifying a searching user as associated with the social network; and processing a query against the index using keywords. 2. The method of claim 1 , further comprising creating a content identifier for each content node and an entity identifier for each entity node. | 0.552795 |
10,043,516 | 1 | 10 | 1. An electronic device for operating an automated assistant, the electronic device comprising: one or more processors; a memory; a speaker; a microphone; and 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: providing, via the speaker of the electronic device, an audio output; while providing the audio output via the speaker of the electronic device, receiving, via the microphone of the electronic device, a natural language speech input; in response to receiving the natural language speech input, determining a type of the audio output; in response to a determination that the audio output is of a first type, adjusting the audio output; in response to a determination that the audio output is of a second type different from the first type, ceasing to provide the audio output; deriving a representation of user intent based on the natural language speech input and the audio output; identifying a task based on the derived user intent; and performing the identified task. | 1. An electronic device for operating an automated assistant, the electronic device comprising: one or more processors; a memory; a speaker; a microphone; and 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: providing, via the speaker of the electronic device, an audio output; while providing the audio output via the speaker of the electronic device, receiving, via the microphone of the electronic device, a natural language speech input; in response to receiving the natural language speech input, determining a type of the audio output; in response to a determination that the audio output is of a first type, adjusting the audio output; in response to a determination that the audio output is of a second type different from the first type, ceasing to provide the audio output; deriving a representation of user intent based on the natural language speech input and the audio output; identifying a task based on the derived user intent; and performing the identified task. 10. The electronic device of claim 1 wherein adjusting the audio output comprises attenuating the audio output. | 0.816832 |
9,171,045 | 1 | 3 | 1. A method comprising: receiving a set of queries at a computing device, wherein each query is associated with a user who provided the query and each query has an associated time; dividing the received set of queries into a set of queries that are commercial queries and a set of queries that are non-commercial queries, by the computing device; generating a first set of query communities from the set of queries that are non-commercial queries and a second set of query communities from the set of queries that are commercial queries by the computing device, wherein each query community comprises a plurality of queries of the set of queries and each query in a query community is related to each other query in the query community, wherein a first query is related to a second query if the first query and the second query were provided by the same user with associated times that are within a time window more than a threshold number of times; determining a set of mappings of query communities from the first set of query communities to query communities from the second set of query communities by the computing device according to the users and times associated with the queries in the query communities; receiving a query from the first set of query communities at the computing device; and recommending one or more queries from the second set of query communities using the received query and the set of mappings. | 1. A method comprising: receiving a set of queries at a computing device, wherein each query is associated with a user who provided the query and each query has an associated time; dividing the received set of queries into a set of queries that are commercial queries and a set of queries that are non-commercial queries, by the computing device; generating a first set of query communities from the set of queries that are non-commercial queries and a second set of query communities from the set of queries that are commercial queries by the computing device, wherein each query community comprises a plurality of queries of the set of queries and each query in a query community is related to each other query in the query community, wherein a first query is related to a second query if the first query and the second query were provided by the same user with associated times that are within a time window more than a threshold number of times; determining a set of mappings of query communities from the first set of query communities to query communities from the second set of query communities by the computing device according to the users and times associated with the queries in the query communities; receiving a query from the first set of query communities at the computing device; and recommending one or more queries from the second set of query communities using the received query and the set of mappings. 3. The method of claim 1 , wherein each community in the second set of query communities corresponds to a product category. | 0.950363 |
8,073,827 | 4 | 6 | 4. A processing method performed by a processing apparatus having a processing unit, the processing method comprising: inputting a first process description document; performing a service, by the processing unit, in accordance with a procedure written in the first process description document including first, second, third and fourth description parts, the first description part describing a receiving processing for receiving data from a client via a network and a service processing for performing the service, the second description part describing a call processing for calling a second processing apparatus which is connected to the network and provides a second web service, the third description part describing a conversion from a variable used by the second processing apparatus to a variable used by a third processing apparatus, and the fourth description part describing a call processing for calling the third processing apparatus which is connected to the network and provides a third web service; generating, by the processing unit, a second process description document including the first and fourth description parts by deleting, from the first process description document, second and third description parts; and sending the second process description document to the next processing apparatus described in the deleted second description part. | 4. A processing method performed by a processing apparatus having a processing unit, the processing method comprising: inputting a first process description document; performing a service, by the processing unit, in accordance with a procedure written in the first process description document including first, second, third and fourth description parts, the first description part describing a receiving processing for receiving data from a client via a network and a service processing for performing the service, the second description part describing a call processing for calling a second processing apparatus which is connected to the network and provides a second web service, the third description part describing a conversion from a variable used by the second processing apparatus to a variable used by a third processing apparatus, and the fourth description part describing a call processing for calling the third processing apparatus which is connected to the network and provides a third web service; generating, by the processing unit, a second process description document including the first and fourth description parts by deleting, from the first process description document, second and third description parts; and sending the second process description document to the next processing apparatus described in the deleted second description part. 6. The processing method according to claim 4 , wherein the first and second process description documents include a description part describing a conversion from at least one variable in which a result of the service is stored into at least one variable used for the processing apparatus to be called. | 0.5 |
