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8,845,428 | 12 | 14 | 12. A non-transitory tangible storage medium storing a computer program for coordinating real-world gathering to promote an event associated with a community of an online game, the computer program comprising executable instructions that cause a computer to: encourage players of the online game community to participate in a real-world gathering by providing incentives for participants at the real-world gathering, wherein the real-world gathering is an event separate from the online game to expand the online game community; promote familiarity among the participants by providing and introducing online game community titles of the participants at the real-world gathering through a mobile device used by each participant; provide online benefits and rewards to the participants for participating in the real-world gathering; enable online participants of the real-world gathering to track the real-world gathering on a map; and enable the online participants to communicate with the participants at the real-world gathering, wherein the communication between the online participant and the participants at the real-world gathering includes sending messages and tips. | 12. A non-transitory tangible storage medium storing a computer program for coordinating real-world gathering to promote an event associated with a community of an online game, the computer program comprising executable instructions that cause a computer to: encourage players of the online game community to participate in a real-world gathering by providing incentives for participants at the real-world gathering, wherein the real-world gathering is an event separate from the online game to expand the online game community; promote familiarity among the participants by providing and introducing online game community titles of the participants at the real-world gathering through a mobile device used by each participant; provide online benefits and rewards to the participants for participating in the real-world gathering; enable online participants of the real-world gathering to track the real-world gathering on a map; and enable the online participants to communicate with the participants at the real-world gathering, wherein the communication between the online participant and the participants at the real-world gathering includes sending messages and tips. 14. The non-transitory tangible storage medium of claim 12 , further comprising executable instructions that cause a computer to communicate with the mobile device to encourage the participants at the real-world gathering to physically participate in a social interaction with other participants at the real-world gathering. | 0.585678 |
8,938,403 | 15 | 18 | 15. A non-transitory computer readable medium for use in a computer to compute token-dependent affective response baseline levels for a user; the computer includes a processor, and the non-transitory computer readable medium comprising: program code for receiving a certain temporal window of token instances; program code for receiving, from a database, first and third affective response annotations corresponding to first and third temporal windows of token instances, respectively; wherein the database further stores a second affective response annotation corresponding to a second temporal window of token instances, the user was exposed to the first, second, and third, temporal windows of token instances during first, second, and third time periods, respectively; and wherein the first time period precedes the second time period, and the second time period precedes the third time period; program code for calculating, using a distance function, a first metric, a second metric, and a third metric between the certain temporal window of token instances and the first temporal window of token instances, the second temporal window of token instances, and the third temporal window of token instances, respectively; wherein the first and third metrics are below a predefined threshold, while the second metric is not below the predefined threshold; program code for computing a first affective response baseline level associated with the certain temporal window of token instances based on data comprising the first and third affective response annotations; program code for receiving an additional temporal window of token instances; program code for receiving, from the database, the second affective response annotation; wherein a fourth metric between the additional temporal window of token instances and the second temporal window of token instances is below the predefined threshold; and program code for computing a second affective response baseline level associated with the additional temporal window of token instances based on data comprising the second affective response annotation; wherein the computed first affective response baseline level is different from the computed second affective response baseline level; wherein the first and second affective response baseline levels each represent a value comprising at least one of: an emotional response, and a value of a user measurement channel. | 15. A non-transitory computer readable medium for use in a computer to compute token-dependent affective response baseline levels for a user; the computer includes a processor, and the non-transitory computer readable medium comprising: program code for receiving a certain temporal window of token instances; program code for receiving, from a database, first and third affective response annotations corresponding to first and third temporal windows of token instances, respectively; wherein the database further stores a second affective response annotation corresponding to a second temporal window of token instances, the user was exposed to the first, second, and third, temporal windows of token instances during first, second, and third time periods, respectively; and wherein the first time period precedes the second time period, and the second time period precedes the third time period; program code for calculating, using a distance function, a first metric, a second metric, and a third metric between the certain temporal window of token instances and the first temporal window of token instances, the second temporal window of token instances, and the third temporal window of token instances, respectively; wherein the first and third metrics are below a predefined threshold, while the second metric is not below the predefined threshold; program code for computing a first affective response baseline level associated with the certain temporal window of token instances based on data comprising the first and third affective response annotations; program code for receiving an additional temporal window of token instances; program code for receiving, from the database, the second affective response annotation; wherein a fourth metric between the additional temporal window of token instances and the second temporal window of token instances is below the predefined threshold; and program code for computing a second affective response baseline level associated with the additional temporal window of token instances based on data comprising the second affective response annotation; wherein the computed first affective response baseline level is different from the computed second affective response baseline level; wherein the first and second affective response baseline levels each represent a value comprising at least one of: an emotional response, and a value of a user measurement channel. 18. The non-transitory computer readable medium of claim 15 wherein the first, second, and third temporal windows of token instances comprise first, second, and third situation identifiers, respectively; and wherein the first and third situation identifiers correspond to a first situation, and the second situation identifier corresponds to a second situation that is different than the first situation. | 0.524706 |
9,336,497 | 7 | 11 | 7. A system comprising: at least one computing device configured to update an expert corpus set, by performing actions including: obtaining a query from a first user; obtaining a first raw data source; determining a first relevance score for the first raw data source with respect to the query, by performing actions including: creating a first vector of statistical variables for the query using at least one natural language processing (NLP) socket, the statistical variables of the first vector having category types; creating a second vector of statistical variables for the first raw data source, the statistical variables for the first raw data source having category types that are the same as the category types of the statistical variables for the query; generating a hypothesis regarding the relevance of the first raw data source with respect to the query; testing the hypothesis by comparing each statistical variable for the query to each same statistical variable for the first raw data source; and calculating a gradient between the first vector and the second vector to determine the first relevance score; updating the expert corpus set by ingesting the first raw data source into the expert corpus in response to determining the first relevance score exceeding a first thresholds; accessing the updated expert corpus; determining at least one candidate answer to the query using an NLP algorithm; calculating a veracity score for each of the at least one candidate answers based on the NLP algorithm; and responding to the first user with at least one first candidate answer based on the veracity score. | 7. A system comprising: at least one computing device configured to update an expert corpus set, by performing actions including: obtaining a query from a first user; obtaining a first raw data source; determining a first relevance score for the first raw data source with respect to the query, by performing actions including: creating a first vector of statistical variables for the query using at least one natural language processing (NLP) socket, the statistical variables of the first vector having category types; creating a second vector of statistical variables for the first raw data source, the statistical variables for the first raw data source having category types that are the same as the category types of the statistical variables for the query; generating a hypothesis regarding the relevance of the first raw data source with respect to the query; testing the hypothesis by comparing each statistical variable for the query to each same statistical variable for the first raw data source; and calculating a gradient between the first vector and the second vector to determine the first relevance score; updating the expert corpus set by ingesting the first raw data source into the expert corpus in response to determining the first relevance score exceeding a first thresholds; accessing the updated expert corpus; determining at least one candidate answer to the query using an NLP algorithm; calculating a veracity score for each of the at least one candidate answers based on the NLP algorithm; and responding to the first user with at least one first candidate answer based on the veracity score. 11. The system of claim 7 , wherein the categories include at least one of lexical answer type, sentence focus frequencies or entity type. | 0.938992 |
8,543,382 | 6 | 7 | 6. A system for diacritizing a text, the system comprising: a memory for storing a Hidden Makov Model, a text, and at least one diacritical mark of a set of diacritical marks, the text comprising a plurality of characters associated with an Arabic language; a processor coupled to the memory, wherein the processor is configured to: analyze the text to determine whether the text requires at least one diacritical mark of the set of diacritical marks; convert each character to an ASCII code; feed each ASCII code in sequence to the Hidden Markov Model; apply an expectation-maximization process to each ASCII code starting at one end of the sequence; transition from one diacritical mark to another diacritical mark from the set of diacritical marks for each ASCII code; record a probability for each diacritical mark when associated with each current ASCII code; change a state of the Hidden Markov Model based on each probability over regularly spaced periods of time; wherein the Hidden Markov Model transitions from state q i at time t to a state q i at time t+1, where t=1, 2, 3, . . . M; and i, j=1, 2, . . . , N, and where M represents a number of the transitions and N represents a number of the states; wherein a transition probability a ij , representing a probability that diacritical mark q j appears directly after diacritical mark q i , equals an expected number of transitions from state q i to state q j divided by an expected number of transitions from state q i , finalize a diacritical mark having the highest probability for the current ASCII code; process each character in the sequence of the text, wherein the Hidden Markov Model bases the probability at least in part on the probability of a diacritical mark applied on one or more preceding characters of the sequence and on a context of the text for determining the probability of a diacritical mark on a given character; generate a sequence of the diacritical marks corresponding to the sequence of characters; match the sequence of diacritical marks with the text to obtain the diacritized text; and a display to present the diacritized text. | 6. A system for diacritizing a text, the system comprising: a memory for storing a Hidden Makov Model, a text, and at least one diacritical mark of a set of diacritical marks, the text comprising a plurality of characters associated with an Arabic language; a processor coupled to the memory, wherein the processor is configured to: analyze the text to determine whether the text requires at least one diacritical mark of the set of diacritical marks; convert each character to an ASCII code; feed each ASCII code in sequence to the Hidden Markov Model; apply an expectation-maximization process to each ASCII code starting at one end of the sequence; transition from one diacritical mark to another diacritical mark from the set of diacritical marks for each ASCII code; record a probability for each diacritical mark when associated with each current ASCII code; change a state of the Hidden Markov Model based on each probability over regularly spaced periods of time; wherein the Hidden Markov Model transitions from state q i at time t to a state q i at time t+1, where t=1, 2, 3, . . . M; and i, j=1, 2, . . . , N, and where M represents a number of the transitions and N represents a number of the states; wherein a transition probability a ij , representing a probability that diacritical mark q j appears directly after diacritical mark q i , equals an expected number of transitions from state q i to state q j divided by an expected number of transitions from state q i , finalize a diacritical mark having the highest probability for the current ASCII code; process each character in the sequence of the text, wherein the Hidden Markov Model bases the probability at least in part on the probability of a diacritical mark applied on one or more preceding characters of the sequence and on a context of the text for determining the probability of a diacritical mark on a given character; generate a sequence of the diacritical marks corresponding to the sequence of characters; match the sequence of diacritical marks with the text to obtain the diacritized text; and a display to present the diacritized text. 7. The system of claim 6 , wherein the at least one diacritical mark associated with the set of diacritical mark is at least one of: Fat-hah, Shaddah+Fat-hah, Tanween Fat-hah, Shaddah+Tanween Fat-hah, Dhammah, Shaddah+Dhammah, Tanween Dhammah, Shaddah+Tanween Dhammah, Kasrah, Shaddah+Kasrah, Tanween Kasrah, Shaddah+Tanween Kasrah, Maddah, Sukun and No diacritic. | 0.5 |
10,147,038 | 1 | 20 | 1. A computer implemented method of integrating a trained model trained on a training data set into an application, the method comprising: instantiating, by a project engine, a mechanical turk project that includes a plurality of tasks available to project members; receiving, by the project engine, project results data from the project members; generating, by the project engine, a training data set from the project results data; training, by the project engine, a trained model from the training data set; converting, by an application generator module, the project results data in the form of the trained model into a trained application module formatted for integration into an application; and causing, by the application generator module, integration of the trained application module into the application. | 1. A computer implemented method of integrating a trained model trained on a training data set into an application, the method comprising: instantiating, by a project engine, a mechanical turk project that includes a plurality of tasks available to project members; receiving, by the project engine, project results data from the project members; generating, by the project engine, a training data set from the project results data; training, by the project engine, a trained model from the training data set; converting, by an application generator module, the project results data in the form of the trained model into a trained application module formatted for integration into an application; and causing, by the application generator module, integration of the trained application module into the application. 20. The method of claim 1 , further comprising updating the project results data as time passes. | 0.838384 |
10,095,668 | 5 | 6 | 5. A method for providing a text automatic response, comprising: receiving a request for a web-based text ARS development tool from a manager device; and providing the manager device with the web-based text ARS development tool; generating, by the manager device, a menu tree for a text ARS, wherein the menu tree is generated to correspond to each menu based on a shape selected according to an attribute of each menu; editing, by the manager device, the generated menu tree; and generating, by the manager device, a pre-defined XML document set based on the edited menu tree. | 5. A method for providing a text automatic response, comprising: receiving a request for a web-based text ARS development tool from a manager device; and providing the manager device with the web-based text ARS development tool; generating, by the manager device, a menu tree for a text ARS, wherein the menu tree is generated to correspond to each menu based on a shape selected according to an attribute of each menu; editing, by the manager device, the generated menu tree; and generating, by the manager device, a pre-defined XML document set based on the edited menu tree. 6. The method of claim 5 , wherein the generating of the XML document set comprises generating an XML document to correspond to each menu included in the menu tree. | 0.5 |
5,509,074 | 3 | 8 | 3. The method of claim 1 wherein said agents are pre-installed as software into each of said plurality of users' computers. | 3. The method of claim 1 wherein said agents are pre-installed as software into each of said plurality of users' computers. 8. The method of claim 3 wherein each of said agents is capable of decrypting only a single, uniquely encoded version of documents published. | 0.5 |
9,472,013 | 1 | 9 | 1. A method comprising: prior to generating display at a first mobile device of a respective animation according to animation rule information, receiving, using one or more processors at the first mobile device associated with a first user, a selection by the first user of a map display position to be incorporated into an animation template of a specific animation rule in the animation rule information; receiving at the first mobile device associated with the first user, a communication request from a second user; accessing social networking information specified by an online social networking account associated with the first user or the second user; determining a relationship between the second user and the first user, based on the accessed social networking information; determining a current location of a second mobile device associated with the second user; accessing animation rule information describing a plurality of animation rules corresponding to a plurality of relationships; and generating a display of an animation, via a user interface in the first mobile device, based on the animation template of the specific animation rule which corresponds to the determined relationship, the animation template specifying insertion of a map of the current location of the second mobile device at the selected map display position within the display of the animation. | 1. A method comprising: prior to generating display at a first mobile device of a respective animation according to animation rule information, receiving, using one or more processors at the first mobile device associated with a first user, a selection by the first user of a map display position to be incorporated into an animation template of a specific animation rule in the animation rule information; receiving at the first mobile device associated with the first user, a communication request from a second user; accessing social networking information specified by an online social networking account associated with the first user or the second user; determining a relationship between the second user and the first user, based on the accessed social networking information; determining a current location of a second mobile device associated with the second user; accessing animation rule information describing a plurality of animation rules corresponding to a plurality of relationships; and generating a display of an animation, via a user interface in the first mobile device, based on the animation template of the specific animation rule which corresponds to the determined relationship, the animation template specifying insertion of a map of the current location of the second mobile device at the selected map display position within the display of the animation. 9. The method of claim 1 , wherein the communication request corresponds to a telephone call, a text message, an instant message, an email message, or a video chat request. | 0.616071 |
9,397,977 | 20 | 21 | 20. The apparatus of claim 16 , wherein the memory holds further instructions for determining a corresponding registration fee based on the count of domain names. | 20. The apparatus of claim 16 , wherein the memory holds further instructions for determining a corresponding registration fee based on the count of domain names. 21. The apparatus of claim 20 , wherein the memory holds further instructions for analyzing an IP address associated with a source of the request, and determining a response to the request based on the IP address associated with the source of the request. | 0.5 |
8,972,376 | 1 | 4 | 1. A system for optimized web domains classification based on progressive crawling with clustering, comprising: a processor configured to: crawl a domain to collect data for a subset of pages of a corpus of content associated with the domain; classify each of the crawled pages into one or more category clusters, wherein the category clusters represent a content categorization of the corpus of content associated with the domain, and wherein the classifying of the each of the crawled pages into the one or more category clusters comprises: determine a category for the each of the crawled pages in the domain; group more than one page having the same category into a first cluster; determine whether a number of the more than one page of the first cluster exceeds a first threshold; and in the event that the number of the more than one page of the first cluster does not exceed the first threshold, select a new page within the domain to crawl and classify; and determine which of the one or more category clusters to publish for the domain; and a memory coupled to the processor and configured to provide the processor with instructions. | 1. A system for optimized web domains classification based on progressive crawling with clustering, comprising: a processor configured to: crawl a domain to collect data for a subset of pages of a corpus of content associated with the domain; classify each of the crawled pages into one or more category clusters, wherein the category clusters represent a content categorization of the corpus of content associated with the domain, and wherein the classifying of the each of the crawled pages into the one or more category clusters comprises: determine a category for the each of the crawled pages in the domain; group more than one page having the same category into a first cluster; determine whether a number of the more than one page of the first cluster exceeds a first threshold; and in the event that the number of the more than one page of the first cluster does not exceed the first threshold, select a new page within the domain to crawl and classify; and determine which of the one or more category clusters to publish for the domain; and a memory coupled to the processor and configured to provide the processor with instructions. 4. The system recited in claim 1 , wherein the processor is further configured to: promote a cluster to a primary category cluster for the domain. | 0.814721 |
9,715,488 | 16 | 20 | 16. The apparatus of claim 9 , wherein the instructions further cause the processor to propagate the at least one knowledge value at least by iteratively applying one or more knowledge reasoners to the logical parse hierarchical representation to propagate the at least one knowledge value of at least a subset of the nodes in the logical parse hierarchical representation to one or more other nodes in the logical parse hierarchical representation based on propagation rules, wherein each application of a knowledge reasoner generates a transaction record in the transaction knowledgebase data structure identifying node affected by the application of the knowledge reasoner. | 16. The apparatus of claim 9 , wherein the instructions further cause the processor to propagate the at least one knowledge value at least by iteratively applying one or more knowledge reasoners to the logical parse hierarchical representation to propagate the at least one knowledge value of at least a subset of the nodes in the logical parse hierarchical representation to one or more other nodes in the logical parse hierarchical representation based on propagation rules, wherein each application of a knowledge reasoner generates a transaction record in the transaction knowledgebase data structure identifying node affected by the application of the knowledge reasoner. 20. The apparatus of claim 16 , wherein each transaction record stores identifiers of nodes in the hierarchical representation affected by application of a corresponding knowledge reasoner and knowledge value states of the nodes corresponding to the identifiers of nodes in the transaction record. | 0.5 |
8,892,495 | 1 | 4 | 1. A programmable environmental controller, comprising: an local area network interface port configured to communicate digital data through a local area network; at least one climate sensor configured to sense environmental climate conditions; at least one movement sensor configured to detect a movement of an individual in a vicinity of the at least one movement sensor; and at least one automated processor configured to receive the sensed environmental climate conditions and the detected movement, to jointly classify a temporal pattern of the sensed environmental climate conditions and the detected movement, and to communicate at least one signal in dependence on the jointly classified pattern, the at least one signal being adapted to at least control an environmental control system. | 1. A programmable environmental controller, comprising: an local area network interface port configured to communicate digital data through a local area network; at least one climate sensor configured to sense environmental climate conditions; at least one movement sensor configured to detect a movement of an individual in a vicinity of the at least one movement sensor; and at least one automated processor configured to receive the sensed environmental climate conditions and the detected movement, to jointly classify a temporal pattern of the sensed environmental climate conditions and the detected movement, and to communicate at least one signal in dependence on the jointly classified pattern, the at least one signal being adapted to at least control an environmental control system. 4. The programmable environmental controller according to claim 1 , wherein the local area network communicates information with a second programmable environmental controller, comprising a second local area network interface port configured to communicate digital data through the local area network; at least one second climate sensor configured to sense second environmental climate conditions; at least one second movement sensor configured to detect a second movement of an individual in a vicinity of the at least one second movement sensor; and at least one second automated processor configured to receive the sensed second environmental conditions and the detected second movement, to jointly classify a temporal pattern of the sensed second environmental climate conditions and the detected second movement, and to communicate at least one second signal in dependence on the jointly classified pattern. | 0.5 |
9,721,207 | 1 | 4 | 1. A method for generating written content in an application, comprising: importing data from a plurality of data sources in response to a user query, wherein the plurality of data sources include at least one website determined to be a reliable source of information by a community of users, at least one internal knowledge management system, and at least one external knowledge management system; ranking the plurality of data sources based on a plurality of ranking factors, wherein the plurality of ranking factors include: total page views for each website, expertise of at least one author of data presented on each website, comments regarding the data presented on each website, crowd-sourced ratings of each website, a number of times the data presented on each website has been shared, and a reputation of an entity associated with the website; ranking the imported data imported from the plurality of data sources based on the ranking of the plurality of data sources to determine a relevance of the imported data; displaying, on a user interface, the imported data having a relevance above a user selected scoring threshold; automatically generating written content using at least a portion of the imported data displayed on the user interface based on the determined relevance of the imported data, wherein a size of the portion of the imported data used to automatically generate the written content decreases as the relevance of the imported data decreases; and automatically customizing the written content based on a file format of the application. | 1. A method for generating written content in an application, comprising: importing data from a plurality of data sources in response to a user query, wherein the plurality of data sources include at least one website determined to be a reliable source of information by a community of users, at least one internal knowledge management system, and at least one external knowledge management system; ranking the plurality of data sources based on a plurality of ranking factors, wherein the plurality of ranking factors include: total page views for each website, expertise of at least one author of data presented on each website, comments regarding the data presented on each website, crowd-sourced ratings of each website, a number of times the data presented on each website has been shared, and a reputation of an entity associated with the website; ranking the imported data imported from the plurality of data sources based on the ranking of the plurality of data sources to determine a relevance of the imported data; displaying, on a user interface, the imported data having a relevance above a user selected scoring threshold; automatically generating written content using at least a portion of the imported data displayed on the user interface based on the determined relevance of the imported data, wherein a size of the portion of the imported data used to automatically generate the written content decreases as the relevance of the imported data decreases; and automatically customizing the written content based on a file format of the application. 4. The method of claim 1 , wherein automatically generating written content is performed by a narrative science engine. | 0.661932 |
8,554,796 | 1 | 5 | 1. A method for analyzing the semantic content of network configuration files of a communication network, comprising: electronically accessing configuration files in a configuration language associated with corresponding network components of said network, said files containing commands that define the configuration of those components in said network; transforming, by using a processor, said commands into a structural database without the need for a grammar to understand the complete configuration language without losing any information in the configuration file; and constructing a semantic database of said configuration files by querying said structural database where the semantic database captures global relationships between commands in different parts of configuration files. | 1. A method for analyzing the semantic content of network configuration files of a communication network, comprising: electronically accessing configuration files in a configuration language associated with corresponding network components of said network, said files containing commands that define the configuration of those components in said network; transforming, by using a processor, said commands into a structural database without the need for a grammar to understand the complete configuration language without losing any information in the configuration file; and constructing a semantic database of said configuration files by querying said structural database where the semantic database captures global relationships between commands in different parts of configuration files. 5. The method of claim 1 wherein said commands are written in command blocks each comprising a main command followed by subcommands. | 0.722689 |
9,770,660 | 25 | 29 | 25. A method performed by a computer system to simulate a physical event, the method comprising: receiving a first input of one or more attributes related to a physical context of the physical event, wherein the receiving the first input of the one or more attributes includes receiving at least one first attribute related to a present location of a physical entity in the physical event; receiving a second input of one or more parameters related to a prediction of future behavior of the physical entity, wherein the receiving the second input of the one or more parameters includes receiving at least one parameter related to information usable to predict the future behavior of the physical entity in the physical event, wherein the at least one parameter is received in response to the at least one first attribute being presented, as a stimulus for the future behavior, on a display; receiving an indication to activate a computer device to start a simulation of the physical event in a virtual context in which the physical entity is simulated, wherein the start of the simulation includes positioning the simulated physical entity in the simulation at a location that corresponds to the present location of the physical entity in the physical event in accordance with the at least one parameter and in accordance with the at least one first attribute; and in response to receipt of the indication, starting the simulation with user control of the simulated physical entity to interact with the simulation from the location at which the simulated physical entity is positioned. | 25. A method performed by a computer system to simulate a physical event, the method comprising: receiving a first input of one or more attributes related to a physical context of the physical event, wherein the receiving the first input of the one or more attributes includes receiving at least one first attribute related to a present location of a physical entity in the physical event; receiving a second input of one or more parameters related to a prediction of future behavior of the physical entity, wherein the receiving the second input of the one or more parameters includes receiving at least one parameter related to information usable to predict the future behavior of the physical entity in the physical event, wherein the at least one parameter is received in response to the at least one first attribute being presented, as a stimulus for the future behavior, on a display; receiving an indication to activate a computer device to start a simulation of the physical event in a virtual context in which the physical entity is simulated, wherein the start of the simulation includes positioning the simulated physical entity in the simulation at a location that corresponds to the present location of the physical entity in the physical event in accordance with the at least one parameter and in accordance with the at least one first attribute; and in response to receipt of the indication, starting the simulation with user control of the simulated physical entity to interact with the simulation from the location at which the simulated physical entity is positioned. 29. The method of claim 25 , further comprising receiving at least one second attribute related to an actual present occurrence in a physical context other than the physical event, wherein the future behavior of the physical entity is stimulated by the at least one second attribute. | 0.596866 |
8,925,108 | 16 | 21 | 16. A machine-implemented method comprising: receiving, by a document archive server, a client request relating to an electronic document stored at the document archive server, wherein (a) the client request is for generation of an audit-enabled document, (b) the electronic document is associated with actions-taken information that exists separately from the electronic document, and (c) the actions-taken information describes actions taken with respect to the electronic document; retrieving, by the document archive server, from an audit information server and in response to the client request, the actions-taken information associated with the electronic document; generating, by the document archive server, an audit-enabled electronic document that includes the actions-taken information and the electronic document; and providing, by the document archive server, the audit-enabled electronic document. | 16. A machine-implemented method comprising: receiving, by a document archive server, a client request relating to an electronic document stored at the document archive server, wherein (a) the client request is for generation of an audit-enabled document, (b) the electronic document is associated with actions-taken information that exists separately from the electronic document, and (c) the actions-taken information describes actions taken with respect to the electronic document; retrieving, by the document archive server, from an audit information server and in response to the client request, the actions-taken information associated with the electronic document; generating, by the document archive server, an audit-enabled electronic document that includes the actions-taken information and the electronic document; and providing, by the document archive server, the audit-enabled electronic document. 21. The method of claim 16 , wherein: the client request is triggered by attaching the electronic document to an email addressed to an email recipient; the audit-enabled electronic document further includes a specification of authorized actions to be associated with the electronic document; and the authorized actions are based on an identity of the email recipient. | 0.5 |
9,547,832 | 12 | 19 | 12. A system comprising: one or more processors; a content monitor executable by the one or more processors and configured to: receive a content item from one or more of a plurality of communication channels, and determine the content item is to be processed further, the content item comprising: a communication from an individual, and a statement by the individual, the statement comprising committing language about an intent to attend an event; an analysis engine executable by the one or more processors and configured to determine a commitment score of the individual to attend the event by: identifying the event as a topic of interest in the content item; calculating a strength value of the intent of the individual to attend the event by performing a natural language analysis of the committing language of the statement by the individual in the content item, calculating a sentiment value of the intent of the individual to attend the event by performing a semantic analysis on the content item, the semantic analysis comprising identifying a description of a probability related to the event identified in the content item, calculating a social impact value of the intent of the individual to attend the event by performing a social impact analysis of the content item based on a number of receiving subscribers to the content item on the communication channel, and calculating a magnitude value of the intent of the individual to attend the event by performing a magnitude of commitment analysis of the content item based on a cost of attending the event, wherein: the commitment score comprises a combination of the strength value, the sentiment value, the social impact value, and the magnitude value; and determining an action based on the commitment score of the individual to attend the event. | 12. A system comprising: one or more processors; a content monitor executable by the one or more processors and configured to: receive a content item from one or more of a plurality of communication channels, and determine the content item is to be processed further, the content item comprising: a communication from an individual, and a statement by the individual, the statement comprising committing language about an intent to attend an event; an analysis engine executable by the one or more processors and configured to determine a commitment score of the individual to attend the event by: identifying the event as a topic of interest in the content item; calculating a strength value of the intent of the individual to attend the event by performing a natural language analysis of the committing language of the statement by the individual in the content item, calculating a sentiment value of the intent of the individual to attend the event by performing a semantic analysis on the content item, the semantic analysis comprising identifying a description of a probability related to the event identified in the content item, calculating a social impact value of the intent of the individual to attend the event by performing a social impact analysis of the content item based on a number of receiving subscribers to the content item on the communication channel, and calculating a magnitude value of the intent of the individual to attend the event by performing a magnitude of commitment analysis of the content item based on a cost of attending the event, wherein: the commitment score comprises a combination of the strength value, the sentiment value, the social impact value, and the magnitude value; and determining an action based on the commitment score of the individual to attend the event. 19. The system of claim 12 , wherein performing the semantic analysis further comprises identifying conflicting language or conflicting sentiment in the content item. | 0.707746 |
9,268,560 | 1 | 2 | 1. A method for indicating a change to a dependent file, the method comprising: receiving a first change to a program file; performing, via a computing device, a second change to code or program data in a first dependent file on the program file; wherein the second change is related to the first change; and displaying, in a document editor via the computing device, a first identifier for the first dependent file in a first text style, if the first dependent file is changed based on the first change to the program file; displaying, in the document editor via the computing device, a second identifier, in a second text style, for a second dependent file, if the second dependent file is not changed based on the first change to the program file wherein: code of the program file calls code or program data of the first dependent file and the second dependent file, the first text style indicates the first dependent file has been changed based on the first change to the program file, and the first text style and the second text style are different styles. | 1. A method for indicating a change to a dependent file, the method comprising: receiving a first change to a program file; performing, via a computing device, a second change to code or program data in a first dependent file on the program file; wherein the second change is related to the first change; and displaying, in a document editor via the computing device, a first identifier for the first dependent file in a first text style, if the first dependent file is changed based on the first change to the program file; displaying, in the document editor via the computing device, a second identifier, in a second text style, for a second dependent file, if the second dependent file is not changed based on the first change to the program file wherein: code of the program file calls code or program data of the first dependent file and the second dependent file, the first text style indicates the first dependent file has been changed based on the first change to the program file, and the first text style and the second text style are different styles. 2. The method of claim 1 , further comprising displaying a user prompt for saving the second change to the first dependent file in a non-transitory computer readable memory device. | 0.621849 |
8,364,509 | 1 | 40 | 1. A method of measuring performance parameters and reporting on at least one aspect of respective performance parameters of a plurality of agents, the method comprising at least the following: measuring in an agent training system at least a plurality of data, including data representing agent performance parameters for each of the plurality, of agents, wherein the plurality of data representing agent performance parameters includes data related to at least one of either sections or teams to which an agent is assigned or associated, personal development meetings (PDMs) during which the agent receives constructive criticism or other remedial instruction related to improving job performance, wherein the plurality of data is related to improving the job performance of at least one of the plurality of agents on an individual agent-basis or a group-level agent-basis; receiving from the agent training system the plurality of data from a first input representing respective performance parameters pertaining to the plurality of agents on the individual agent-basis or the group-level agent-basis working at least one call center; organizing the at least plurality of data related to improving the job performance received from the first input in a data store in at least one of a plurality of user-specified parameters; storing the at least plurality of data related to improving the job performance received from the first input in the data store arranged in at least one of the plurality of user-specified parameters for subsequent query and retrieval; receiving at least one query of the data store by a user pertaining to the improving job performance of at least one of the agents on the individual agent-basis or the group-level agent-basis; processing an output determined by the at least first input of at least the plurality of the data related to the improving job performance in response to the query and processing the output results in the plurality of data related to remedial instruction related to improving a job performance arranged in one of the plurality of user-specified parameters, including at least one of a productivity parameter, a conversion parameter, a schedule adherence parameter, and a number of calls parameter by a given agent; and presenting at least one report as determined by the output of the query to the at least one user. | 1. A method of measuring performance parameters and reporting on at least one aspect of respective performance parameters of a plurality of agents, the method comprising at least the following: measuring in an agent training system at least a plurality of data, including data representing agent performance parameters for each of the plurality, of agents, wherein the plurality of data representing agent performance parameters includes data related to at least one of either sections or teams to which an agent is assigned or associated, personal development meetings (PDMs) during which the agent receives constructive criticism or other remedial instruction related to improving job performance, wherein the plurality of data is related to improving the job performance of at least one of the plurality of agents on an individual agent-basis or a group-level agent-basis; receiving from the agent training system the plurality of data from a first input representing respective performance parameters pertaining to the plurality of agents on the individual agent-basis or the group-level agent-basis working at least one call center; organizing the at least plurality of data related to improving the job performance received from the first input in a data store in at least one of a plurality of user-specified parameters; storing the at least plurality of data related to improving the job performance received from the first input in the data store arranged in at least one of the plurality of user-specified parameters for subsequent query and retrieval; receiving at least one query of the data store by a user pertaining to the improving job performance of at least one of the agents on the individual agent-basis or the group-level agent-basis; processing an output determined by the at least first input of at least the plurality of the data related to the improving job performance in response to the query and processing the output results in the plurality of data related to remedial instruction related to improving a job performance arranged in one of the plurality of user-specified parameters, including at least one of a productivity parameter, a conversion parameter, a schedule adherence parameter, and a number of calls parameter by a given agent; and presenting at least one report as determined by the output of the query to the at least one user. 40. The method of claim 1 , wherein receiving at least one query includes receiving a query for agent productivity data, the agent productivity data being specific to at least one given date. | 0.741892 |
8,892,423 | 1 | 3 | 1. A computer-implemented method for generating examples for electronic dictionaries to serve as an aid to translation between languages performed by one or more processors, the method comprising: creating an electronic dictionary example by: acquiring at least one dictionary entry comprising a headword W j in a source language and at least one translation T j1 , T j2 , . . . T jn for the headword W j in a target language; generating a first set comprising possible forms for the headword W j in the source language and a second set comprising possible forms for each translation T j1 , T j2 , . . . T jn in the target language; searching a corpus of translations, where the corpus of translations is a preexisting corpus of translation sentence pairs, each translation sentence pair comprising a first sentence in the source language and a second sentence in the target language, where the first sentence is a translation of the second sentence, and the searching includes searching at least one first sentence in the source language included in the corpus of translations and searching at least one second sentence in the target language in the corpus of translations; identifying in the corpus of translations at least one translation sentence pair, from either the searching of the at least one first sentence in the source language or the searching of the at least one second sentence in the target language, that consists of the first sentence that incorporates the headword W j , or one of its generated forms, and the second sentence that incorporates the translation T jn or one of its generated forms; and providing the at least one translation sentence pair to a user. | 1. A computer-implemented method for generating examples for electronic dictionaries to serve as an aid to translation between languages performed by one or more processors, the method comprising: creating an electronic dictionary example by: acquiring at least one dictionary entry comprising a headword W j in a source language and at least one translation T j1 , T j2 , . . . T jn for the headword W j in a target language; generating a first set comprising possible forms for the headword W j in the source language and a second set comprising possible forms for each translation T j1 , T j2 , . . . T jn in the target language; searching a corpus of translations, where the corpus of translations is a preexisting corpus of translation sentence pairs, each translation sentence pair comprising a first sentence in the source language and a second sentence in the target language, where the first sentence is a translation of the second sentence, and the searching includes searching at least one first sentence in the source language included in the corpus of translations and searching at least one second sentence in the target language in the corpus of translations; identifying in the corpus of translations at least one translation sentence pair, from either the searching of the at least one first sentence in the source language or the searching of the at least one second sentence in the target language, that consists of the first sentence that incorporates the headword W j , or one of its generated forms, and the second sentence that incorporates the translation T jn or one of its generated forms; and providing the at least one translation sentence pair to a user. 3. The computer-implemented method of claim 1 , wherein the first set comprises all possible forms for the headword W j . | 0.81994 |