7,827,029 | 1 | 14 | 1. A method comprising the steps of: determining a user selected passage by highlighting, marking, or cut and pasting a block of text using a computer operable input device; determining user interest information; determining a parsing grammar and a generation grammar; determining a meaning structure based on the selected passage; transforming the user selected passage and the user interest information into condensation transformations comprised of rewrite rules which delete, merge and change elements of the meaning structure; determining meaning distortion constraints beyond which the meaning structure is identified as being distorted after the condensation transformations are applied; determining a packed meaning structure based on the selected passage and the determined parsing grammar; determining a reduced meaning structure based on the condensation transformations, the packed meaning structure, the user information, and the meaning distortion constraints; determining at least one candidate condensation structure based on the reduced meaning structure and a predictive model or a statistical disambiguation model; and determining a user-interest note based on the candidate condensation structures and the determined generation grammar. | 1. A method comprising the steps of: determining a user selected passage by highlighting, marking, or cut and pasting a block of text using a computer operable input device; determining user interest information; determining a parsing grammar and a generation grammar; determining a meaning structure based on the selected passage; transforming the user selected passage and the user interest information into condensation transformations comprised of rewrite rules which delete, merge and change elements of the meaning structure; determining meaning distortion constraints beyond which the meaning structure is identified as being distorted after the condensation transformations are applied; determining a packed meaning structure based on the selected passage and the determined parsing grammar; determining a reduced meaning structure based on the condensation transformations, the packed meaning structure, the user information, and the meaning distortion constraints; determining at least one candidate condensation structure based on the reduced meaning structure and a predictive model or a statistical disambiguation model; and determining a user-interest note based on the candidate condensation structures and the determined generation grammar. 14. The method of claim 1 , in which the condensation transformations are applied to the meaning structures based on meaning distortion constraints. | 0.805774 |
7,765,271 | 43 | 51 | 43. A method as claimed in claim 35 , further comprising the step of: o) adjusting the display of the scanned document via a user's operation of a control element defined by the HTML document displayed by the web browser within the user interface. | 43. A method as claimed in claim 35 , further comprising the step of: o) adjusting the display of the scanned document via a user's operation of a control element defined by the HTML document displayed by the web browser within the user interface. 51. A method as claimed in claim 43 , wherein the document data and the index data are transmitted in said step (l) between the server and client device in hypertext transfer protocol (HTTP) format. | 0.786638 |
8,438,007 | 29 | 30 | 29. A computer-readable storage device encoded with a computer program product for use in generating a second human language user interface for a software product having a first human language user interface, the product comprising instructions operable to cause data processing apparatus to perform operations comprising: opening a glossary database that includes a plurality of string sets at least some of which include a user interface string in a first human language and a corresponding user interface string in a second human language, wherein a string set comprises user interface strings having the same set identifier, a set identifier comprising context information about a previous use of a user interface string in a user interface of a software product including a name of the software product and an identifier specifying a type of user interface string and each user interface string comprises a string displayed in a user interface of a software product; selecting a user interface string in the first human language user interface; finding, using a processor, a string set in the glossary database having the selected user interface string and a user interface string in the second human language, wherein the user interface strings in the string set were previously used in a software product different from the software product for which the second human language user interface is being generated; and using the user interface string in the second human language in the second human language user interface, wherein finding a string set in the glossary database having the selected user interface string comprises: searching for one or more literal user interface strings in the glossary database that matches the selected user interface string, generating a score for each matching user interface string based on a comparison of (i) context information about a previous use of the matching user interface string with (ii) context information about a previous use of the selected user interface string, and deciding, based on the score, whether or not to select a string set. | 29. A computer-readable storage device encoded with a computer program product for use in generating a second human language user interface for a software product having a first human language user interface, the product comprising instructions operable to cause data processing apparatus to perform operations comprising: opening a glossary database that includes a plurality of string sets at least some of which include a user interface string in a first human language and a corresponding user interface string in a second human language, wherein a string set comprises user interface strings having the same set identifier, a set identifier comprising context information about a previous use of a user interface string in a user interface of a software product including a name of the software product and an identifier specifying a type of user interface string and each user interface string comprises a string displayed in a user interface of a software product; selecting a user interface string in the first human language user interface; finding, using a processor, a string set in the glossary database having the selected user interface string and a user interface string in the second human language, wherein the user interface strings in the string set were previously used in a software product different from the software product for which the second human language user interface is being generated; and using the user interface string in the second human language in the second human language user interface, wherein finding a string set in the glossary database having the selected user interface string comprises: searching for one or more literal user interface strings in the glossary database that matches the selected user interface string, generating a score for each matching user interface string based on a comparison of (i) context information about a previous use of the matching user interface string with (ii) context information about a previous use of the selected user interface string, and deciding, based on the score, whether or not to select a string set. 30. The product encoded on the computer-readable storage device of claim 29 , wherein, if multiple matches are found, selecting a string set that includes the matching user interface string having the highest score and having a score that equals or exceeds a specified minimum score value and if no string set includes such a user interface string, then not selecting a string set and delegating to a human translator the translation of the user interface string in the first human language into the second human language. | 0.539683 |
8,266,068 | 21 | 24 | 21. A system for interviewing a candidate, comprising: a processor; and a memory comprising a plurality of instructions executed by the processor, wherein the plurality of instructions are configured to: provide a virtual interview assistant comprising an interview plan, a recording module, an analysis module, and candidate screening criteria, wherein the interview plan comprises one or more interview sessions, wherein the recording module is configured to record at least one recording selected from a group consisting of video recording, audio recording, and physiological parameter recording, wherein the analysis module is configured for analyzing the at least one recording, and wherein the candidate screening criteria comprise an acceptance criterion for each of the one or more interview sessions; obtain a pre-determined qualification score representing a level of a current employee fitting a target requirement, wherein the pre-determined qualification score is assigned to the current employee based on a performance track record of the current employee in a position held by the current employee, and wherein the current employee is identified as a qualified candidate by a recruiter based on the target requirement; interviewing, in a mock interview subsequent to identifying the current employee, the current employee using the virtual interview assistant to generate score qualified candidate profile; adjust the interview plan to generate an adjusted interview plan based on the qualified candidate profile and the pre-determined qualification score; collect a candidate interview response by interviewing the candidate using the virtual interview assistant based on the adjusted interview plan, wherein at least a portion of the candidate interview response is collected using the recording module; analyze the candidate interview response using the analysis module to generate a candidate profile information comprising a score for each of the one or more interview sessions; and selectively present the candidate profile information to the recruiter in response to the candidate profile information meeting the candidate screening criteria, wherein each score in the candidate profile confirms to the acceptance criterion in the candidate screening criteria for a corresponding one of the one or more interview sessions, and wherein the recruiter makes a recruiting decision regarding the candidate based on the candidate