8,484,028 | 24 | 32 | 24. A non-transitory computer readable medium comprising instructions for executing a process for document navigation, the instructions comprising: displaying a text document on a display; converting a text of the text document into at least one audible sound; presenting the at least one audible sound; displaying a section of the document corresponding to the audible sound, and navigating a displayed cursor indicating text corresponding to the at least one audible sound as the at least one audible sound is played; wherein upon displaying the section of the document containing a link which points to a region of interest, panning to the region of interest pointed to by the link when the link is referenced by the audible sound. | 24. A non-transitory computer readable medium comprising instructions for executing a process for document navigation, the instructions comprising: displaying a text document on a display; converting a text of the text document into at least one audible sound; presenting the at least one audible sound; displaying a section of the document corresponding to the audible sound, and navigating a displayed cursor indicating text corresponding to the at least one audible sound as the at least one audible sound is played; wherein upon displaying the section of the document containing a link which points to a region of interest, panning to the region of interest pointed to by the link when the link is referenced by the audible sound. 32. The non-transitory computer readable medium of claim 24 , wherein the link comprises a macro. | 0.806773 |
7,623,711 | 65 | 66 | 65. The method defined in claim 64 further comprising: computing a white space graph for each document; and determining a minimal scaling factor for each document. | 65. The method defined in claim 64 further comprising: computing a white space graph for each document; and determining a minimal scaling factor for each document. 66. The method defined in claim 65 further comprising: scaling each document in the collection by its minimal scaling factor. | 0.634503 |
8,005,643 | 44 | 46 | 44. The system according to claim 36 , further comprising an act of dynamically generating the baseline statistical distribution. | 44. The system according to claim 36 , further comprising an act of dynamically generating the baseline statistical distribution. 46. The system according to claim 44 , wherein the dynamically generated baseline distribution is adapted from previous execution of the act of analyzing the result to obtain a statistical distribution of at least one identifying characteristic within the result. | 0.600304 |
9,483,768 | 1 | 7 | 1. A computer-implemented method, comprising: receiving, by a processor, interaction data corresponding to one or more interactions between a customer and a customer support representative and storing said interaction data in a memory; extracting the stored interaction data from the memory and detecting, by the processor, the presence of at least one language associated with the interaction data by comparing whole text strings or portions of text in the interaction data with available language detection models for different languages, and predicting a best matching language corresponding to the interaction data; generating, by the processor, textual content in a plurality of languages corresponding to the interaction data based at least in part on translating the interaction data using two or more languages different than the at least one language; determining, by the processor, at least one emotion score for text corresponding to each language from among the plurality of languages; determining, by the processor, an aggregate emotion score using the at least one emotion score for the text corresponding to the each language; modeling, by the processor, an interaction experience of the customer based at least in part on the aggregate emotion score; and providing at least one recommendation to the customer based on said modeled interaction experience. | 1. A computer-implemented method, comprising: receiving, by a processor, interaction data corresponding to one or more interactions between a customer and a customer support representative and storing said interaction data in a memory; extracting the stored interaction data from the memory and detecting, by the processor, the presence of at least one language associated with the interaction data by comparing whole text strings or portions of text in the interaction data with available language detection models for different languages, and predicting a best matching language corresponding to the interaction data; generating, by the processor, textual content in a plurality of languages corresponding to the interaction data based at least in part on translating the interaction data using two or more languages different than the at least one language; determining, by the processor, at least one emotion score for text corresponding to each language from among the plurality of languages; determining, by the processor, an aggregate emotion score using the at least one emotion score for the text corresponding to the each language; modeling, by the processor, an interaction experience of the customer based at least in part on the aggregate emotion score; and providing at least one recommendation to the customer based on said modeled interaction experience. 7. The method of claim 1 , further comprising: performing, by the processor, correction of the at least one emotion score based on at least one of a semantic analysis, a sarcasm analysis, an experiential analysis, and an engagement-based analysis of the text. | 0.830497 |
8,548,807 | 9 | 12 | 9. A system comprising: a processor; and a computer-readable storage medium storing instructions which, when executed by the processor, cause the processor to perform a method comprising: identifying an acoustic model and a pronouncing dictionary, wherein the acoustic model and the pronouncing dictionary are trained on native speech in a target dialect; collecting speech from a speaker, resulting in collected speech; transcribing the collected speech to generate a lattice of plausible phonemes which depend on a property of the target dialect; replacing each phoneme used in the acoustic model with a modified phoneme, wherein the modified phoneme is a weighted sum of plausible phonemes in the lattice of plausible phonemes, to yield a modified acoustic model; and recognizing additional speech from the speaker using the modified acoustic model. | 9. A system comprising: a processor; and a computer-readable storage medium storing instructions which, when executed by the processor, cause the processor to perform a method comprising: identifying an acoustic model and a pronouncing dictionary, wherein the acoustic model and the pronouncing dictionary are trained on native speech in a target dialect; collecting speech from a speaker, resulting in collected speech; transcribing the collected speech to generate a lattice of plausible phonemes which depend on a property of the target dialect; replacing each phoneme used in the acoustic model with a modified phoneme, wherein the modified phoneme is a weighted sum of plausible phonemes in the lattice of plausible phonemes, to yield a modified acoustic model; and recognizing additional speech from the speaker using the modified acoustic model. 12. The system of claim 9 , wherein the target dialect comprises at least one of a regional dialect and a foreign accent. | 0.617089 |
9,323,838 | 7 | 8 | 7. A method comprising: extracting description information of multiple products; clustering the description information of the multiple products belonging to a particular model into a first text; processing the first text by segmenting the first text to one of: remove from the first text one or more terms whose term frequencies are higher than a first set threshold, and remove from the first text one or more terms whose term frequencies are lower than a second set threshold; clustering, after processing the first text, first texts of products belonging to different models into a second text; applying a subject analysis to the second text to obtain one or more subjects; correlating the multiple products to the one or more subjects; and navigating the multiple products according to a respective subject correlated to a respective product, wherein the applying the subject analysis to the second text to obtain one or more subjects comprises: setting a number of subjects in one or more subject models; applying the subject analysis to the second text by using a text analysis method based on the one or more subject models; obtaining a number of subsets corresponding to the number of subjects from a set of terms included in the second text, the number of subsets being equal to the number of subjects, a respective subset corresponding to a respective subject; and according to the respective subset that one or more terms in the description information of the products locate, correlating the description information of the products to the respective subject corresponding to the respective subset. | 7. A method comprising: extracting description information of multiple products; clustering the description information of the multiple products belonging to a particular model into a first text; processing the first text by segmenting the first text to one of: remove from the first text one or more terms whose term frequencies are higher than a first set threshold, and remove from the first text one or more terms whose term frequencies are lower than a second set threshold; clustering, after processing the first text, first texts of products belonging to different models into a second text; applying a subject analysis to the second text to obtain one or more subjects; correlating the multiple products to the one or more subjects; and navigating the multiple products according to a respective subject correlated to a respective product, wherein the applying the subject analysis to the second text to obtain one or more subjects comprises: setting a number of subjects in one or more subject models; applying the subject analysis to the second text by using a text analysis method based on the one or more subject models; obtaining a number of subsets corresponding to the number of subjects from a set of terms included in the second text, the number of subsets being equal to the number of subjects, a respective subset corresponding to a respective subject; and according to the respective subset that one or more terms in the description information of the products locate, correlating the description information of the products to the respective subject corresponding to the respective subset. 8. The method as recited in claim 7 , further comprising: prior to the extracting the description information of the multiple products, categorizing the multiple products, wherein the extracting the description information of the multiple products comprises extracting description information of multiple products under a particular category and the second text includes the description information of the multiple products under the particular category. | 0.5 |
8,504,915 | 5 | 11 | 5. A computer-implemented method comprising: detecting an input operation associated with an electronic note page, wherein the input operation coincides with a first page location; positioning an originating container and a cursor at a default insertion point when the electronic note page is absent of a container and an input state corresponds to a first input operation in conjunction with the electronic note page; removing the originating container and positioning a new container and the cursor at the insertion point when the input state corresponds to a second input operation; and transitioning a display of the new container and content rendered on the electronic note page using an interaction model that includes resizing ranges to automatically adjust the new container to a first percentage of an available page dimension and to a second percentage of the available page dimension during resizing operations. | 5. A computer-implemented method comprising: detecting an input operation associated with an electronic note page, wherein the input operation coincides with a first page location; positioning an originating container and a cursor at a default insertion point when the electronic note page is absent of a container and an input state corresponds to a first input operation in conjunction with the electronic note page; removing the originating container and positioning a new container and the cursor at the insertion point when the input state corresponds to a second input operation; and transitioning a display of the new container and content rendered on the electronic note page using an interaction model that includes resizing ranges to automatically adjust the new container to a first percentage of an available page dimension and to a second percentage of the available page dimension during resizing operations. 11. The computer-implemented method of claim 5 , further comprising reflowing content based in part on container resizing operations. | 0.812676 |
9,542,436 | 1 | 10 | 1. A lifecycle management system for a platform, the system comprising: associative memory populated with a plurality of matrices, each matrix including entity values and attribute values pertaining to life cycle of the platform, the associative memory providing unfiltered correlations between each matrix relative to the other matrices; a processor executing an entity analytics engine to perform a search of the unfiltered correlations with respect to a specified component of the platform, the search returning any highly correlated entity and attribute values that do not include the specified component but are highly correlated to the specified component; wherein the highly correlated entity is the entity with the highest value, that correspond to a number of times the entity is returned within a repetitive search executed by the entity analytic engine; and the processor updating a plurality of different information tools with reports based on other similar components that are either further ahead or behind in the lifecycle of the specified component. | 1. A lifecycle management system for a platform, the system comprising: associative memory populated with a plurality of matrices, each matrix including entity values and attribute values pertaining to life cycle of the platform, the associative memory providing unfiltered correlations between each matrix relative to the other matrices; a processor executing an entity analytics engine to perform a search of the unfiltered correlations with respect to a specified component of the platform, the search returning any highly correlated entity and attribute values that do not include the specified component but are highly correlated to the specified component; wherein the highly correlated entity is the entity with the highest value, that correspond to a number of times the entity is returned within a repetitive search executed by the entity analytic engine; and the processor updating a plurality of different information tools with reports based on other similar components that are either further ahead or behind in the lifecycle of the specified component. 10. The system of claim 1 , wherein the lifecycle includes component material, maintenance, location and use. | 0.826984 |
7,895,144 | 1 | 7 | 1. A risk assessment method comprising: receiving, by an inference engine within a computing system, first sensor cohort data associated with a first cohort, said first cohort located within a pre/post security area within an airport; receiving, by said inference engine, first group technology inferences associated with said first cohort; generating, by said inference engine, first risk cohort inferences, said generating said first risk cohort inferences based on said first group technology inferences and said first sensor cohort data; receiving, by said inference engine, first inference data generated by said inference engine, said first inference data comprising a first plurality of inferences associated with said first cohort and a security perimeter surrounding said airport; receiving, by said inference engine, second inference data generated by said inference engine, said second inference data comprising a second of plurality of inferences associated with said first cohort and said pre/post security area within said airport; generating, by said inference engine, third inference data, said third inference data comprising a third plurality of inferences associated with said first cohort and said pre/post security area within said airport, wherein said generating said third inference data is based on said first risk cohort inferences, said first inference data, and said second inference data; generating, by said inference engine based on said third inference data, a first associated risk level score for said first cohort; and storing, by said computing system, said third inference data and said first associated risk level score. | 1. A risk assessment method comprising: receiving, by an inference engine within a computing system, first sensor cohort data associated with a first cohort, said first cohort located within a pre/post security area within an airport; receiving, by said inference engine, first group technology inferences associated with said first cohort; generating, by said inference engine, first risk cohort inferences, said generating said first risk cohort inferences based on said first group technology inferences and said first sensor cohort data; receiving, by said inference engine, first inference data generated by said inference engine, said first inference data comprising a first plurality of inferences associated with said first cohort and a security perimeter surrounding said airport; receiving, by said inference engine, second inference data generated by said inference engine, said second inference data comprising a second of plurality of inferences associated with said first cohort and said pre/post security area within said airport; generating, by said inference engine, third inference data, said third inference data comprising a third plurality of inferences associated with said first cohort and said pre/post security area within said airport, wherein said generating said third inference data is based on said first risk cohort inferences, said first inference data, and said second inference data; generating, by said inference engine based on said third inference data, a first associated risk level score for said first cohort; and storing, by said computing system, said third inference data and said first associated risk level score. 7. The method of claim 1 , further comprising: generating, by said computing system, an alert associated with said first associated risk level score for said first cohort; and presenting by said computing system, said alert. | 0.915535 |
4,831,526 | 1 | 7 | 1. A computerized insurance system for preparing and processing applications for insurance and premium quotations, and for preparing and writing insurance contracts requested by clients, said system comprising: processing means, including an interactive data bank into which data is written and from which data is read, said data bank storing information regarding a risk to be insured, client information, insurance premium information and the full text of all contract provisions terminal means for interactively communicating on-line with said processing means and accessibly by an operator to produce requestes and to enter information and/or retrieve information for writing into and/or reading from said data bank; display means for displaying information that is entered and retrieved; merging means included in said processing means for reading out from said data bank selected client information and only the text data of those contract provisions which apply to the particular contract and merging said read out client information and said read out particularized text data to compile final insurance contract documents tailored to each client; and print means coupled to said merging means for printing said final insurance contracts. | 1. A computerized insurance system for preparing and processing applications for insurance and premium quotations, and for preparing and writing insurance contracts requested by clients, said system comprising: processing means, including an interactive data bank into which data is written and from which data is read, said data bank storing information regarding a risk to be insured, client information, insurance premium information and the full text of all contract provisions terminal means for interactively communicating on-line with said processing means and accessibly by an operator to produce requestes and to enter information and/or retrieve information for writing into and/or reading from said data bank; display means for displaying information that is entered and retrieved; merging means included in said processing means for reading out from said data bank selected client information and only the text data of those contract provisions which apply to the particular contract and merging said read out client information and said read out particularized text data to compile final insurance contract documents tailored to each client; and print means coupled to said merging means for printing said final insurance contracts. 7. A computerized insurance system as in claim 1 wherein the risk insured relates to a dwelling. | 0.854103 |
10,120,933 | 1 | 27 | 1. A method of language processing that represents both sematic and orientation content within a text, the method being performed by circuitry included in a computing device, the method comprising: receiving, by the circuitry, ordered data elements representing respective words in a text, the ordered data elements including a first data element including one or more words and a second data element, which is sequential with the first data element and include another one or more words; generating, based on a plurality of semantic classes corresponding respectively to blades of a graded vector space representing clusters of semantic meaning, a first subspace formed within the graded vector space by the first data element, a second subspace within the graded vector space by the second data element, a first set of data elements corresponding to one or more first semantic classes that include the first data element, and a second set of data elements corresponding to one or more second semantic classes that include the second data element; encoding by the circuitry, when the one or more first semantic classes is non-degenerate with the one or more second semantic classes, the first data element with respect to the second data element such that an encoded subspace formed by encoding the first data element with respect to the second data element is oriented with respect to an order of the first data element with respect to the second data element, the encoding being performed by computing one of a left contraction and a right contraction of the first set of data elements with respect to the second set of data elements; computing by the circuitry, respective components of a weight distribution of the first data element with respect to the second data element, the components of the weight distribution being computed using respective ordered pairs for each of the one or more first semantic classes with respect to each of the one or more second semantic classes; and determining a dominant semantic class of an ordered sequence of the first data element and the second data element, the dominant semantic class representing an oriented semantic context of an ordered sequence of the first and second data elements, and the dominant semantic class being determined as a semantic class corresponding to a component having a maximum magnitude of the weight distribution, wherein the left contraction is one of a Clifford Algebra left contraction and a Geometric Algebra left contraction, and the right contraction is one of a Clifford Algebra right contraction and a Geometric Algebra right contraction. | 1. A method of language processing that represents both sematic and orientation content within a text, the method being performed by circuitry included in a computing device, the method comprising: receiving, by the circuitry, ordered data elements representing respective words in a text, the ordered data elements including a first data element including one or more words and a second data element, which is sequential with the first data element and include another one or more words; generating, based on a plurality of semantic classes corresponding respectively to blades of a graded vector space representing clusters of semantic meaning, a first subspace formed within the graded vector space by the first data element, a second subspace within the graded vector space by the second data element, a first set of data elements corresponding to one or more first semantic classes that include the first data element, and a second set of data elements corresponding to one or more second semantic classes that include the second data element; encoding by the circuitry, when the one or more first semantic classes is non-degenerate with the one or more second semantic classes, the first data element with respect to the second data element such that an encoded subspace formed by encoding the first data element with respect to the second data element is oriented with respect to an order of the first data element with respect to the second data element, the encoding being performed by computing one of a left contraction and a right contraction of the first set of data elements with respect to the second set of data elements; computing by the circuitry, respective components of a weight distribution of the first data element with respect to the second data element, the components of the weight distribution being computed using respective ordered pairs for each of the one or more first semantic classes with respect to each of the one or more second semantic classes; and determining a dominant semantic class of an ordered sequence of the first data element and the second data element, the dominant semantic class representing an oriented semantic context of an ordered sequence of the first and second data elements, and the dominant semantic class being determined as a semantic class corresponding to a component having a maximum magnitude of the weight distribution, wherein the left contraction is one of a Clifford Algebra left contraction and a Geometric Algebra left contraction, and the right contraction is one of a Clifford Algebra right contraction and a Geometric Algebra right contraction. 27. A non-transitory computer-readable medium including computer program instructions, which when executed by a computer, cause the computer to perform the method according to claim 1 . | 0.792601 |