profile information. | 21. A system for interviewing a candidate, comprising: a processor; and a memory comprising a plurality of instructions executed by the processor, wherein the plurality of instructions are configured to: provide a virtual interview assistant comprising an interview plan, a recording module, an analysis module, and candidate screening criteria, wherein the interview plan comprises one or more interview sessions, wherein the recording module is configured to record at least one recording selected from a group consisting of video recording, audio recording, and physiological parameter recording, wherein the analysis module is configured for analyzing the at least one recording, and wherein the candidate screening criteria comprise an acceptance criterion for each of the one or more interview sessions; obtain a pre-determined qualification score representing a level of a current employee fitting a target requirement, wherein the pre-determined qualification score is assigned to the current employee based on a performance track record of the current employee in a position held by the current employee, and wherein the current employee is identified as a qualified candidate by a recruiter based on the target requirement; interviewing, in a mock interview subsequent to identifying the current employee, the current employee using the virtual interview assistant to generate score qualified candidate profile; adjust the interview plan to generate an adjusted interview plan based on the qualified candidate profile and the pre-determined qualification score; collect a candidate interview response by interviewing the candidate using the virtual interview assistant based on the adjusted interview plan, wherein at least a portion of the candidate interview response is collected using the recording module; analyze the candidate interview response using the analysis module to generate a candidate profile information comprising a score for each of the one or more interview sessions; and selectively present the candidate profile information to the recruiter in response to the candidate profile information meeting the candidate screening criteria, wherein each score in the candidate profile confirms to the acceptance criterion in the candidate screening criteria for a corresponding one of the one or more interview sessions, and wherein the recruiter makes a recruiting decision regarding the candidate based on the candidate profile information. 24. The system of claim 21 , wherein at least one selected from a group consisting of the interview plan and the candidate screening criteria is selected from a predefined library based on the target requirement. | 0.883002 |
8,185,830 | 14 | 19 | 14. A system comprising one or more computing devices operable to perform operations including: receiving an identification of user group configuration settings for a user group comprising a plurality of users, the user group configuration settings identifying a first plurality of content items to be retrieved from a first plurality of remote content provider servers and to be provided in a user group container document; generating the user group container document based on the user group configuration settings, the user group container document comprising a first plurality of content modules that provide the first plurality of content items from the first plurality of remote content provider servers; sending the user group container document over a public network to each of a plurality of client devices; receiving from the client devices data identifying personal configuration settings for each of the plurality of users in the user group, each of the personal configuration settings specifying a second plurality of content items to be retrieved from a second plurality of remote content provider servers and to be included in personal container documents; generating a first personal container document based on the user group configuration settings and the personal configuration settings for a first user, the container document comprising: one or more of the first plurality of content modules that provide the first plurality of content items from the first plurality of remote content provider servers; and a second plurality of content modules that provide the second plurality of content items from a second plurality of remote content provider servers. | 14. A system comprising one or more computing devices operable to perform operations including: receiving an identification of user group configuration settings for a user group comprising a plurality of users, the user group configuration settings identifying a first plurality of content items to be retrieved from a first plurality of remote content provider servers and to be provided in a user group container document; generating the user group container document based on the user group configuration settings, the user group container document comprising a first plurality of content modules that provide the first plurality of content items from the first plurality of remote content provider servers; sending the user group container document over a public network to each of a plurality of client devices; receiving from the client devices data identifying personal configuration settings for each of the plurality of users in the user group, each of the personal configuration settings specifying a second plurality of content items to be retrieved from a second plurality of remote content provider servers and to be included in personal container documents; generating a first personal container document based on the user group configuration settings and the personal configuration settings for a first user, the container document comprising: one or more of the first plurality of content modules that provide the first plurality of content items from the first plurality of remote content provider servers; and a second plurality of content modules that provide the second plurality of content items from a second plurality of remote content provider servers. 19. The system of claim 14 , wherein the user group configuration settings specify an authentication method to authenticate the users in the user group. | 0.892807 |
7,475,416 | 1 | 9 | 1. In a system including a television and a video transmission medium, wherein interactive broadcast data text descriptions such as electronic program guide information, news headlines, sports scores, or other similar kinds of periodically updated information that can be displayed as text simultaneously with other programming is transmitted across the video transmission medium, and wherein the system also includes a management system having a digital processor for processing one or more unique digital signatures that correspond to the interactive broadcast data, and an input device for inputting other digital data that corresponds to user instructions input by a user when searching for particular interactive broadcast data, a method for efficiently searching the interactive broadcast data in response to a string of text input by a user in order to identify the particular interactive broadcast data desired by the user, the method comprising: receiving interactive broadcast data at the management system, said interactive broadcast data having unique binary signatures, each unique binary signature generated for an electronic program guide entry using programming information from a plurality of information fields of the electronic program guide entry, wherein each of the unique binary signatures is created prior to transmission across the video transmission medium using a first function adapted to convert alphanumeric text in fields of the electronic program guide entries into unique binary signatures having a fixed number of bytes, wherein at least one of the unique binary signatures includes a plurality of distinct four bit binary representations corresponding to a plurality of distinct terms found within a single electronic program guide entry, with each of the distinct four bit binary representations in the unique binary signature corresponding to a distinct term, and with all of the distinct four bit binary representations being concatenated into a single binary signature comprising the fixed number of bytes, storing the unique binary signatures at the management system; receiving a first user-entered text string; using a second function to convert the first user-entered text string into a unique binary signature; retrieving and comparing the stored unique binary signatures corresponding to the interactive broadcast data text descriptions to the unique binary signature of the user-entered text string; and based on the comparison, the management system identifying at least one item of interactive broadcast data that matches the user-entered text string. | 1. In a system including a television and a video transmission medium, wherein interactive broadcast data text descriptions such as electronic program guide information, news headlines, sports scores, or other similar kinds of periodically updated information that can be displayed as text simultaneously with other programming is transmitted across the video transmission medium, and wherein the system also includes a management system having a digital processor for processing one or more unique digital signatures that correspond to the interactive broadcast data, and an input device for inputting other digital data that corresponds to user instructions input by a user when