8,370,949 | 1 | 8 | 1. A method comprising: identifying, by one or more processors of one or more server devices, an advertisement; determining, by one or more processors of the one or more server devices, that one or more terms, associated with the advertisement, include a potential violation of intellectual property rights; and providing, by one or more processors of the one or more server devices and to an advertiser associated with the advertisement, an option for the advertiser to request further review of the advertisement when the one or more terms, associated with the advertisement, include the potential violation of intellectual property rights. | 1. A method comprising: identifying, by one or more processors of one or more server devices, an advertisement; determining, by one or more processors of the one or more server devices, that one or more terms, associated with the advertisement, include a potential violation of intellectual property rights; and providing, by one or more processors of the one or more server devices and to an advertiser associated with the advertisement, an option for the advertiser to request further review of the advertisement when the one or more terms, associated with the advertisement, include the potential violation of intellectual property rights. 8. The method of claim 1 , further comprising: providing, before providing the option for the advertiser to request further review and to the advertiser, an opportunity to change the advertisement; receiving, after providing the option for the advertiser to request further review of the advertisement and from the advertiser, an indication that use of the one or more terms is authorized; and allowing the advertisement to be displayed after receiving the indication. | 0.5 |
8,185,523 | 1 | 17 | 1. A computer-implemented method of ranking document returned in a results list in response to a search query, comprising entering a search query; displaying a results list of documents returned in response to the search query; receiving and storing input from the user indicating the relevance to the user of a document in the results list of documents; modifying the weight of previous input from the user indicating the relevance of the document to the user; associating the user input indicating the relevance to the user of the document in the results list with the search query and with the document; determining a relevance formula having variables and parameters, the relevance formula for computing a relevance score for the document and the search query, wherein the variables comprise a plurality of features, a feature of the plurality of features comprises one of: a frequency of occurrence of a term in the document, a frequency of hyperlinks containing a term in the document, and a frequency of occurrence of a term in an abstract of the document, and the parameters comprise a plurality of weighting factors corresponding to each of the features; ranking the document based on the relevance score, wherein the relevance score is dependent on the user input associated with the document and the search query; and presenting the ranked results list of documents to the user. | 1. A computer-implemented method of ranking document returned in a results list in response to a search query, comprising entering a search query; displaying a results list of documents returned in response to the search query; receiving and storing input from the user indicating the relevance to the user of a document in the results list of documents; modifying the weight of previous input from the user indicating the relevance of the document to the user; associating the user input indicating the relevance to the user of the document in the results list with the search query and with the document; determining a relevance formula having variables and parameters, the relevance formula for computing a relevance score for the document and the search query, wherein the variables comprise a plurality of features, a feature of the plurality of features comprises one of: a frequency of occurrence of a term in the document, a frequency of hyperlinks containing a term in the document, and a frequency of occurrence of a term in an abstract of the document, and the parameters comprise a plurality of weighting factors corresponding to each of the features; ranking the document based on the relevance score, wherein the relevance score is dependent on the user input associated with the document and the search query; and presenting the ranked results list of documents to the user. 17. The method of claim 1 , further comprising updating the relevance formula wherein updating the relevance formula comprises updating the parameters, and the plurality of features comprise at least two from the group consisting of a tag, a term within the document, a frequency of occurrence of a term in the link text of the document, a frequency of occurrence of a term in the abstract of the document, a frequency of occurrence of a term in the summary of the document, a frequency of occurrence of a term in anchor text in the document, a frequency of occurrence of a term in the summary information in the document, a structure of the document, the length of the document, the type of the document, the date of creation of the document, a link to the document, a position of the document in a search results list, a number of times the document has been accessed from a search results list, term scores, link structures, a user representation, a time of a user input, a user blocking the document, a user identifier for the document, a user saving the document, a user bookmarking the document, a user tagging the document, and a user rating of the document. | 0.5 |
8,799,765 | 1 | 2 | 1. A digital computing device for viewing shared annotations of at least one electronic reference document, the device comprising a digital memory, a display, a user input interface, and a network interface; and said computing device being configured to: receive, over a network, via the network interface, a digital location identifier for navigation to a shared reading location with an associated annotation, said digital location identifier specifying the shared reading location within a version of the electronic reference document and does not include the version of the electronic reference document; upon receipt of a selection to navigate to the shared reading location, determine if a user of the digital computing device has sufficient rights to use a copy of the electronic reference document referenced by the digital location identifier, and, if the user does not have sufficient rights, (i) provide a prompt with an option for the user to purchase said rights, (ii) responsive to confirmation of the purchase of the rights to use the copy of the electronic reference document, receive the copy of the electronic reference document, and (iii) store the copy of the electronic reference document in the digital memory; and upon successful confirmation of the user's rights to use the copy of the electronic reference document and responsive to the selection to navigate to the shared reading location (i) determining that the content referenced by the digital location identifier exists in the copy of the reference document, (ii) displaying at least a portion of the copy of the electronic reference document at the shared reading location and the associated annotation within the copy of the electronic reference document at the shared reading location. | 1. A digital computing device for viewing shared annotations of at least one electronic reference document, the device comprising a digital memory, a display, a user input interface, and a network interface; and said computing device being configured to: receive, over a network, via the network interface, a digital location identifier for navigation to a shared reading location with an associated annotation, said digital location identifier specifying the shared reading location within a version of the electronic reference document and does not include the version of the electronic reference document; upon receipt of a selection to navigate to the shared reading location, determine if a user of the digital computing device has sufficient rights to use a copy of the electronic reference document referenced by the digital location identifier, and, if the user does not have sufficient rights, (i) provide a prompt with an option for the user to purchase said rights, (ii) responsive to confirmation of the purchase of the rights to use the copy of the electronic reference document, receive the copy of the electronic reference document, and (iii) store the copy of the electronic reference document in the digital memory; and upon successful confirmation of the user's rights to use the copy of the electronic reference document and responsive to the selection to navigate to the shared reading location (i) determining that the content referenced by the digital location identifier exists in the copy of the reference document, (ii) displaying at least a portion of the copy of the electronic reference document at the shared reading location and the associated annotation within the copy of the electronic reference document at the shared reading location. 2. The computing device of claim 1 , wherein the digital location identifier specifies the reading location by specifying one or more of: a letter, a word, a group of words, a title, a sentence, a paragraph, a chapter, a section, an image, a table, an interactive object, an assessment, and a page. | 0.604775 |
8,024,190 | 5 | 7 | 5. The method of claim 4 , further comprising: selecting k utterances that have smallest confidence scores from the set of candidates and transcribing the k utterances into a first additional transcribed set. | 5. The method of claim 4 , further comprising: selecting k utterances that have smallest confidence scores from the set of candidates and transcribing the k utterances into a first additional transcribed set. 7. The method of claim 5 , wherein selecting k utterances further comprises leaving out utterances with confidence scores indicating that the utterances were correctly recognized. | 0.5 |
7,778,954 | 1 | 2 | 1. A method for automatically processing a textual document, comprising: marking up citations in the textual document, the citations identifying other textual documents relied on in the textual document; locating quotations in the textual document which are candidates for being associated with a marked-up citation; identifying in the textual document indicating a negative treatment of another textual document; generating a depth-of-treatment value for each citation in the textual document, the depth-of-treatment value indicating a potential significance of the piece of text to the reasoning in the textual document; determining a source textual document for each candidate quotation in order to verify the origin of the quotation and generate verified quotations; producing a plurality of pieces of text surrounding the citations based on the verified quotations; assigning an abstract to each citation based on the plurality of pieces of text surrounding the citations; and grouping the citations and the verified quotations, the depth-of-treatment values and the abstracts for each citation together into a data record containing information about the reasoning contained in the textual document. | 1. A method for automatically processing a textual document, comprising: marking up citations in the textual document, the citations identifying other textual documents relied on in the textual document; locating quotations in the textual document which are candidates for being associated with a marked-up citation; identifying in the textual document indicating a negative treatment of another textual document; generating a depth-of-treatment value for each citation in the textual document, the depth-of-treatment value indicating a potential significance of the piece of text to the reasoning in the textual document; determining a source textual document for each candidate quotation in order to verify the origin of the quotation and generate verified quotations; producing a plurality of pieces of text surrounding the citations based on the verified quotations; assigning an abstract to each citation based on the plurality of pieces of text surrounding the citations; and grouping the citations and the verified quotations, the depth-of-treatment values and the abstracts for each citation together into a data record containing information about the reasoning contained in the textual document. 2. The method of claim 1 , further comprising a step of displaying an interactive hierarchical chart representation of a case history for a legal case corresponding to at least one of the citations, wherein the hierarchical chart representation is organized and displayed according to a set of rules and includes a plurality of horizontally oriented rectangular regions, with each rectangular region corresponding to a level of a court system. | 0.5 |
9,990,652 | 23 | 24 | 23. A computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to perform steps comprising: receiving a plurality of advertisement requests, each advertising request comprising advertising content and an identification of one or more objects in a social networking system; identifying a viewing user to receive advertising; identifying one or more other users who are connected to the viewing user in the social networking system; identifying a plurality of objects in the social networking system with which the identified one or more other users have interacted; identifying one or more candidate advertisements based on the advertisement requests, where each candidate advertisement is associated with an advertisement request that identified at least one of the identified objects with which the identified one or more other users have interacted; selecting a candidate advertisement for display to the viewing user; generating by a processor a social advertisement that comprises (1) the advertising content for the advertisement request associated with the selected candidate advertisement and (2) a social story that describes an interaction between a user who is connected with the viewing user and an object in the social networking system; and sending the social advertisement for display to the viewing user. | 23. A computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to perform steps comprising: receiving a plurality of advertisement requests, each advertising request comprising advertising content and an identification of one or more objects in a social networking system; identifying a viewing user to receive advertising; identifying one or more other users who are connected to the viewing user in the social networking system; identifying a plurality of objects in the social networking system with which the identified one or more other users have interacted; identifying one or more candidate advertisements based on the advertisement requests, where each candidate advertisement is associated with an advertisement request that identified at least one of the identified objects with which the identified one or more other users have interacted; selecting a candidate advertisement for display to the viewing user; generating by a processor a social advertisement that comprises (1) the advertising content for the advertisement request associated with the selected candidate advertisement and (2) a social story that describes an interaction between a user who is connected with the viewing user and an object in the social networking system; and sending the social advertisement for display to the viewing user. 24. The computer program product of claim 23 , wherein the viewing user is identified responsive to the viewing user requesting content with which an advertisement is to be served. | 0.784173 |
9,674,132 | 8 | 9 | 8. The method of claim 3 , further comprising: providing a user interface object, wherein the user interface object allows the user to change to reply to only the sender of the first message by acting on the user interface object, or to automatically remove the multiple recipients of the first message from the recipient list. | 8. The method of claim 3 , further comprising: providing a user interface object, wherein the user interface object allows the user to change to reply to only the sender of the first message by acting on the user interface object, or to automatically remove the multiple recipients of the first message from the recipient list. 9. The method of claim 8 , wherein the user interface object is a button or icon, wherein the user action is a click or a touch on the button or icon. | 0.5 |
8,775,933 | 14 | 18 | 14. A method for generating a document, comprising: (a) accessing a referencing document; (b) invoking a fragment object referenced by the referencing document, the fragment object including a first set of data corresponding to a document fragment of a source document and a second set of data corresponding to a transformation of the document fragment of the source document; (c) determining the format of the referencing document based on the second set of data; (d) transforming the document fragment of the source document to correspond to the determined format of the referencing document; and (e) incorporating the transformed document fragment of the source document into the referencing document to generate a document. | 14. A method for generating a document, comprising: (a) accessing a referencing document; (b) invoking a fragment object referenced by the referencing document, the fragment object including a first set of data corresponding to a document fragment of a source document and a second set of data corresponding to a transformation of the document fragment of the source document; (c) determining the format of the referencing document based on the second set of data; (d) transforming the document fragment of the source document to correspond to the determined format of the referencing document; and (e) incorporating the transformed document fragment of the source document into the referencing document to generate a document. 18. The method as claimed in claim 14 , wherein the transformation of the document fragment of the source document further changes a color characteristic of the document fragment of the source document. | 0.752451 |
8,103,691 | 1 | 15 | 1. A method for dynamically generating a survey result(s) comprising: storing and managing each registered user's one or more profile(s), preferences and relational connections or dynamic relationships at a central server; allowing each user to manage a Human Operating System (HOS) including one or more profiles, activities, applications, services, actions, transactions, groups, searching, sharing, communication, contents and connections; presenting one or more domain or subject or taxonomy specific survey forms to user; receiving, via categories survey forms, a plurality of categories survey data or selections from the user, wherein survey data or selections relate or map, for each of plurality of different categories of user data for sharing with one or more other connected users who can access that category of user data and customization, personalization and configuration data utilize for customization of the user's Human Operating System (HOS) including dynamically creating one or more social networks, establishing communication and sharing selective one or more user resources or profiles with one or more other connected users, customize searching and matching, e-commerce, receiving customized advertisements, applications and services lists and contents; updating survey data and survey analysis to the related categories profile(s) of the user for applying or use the survey data for customization, personalization and configuration of each user's Human Operating System (HOS); and generating and presenting a survey results to the user, wherein survey results comprises a details of customization, personalization and configuration of each user's Human Operating System (HOS) and which other connected users can access which categories of user data based on the survey data or selections. | 1. A method for dynamically generating a survey result(s) comprising: storing and managing each registered user's one or more profile(s), preferences and relational connections or dynamic relationships at a central server; allowing each user to manage a Human Operating System (HOS) including one or more profiles, activities, applications, services, actions, transactions, groups, searching, sharing, communication, contents and connections; presenting one or more domain or subject or taxonomy specific survey forms to user; receiving, via categories survey forms, a plurality of categories survey data or selections from the user, wherein survey data or selections relate or map, for each of plurality of different categories of user data for sharing with one or more other connected users who can access that category of user data and customization, personalization and configuration data utilize for customization of the user's Human Operating System (HOS) including dynamically creating one or more social networks, establishing communication and sharing selective one or more user resources or profiles with one or more other connected users, customize searching and matching, e-commerce, receiving customized advertisements, applications and services lists and contents; updating survey data and survey analysis to the related categories profile(s) of the user for applying or use the survey data for customization, personalization and configuration of each user's Human Operating System (HOS); and generating and presenting a survey results to the user, wherein survey results comprises a details of customization, personalization and configuration of each user's Human Operating System (HOS) and which other connected users can access which categories of user data based on the survey data or selections. 15. The method as claimed in claim 1 , wherein the central server can monitor, manage, analyze, update and use or apply said one or more survey data for customizations, personalization and configurations of user's Human Operating System (HOS). | 0.860505 |
9,037,449 | 1 | 7 | 1. A method for establishing paraphrasing data for a machine translation system comprising: selecting a paraphrasing target sentence through application of an object language model to a translated sentence that is obtained by machine-translating a source language sentence; extracting, from a corpus DB for the source language, paraphrasing candidates that can be paraphrased with the selected paraphrasing target sentence; performing machine translation with respect to the extracted paraphrasing candidates; selecting a final paraphrasing candidate from the extracted paraphrasing candidates by applying the object language model to a result of the machine translation with respect to the extracted paraphrasing candidates; and confirming a paraphrasing relationship of the paraphrasing target sentence and the selected final paraphrasing candidate as paraphrasing lexical patterns using a bilingual corpus and storing the paraphrasing lexical patterns in a paraphrasing DB. | 1. A method for establishing paraphrasing data for a machine translation system comprising: selecting a paraphrasing target sentence through application of an object language model to a translated sentence that is obtained by machine-translating a source language sentence; extracting, from a corpus DB for the source language, paraphrasing candidates that can be paraphrased with the selected paraphrasing target sentence; performing machine translation with respect to the extracted paraphrasing candidates; selecting a final paraphrasing candidate from the extracted paraphrasing candidates by applying the object language model to a result of the machine translation with respect to the extracted paraphrasing candidates; and confirming a paraphrasing relationship of the paraphrasing target sentence and the selected final paraphrasing candidate as paraphrasing lexical patterns using a bilingual corpus and storing the paraphrasing lexical patterns in a paraphrasing DB. 7. The method for establishing paraphrasing data for a machine translation system of claim 1 , wherein the extracting the paraphrasing candidates extracts the paraphrasing candidates in an n-best form. | 0.844907 |