searching for particular interactive broadcast data, a method for efficiently searching the interactive broadcast data in response to a string of text input by a user in order to identify the particular interactive broadcast data desired by the user, the method comprising: receiving interactive broadcast data at the management system, said interactive broadcast data having unique binary signatures, each unique binary signature generated for an electronic program guide entry using programming information from a plurality of information fields of the electronic program guide entry, wherein each of the unique binary signatures is created prior to transmission across the video transmission medium using a first function adapted to convert alphanumeric text in fields of the electronic program guide entries into unique binary signatures having a fixed number of bytes, wherein at least one of the unique binary signatures includes a plurality of distinct four bit binary representations corresponding to a plurality of distinct terms found within a single electronic program guide entry, with each of the distinct four bit binary representations in the unique binary signature corresponding to a distinct term, and with all of the distinct four bit binary representations being concatenated into a single binary signature comprising the fixed number of bytes, storing the unique binary signatures at the management system; receiving a first user-entered text string; using a second function to convert the first user-entered text string into a unique binary signature; retrieving and comparing the stored unique binary signatures corresponding to the interactive broadcast data text descriptions to the unique binary signature of the user-entered text string; and based on the comparison, the management system identifying at least one item of interactive broadcast data that matches the user-entered text string. 9. The method as recited in claim 1 , wherein comparing the binary signatures of the interactive broadcast data text descriptions to the binary signature of the user-entered text string comprises the following: comparing each binary signature of an interactive broadcast data text description to the results of a logical OR operation performed on each binary signature of an interactive broadcast data text description and the binary signature of the user-entered text string. | 0.566485 |
8,812,480 | 24 | 28 | 24. A method, performed by a content search system including a parser, a rules database memory device, a normalizer having a decoder and a plurality of transducers, and a search engine, for determining whether an input string matches one or more rules stored in the rules database, comprising: extracting selected portions of the input string, using the parser, to form a filtered input string; forwarding, by the parser, the filtered input string to the normalizer; generating, by the parser, an activation signal for at least one of the plurality of transducers within the normalizer; decoding the filtered input string, by the decoder, to produce an un-encoded filtered input string; activating each transducer of the plurality of transducers for which an activation signal has been generated; removing a field-specific obfuscation from the un-encoded filtered input string using the at least one activated transducer; forwarding the normalized un-encoded filtered input string to the search engine; generating a rule select signal, using the parser, in response to the selected portions of the input string; selecting a set of rules in the rules database memory device in response to the rule select signal; and loading the selected set of rules from the rules database memory device into the search engine; wherein each transducer of the plurality of transducers is configured to be activated or deactivated in response to a signal from the parser. | 24. A method, performed by a content search system including a parser, a rules database memory device, a normalizer having a decoder and a plurality of transducers, and a search engine, for determining whether an input string matches one or more rules stored in the rules database, comprising: extracting selected portions of the input string, using the parser, to form a filtered input string; forwarding, by the parser, the filtered input string to the normalizer; generating, by the parser, an activation signal for at least one of the plurality of transducers within the normalizer; decoding the filtered input string, by the decoder, to produce an un-encoded filtered input string; activating each transducer of the plurality of transducers for which an activation signal has been generated; removing a field-specific obfuscation from the un-encoded filtered input string using the at least one activated transducer; forwarding the normalized un-encoded filtered input string to the search engine; generating a rule select signal, using the parser, in response to the selected portions of the input string; selecting a set of rules in the rules database memory device in response to the rule select signal; and loading the selected set of rules from the rules database memory device into the search engine; wherein each transducer of the plurality of transducers is configured to be activated or deactivated in response to a signal from the parser. 28. The method of claim 24 , wherein the input string comprises an HTTP request message, and the method further comprises: examining the input string, using the parser, to identify a Universal Resource Identifier (URI) field of the request message. | 0.699029 |
7,937,663 | 11 | 13 | 11. A computer storage media medium having computer executable instructions for providing collaborative authoring features in a document editor program, the instructions comprising: providing a document editor program that includes a line of business integration mode; activating the line of business integration mode; displaying a document editing pane in the document editor program, wherein the document editing pane includes a document having at least one section; displaying a document assembly pane in the document editing program, wherein the document assembly pane includes a document details pane and a section details pane; obtaining document details metadata, wherein the document details metadata includes metadata associated with collaboratively authoring the document; obtaining section details metadata, wherein the section details metadata includes data associated with an author assigned to the at least one section of the document; displaying the document details metadata in the document details pane; displaying the section details metadata in the section details pane; and publishing the document, wherein publishing the document includes disabling the line of business integration mode and hiding the document assembly pane. | 11. A computer storage media medium having computer executable instructions for providing collaborative authoring features in a document editor program, the instructions comprising: providing a document editor program that includes a line of business integration mode; activating the line of business integration mode; displaying a document editing pane in the document editor program, wherein the document editing pane includes a document having at least one section; displaying a document assembly pane in the document editing program, wherein the document assembly pane includes a document details pane and a section details pane; obtaining document details metadata, wherein the document details metadata includes metadata associated with collaboratively authoring the document; obtaining section details metadata, wherein the section details metadata includes data associated with an author assigned to the at least one section of the document; displaying the document details metadata in the document details pane; displaying the section details metadata in the section details pane; and publishing the document, wherein publishing the document includes disabling the line of business integration mode and hiding the document assembly pane. 13. The computer storage media medium of claim 11 , wherein the section details metadata includes at least one member of a group comprising: a section title, a section owner, a start date, a due date, a status, authoring instructions, reassignment privileges, view protection, and a notification history. | 0.5 |
9,787,637 | 29 | 36 | 29. A method comprising: establishing, using computing instructions comprised in at least one memory of a computing apparatus defining a first location in a network, a web browsing session with the network; selecting, using a traffic routing software unit comprised in at least one computing processor of the computing apparatus, a secure web container through which web content is to be accessed during the web browsing session, wherein the network comprises two or more secure web containers, and wherein a first secure web container, of the two or more secure web containers, is located in a first secure web container location of the network and a second secure web container, of the two or more secure web containers, is located in a second secure web container location of the network; selecting, using the traffic routing software unit, an egress node through which the web content is to be routed during the web browsing session, wherein the network comprises two or more egress nodes, and wherein a first egress node, of the two or more egress nodes, is located in a first egress node location of the network and a second egress node, of the two or more egress nodes, is located in a second egress node location of the network; and providing, via a communication connection between at least one of the two or more secure web containers and a user device defining a second location in the network, a user of the user device with secure access to the web content, wherein the web content is rendered or accessed at the secure web container, and wherein providing the user of the user device with the secure access to the web content comprises manipulating, at the secure web container, one or more elements of a browser fingerprint presented to or accessed by a web server associated with the web content such that a characteristic of the user device comprised in the manipulated browser fingerprint and a third location presented to or accessed by the web server is different from an actual characteristic of the user device and the second location, respectively. | 29. A method comprising: establishing, using computing instructions comprised in at least one memory of a computing apparatus defining a first location in a network, a web browsing session with the network; selecting, using a traffic routing software unit comprised in at least one computing processor of the computing apparatus, a secure web container through which web content is to be accessed during the web browsing session, wherein the network comprises two or more secure web containers, and wherein a first secure web container, of the two or more secure web containers, is located in a first secure web container location of the network and a second secure web container, of the two or more secure web containers, is located in a second secure web container location of the network; selecting, using the traffic routing software unit, an egress node through which the web content is to be routed during the web browsing session, wherein the network comprises two or more egress nodes, and wherein a first egress node, of the two or more egress nodes, is located in a first egress node location of the network and a second egress node, of the two or more egress nodes, is located in a second egress node location of the network; and providing, via a communication connection between at least one of the two or more secure web containers and a user device defining a second location in the network, a user of the user device with secure access to the web content, wherein the web content is rendered or accessed at the secure web container, and wherein providing the user of the user device with the secure access to the web content comprises manipulating, at the secure web container, one or more elements of a browser fingerprint presented to or accessed by a web server associated with the web content such that a characteristic of the user device comprised in the manipulated browser fingerprint and a third location presented to or accessed by the web server is different from an actual characteristic of the user device and the second location, respectively. 36. The method of claim 29 , wherein the first location is the same as or different from at least one of the second location, the third location, the first secure web container location, the second secure web container location, the first egress node location, or the second egress node location. | 0.719697 |
8,793,646 | 1 | 3 | 1. A method comprising: reading a stereotype of a first profile from a computer readable storage medium, wherein the stereotype defines constraints to be applied to a model associated with the first profile; determining that the stereotype indicates a second profile and a third profile; accessing the second profile and the third profile; and aggregating a plurality of constraints from across the second profile and the third profile for use as constraints for the stereotype of the first profile, wherein the profiles and the stereotype comport with semantics of a modeling language. | 1. A method comprising: reading a stereotype of a first profile from a computer readable storage medium, wherein the stereotype defines constraints to be applied to a model associated with the first profile; determining that the stereotype indicates a second profile and a third profile; accessing the second profile and the third profile; and aggregating a plurality of constraints from across the second profile and the third profile for use as constraints for the stereotype of the first profile, wherein the profiles and the stereotype comport with semantics of a modeling language. 3. The method of claim 1 , wherein said aggregating comprises writing references to the plurality of constraints into a definition of the stereotype of the first profile. | 0.532967 |
7,934,194 | 33 | 34 | 33. The medium of claim 32 , wherein the block diagram includes at least one propagation characteristic. | 33. The medium of claim 32 , wherein the block diagram includes at least one propagation characteristic. 34. The medium of claim 33 , wherein the at least one propagation characteristic is one of a sample time or a data type. | 0.5 |
9,319,556 | 1 | 5 | 1. A document authentication method implemented in a data processing system, comprising: (a) obtaining an original grayscale image representing an original document, the original grayscale image including one or more halftone text areas or light text areas and one or more non-halftone text areas or dark text areas, wherein each halftone text area includes a plurality of halftone dots, each halftone dot being formed by a plurality of pixels, each pixel being a smallest unit of the original grayscale image and each pixel having a grayscale pixel value; (b) separating the halftone or light text areas from the non-halftone or dark text areas of the original grayscale image; (c) separately binarizing the halftone or light text areas and the non-halftone or dark text areas generated by step (b); (d) down-sampling only the binarized non-halftone or dark text areas generated by step (c) which correspond to the non-halftone or dark text areas of the original grayscale image, without down-sampling the binarized halftone or light text areas which correspond to the halftone or light text areas of the same original grayscale image, whereby a binarized original image is generated; (e) obtaining a target grayscale image representing a hardcopy target document, the target grayscale image including one or more halftone text areas and one or more non-halftone text areas, wherein each halftone text area includes a plurality of halftone dots, each halftone dot being formed by a plurality of pixels, each pixel being a smallest unit of the target grayscale image and each pixel having a grayscale pixel value; (f) separating the halftone text areas and the non-halftone text areas of the target grayscale image; (g) separately binarizing the halftone text areas and the non-halftone text areas generated by step (f); (h) down-sampling only the binarized non-halftone text areas generated by step (g) which correspond to the non-halftone text areas of the target grayscale image, without down-sampling the binarized halftone text areas which correspond to the halftone text areas of the same target grayscale image, whereby a binarized target image is generated; and (i) comparing the binarized target image with the binarized original image to determine whether the target document is an authentic copy of the original document. | 1. A document authentication method implemented in a data processing system, comprising: (a) obtaining an original grayscale image representing an original document, the original grayscale image including one or more halftone text areas or light text areas and one or more non-halftone text areas or dark text areas, wherein each halftone text area includes a plurality of halftone dots, each halftone dot being formed by a plurality of pixels, each pixel being a smallest unit of the original grayscale image and each pixel having a grayscale pixel value; (b) separating the halftone or light text areas from the non-halftone or dark text areas of the original grayscale image; (c) separately binarizing the halftone or light text areas and the non-halftone or dark text areas generated by step (b); (d) down-sampling only the binarized non-halftone or dark text areas generated by step (c) which correspond to the non-halftone or dark text areas of the original grayscale image, without down-sampling the binarized halftone or light text areas which correspond to the halftone or light text areas of the same original grayscale image, whereby a binarized original image is generated; (e) obtaining a target grayscale image representing a hardcopy target document, the target grayscale image including one or more halftone text areas and one or more non-halftone text areas, wherein each halftone text area includes a plurality of halftone dots, each halftone dot being formed by a plurality of pixels, each pixel being a smallest unit of the target grayscale image and each pixel having a grayscale pixel value; (f) separating the halftone text areas and the non-halftone text areas of the target grayscale image; (g) separately binarizing the halftone text areas and the non-halftone text areas generated by step (f); (h) down-sampling only the binarized non-halftone text areas generated by step (g) which correspond to the non-halftone text areas of the target grayscale image, without down-sampling the binarized halftone text areas which correspond to the halftone text areas of the same target grayscale image, whereby a binarized target image is generated; and (i) comparing the binarized target image with the binarized original image to determine whether the target document is an authentic copy of the original document. 5. The method of claim 1 , wherein step (b) includes: identifying text characters in the original grayscale image; classifying each identified text character as either a halftone text character or a non-halftone text character based on a topological analysis of the text character; and dividing the original grayscale image into halftone text areas containing only halftone text characters and non-halftone text areas containing non-halftone text characters; where step (c) includes: binarizing each halftone text area using pixel value statistics calculated from pixels in that area only; wherein step (f) includes: identifying text characters in the target grayscale image; classifying each identified text character as either a halftone text character or a non-halftone text character based on a topological analysis of the text character; and dividing the target grayscale image into halftone text areas containing only halftone text characters and non-halftone text areas containing non-halftone text characters; and where step (g) includes: binarizing each halftone text area using pixel value statistics calculated from pixels in that area only. | 0.5 |