8,131,731 | 1 | 4 | 1. A computer-implemented method comprising: maintaining metadata associated with browser-accessible items, wherein the metadata includes: a title associated with an item, a URL associated with an item, a last date an item was visited by a user, frequency with which an item has been visited by a user, and whether or not an item was selected from a list; assigning numerical weights to the metadata to represent how important a particular piece of metadata is in respect to total relevance of maintained metadata; processing weighted metadata using a relevancy algorithm; receiving a user action associated with accessing one or more items; and responsive to receiving the user action, presenting one or more suggestions based upon the user action and an output of the relevancy algorithm, wherein presenting comprises displaying the one or more suggestions grouped according to types of suggestions, wherein individual types of suggestions are associated with a different user action related to the browser that caused an associated suggestion to be presented. | 1. A computer-implemented method comprising: maintaining metadata associated with browser-accessible items, wherein the metadata includes: a title associated with an item, a URL associated with an item, a last date an item was visited by a user, frequency with which an item has been visited by a user, and whether or not an item was selected from a list; assigning numerical weights to the metadata to represent how important a particular piece of metadata is in respect to total relevance of maintained metadata; processing weighted metadata using a relevancy algorithm; receiving a user action associated with accessing one or more items; and responsive to receiving the user action, presenting one or more suggestions based upon the user action and an output of the relevancy algorithm, wherein presenting comprises displaying the one or more suggestions grouped according to types of suggestions, wherein individual types of suggestions are associated with a different user action related to the browser that caused an associated suggestion to be presented. 4. The method of claim 1 , wherein the user action comprises accessing a drop down menu associated with a Web browser. | 0.831909 |
8,392,421 | 9 | 13 | 9. A non-transitory computer readable medium, embodying instructions executable by a computer to perform method steps for profiling an Internet endpoint associated with an Internet Protocol (IP) prefix, the instructions, when executed, comprising functionality for: generating a profiling rule using an Internet search engine; obtaining a search result by inputting the IP prefix to the Internet search engine; and classifying the Internet endpoint based on the search result using the profiling rule, wherein the profiling rule comprises a IP tag and a key phrase list having a phrase associated with a URL class, the URL class being associated with the IP tag, wherein the profiling rule further comprises a data structure having an entry, the entry having a first domain name associated with the phrase, wherein the data structure is a cache having a plurality of indexes corresponding to a plurality of values, the plurality of indexes comprising the first domain name, the plurality of values comprising the phrase, wherein the search result comprises: a Uniform Resource Locator (URL) having a second domain name; and a hit text associated with the URL, the hit text comprising a phrase in the key phrase list, wherein classifying the Internet endpoint comprises: assigning the URL class to the URL and assigning the IP tag to the Internet end point if the first domain name is the same as the second domain name, wherein generating the profiling rule comprises: adding an index comprising the second domain name to the cache if the plurality of indexes do not comprise the second domain name; setting a counter to an initial count; obtaining another search result by inputting another Internet endpoint domain name to the Internet search engine, the another search result comprising another hit text associated with another URL; the another hit text comprising another phrase in the key phrase list; incrementing the counter if the another URL comprises the second domain name; and setting a value in the cache corresponding to the index based on at least one selected from the group consisting of the phrase and the another phrase if the counter exceeds a pre-determined threshold. | 9. A non-transitory computer readable medium, embodying instructions executable by a computer to perform method steps for profiling an Internet endpoint associated with an Internet Protocol (IP) prefix, the instructions, when executed, comprising functionality for: generating a profiling rule using an Internet search engine; obtaining a search result by inputting the IP prefix to the Internet search engine; and classifying the Internet endpoint based on the search result using the profiling rule, wherein the profiling rule comprises a IP tag and a key phrase list having a phrase associated with a URL class, the URL class being associated with the IP tag, wherein the profiling rule further comprises a data structure having an entry, the entry having a first domain name associated with the phrase, wherein the data structure is a cache having a plurality of indexes corresponding to a plurality of values, the plurality of indexes comprising the first domain name, the plurality of values comprising the phrase, wherein the search result comprises: a Uniform Resource Locator (URL) having a second domain name; and a hit text associated with the URL, the hit text comprising a phrase in the key phrase list, wherein classifying the Internet endpoint comprises: assigning the URL class to the URL and assigning the IP tag to the Internet end point if the first domain name is the same as the second domain name, wherein generating the profiling rule comprises: adding an index comprising the second domain name to the cache if the plurality of indexes do not comprise the second domain name; setting a counter to an initial count; obtaining another search result by inputting another Internet endpoint domain name to the Internet search engine, the another search result comprising another hit text associated with another URL; the another hit text comprising another phrase in the key phrase list; incrementing the counter if the another URL comprises the second domain name; and setting a value in the cache corresponding to the index based on at least one selected from the group consisting of the phrase and the another phrase if the counter exceeds a pre-determined threshold. 13. The non-transitory computer readable medium of claim 9 , wherein the instructions, when executed, further comprise functionality for: assigning the URL class to the URL and assigning the IP tag to the Internet end point if the URL domain name comprises the phrase. | 0.776294 |
8,572,747 | 6 | 10 | 6. A computer program product, comprising: a non-transitory computer readable storage medium having computer readable program code embodied therewith, the computer readable program code comprising code suitable to cause a computer system to perform at least the following: performing a static analysis on a program having sources and sinks to track string flow from the sources to the sinks, comprising: for string variables in the program that begin at sources, computing grammar of all possible string values for each of the string variables; for methods in the program operating on any of the string variables, computing grammar of string variables returned by the methods; determining that one of the string variables reaches a sink that performs a security-sensitive operation; determining that one of methods in a path from a source to the sink that performs the security-sensitive operation is one of a user-defined sanitizer or a user-defined validator; in response to the one of the string variables reaching the sink and to the one of the methods in the path being one of a user-defined sanitizer or a user-defined validator, comparing current grammar of the one of the string variables with a policy corresponding to the security-sensitive operation and determining based on the comparing whether the one of the user-defined sanitizer or the user-defined validator presents or does not present a possible security problem; and performing a reporting operation based on the comparing, wherein performing the reporting operation further comprises reporting, in response to the comparing, whether the one of the user-defined sanitizer or the user-defined validator presents or does not present the possible security problem, wherein the performing the reporting operation further comprises declaring the sink safe with respect to the security-sensitive operation in response to the one of the user-defined sanitizer or the user-defined validator not presenting the possible security problem and outputting indicia indicating the one of the user-defined sanitizer or the user-defined validator appears safe, and wherein the performing the reporting operation further comprises declaring the sink unsafe with respect to the security-sensitive operation in response to the one of the user-defined sanitizer or the user-defined validator presenting the possible security problem and outputting indicia indicating the one of the user-defined sanitizer or the user-defined validator has a possible security problem. | 6. A computer program product, comprising: a non-transitory computer readable storage medium having computer readable program code embodied therewith, the computer readable program code comprising code suitable to cause a computer system to perform at least the following: performing a static analysis on a program having sources and sinks to track string flow from the sources to the sinks, comprising: for string variables in the program that begin at sources, computing grammar of all possible string values for each of the string variables; for methods in the program operating on any of the string variables, computing grammar of string variables returned by the methods; determining that one of the string variables reaches a sink that performs a security-sensitive operation; determining that one of methods in a path from a source to the sink that performs the security-sensitive operation is one of a user-defined sanitizer or a user-defined validator; in response to the one of the string variables reaching the sink and to the one of the methods in the path being one of a user-defined sanitizer or a user-defined validator, comparing current grammar of the one of the string variables with a policy corresponding to the security-sensitive operation and determining based on the comparing whether the one of the user-defined sanitizer or the user-defined validator presents or does not present a possible security problem; and performing a reporting operation based on the comparing, wherein performing the reporting operation further comprises reporting, in response to the comparing, whether the one of the user-defined sanitizer or the user-defined validator presents or does not present the possible security problem, wherein the performing the reporting operation further comprises declaring the sink safe with respect to the security-sensitive operation in response to the one of the user-defined sanitizer or the user-defined validator not presenting the possible security problem and outputting indicia indicating the one of the user-defined sanitizer or the user-defined validator appears safe, and wherein the performing the reporting operation further comprises declaring the sink unsafe with respect to the security-sensitive operation in response to the one of the user-defined sanitizer or the user-defined validator presenting the possible security problem and outputting indicia indicating the one of the user-defined sanitizer or the user-defined validator has a possible security problem. 10. The computer program product of claim 6 , wherein the current grammar of the one string variable does not meet the policy, and the reporting operation declares that a potential security problem exists for the sink and the corresponding security-sensitive operation. | 0.658629 |
10,013,414 | 21 | 27 | 21. A management entity comprising: a memory comprising instructions; and a processor in communication with the memory wherein the processor executes the instructions to: parse a request to collect data about a communications system for an entity in the communications system, the parsing to produce a parsed request and dependency information, generate sets of model elements in accordance with context tokens and content tokens derived from the parsed request, the content tokens including extrinsic metadata and intrinsic metadata of the parsed request, wherein the processor executing the instructions to generate the sets of model elements comprises the processor executing the instructions to: evaluate content of the request to collect the data to produce links between context tokens and the content tokens, and map the links into a model element graph, analyze context metadata tokens and content metadata tokens in accordance with the dependency information, analyze the model element graph in accordance with the dependency information, modify the model element graph for each dependency and dependency type in the dependency information, generate a platform-neutral description of results of the request from the model element graph derived from the sets of model elements, the platform-neutral description being independent of a protocol used by the management entity to collect the data about the communications system, and execute the request to collect the data as requested in accordance with the platform-neutral description; and store the data as collected in the memory. | 21. A management entity comprising: a memory comprising instructions; and a processor in communication with the memory wherein the processor executes the instructions to: parse a request to collect data about a communications system for an entity in the communications system, the parsing to produce a parsed request and dependency information, generate sets of model elements in accordance with context tokens and content tokens derived from the parsed request, the content tokens including extrinsic metadata and intrinsic metadata of the parsed request, wherein the processor executing the instructions to generate the sets of model elements comprises the processor executing the instructions to: evaluate content of the request to collect the data to produce links between context tokens and the content tokens, and map the links into a model element graph, analyze context metadata tokens and content metadata tokens in accordance with the dependency information, analyze the model element graph in accordance with the dependency information, modify the model element graph for each dependency and dependency type in the dependency information, generate a platform-neutral description of results of the request from the model element graph derived from the sets of model elements, the platform-neutral description being independent of a protocol used by the management entity to collect the data about the communications system, and execute the request to collect the data as requested in accordance with the platform-neutral description; and store the data as collected in the memory. 27. The management entity of claim 21 , wherein the processor, for each dependency and dependency type combination, executes the instructions to perform a dependency analysis in accordance with the dependency and dependency type combination to produce a first list of dependencies, and to adjust the model element graph in accordance with the dependency and dependency type combination. | 0.578603 |
9,747,358 | 10 | 11 | 10. An apparatus comprising: a processor; a pattern services module executed by the processor to receive a user input and provide, through a display of the computer system, a guided user interface that restricts the user input to one of a plurality of defined patterns that are available at a multi-dimensional data source; and a pattern engine module coupled to the pattern services module, the pattern engine module to execute a pattern available at the multi-dimensional data source selected by the user on the multi-dimensional data source to generate a report with deterministic values. | 10. An apparatus comprising: a processor; a pattern services module executed by the processor to receive a user input and provide, through a display of the computer system, a guided user interface that restricts the user input to one of a plurality of defined patterns that are available at a multi-dimensional data source; and a pattern engine module coupled to the pattern services module, the pattern engine module to execute a pattern available at the multi-dimensional data source selected by the user on the multi-dimensional data source to generate a report with deterministic values. 11. The apparatus of claim 10 , wherein the pattern services module restricts parameters input to at least one of a plurality of parameters specific to the pattern. | 0.5 |
8,024,317 | 1 | 3 | 1. A method comprising the steps: receiving, over a network, by a computing device, a user context query from a user, wherein the user context query is formatted as a parameter of a universal resource locator (URL) and comprises user context criteria; formulating, via the network, a network data query based on the user context criteria so as to search, via the network, for user profile data, social network data, spatial data, temporal data, topical data and context query bid data that is available via the network and relates to the user context criteria so as to identify a plurality of entries in a context query bid database that relate to the user context criteria, each of the plurality of entries in the context query bid database comprising bid context criteria, a bid amount, an identification of a bid advertiser, and an identification of a bid advertisement; selecting, via the network, a selected context query bid database entry from the plurality of entries in the context query bid database, such that the selected context query bid database entry has a highest bid amount; retrieving, via the network, a selected advertisement database entry from an advertisement database such that an identification of an advertiser and an identification of an advertisement on the selected advertisement database entry matches the identification of the bid advertiser and the identification of the bid advertisement on the selected context query bid database entry, the selected advertisement database entry additionally comprising an advertisement data object; generating, via the network, a dynamic webpage having content relating to the user context query; inserting, via the network, the data object into the dynamic webpage; transmitting, over the network, the dynamic webpage to the user; charging the advertiser a fee associated with the selected context query bid database entry when a user interface event relating to the dynamic webpage occurs. | 1. A method comprising the steps: receiving, over a network, by a computing device, a user context query from a user, wherein the user context query is formatted as a parameter of a universal resource locator (URL) and comprises user context criteria; formulating, via the network, a network data query based on the user context criteria so as to search, via the network, for user profile data, social network data, spatial data, temporal data, topical data and context query bid data that is available via the network and relates to the user context criteria so as to identify a plurality of entries in a context query bid database that relate to the user context criteria, each of the plurality of entries in the context query bid database comprising bid context criteria, a bid amount, an identification of a bid advertiser, and an identification of a bid advertisement; selecting, via the network, a selected context query bid database entry from the plurality of entries in the context query bid database, such that the selected context query bid database entry has a highest bid amount; retrieving, via the network, a selected advertisement database entry from an advertisement database such that an identification of an advertiser and an identification of an advertisement on the selected advertisement database entry matches the identification of the bid advertiser and the identification of the bid advertisement on the selected context query bid database entry, the selected advertisement database entry additionally comprising an advertisement data object; generating, via the network, a dynamic webpage having content relating to the user context query; inserting, via the network, the data object into the dynamic webpage; transmitting, over the network, the dynamic webpage to the user; charging the advertiser a fee associated with the selected context query bid database entry when a user interface event relating to the dynamic webpage occurs. 3. The method of claim 1 wherein in the formulating step, entries in the context query bid database are identified wherein the respective bid context criteria on each entry of the plurality of entries in the context query bid database and the context criteria on the user context query both relate to a single entity. | 0.6067 |
8,554,742 | 8 | 11 | 8. A process operable on one or more computers for identifying duplicate records among a plurality of records in a database, each record having a plurality of fields, comprising: (a) setting a threshold match probability; (b) calculating record match probabilities for each of a plurality of possible patterns, wherein each pattern is a different permutation of comparisons between said fields and wherein each record match probability is a posterior probability that two records are duplicates given that the two records fit the respective pattern; (c) identifying patterns having record match probabilities meeting or exceeding said threshold match probability; (d) disregarding patterns having record match probabilities lower than said threshold match probability; (e) determining which records pairs within the plurality of records have one or more of said identified patterns; and (f) analyzing said record pairs to determine whether said record pairs are duplicates; wherein steps (b)-(d) occur prior to steps (e) and (f). | 8. A process operable on one or more computers for identifying duplicate records among a plurality of records in a database, each record having a plurality of fields, comprising: (a) setting a threshold match probability; (b) calculating record match probabilities for each of a plurality of possible patterns, wherein each pattern is a different permutation of comparisons between said fields and wherein each record match probability is a posterior probability that two records are duplicates given that the two records fit the respective pattern; (c) identifying patterns having record match probabilities meeting or exceeding said threshold match probability; (d) disregarding patterns having record match probabilities lower than said threshold match probability; (e) determining which records pairs within the plurality of records have one or more of said identified patterns; and (f) analyzing said record pairs to determine whether said record pairs are duplicates; wherein steps (b)-(d) occur prior to steps (e) and (f). 11. The process according to claim 8 , wherein said analyzed record pairs are categorized as duplicates, not duplicates, or indeterminate. | 0.707627 |
10,133,993 | 14 | 15 | 14. A non-transitory machine-readable storage medium comprising instructions that, when executed by one or more processors of a machine, cause the machine to perform operations for reducing computing resources used by an online social network by generating a user interface to structure search results corresponding to a plurality of potential experts in a specific skill and to limit presentation of endorsement data of an expert presented in the search results to an inquirer that is within a threshold degree of connection from the expert, the operations comprising: receiving, from a device of the inquirer, a search request for the expert in the specific skill; accessing, from a database in the online social network, profile data of members in the online social network, the profile data including the endorsement data; determining the plurality of potential experts from the members in the online social network based on the profile data; accessing social graph data of the plurality of potential experts, the social graph data including degrees of connections between the inquirer and the plurality of potential experts; calculating a ranking value for each member of the plurality of potential experts based on the accessed profile data and the accessed social graph data; verifying the expert from the plurality of potential experts based on the calculated ranking value for each member of the plurality of potential experts, the calculated ranking value of the expert being higher than a predetermined threshold; and including the profile data for the verified expert in the search results for presentation on a display of the device based on a determination that a degree of connection between the verified expert and the inquirer is within the threshold degree of connection. | 14. A non-transitory machine-readable storage medium comprising instructions that, when executed by one or more processors of a machine, cause the machine to perform operations for reducing computing resources used by an online social network by generating a user interface to structure search results corresponding to a plurality of potential experts in a specific skill and to limit presentation of endorsement data of an expert presented in the search results to an inquirer that is within a threshold degree of connection from the expert, the operations comprising: receiving, from a device of the inquirer, a search request for the expert in the specific skill; accessing, from a database in the online social network, profile data of members in the online social network, the profile data including the endorsement data; determining the plurality of potential experts from the members in the online social network based on the profile data; accessing social graph data of the plurality of potential experts, the social graph data including degrees of connections between the inquirer and the plurality of potential experts; calculating a ranking value for each member of the plurality of potential experts based on the accessed profile data and the accessed social graph data; verifying the expert from the plurality of potential experts based on the calculated ranking value for each member of the plurality of potential experts, the calculated ranking value of the expert being higher than a predetermined threshold; and including the profile data for the verified expert in the search results for presentation on a display of the device based on a determination that a degree of connection between the verified expert and the inquirer is within the threshold degree of connection. 15. The storage medium of claim 14 , further comprising instructions that cause the machine to perform operations comprising: accessing an organization chart from an employer database; determining a connection path between the inquirer and the expert based on the social graph data and the organization chart; and presenting, on the display of the device, information associated with the determined connection path. | 0.5 |