7,783,626 | 3 | 4 | 3. The method of claim 1 , further comprising: building a new version of a delta index using the previously generated global analysis computations, a current version of a delta store, and newly crawled documents. | 3. The method of claim 1 , further comprising: building a new version of a delta index using the previously generated global analysis computations, a current version of a delta store, and newly crawled documents. 4. The method of claim 3 , wherein the creation of the new delta index allows new documents to be indexed and retrieved before global analysis computations for the new documents are performed. | 0.578947 |
9,720,967 | 12 | 20 | 12. A method comprising: in response to receiving a query, determining an execution plan that comprises a plurality of sub-plans that includes a first sub-plan and a second sub-plan; wherein the first sub-plan comprises a first operation, of a first type, that can be performed during execution of the execution plan; wherein the second sub-plan comprises a second operation, of a second type that is different than the first type, that can be performed during execution of the execution plan; wherein no sub-plan in the plurality of sub-plans contains all operations that are required to identify a valid result of the query; after determining the execution plan, executing a portion of the execution plan, wherein executing the portion of the execution plan comprises storing, in a buffer, temporary results of executing the portion; analyzing the temporary results to determine a minimum value, a maximum value, or a number of distinct values, wherein the number of distinct values is less than a number of rows in the buffer; based on the minimum value, the maximum value, or the number of distinct values, determining whether one or more criteria are satisfied; if the one or more criteria are satisfied, then executing the first sub-plan of the plurality of sub-plans without executing the second sub-plan; if the one or more criteria are not satisfied, then executing the second sub-plan of the plurality of sub-plans without executing the first sub-plan; generating a result of the query by completing execution of the execution plan and without reoptimizing the query; wherein the method is performed by one or more computing devices. | 12. A method comprising: in response to receiving a query, determining an execution plan that comprises a plurality of sub-plans that includes a first sub-plan and a second sub-plan; wherein the first sub-plan comprises a first operation, of a first type, that can be performed during execution of the execution plan; wherein the second sub-plan comprises a second operation, of a second type that is different than the first type, that can be performed during execution of the execution plan; wherein no sub-plan in the plurality of sub-plans contains all operations that are required to identify a valid result of the query; after determining the execution plan, executing a portion of the execution plan, wherein executing the portion of the execution plan comprises storing, in a buffer, temporary results of executing the portion; analyzing the temporary results to determine a minimum value, a maximum value, or a number of distinct values, wherein the number of distinct values is less than a number of rows in the buffer; based on the minimum value, the maximum value, or the number of distinct values, determining whether one or more criteria are satisfied; if the one or more criteria are satisfied, then executing the first sub-plan of the plurality of sub-plans without executing the second sub-plan; if the one or more criteria are not satisfied, then executing the second sub-plan of the plurality of sub-plans without executing the first sub-plan; generating a result of the query by completing execution of the execution plan and without reoptimizing the query; wherein the method is performed by one or more computing devices. 20. The method of claim 12 , further comprising: in response to receiving a second query, determining a second execution plan that comprises a second plurality of sub-plans that includes a third sub-plan, a fourth sub-plan, and a fifth sub-plan; after determining the second execution plan, executing a portion of the second execution plan, wherein executing the portion of the second execution plan comprises collecting statistics; based on the statistics, selecting one of the third sub-plan, the fourth sub-plan, or the fifth sub-plan. | 0.808131 |
9,990,919 | 1 | 6 | 1. A method, comprising: performing, using a computer processor, speech recognition on user dictated words to generate a dictation; parsing the dictation using a deterministic formatting grammar module to build a concept-tagged formatting graph; extracting features from the formatting graph; estimating scores for the extracted features using a discriminative statistical model, wherein the discriminative statistical model is derived from a deterministic formatting grammar module and user formatted documents; choosing a path in the formatting graph as a formatting selection based on the estimated scores; and outputting the dictation as formatted text based on the formatting selection to provide an integrated stochastic and deterministic formatting of the dictation for disambiguation of the user dictated words. | 1. A method, comprising: performing, using a computer processor, speech recognition on user dictated words to generate a dictation; parsing the dictation using a deterministic formatting grammar module to build a concept-tagged formatting graph; extracting features from the formatting graph; estimating scores for the extracted features using a discriminative statistical model, wherein the discriminative statistical model is derived from a deterministic formatting grammar module and user formatted documents; choosing a path in the formatting graph as a formatting selection based on the estimated scores; and outputting the dictation as formatted text based on the formatting selection to provide an integrated stochastic and deterministic formatting of the dictation for disambiguation of the user dictated words. 6. The method according to claim 1 , wherein the user dictated words include a website address. | 0.81 |
8,554,796 | 2 | 6 | 2. The method of claim 1 including using said semantic database to detect communication network policy violations in said communication network. | 2. The method of claim 1 including using said semantic database to detect communication network policy violations in said communication network. 6. The method of claim 2 wherein said commands are written in command blocks each comprising a main command followed by subcommands. | 0.725 |
9,501,592 | 1 | 11 | 1. A computer implemented method for implementing analog behavioral modeling and IP (intellectual property) integration using a Hardware Description Language, comprising: determining or identifying, with a nettype and resolution function management module at least partially stored in memory and configured to include or function in conjunction with at least one processor or at least one processor core, a set of built-in nettypes including at least one wire-real nettype in a SystemVerilog modeling environment; defining a discipline with a domain type of a domain as a native capability of the SystemVerilog modeling environment at least by binding one or more attributes of the domain; characterizing at least one real-valued net of one or more real-valued nets with the discipline associated with the at least one real-valued net and one or more characteristics of the discipline; determining or identifying a plurality of resolution functions for the set of built-in nettypes natively in the SystemVerilog modeling-environment, the plurality of resolution functions including at least one built-in resolution function and at least one modified resolution function modified from a built-in resolution function; porting an analog or a mixed-signal portion including at least the at least one real-valued net of the one or more real-valued nets and information about the domain and the discipline into an electronic design in the SystemVerilog modeling environment by using at least native real modeling capabilities of the SystemVerilog modeling environment without recreating details of the analog or mixed-signal portion; and reducing computational resource utilization at least by elaborating the electronic design into an elaborated electronic design and by performing multiple verification or simulation tasks, at a simulation or verification module residing on one computing node and receiving the electronic design from a separate computing node, for the elaborated electronic design with the set of native real modeling capabilities of the SystemVerilog modeling environment. | 1. A computer implemented method for implementing analog behavioral modeling and IP (intellectual property) integration using a Hardware Description Language, comprising: determining or identifying, with a nettype and resolution function management module at least partially stored in memory and configured to include or function in conjunction with at least one processor or at least one processor core, a set of built-in nettypes including at least one wire-real nettype in a SystemVerilog modeling environment; defining a discipline with a domain type of a domain as a native capability of the SystemVerilog modeling environment at least by binding one or more attributes of the domain; characterizing at least one real-valued net of one or more real-valued nets with the discipline associated with the at least one real-valued net and one or more characteristics of the discipline; determining or identifying a plurality of resolution functions for the set of built-in nettypes natively in the SystemVerilog modeling-environment, the plurality of resolution functions including at least one built-in resolution function and at least one modified resolution function modified from a built-in resolution function; porting an analog or a mixed-signal portion including at least the at least one real-valued net of the one or more real-valued nets and information about the domain and the discipline into an electronic design in the SystemVerilog modeling environment by using at least native real modeling capabilities of the SystemVerilog modeling environment without recreating details of the analog or mixed-signal portion; and reducing computational resource utilization at least by elaborating the electronic design into an elaborated electronic design and by performing multiple verification or simulation tasks, at a simulation or verification module residing on one computing node and receiving the electronic design from a separate computing node, for the elaborated electronic design with the set of native real modeling capabilities of the SystemVerilog modeling environment. 11. The computer implemented method of claim 1 , wherein the set of built-in nettypes including the at least one wire-real nettype provides the SystemVerilog modeling environment with at least one native wire-real capability for directly modeling behavior of one or more real-valued nets with the at least one wire-real nettype in the SystemVerilog modeling environment. | 0.717125 |
9,368,114 | 21 | 22 | 21. A system, comprising: one or more processors; a display, and memory having instructions stored thereon, the instructions, when executed by the one or more processors, cause the processors to: receive a speech output to be provided to a user; determine if speech input is currently being received from the user, upon determining that the user is not currently providing speech input, cause the speech output to be provided to the user; upon determining that the user is currently providing speech input: determine if the speech output is urgent; upon determining that the speech output is urgent, cause the speech output to be provided to the user; and upon determining that provision of the speech output is not urgent, stay providing the speech output to the user, and provide a displayed output corresponding to the speech output. | 21. A system, comprising: one or more processors; a display, and memory having instructions stored thereon, the instructions, when executed by the one or more processors, cause the processors to: receive a speech output to be provided to a user; determine if speech input is currently being received from the user, upon determining that the user is not currently providing speech input, cause the speech output to be provided to the user; upon determining that the user is currently providing speech input: determine if the speech output is urgent; upon determining that the speech output is urgent, cause the speech output to be provided to the user; and upon determining that provision of the speech output is not urgent, stay providing the speech output to the user, and provide a displayed output corresponding to the speech output. 22. The system of claim 21 , further comprising instructions for causing the one or more processors to, prior to receiving the speech output: receive a request from the user to perform a digital assistant task. | 0.653465 |
9,129,212 | 6 | 9 | 6. A non-transitory computer-readable storage medium containing instructions that, when executed on a network-based transaction system, cause the network-based transaction system to perform operations comprising: receiving a selected entity on the network-based transaction system; determining a plurality of related entities based at least in part on aggregated navigation transitions between the selected entity and each related entity of the plurality of related entities; and generating a recommendation set of related entities, for publication via the network-based transaction system over a network connection, from the plurality of related entities based at least in part on a velocity for each related entity of the plurality of related entities, the velocity is a measure of directional change in popularity. | 6. A non-transitory computer-readable storage medium containing instructions that, when executed on a network-based transaction system, cause the network-based transaction system to perform operations comprising: receiving a selected entity on the network-based transaction system; determining a plurality of related entities based at least in part on aggregated navigation transitions between the selected entity and each related entity of the plurality of related entities; and generating a recommendation set of related entities, for publication via the network-based transaction system over a network connection, from the plurality of related entities based at least in part on a velocity for each related entity of the plurality of related entities, the velocity is a measure of directional change in popularity. 9. The non-transitory computer-readable storage medium of claim 6 , wherein the instructions that cause the network-based transaction system to determine the plurality of related entities include instructions that cause the network-based transaction system to calculate and aggregate navigation transitions between the selected entity and the plurality of entities based on community behavior tracked by the network-based transaction system. | 0.5 |
8,073,683 | 1 | 7 | 1. A method for augmenting an initial training database, comprising: receiving a training example from the initial training database, wherein the training example is from an action pair comprising the training example and an action command associated with the training example; translating the training example to an output, wherein the output comprises a variation of the training sample; translating the output to a first variant of the training example; and storing an augmented action pair including the first variant of the training example and the action command associated with the training example in an augmented training database. | 1. A method for augmenting an initial training database, comprising: receiving a training example from the initial training database, wherein the training example is from an action pair comprising the training example and an action command associated with the training example; translating the training example to an output, wherein the output comprises a variation of the training sample; translating the output to a first variant of the training example; and storing an augmented action pair including the first variant of the training example and the action command associated with the training example in an augmented training database. 7. The method of claim 1 , wherein the first variant is in a natural language. | 0.840164 |