9,280,742 | 14 | 15 | 14. A non-transitory computer readable storage medium, having instructions stored therein, which when executed, cause a processing device to perform operations comprising: identifying a new media clip provided by a first user; providing a feature vector associated with the new media clip to a first classifier, wherein the feature vector associated with the new media clip includes a plurality of values representing one or more features extracted from content of the new media clip, wherein the first classifier was trained using a plurality of features vectors of a plurality of existing media clips to produce suggested semantic tags for respective existing media clips; obtaining, by processing device, a first set of semantic tags for the new media clip from the first classifier; providing, by the processing device, the first set of semantic tags for the new media clip to a second classifier that was trained using input-output pairs, wherein an input of each input-output pair includes a subset of the suggested semantic tags that was previously produced by the first classifier and suggested to a second user for one of the respective existing clips, and wherein an output of the input-output pair includes one or more suggested semantic tags selected by the second user from the subset of the suggested semantic tags for the one of the respective existing clips; obtaining, by the computer system, a second set of semantic tags for the new media clip from the second classifier; and suggesting to the first user, by the computer system, the second set of semantic tags for the new media clip. | 14. A non-transitory computer readable storage medium, having instructions stored therein, which when executed, cause a processing device to perform operations comprising: identifying a new media clip provided by a first user; providing a feature vector associated with the new media clip to a first classifier, wherein the feature vector associated with the new media clip includes a plurality of values representing one or more features extracted from content of the new media clip, wherein the first classifier was trained using a plurality of features vectors of a plurality of existing media clips to produce suggested semantic tags for respective existing media clips; obtaining, by processing device, a first set of semantic tags for the new media clip from the first classifier; providing, by the processing device, the first set of semantic tags for the new media clip to a second classifier that was trained using input-output pairs, wherein an input of each input-output pair includes a subset of the suggested semantic tags that was previously produced by the first classifier and suggested to a second user for one of the respective existing clips, and wherein an output of the input-output pair includes one or more suggested semantic tags selected by the second user from the subset of the suggested semantic tags for the one of the respective existing clips; obtaining, by the computer system, a second set of semantic tags for the new media clip from the second classifier; and suggesting to the first user, by the computer system, the second set of semantic tags for the new media clip. 15. The non-transitory computer readable storage medium of claim 14 wherein the first classifier is trained for each of the suggested semantic tags using positive feature vector examples and negative feature vector examples, wherein the positive feature vector examples are feature vectors associated with existing media clips that have a corresponding tag, and the negative feature vector examples are feature vectors associated with existing media clips that do not have the corresponding tag. | 0.5 |
5,463,773 | 2 | 6 | 2. A document classifying system comprising: a document data classifying system for inputting document data and defining a classification to which the document data belongs; a document classifying function building system operatively connected to the document data classifying system for automatically and recursively building a classification decision tree in the document data classifying system; a sample data storage apparatus operatively connected to the document classifying function building system for storing sample data formed by a set comprised of the document data and the classification to which the document data belongs; and a keyword storage apparatus operatively connected to the document classifying function building system for storing keywords extracted thereby, wherein said document classifying function building system comprises: extraction means operatively connected to the sample data storage apparatus and the keyword storage apparatus for referencing the sample data consisting of the set of the document data and the classification to which the document data belongs and extracting keywords defined by a connection of a word sequence from the sample data, calculation means for calculating an evaluation value assigned to a designated keyword by the calculation of the Loss function obtained by one attribute value for the document data including the designated keyword within an object document data and another attribute value for the document data not including the designated keyword within the object document data, setting means, operatively connected to the calculation means, for determining the document data for each node such that it assigns the sample data to a root node, and, except for the root node, assigns the document data separated until it reaches an upper node and is classified by keywords allocated by the upper node, decision means operatively connected to the setting means for determining whether or not the attribute included in the object document data is sufficient to satisfy predetermined completion conditions when the setting means determines the object document data, and allocation means operatively connected to the calculation means, the setting means and the decision means, and for, on the one hand, sequentially designating non-allocated keywords and calling the calculation means when the decision means does not determine that the completion conditions are satisfied, obtaining the evaluation value determined from said calculation means for each non-allocated keyword, allocating one of the non-allocated keywords to the node in accordance with the evaluation value; and, on the other hand, when the decision means determines that the completion conditions are satisfied, allocating the classification indicated by the object document data to the node instead of keywords, wherein when the allocation means allocates keywords, the setting means is recursively called in an allocation process. | 2. A document classifying system comprising: a document data classifying system for inputting document data and defining a classification to which the document data belongs; a document classifying function building system operatively connected to the document data classifying system for automatically and recursively building a classification decision tree in the document data classifying system; a sample data storage apparatus operatively connected to the document classifying function building system for storing sample data formed by a set comprised of the document data and the classification to which the document data belongs; and a keyword storage apparatus operatively connected to the document classifying function building system for storing keywords extracted thereby, wherein said document classifying function building system comprises: extraction means operatively connected to the sample data storage apparatus and the keyword storage apparatus for referencing the sample data consisting of the set of the document data and the classification to which the document data belongs and extracting keywords defined by a connection of a word sequence from the sample data, calculation means for calculating an evaluation value assigned to a designated keyword by the calculation of the Loss function obtained by one attribute value for the document data including the designated keyword within an object document data and another attribute value for the document data not including the designated keyword within the object document data, setting means, operatively connected to the calculation means, for determining the document data for each node such that it assigns the sample data to a root node, and, except for the root node, assigns the document data separated until it reaches an upper node and is classified by keywords allocated by the upper node, decision means operatively connected to the setting means for determining whether or not the attribute included in the object document data is sufficient to satisfy predetermined completion conditions when the setting means determines the object document data, and allocation means operatively connected to the calculation means, the setting means and the decision means, and for, on the one hand, sequentially designating non-allocated keywords and calling the calculation means when the decision means does not determine that the completion conditions are satisfied, obtaining the evaluation value determined from said calculation means for each non-allocated keyword, allocating one of the non-allocated keywords to the node in accordance with the evaluation value; and, on the other hand, when the decision means determines that the completion conditions are satisfied, allocating the classification indicated by the object document data to the node instead of keywords, wherein when the allocation means allocates keywords, the setting means is recursively called in an allocation process. 6. A document classifying system as claimed in claim 2, wherein said decision means determines that the completion conditions are satisfied when the classification included in the object document data becomes the same as the classification to which the document data belongs when a ratio of correctly classified document data to total document data exceeds a predetermined value. | 0.5 |
8,307,403 | 1 | 4 | 1. A computer-implemented method comprising: receiving at least one broadcast stream of a television show at a computer operating a search program for processing the at least one broadcast stream for the television show; receiving, at the search program operating on the computer from a transmission path separate from the at least one broadcast stream of the television show, a web page that contains executable code and a set of search instructions to process the at least one broadcast stream of the television show, the executable code implementing one or more callback methods within the web page that perform associated actions when called, the set of search instructions comprising a search/action pair indicating at least one search criteria and a callback method located within the web page that is to be called to initiate at least one associated action when the at least one search criteria is found in the at least one broadcast stream of the television show; creating, by the search program, a search object comprising an instance of a search class for extracting portions of content from the broadcast stream of the television show and performing a search of extracted portions of content for a match of the at least one search criteria indicated by the search/action pair; registering, from the search object, the callback method that is indicated by the search/action pair and located within the web page; determining, by the search object, that the at least one search criteria indicated by the search/action pair has been found in the extracted portions of content; clearing, by the search object, system resources associated with the search of the extracted portions of content performed by the search object; calling the callback method that is indicated by the search/action pair and located within the web page for initiating the at least one associated action; and synchronizing the at least one associated action with content of the television show being displayed on the computer. | 1. A computer-implemented method comprising: receiving at least one broadcast stream of a television show at a computer operating a search program for processing the at least one broadcast stream for the television show; receiving, at the search program operating on the computer from a transmission path separate from the at least one broadcast stream of the television show, a web page that contains executable code and a set of search instructions to process the at least one broadcast stream of the television show, the executable code implementing one or more callback methods within the web page that perform associated actions when called, the set of search instructions comprising a search/action pair indicating at least one search criteria and a callback method located within the web page that is to be called to initiate at least one associated action when the at least one search criteria is found in the at least one broadcast stream of the television show; creating, by the search program, a search object comprising an instance of a search class for extracting portions of content from the broadcast stream of the television show and performing a search of extracted portions of content for a match of the at least one search criteria indicated by the search/action pair; registering, from the search object, the callback method that is indicated by the search/action pair and located within the web page; determining, by the search object, that the at least one search criteria indicated by the search/action pair has been found in the extracted portions of content; clearing, by the search object, system resources associated with the search of the extracted portions of content performed by the search object; calling the callback method that is indicated by the search/action pair and located within the web page for initiating the at least one associated action; and synchronizing the at least one associated action with content of the television show being displayed on the computer. 4. The method of claim 1 , wherein the web page containing the set of search instructions is received in response to a user of the computer navigating to the web page. | 0.641631 |
6,044,387 | 22 | 23 | 22. A system for effecting an automatic editing operation on a plurality of files at one time, comprising: (a) a memory in which a plurality of machine instructions are stored; (b) a storage medium in which the plurality of files are stored; (c) a processor that is communicatively coupled to the memory and to the storage medium, said processor executing the machine instructions stored in the memory and in response thereto, performing a plurality of functions, including: (i) enabling a user to specify the editing operation that is to be automatically effected on the plurality of files by the processor; (ii) without requiring intervention by the user, automatically opening each file of the plurality of files on the storage medium in order to identify each file for which the editing operation is applicable; and (iii) implementing the editing operation on each file for which the editing operation is applicable. | 22. A system for effecting an automatic editing operation on a plurality of files at one time, comprising: (a) a memory in which a plurality of machine instructions are stored; (b) a storage medium in which the plurality of files are stored; (c) a processor that is communicatively coupled to the memory and to the storage medium, said processor executing the machine instructions stored in the memory and in response thereto, performing a plurality of functions, including: (i) enabling a user to specify the editing operation that is to be automatically effected on the plurality of files by the processor; (ii) without requiring intervention by the user, automatically opening each file of the plurality of files on the storage medium in order to identify each file for which the editing operation is applicable; and (iii) implementing the editing operation on each file for which the editing operation is applicable. 23. The system of claim 22, wherein the editing operation effected by the processor in response to the machine instructions comprises one of: (a) spell checking text included in the plurality of files to identify words that may be misspelled; and (b) finding characters specified by the user in the plurality of files and replacing said characters with replacement characters entered by the user. | 0.5 |
10,049,482 | 17 | 18 | 17. The animation server system of claim 16 , wherein the metadata for the first animation comprises: information about a start and end pose; a number of frames of the first animation; a global orientation of the 3D character in a first frame of the first animation; a global orientation of the 3D character in a last frame of the first animation; whether the first animation is in place or in motion; and whether there is any foot planting in the first animation. | 17. The animation server system of claim 16 , wherein the metadata for the first animation comprises: information about a start and end pose; a number of frames of the first animation; a global orientation of the 3D character in a first frame of the first animation; a global orientation of the 3D character in a last frame of the first animation; whether the first animation is in place or in motion; and whether there is any foot planting in the first animation. 18. The animation server system of claim 17 , wherein the metadata for the first animation further comprises whether the 3D character in the first animation ends on a floor level, a higher level, or a lower level than at the start of the first animation. | 0.5 |
9,064,201 | 12 | 13 | 12. A non-transitory computer-readable medium having recorded thereon a program for causing a computer to execute processing for: receiving a document for variable printing and a lob ticket including print settings, the print settings including a conditional print setting that depends on metadata included in the document; replacing, in the received document, information that is common between the conditional print setting and metadata included in the document with unique information, replacing in the received job ticket, a condition portion of the conditional print setting with the unique information, and deleting, in the received document, information that is not common between the conditional print setting and metadata included in the document such that the deleted information is removed from the received document; and instructing printing, using the conditional print setting and the document in which the common information is replaced with the unique information. | 12. A non-transitory computer-readable medium having recorded thereon a program for causing a computer to execute processing for: receiving a document for variable printing and a lob ticket including print settings, the print settings including a conditional print setting that depends on metadata included in the document; replacing, in the received document, information that is common between the conditional print setting and metadata included in the document with unique information, replacing in the received job ticket, a condition portion of the conditional print setting with the unique information, and deleting, in the received document, information that is not common between the conditional print setting and metadata included in the document such that the deleted information is removed from the received document; and instructing printing, using the conditional print setting and the document in which the common information is replaced with the unique information. 13. The medium according to claim 12 , wherein the common information is replaced with the unique information by changing a conditional expression of the conditional print setting to a predetermined character string. | 0.5 |
8,479,106 | 8 | 14 | 8. A device configured for communications with a network, the device comprising; a display; a communication subsystem for sending and receiving messages over the network; a memory; an user input device; and a processor for controlling the operation of the display, wherein the processor is configured to display a scrollable message composition window showing a reply to a message, wherein the reply includes a primary area and a secondary area, and wherein the secondary area includes a copy of the message; detect a first event when the message composition window has been scrolled such that the primary area of the reply is not visible; display an overlay in response to the first event, the overlay including an input area for the input of elements; detect a second event; and in response to the second event, remove the overlay and insert the elements entered in the input area of the overlay into the primary area of the reply. | 8. A device configured for communications with a network, the device comprising; a display; a communication subsystem for sending and receiving messages over the network; a memory; an user input device; and a processor for controlling the operation of the display, wherein the processor is configured to display a scrollable message composition window showing a reply to a message, wherein the reply includes a primary area and a secondary area, and wherein the secondary area includes a copy of the message; detect a first event when the message composition window has been scrolled such that the primary area of the reply is not visible; display an overlay in response to the first event, the overlay including an input area for the input of elements; detect a second event; and in response to the second event, remove the overlay and insert the elements entered in the input area of the overlay into the primary area of the reply. 14. The device of claim 8 wherein the element comprises alphabetic and numeric characters. | 0.78972 |
9,928,310 | 1 | 9 | 1. A method comprising: receiving a query graph that includes a plurality of query nodes; selecting a first query node from among the plurality of query nodes; storing a first data structure that is accessible to a first plurality of threads; for each thread in the first plurality of threads: assigning, to said each thread, a different set of nodes in a particular graph; identifying, in the set of nodes assigned to said each thread, one or more nodes that match the first query node; storing, in the first data structure, one or more node identities of the one or more nodes identified by said each thread; selecting, from among the plurality of query nodes, a second query node that is different than the first query node; storing a second data structure that is accessible to a second plurality of threads; for each thread in the second plurality of threads: assigning, to said each thread, a different set of nodes identified in the first data structure; identifying, in the particular graph, one or more neighbor nodes, of each node that is identified in the first data structure and is assigned to said each thread, that match the second query node; storing, in the second data structure, one or more identities of the one or more neighbor nodes identified by said each thread; wherein the method is performed by one or more computing devices. | 1. A method comprising: receiving a query graph that includes a plurality of query nodes; selecting a first query node from among the plurality of query nodes; storing a first data structure that is accessible to a first plurality of threads; for each thread in the first plurality of threads: assigning, to said each thread, a different set of nodes in a particular graph; identifying, in the set of nodes assigned to said each thread, one or more nodes that match the first query node; storing, in the first data structure, one or more node identities of the one or more nodes identified by said each thread; selecting, from among the plurality of query nodes, a second query node that is different than the first query node; storing a second data structure that is accessible to a second plurality of threads; for each thread in the second plurality of threads: assigning, to said each thread, a different set of nodes identified in the first data structure; identifying, in the particular graph, one or more neighbor nodes, of each node that is identified in the first data structure and is assigned to said each thread, that match the second query node; storing, in the second data structure, one or more identities of the one or more neighbor nodes identified by said each thread; wherein the method is performed by one or more computing devices. 9. The method of claim 1 , wherein: for each thread of the first plurality of threads, identifying, in the set of nodes assigned to said each thread, the one or more nodes that match the first query node comprises: identifying one or more attributes of the first query node, for each node in the set of nodes assigned to said each thread: identifying one or more attributes of said each node, comparing one or more attributes of the first query node with the one or more attributes of said each node, and wherein storing in the first data structure comprises storing an identity of said each node in the first data structure only if the one or more attributes of the first query node match the one or more attributes of said each node. | 0.556695 |