8,005,823 | 1 | 6 | 1. A system, comprising: a processor; and a memory coupled to the processor, wherein the memory comprises program instructions executable by the processor to: group users into a plurality of distinct communities according to one or more grouping criteria, such that for each particular one of the communities, members of the particular community share at least one grouping criterion in common, and wherein membership of at least some of the communities does not completely overlap; store indications of membership of each of the communities; receive a query from a member of one or more of the communities, and in response to the query: obtain results of the query; determine one or more of the plurality of communities to associate with the query, wherein the determining comprises comparing the query to queries entered by other members of the one or more of the plurality of communities such that the member from which the query is received is a member of the determined one or more communities, and the determined one or more communities are distinct from others of the plurality of communities; identify other results from only the determined one or more communities, wherein the other results are for at least one other query from at least one other member of the determined one or more communities, and wherein the other results reflect user feedback from the at least one other member of the determined one or more communities; compare the results to the other results from the determined one or more communities to determine a measure of similarity between the results and the other results; and modify the results according to the other results in response to determining that the measure of similarity is above a predetermined threshold, wherein the results are not modified according to the other results if the measure of similarity is not above the predetermined threshold. | 1. A system, comprising: a processor; and a memory coupled to the processor, wherein the memory comprises program instructions executable by the processor to: group users into a plurality of distinct communities according to one or more grouping criteria, such that for each particular one of the communities, members of the particular community share at least one grouping criterion in common, and wherein membership of at least some of the communities does not completely overlap; store indications of membership of each of the communities; receive a query from a member of one or more of the communities, and in response to the query: obtain results of the query; determine one or more of the plurality of communities to associate with the query, wherein the determining comprises comparing the query to queries entered by other members of the one or more of the plurality of communities such that the member from which the query is received is a member of the determined one or more communities, and the determined one or more communities are distinct from others of the plurality of communities; identify other results from only the determined one or more communities, wherein the other results are for at least one other query from at least one other member of the determined one or more communities, and wherein the other results reflect user feedback from the at least one other member of the determined one or more communities; compare the results to the other results from the determined one or more communities to determine a measure of similarity between the results and the other results; and modify the results according to the other results in response to determining that the measure of similarity is above a predetermined threshold, wherein the results are not modified according to the other results if the measure of similarity is not above the predetermined threshold. 6. The system of claim 1 , wherein to modify the results the program instructions are further executable to aggregate the results and the other results to produce aggregated results, wherein the aggregated results include at least one result from the other results that was not in the results obtained for the query. | 0.627358 |
8,903,862 | 9 | 10 | 9. A method comprising: receiving a first triple including a subject, predicate and object, the object comprising a literal-type object; identifying, by a processor, a subtype of the object of the first triple; semantically marking the object with the identified subtype, semantically marking the object including generating: a second triple including the subject and predicate of the first triple as the subject and predicate thereof, and a third triple including the object of the first triple or a representation of the object of the first triple as the object thereof; and storing the second and third triples in a triple store. | 9. A method comprising: receiving a first triple including a subject, predicate and object, the object comprising a literal-type object; identifying, by a processor, a subtype of the object of the first triple; semantically marking the object with the identified subtype, semantically marking the object including generating: a second triple including the subject and predicate of the first triple as the subject and predicate thereof, and a third triple including the object of the first triple or a representation of the object of the first triple as the object thereof; and storing the second and third triples in a triple store. 10. A method according to claim 9 further comprising: processing the object of the first triple, including one or more of compressing, reformatting or encrypting the object, the representation of the object comprising the processed object or a representation thereof. | 0.705298 |
8,265,377 | 10 | 11 | 10. A system for improving cursive handwriting recognition, comprising: a mixed prototype database trained at least in part with one or more print and cursive samples; a cursive prototype database trained at least in part with one or more cursive samples; and a template matching module configured to be used for a cursive handwriting recognition operation using at least some of the mixed and cursive prototype databases. | 10. A system for improving cursive handwriting recognition, comprising: a mixed prototype database trained at least in part with one or more print and cursive samples; a cursive prototype database trained at least in part with one or more cursive samples; and a template matching module configured to be used for a cursive handwriting recognition operation using at least some of the mixed and cursive prototype databases. 11. The system of claim 10 , the cursive prototype database not trained with one or more print samples. | 0.840557 |
9,996,523 | 11 | 14 | 11. A computing system for autosuggesting related objects to a user, comprising: one or more processors; and one or more memory devices, the one or more memory devices storing instructions that when executed by the one or more processors cause the one or more processors to perform operations, the operations comprising: receiving data indicative of a user input that comprises an n-gram of one or more characters; identifying one or more autocomplete suggestions for the user input; identifying one or more ontologies based at least in part on at least one of the n-gram of one or more characters and the autocomplete suggestions for the user input, wherein each ontology is associated with a category that is related to at least one of the user input and the autocomplete suggestions for the user input, and wherein each ontology comprises a plurality of object types, each object type comprises one or more terms; determining one or more suggested related objects based at least in part on one or more of the plurality of object types, wherein the one or more suggested related objects comprise one or more of the terms that are related to at least one of the user input and the autocomplete suggestions; and providing data indicative of the suggested related objects for display on a user interface via a display device. | 11. A computing system for autosuggesting related objects to a user, comprising: one or more processors; and one or more memory devices, the one or more memory devices storing instructions that when executed by the one or more processors cause the one or more processors to perform operations, the operations comprising: receiving data indicative of a user input that comprises an n-gram of one or more characters; identifying one or more autocomplete suggestions for the user input; identifying one or more ontologies based at least in part on at least one of the n-gram of one or more characters and the autocomplete suggestions for the user input, wherein each ontology is associated with a category that is related to at least one of the user input and the autocomplete suggestions for the user input, and wherein each ontology comprises a plurality of object types, each object type comprises one or more terms; determining one or more suggested related objects based at least in part on one or more of the plurality of object types, wherein the one or more suggested related objects comprise one or more of the terms that are related to at least one of the user input and the autocomplete suggestions; and providing data indicative of the suggested related objects for display on a user interface via a display device. 14. The computing system of claim 11 , wherein providing the data indicative of the suggested related objects for display on the user interface via the display device comprises: providing the data indicative of the suggested related objects for display on the user interface via the display device while a user is providing the user input. | 0.778431 |
5,493,658 | 3 | 4 | 3. The tutorial method of claim 2 wherein said comparison step compares user input actions with a lesson control table containing a list of user input action statements. | 3. The tutorial method of claim 2 wherein said comparison step compares user input actions with a lesson control table containing a list of user input action statements. 4. The tutorial method of claim 3 further including the step of providing a lesson control pointer on said lesson control table and incrementally adjusting said pointer to point to successive command action statements in said lesson control table as correct user input actions are input by a user, in order to indicate which of the command action statements in said lesson control table should be compared to said user input actions. | 0.5 |
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