9,129,306 | 1 | 5 | 1. A method comprising: identifying, by one or more servers, an opportunity to provide a content item having distribution criteria matching a phrase of one or more words; determining, by the one or more servers, that the phrase matches each of a first distribution criterion and a second distribution criterion that are used to distribute content for a given content sponsor; determining, by the one or more servers, that a first match between the first distribution criterion and the phrase is a more specific match than a second match between the second distribution criterion and the phrase; determining, by the one or more servers, that the first distribution criterion is associated with a first bid that is lower than a second bid associated with the second distribution criterion; in response to determining that the first match is more specific that the second match and that the first bid is lower than the second bid, reducing, by the one or more servers, the second bid by a specified amount to obtain a reduced bid; selecting, by the one or more servers, one of the first distribution criterion or the second distribution criterion as a winning distribution criterion based, at least in part, on the first bid and the reduced second bid; and providing, by the one or more servers, data specifying the winning distribution criterion. | 1. A method comprising: identifying, by one or more servers, an opportunity to provide a content item having distribution criteria matching a phrase of one or more words; determining, by the one or more servers, that the phrase matches each of a first distribution criterion and a second distribution criterion that are used to distribute content for a given content sponsor; determining, by the one or more servers, that a first match between the first distribution criterion and the phrase is a more specific match than a second match between the second distribution criterion and the phrase; determining, by the one or more servers, that the first distribution criterion is associated with a first bid that is lower than a second bid associated with the second distribution criterion; in response to determining that the first match is more specific that the second match and that the first bid is lower than the second bid, reducing, by the one or more servers, the second bid by a specified amount to obtain a reduced bid; selecting, by the one or more servers, one of the first distribution criterion or the second distribution criterion as a winning distribution criterion based, at least in part, on the first bid and the reduced second bid; and providing, by the one or more servers, data specifying the winning distribution criterion. 5. The method of claim 1 , further comprising distributing a content item based on the winning distribution criterion. | 0.873391 |
8,972,390 | 1 | 2 | 1. A method of identifying web pages of a world wide web having relevance to a first file, comprising: identifying a plurality of web pages within the world wide web, wherein the plurality of web pages each have a relationship with the first file, wherein the world wide web provides a platform for sharing web pages, and wherein each web page includes a document or information resource that is suitable for the world wide web and is accessible through a web browser; generating, by a system server, a list of inquiries based on the plurality of web pages; providing, by the system server, the list of inquiries to at least one first author of the first file; receiving from the at least one first author at least one response to the list of inquiries; selecting a subset of the plurality of web pages based on the at least one response; storing information related to the selected subset of the plurality of web pages for access if the first file is selected; generating, by the system server, a second list of inquiries based on the plurality of web pages; providing, by the system server, the second list of inquiries to at least one second author of the plurality of web pages; receiving from the at least one second author of the plurality of web pages at least one second response to the second list of inquiries; re-selecting the subset of the plurality of web pages based on the at least one response and the at least one second response; and storing information related to the re-selected subset of the plurality of web pages for access if the first file is selected; providing, by the system server, the re-selected subset of the plurality of web pages or the selected subset of the plurality of web pages to a user that selects the first file; and identifying the at least one second author or the at least one first author to the user. | 1. A method of identifying web pages of a world wide web having relevance to a first file, comprising: identifying a plurality of web pages within the world wide web, wherein the plurality of web pages each have a relationship with the first file, wherein the world wide web provides a platform for sharing web pages, and wherein each web page includes a document or information resource that is suitable for the world wide web and is accessible through a web browser; generating, by a system server, a list of inquiries based on the plurality of web pages; providing, by the system server, the list of inquiries to at least one first author of the first file; receiving from the at least one first author at least one response to the list of inquiries; selecting a subset of the plurality of web pages based on the at least one response; storing information related to the selected subset of the plurality of web pages for access if the first file is selected; generating, by the system server, a second list of inquiries based on the plurality of web pages; providing, by the system server, the second list of inquiries to at least one second author of the plurality of web pages; receiving from the at least one second author of the plurality of web pages at least one second response to the second list of inquiries; re-selecting the subset of the plurality of web pages based on the at least one response and the at least one second response; and storing information related to the re-selected subset of the plurality of web pages for access if the first file is selected; providing, by the system server, the re-selected subset of the plurality of web pages or the selected subset of the plurality of web pages to a user that selects the first file; and identifying the at least one second author or the at least one first author to the user. 2. The method of claim 1 , wherein identifying the at least one first author comprises providing the user with a name, qualifications or institution of the at least one first author. | 0.80303 |
7,734,094 | 11 | 20 | 11. A computer-readable medium having computer-executable instructions for causing a computer to perform a method comprising: receive a handwritten input from a user; perform a recognition operation on the handwritten input to produce an initial recognition result; perform a comparison of the initial recognition result with at least one explicit sample previously provided by the user using a Kullback-Leibler distance measure operation; and identify at least part of the initial recognition result as a possible incorrect recognition result if the comparison reveals that the at least part of the initial recognition result is not consistent with the at least one explicit sample. | 11. A computer-readable medium having computer-executable instructions for causing a computer to perform a method comprising: receive a handwritten input from a user; perform a recognition operation on the handwritten input to produce an initial recognition result; perform a comparison of the initial recognition result with at least one explicit sample previously provided by the user using a Kullback-Leibler distance measure operation; and identify at least part of the initial recognition result as a possible incorrect recognition result if the comparison reveals that the at least part of the initial recognition result is not consistent with the at least one explicit sample. 20. The computer-readable medium of claim 11 , having computer-executable instructions for causing a computer to perform the method comprising: perform the Kullback-Leibler distance measure operation using
KL( s,k )=Σ c p k ( c|I k )log( p k ( c|I k )/ p s ( c|I s )). | 0.545455 |
9,965,548 | 6 | 7 | 6. A computer program product comprising one or more non-transitory computer-readable storage media and program instructions stored on the one or more non-transitory computer-readable storage media, the program instructions comprising: program instructions to receive an input question; program instructions to process the input question to generate at least one query; program instructions to extract one or more selections of evidence portions from a corpus of information which match the at least one query; program instructions to generate one or more candidate answers based on the one or more selections of evidence portions; for at least one candidate answer in the one or more candidate answers, program instructions to determine whether there are one or more missing or ambiguous pieces of information that would refine an inclusion or exclusion of the candidate answer in the one or more candidate answers; program instructions to output, by the data processing system, the one or more candidate answers, the one or more missing or ambiguous pieces of information that would refine the inclusion or exclusion of the candidate answer in the one or more candidate answers, and the selections of evidence portions for evaluation by the user; program instructions to output, by the data processing system, one or more pieces of criteria that identifies conditions met by each candidate answer in the one or more candidate answers in relation to the input question; program instructions to generate one or more confidence scores for each of the one or more candidate answers by applying one or more reasoning algorithms to language of the input question and language of the one or more candidate answers; program instructions to compare results of each of the one or more reasoning algorithms to a statistical model; based on the comparison of the results to the statistical model, program instructions to modify the statistical model; program instructions to apply statistical weights to confidence scores associated with each of the one or more reasoning algorithms in accordance with the statistical model; and program instructions to synthesize the one or more confidence scores for each of the one or more candidate answers, thereby creating a one or more final answers. | 6. A computer program product comprising one or more non-transitory computer-readable storage media and program instructions stored on the one or more non-transitory computer-readable storage media, the program instructions comprising: program instructions to receive an input question; program instructions to process the input question to generate at least one query; program instructions to extract one or more selections of evidence portions from a corpus of information which match the at least one query; program instructions to generate one or more candidate answers based on the one or more selections of evidence portions; for at least one candidate answer in the one or more candidate answers, program instructions to determine whether there are one or more missing or ambiguous pieces of information that would refine an inclusion or exclusion of the candidate answer in the one or more candidate answers; program instructions to output, by the data processing system, the one or more candidate answers, the one or more missing or ambiguous pieces of information that would refine the inclusion or exclusion of the candidate answer in the one or more candidate answers, and the selections of evidence portions for evaluation by the user; program instructions to output, by the data processing system, one or more pieces of criteria that identifies conditions met by each candidate answer in the one or more candidate answers in relation to the input question; program instructions to generate one or more confidence scores for each of the one or more candidate answers by applying one or more reasoning algorithms to language of the input question and language of the one or more candidate answers; program instructions to compare results of each of the one or more reasoning algorithms to a statistical model; based on the comparison of the results to the statistical model, program instructions to modify the statistical model; program instructions to apply statistical weights to confidence scores associated with each of the one or more reasoning algorithms in accordance with the statistical model; and program instructions to synthesize the one or more confidence scores for each of the one or more candidate answers, thereby creating a one or more final answers. 7. The computer program product of claim 6 , further comprising: program instructions to store the one or more final answers and corresponding selections of evidence portions in association with the input question; program instructions to receive the input question again after a period of time, wherein the corpus of information has been updated with new information during the period of time; program instructions to process the input question to generate the at least one query; program instructions to extract one or more second selections of evidence portions from the corpus of information which match the at least one query; program instructions to generate one or more second candidate answers based on the one or more second selections of evidence portions; program instructions to generate one or more second confidence scores for each of the one or more second candidate answers by applying the one or more reasoning algorithms to language of the input question and language of the one or more second candidate answers; program instructions to compare second results of each of the one or more reasoning algorithms to the statistical model; based on the comparison of the second results to the statistical model, program instructions to modify the statistical model; program instructions to apply statistical weights to confidence scores associated with each of the one or more reasoning algorithms in accordance with the statistical model; program instructions to synthesize the one or more second confidence scores for each of the one or more second candidate answers, thereby creating one or more second final answers; program instructions to store the one or more second final answers and corresponding second selections of evidence portions in association with the input question; and program instructions to compare the one or more final answers with the one or more second final answers to identify one or more trends describing how the corpus of information has changed during the period of time and how changes to the corpus of information have altered the one or more final answers. | 0.5 |
8,458,197 | 12 | 13 | 12. The method of claim 1 , wherein all the steps are repeated for each topic in a set of topics. | 12. The method of claim 1 , wherein all the steps are repeated for each topic in a set of topics. 13. The method of claim 12 , further comprising: outputting one or more groups of topics, where each topic in the set of topics is assigned to a group based on the generated similarity scores. | 0.5 |
8,140,410 | 6 | 7 | 6. A system in communication with a plurality of data pools, comprising: a processor; a computer readable storage device in communication with the processor, comprising: an element mapping for each of the plurality of data pools mapping user elements and attributes to the data pool elements and attributes, wherein the data pools maintain product information; a message mapping for each of the plurality of data pools mapping user messages and their parameters to data pool messages and their parameters; and code, for each of the plurality of data pools, enabled to cause the processor to receive a first document including user elements and messages and map the user elements and messages in the first document to a second document including data pool elements and attributes corresponding to the user elements and messages in the first document, wherein the first document includes one user message to publish product information in a specified data pool to at least one retailer; accessing the code for the specified data pool and the message mapping to map the user message and parameters to at least one mapped specified data pool message and parameters to publish the product information in the specified data pool to the at least one retailer; adding the at least one mapped specified data pool message and parameters to the second document; and transmitting the second document to the specified data pool. | 6. A system in communication with a plurality of data pools, comprising: a processor; a computer readable storage device in communication with the processor, comprising: an element mapping for each of the plurality of data pools mapping user elements and attributes to the data pool elements and attributes, wherein the data pools maintain product information; a message mapping for each of the plurality of data pools mapping user messages and their parameters to data pool messages and their parameters; and code, for each of the plurality of data pools, enabled to cause the processor to receive a first document including user elements and messages and map the user elements and messages in the first document to a second document including data pool elements and attributes corresponding to the user elements and messages in the first document, wherein the first document includes one user message to publish product information in a specified data pool to at least one retailer; accessing the code for the specified data pool and the message mapping to map the user message and parameters to at least one mapped specified data pool message and parameters to publish the product information in the specified data pool to the at least one retailer; adding the at least one mapped specified data pool message and parameters to the second document; and transmitting the second document to the specified data pool. 7. The system of claim 6 , wherein the code is further enabled to cause the processor to perform: receiving from the specified data pool a third document including at least one element and attributes for the product information to publish to the at least one retailer; accessing the code for the specified data pool and the message mapping to map the at least one specified data pool element and attributes to at least one mapped user element and attributes to publish the product information in the specified data pool to the at least one retailer; adding the at least one mapped user message and parameters to a fourth document; and transmitting the fourth document to the at least one retailer. | 0.5 |
10,114,897 | 1 | 4 | 1. A method comprising: identifying a most recent interest from user device submitted data; searching a database for instances of the most recent interest; creating a new category based on the most recent interest; storing the new category in a memory; combining the new category with weighted query search terms and submitting a combined query, separate weights assigned to query search terms according to validity of information found in each of local and remote memories, information found in local memories contributing to higher weights than information found in remote memories in response to private browsing not enabled on the user device, information found in remote memories contributing to higher weights than information found in local memories in response to private browsing being enabled on the user device; receiving combined query results; and creating a modified user interface based on the results of the combined query. | 1. A method comprising: identifying a most recent interest from user device submitted data; searching a database for instances of the most recent interest; creating a new category based on the most recent interest; storing the new category in a memory; combining the new category with weighted query search terms and submitting a combined query, separate weights assigned to query search terms according to validity of information found in each of local and remote memories, information found in local memories contributing to higher weights than information found in remote memories in response to private browsing not enabled on the user device, information found in remote memories contributing to higher weights than information found in local memories in response to private browsing being enabled on the user device; receiving combined query results; and creating a modified user interface based on the results of the combined query. 4. The method of claim 1 , further comprising: adding a new table parameter to existing table parameters of a table included in the user interface to include the new category; and populating the new and existing table parameters with the results of the combined query. | 0.582555 |
7,769,591 | 1 | 6 | 1. A local device comprising: a primary functionality component; an input component configured to receive speech input; a processing component coupled to the input component, the processing component configured to: identify keywords in the speech input, determine whether the local device is capable of processing the speech input based on whether one or more keywords are identified in the speech input, if the local device is capable of processing the speech input, process the speech input, generate corresponding local control signals, and transmit the local control signals to the primary functionality component to direct an action in the primary functionality component, and if the local device is not capable of processing the speech input, extract feature parameters from the speech input for processing at a remote system, receive remote control signals from the remote system responsive to the remote system performing speech recognition on the feature parameters by storing an acoustic model of the feature parameters and recognizing a command based on a previously stored acoustic model associated with the local device to address specific characteristics of the feature parameters, and send the remote control signals to the primary functionality component; and a transceiver coupled to the processing component and configured to establish communications between the local device and the remote system, wherein the communications comprise: high bandwidth communications configured to return data supporting audio or video output at the local device, and low bandwidth communications configured to return data supporting the remote control signals. | 1. A local device comprising: a primary functionality component; an input component configured to receive speech input; a processing component coupled to the input component, the processing component configured to: identify keywords in the speech input, determine whether the local device is capable of processing the speech input based on whether one or more keywords are identified in the speech input, if the local device is capable of processing the speech input, process the speech input, generate corresponding local control signals, and transmit the local control signals to the primary functionality component to direct an action in the primary functionality component, and if the local device is not capable of processing the speech input, extract feature parameters from the speech input for processing at a remote system, receive remote control signals from the remote system responsive to the remote system performing speech recognition on the feature parameters by storing an acoustic model of the feature parameters and recognizing a command based on a previously stored acoustic model associated with the local device to address specific characteristics of the feature parameters, and send the remote control signals to the primary functionality component; and a transceiver coupled to the processing component and configured to establish communications between the local device and the remote system, wherein the communications comprise: high bandwidth communications configured to return data supporting audio or video output at the local device, and low bandwidth communications configured to return data supporting the remote control signals. 6. The local device of claim 1 , wherein the processing component comprises a speech generation engine configured to generate speech output. | 0.72549 |
8,943,394 | 1 | 2 | 1. An apparatus allowing agent intervention in an automated call center application configured to handle multiple simultaneous calls, the method comprising: a processor-based automated dialog system receiving spoken input from the caller in a dialog between the caller, translating the spoken input into a series of words to form a hypothesis regarding the caller input; a notification module configured to automatically notify the agent upon recognition of a potential problem indicated by one of: a repeat by the caller of a phrase, a pause of a duration exceeding a defined time limit, and an increase in volume by the caller over a defined threshold volume; a call monitoring module monitoring the dialog between a caller and an automated dialog system, and allowing the agent to intervene in the event that a confidence level in the hypothesis formed by the automated dialog system does not exceed a defined threshold confidence level; a graphical user interface component displaying dialog information for a plurality of conversations comprising the multiple simultaneous calls through respective tabbed subwindows; a user interface providing the agent with information for each conversation of the plurality of conversations, regarding the conversation flow between the caller and the automated dialog system, obtained semantic information for the dialog, and waveform information for the recognized utterances within the dialog of a respective conversation, and wherein the notification module provides an automatic notification for any of the plurality of conversations as a potential problem occurs between the automated dialog system and a respective caller, and wherein each tabbed subwindow includes respective display areas displaying information for the conversation flow in a first display window showing dialog flow, obtained semantic information in a second display window showing a plurality of active slots associated with a current state of the respective conversation and respective slot values for each of the plurality of active slots, and waveform information in a third display window showing actual waveforms of caller utterances provided by waveform files, for a respective call. | 1. An apparatus allowing agent intervention in an automated call center application configured to handle multiple simultaneous calls, the method comprising: a processor-based automated dialog system receiving spoken input from the caller in a dialog between the caller, translating the spoken input into a series of words to form a hypothesis regarding the caller input; a notification module configured to automatically notify the agent upon recognition of a potential problem indicated by one of: a repeat by the caller of a phrase, a pause of a duration exceeding a defined time limit, and an increase in volume by the caller over a defined threshold volume; a call monitoring module monitoring the dialog between a caller and an automated dialog system, and allowing the agent to intervene in the event that a confidence level in the hypothesis formed by the automated dialog system does not exceed a defined threshold confidence level; a graphical user interface component displaying dialog information for a plurality of conversations comprising the multiple simultaneous calls through respective tabbed subwindows; a user interface providing the agent with information for each conversation of the plurality of conversations, regarding the conversation flow between the caller and the automated dialog system, obtained semantic information for the dialog, and waveform information for the recognized utterances within the dialog of a respective conversation, and wherein the notification module provides an automatic notification for any of the plurality of conversations as a potential problem occurs between the automated dialog system and a respective caller, and wherein each tabbed subwindow includes respective display areas displaying information for the conversation flow in a first display window showing dialog flow, obtained semantic information in a second display window showing a plurality of active slots associated with a current state of the respective conversation and respective slot values for each of the plurality of active slots, and waveform information in a third display window showing actual waveforms of caller utterances provided by waveform files, for a respective call. 2. The apparatus of claim 1 wherein the dialog comprises part of a telephone conversation between the caller and the automated dialog system. | 0.777603 |
8,352,855 | 19 | 20 | 19. A method for defining a selection of text in a document, the method comprising: receiving an unstructured document comprising a plurality of unassociated glyphs; associating sets of glyphs in a plurality of layouts, the layouts comprising separate ordered sequences of text columns; identifying a reading order that specifies a flow of reading through the glyphs; creating a structured document from the unstructured document, the structured document comprising the plurality of layouts and the reading order through the glyphs; displaying the structured document; receiving a start point in a first layout and an initial end point in the structured document for a selection of text within the displayed structured document; calculating a line from the start point to the initial end point; automatically selecting a new end point along the line; and defining a selection of text from the start point to the new end point by using the identified sets of glyphs and the intended flow of reading. | 19. A method for defining a selection of text in a document, the method comprising: receiving an unstructured document comprising a plurality of unassociated glyphs; associating sets of glyphs in a plurality of layouts, the layouts comprising separate ordered sequences of text columns; identifying a reading order that specifies a flow of reading through the glyphs; creating a structured document from the unstructured document, the structured document comprising the plurality of layouts and the reading order through the glyphs; displaying the structured document; receiving a start point in a first layout and an initial end point in the structured document for a selection of text within the displayed structured document; calculating a line from the start point to the initial end point; automatically selecting a new end point along the line; and defining a selection of text from the start point to the new end point by using the identified sets of glyphs and the intended flow of reading. 20. The method of claim 19 , wherein the initial end point is not in one of the layouts, wherein automatically selecting the new point along the line comprises selecting the new end point at a point along the line in any layout closest to the initial end point. | 0.565 |
9,384,295 | 4 | 5 | 4. The method as in claim 3 , further comprising displaying a second document tab identifying an inactive view of the single switchable view. | 4. The method as in claim 3 , further comprising displaying a second document tab identifying an inactive view of the single switchable view. 5. The method as in claim 4 , wherein the user selection is one of the first and second document tabs. | 0.627737 |
9,514,116 | 19 | 20 | 19. The system of claim 18 , further comprising automatically generating a report for the gadget in response to an event. | 19. The system of claim 18 , further comprising automatically generating a report for the gadget in response to an event. 20. The system of claim 19 , wherein the event includes one of opening the spreadsheet, selecting a Named Object View option or performing a search. | 0.5 |
10,121,071 | 17 | 18 | 17. A document verification system as claimed in claim 16 , wherein the second branch is configured to perform personalisation of the documents using said facts. | 17. A document verification system as claimed in claim 16 , wherein the second branch is configured to perform personalisation of the documents using said facts. 18. A document verification system as claimed in claim 17 , wherein the second branch is configured to collate said facts to generate a complex decision rule which is executed in real time to ensure that a recipient receives personalised document relevant to their context. | 0.5 |
7,584,181 | 14 | 15 | 14. The computer-implemented method as set forth in claim 8 , wherein re-ranking further comprises using an order-based re-ranking technique. | 14. The computer-implemented method as set forth in claim 8 , wherein re-ranking further comprises using an order-based re-ranking technique. 15. The computer-implemented method as set forth in claim 14 , wherein the order-based re-ranking technique further comprises using a linear combination of page positions contained on two lists. | 0.5 |
8,132,060 | 6 | 10 | 6. An apparatus comprising: a computer-readable storage device to store a library having a set of application programming interfaces to interface an application and a logical volume manager (LVM), wherein the set of application programming interfaces comprises a set of error reporting interfaces that are configured to report an error using a handle corresponding to an object and a plurality of error reporting handles; and a processing device to execute the application in operation with the LVM, and wherein the processing device is to: initialize a handle for a logical object; retrieve the logical object for the application using the handle; execute a function in the application to act on the logical object; determine if an error occurred resulting from executing the function; if there is an error, report the error using the handle and the plurality of error reporting handles, wherein one of the plurality of error reporting handles comprises an error code associated with the error, wherein another one of the plurality of error reporting handles comprises an error string in a natural language describing the error, and wherein another one of the plurality of error reporting handles comprises a section of programming code of the function associated with the error; and if there is no error, release the logical object from the handle and return the handle to the application. | 6. An apparatus comprising: a computer-readable storage device to store a library having a set of application programming interfaces to interface an application and a logical volume manager (LVM), wherein the set of application programming interfaces comprises a set of error reporting interfaces that are configured to report an error using a handle corresponding to an object and a plurality of error reporting handles; and a processing device to execute the application in operation with the LVM, and wherein the processing device is to: initialize a handle for a logical object; retrieve the logical object for the application using the handle; execute a function in the application to act on the logical object; determine if an error occurred resulting from executing the function; if there is an error, report the error using the handle and the plurality of error reporting handles, wherein one of the plurality of error reporting handles comprises an error code associated with the error, wherein another one of the plurality of error reporting handles comprises an error string in a natural language describing the error, and wherein another one of the plurality of error reporting handles comprises a section of programming code of the function associated with the error; and if there is no error, release the logical object from the handle and return the handle to the application. 10. The apparatus of claim 6 , wherein the error string, the piece of programming code associated with the error, and the error code associated with the error returned are available to the LVM. | 0.60124 |
8,761,351 | 15 | 19 | 15. A computer readable storage medium that is not a transient signal per se, the computer readable storage medium having stored thereon executable instructions that when executed by a processor cause the performance of operations comprising: receiving, by a processor, via an internet protocol (IP) network, information pertaining to an event, wherein: the information is indicative of speech; the information is formatted in accordance with a voice over internet protocol (VoIP); the information is indicative of being provided by a P25 land mobile radio, via a federally regulated land mobile radio system; providing the information for storage, wherein the stored information is available for access by at least one entity associated with an emergency operations center; automatically converting the VoIP formatted information to text; automatically formatting, by the processor, the text in accordance with a WebEOC emergency operations center log format; and providing the formatted text for storage, wherein the stored formatted text is available for access by at least one entity associated with the emergency operations center. | 15. A computer readable storage medium that is not a transient signal per se, the computer readable storage medium having stored thereon executable instructions that when executed by a processor cause the performance of operations comprising: receiving, by a processor, via an internet protocol (IP) network, information pertaining to an event, wherein: the information is indicative of speech; the information is formatted in accordance with a voice over internet protocol (VoIP); the information is indicative of being provided by a P25 land mobile radio, via a federally regulated land mobile radio system; providing the information for storage, wherein the stored information is available for access by at least one entity associated with an emergency operations center; automatically converting the VoIP formatted information to text; automatically formatting, by the processor, the text in accordance with a WebEOC emergency operations center log format; and providing the formatted text for storage, wherein the stored formatted text is available for access by at least one entity associated with the emergency operations center. 19. The computer readable storage medium of claim 15 , the operations further comprising providing at least one of: a location based service to an originator of the information; a wireless priority service to an originator of the information; or a multimedia priority service to an originator of the information. | 0.5 |
9,489,371 | 1 | 4 | 1. A machine-implemented method of detecting a plurality of types of data in a sequence of characters representing text in a human language, the method comprising: converting, by a statistical learning method executing on a processor, the sequence of characters into blocks of input text by detecting text in the sequence of characters that correspond to the plurality of types of data, each block of input text comprising text corresponding to a single one of the plurality of types of data and assigned a tag by the statistical learning method to indicate the type of data detected and assigned a numerical value by the statistical learning method representing a probability that the block of text comprises the type of data indicated by the tag; parsing, by a pattern detection method executing on a processor, the blocks of input text having a numerical value representing at least a pre-determined probability into blocks of output text, the blocks of output text comprising a block of output text directly corresponding to a block of input text and having the tag assigned by the statistical learning method to the corresponding block of input text; and decomposing, by the pattern detection method, one or more blocks of output text from a block of input text using grammatical patterns of the human language to detect text corresponding to subsets of the type of data indicated by the tag assigned by the statistical learning method, each of the one or more blocks of output text having a tag assigned by the pattern detection method to indicate the subset detected, and each of the decomposed one or more blocks of output text comprising at least one lexeme for subsequent processing by an application designed to process a lexeme having the type identified by the tag assigned by the pattern detection method. | 1. A machine-implemented method of detecting a plurality of types of data in a sequence of characters representing text in a human language, the method comprising: converting, by a statistical learning method executing on a processor, the sequence of characters into blocks of input text by detecting text in the sequence of characters that correspond to the plurality of types of data, each block of input text comprising text corresponding to a single one of the plurality of types of data and assigned a tag by the statistical learning method to indicate the type of data detected and assigned a numerical value by the statistical learning method representing a probability that the block of text comprises the type of data indicated by the tag; parsing, by a pattern detection method executing on a processor, the blocks of input text having a numerical value representing at least a pre-determined probability into blocks of output text, the blocks of output text comprising a block of output text directly corresponding to a block of input text and having the tag assigned by the statistical learning method to the corresponding block of input text; and decomposing, by the pattern detection method, one or more blocks of output text from a block of input text using grammatical patterns of the human language to detect text corresponding to subsets of the type of data indicated by the tag assigned by the statistical learning method, each of the one or more blocks of output text having a tag assigned by the pattern detection method to indicate the subset detected, and each of the decomposed one or more blocks of output text comprising at least one lexeme for subsequent processing by an application designed to process a lexeme having the type identified by the tag assigned by the pattern detection method. 4. The method of claim 1 , wherein the predetermined probability is low and the pattern detection method parses a correspondingly large number of the blocks of text. | 0.556452 |
8,311,796 | 1 | 4 | 1. A system for recognizing an input signal entered via a shorthand-on-keyboard interface and for allowing a stem and an affix of an input text to be combined, the system comprising: a memory; a concatenation module stored on the memory that is configured, when executed, to recognize the input signal as an input affix, wherein the concatenation module further recognizes a candidate word as a neighboring candidate word; a compound word module stored on the memory that is configured, when executed, to retrieve a set of words in a lexicon containing the input affix; a ranking module stored on the memory that is configured, when executed, to rank the set of words containing the input affix according to a similarity function that compares each lexicon word in the set of words containing the input affix, with a string containing the candidate word and the input affix, and wherein the compound word module outputs a highest ranked lexicon word in the set of words containing the input affix; and a module stored on the memory that is configured, when executed, to separately display at least two words or affixes and concatenate the at least two words or affixes in response to a circular motion touching inner edges of the at least two words or affixes, wherein the at least two words or affixes correspond to the candidate word and the input affix. | 1. A system for recognizing an input signal entered via a shorthand-on-keyboard interface and for allowing a stem and an affix of an input text to be combined, the system comprising: a memory; a concatenation module stored on the memory that is configured, when executed, to recognize the input signal as an input affix, wherein the concatenation module further recognizes a candidate word as a neighboring candidate word; a compound word module stored on the memory that is configured, when executed, to retrieve a set of words in a lexicon containing the input affix; a ranking module stored on the memory that is configured, when executed, to rank the set of words containing the input affix according to a similarity function that compares each lexicon word in the set of words containing the input affix, with a string containing the candidate word and the input affix, and wherein the compound word module outputs a highest ranked lexicon word in the set of words containing the input affix; and a module stored on the memory that is configured, when executed, to separately display at least two words or affixes and concatenate the at least two words or affixes in response to a circular motion touching inner edges of the at least two words or affixes, wherein the at least two words or affixes correspond to the candidate word and the input affix. 4. The system of claim 1 , wherein the input affix is a prefix. | 0.5 |
7,870,295 | 9 | 12 | 9. A computer implemented method of parsing a message containing a plurality of data formats, the computer implemented method comprising: responsive to identifying a data format of a first component of the message, selecting and invoking, by a data processing apparatus, a first parser to parse the first component; identifying the data format of a second component of the message using the first parser; and responsive to said identification, using the first selected parser to select and invoke, based on the data format of the second component, a second parser for parsing the second message component, wherein invoking the second parser includes inputting the second component to the second parser; and parsing the second component using the second parser. | 9. A computer implemented method of parsing a message containing a plurality of data formats, the computer implemented method comprising: responsive to identifying a data format of a first component of the message, selecting and invoking, by a data processing apparatus, a first parser to parse the first component; identifying the data format of a second component of the message using the first parser; and responsive to said identification, using the first selected parser to select and invoke, based on the data format of the second component, a second parser for parsing the second message component, wherein invoking the second parser includes inputting the second component to the second parser; and parsing the second component using the second parser. 12. The computer implemented method according to claim 9 , wherein identifying the data format of the second component comprises analyzing a format field of the second component. | 0.755495 |
8,578,262 | 8 | 9 | 8. The computer implemented method of claim 5 , wherein the computer aided design interface is operable to depict the architectural feature when the geometry of the architectural feature is encoded using one or more vector equations. | 8. The computer implemented method of claim 5 , wherein the computer aided design interface is operable to depict the architectural feature when the geometry of the architectural feature is encoded using one or more vector equations. 9. The computer implemented method of claim 8 , wherein automatically updating the computer aided design interface comprises modifying the one or more vector equations encoding the geometry of the architectural feature to include the value for the dimension entered into the form specific to the architectural work category. | 0.5 |
7,828,552 | 1 | 2 | 1. A computer-implemented method of administering an assessment to a student, the method comprising: for one or more tasks, calculating an expected weight of evidence using a computer processor for the task based on a student model pertaining to a particular student, wherein the student model comprises one or more variables, wherein each of the one or more variables corresponds to a proficiency of the student and is based on student specific information collected prior to the assessment, wherein each of the one or more variables includes a probability of a plurality of probabilities, wherein each of the plurality of probabilities corresponds to a likelihood that the student has a particular proficiency level, wherein the expected weight of evidence is calculated based on the one or more variables corresponding to the proficiency for the particular student and the student specific information collected prior to the assessment; selecting one of the one or more tasks based on the calculated expected weights of evidence using the computer processor; administering the selected task to the student; collecting evidence regarding the selected task; updating the student model pertaining to the student based on the evidence using the computer processor; determining whether additional information is required to assess the student using the computer processor; if so, repeating the above steps; and if not, assigning a proficiency status to the student based on the student model using the computer processor. | 1. A computer-implemented method of administering an assessment to a student, the method comprising: for one or more tasks, calculating an expected weight of evidence using a computer processor for the task based on a student model pertaining to a particular student, wherein the student model comprises one or more variables, wherein each of the one or more variables corresponds to a proficiency of the student and is based on student specific information collected prior to the assessment, wherein each of the one or more variables includes a probability of a plurality of probabilities, wherein each of the plurality of probabilities corresponds to a likelihood that the student has a particular proficiency level, wherein the expected weight of evidence is calculated based on the one or more variables corresponding to the proficiency for the particular student and the student specific information collected prior to the assessment; selecting one of the one or more tasks based on the calculated expected weights of evidence using the computer processor; administering the selected task to the student; collecting evidence regarding the selected task; updating the student model pertaining to the student based on the evidence using the computer processor; determining whether additional information is required to assess the student using the computer processor; if so, repeating the above steps; and if not, assigning a proficiency status to the student based on the student model using the computer processor. 2. The method of claim 1 wherein the evidence comprises a scored response to the selected task. | 0.901452 |
4,397,635 | 6 | 7 | 6. The teaching method of claim 1 wherein the second displaying step includes the step of sequentially displaying said chosen words at said main portion. | 6. The teaching method of claim 1 wherein the second displaying step includes the step of sequentially displaying said chosen words at said main portion. 7. The teaching method of claim 6 further comprising the step of spacing the baseline words along said baseline portion according to the temporal spacing between the vocalizing of said sequentially displayed chosen words. | 0.5 |
9,208,179 | 10 | 14 | 10. A system for comparing data records, comprising: a processor; a data record tokenizer executing on the processor and configured to: alphanumeric tokens from a plurality of data records, wherein the plurality of data records is generated by a plurality of entities, wherein the plurality of entities includes network devices, wherein the plurality of data records includes semi-structured data records and network management messages; generate a plurality of indexes each referencing an entity of the plurality of entities by at least one of the alphanumeric tokens that is associated with the entity; and extract target alphanumeric tokens from a target data record of a target entity; and a data record matcher executing on the processor and configured to: identify a candidate entity from the plurality of entities based on the target alphanumeric tokens and a first index of the plurality of indexes; calculate a first score representing a first similarity measure between a candidate data record selected from the plurality of data records that belongs to the candidate entity and the target data record of the target entity; and the data record matcher further configured to: store, in response to the first score exceeding a first pre-determined threshold, a combination of a portion of the target data record and a portion of the candidate data record as an expanded profile of the target entity. | 10. A system for comparing data records, comprising: a processor; a data record tokenizer executing on the processor and configured to: alphanumeric tokens from a plurality of data records, wherein the plurality of data records is generated by a plurality of entities, wherein the plurality of entities includes network devices, wherein the plurality of data records includes semi-structured data records and network management messages; generate a plurality of indexes each referencing an entity of the plurality of entities by at least one of the alphanumeric tokens that is associated with the entity; and extract target alphanumeric tokens from a target data record of a target entity; and a data record matcher executing on the processor and configured to: identify a candidate entity from the plurality of entities based on the target alphanumeric tokens and a first index of the plurality of indexes; calculate a first score representing a first similarity measure between a candidate data record selected from the plurality of data records that belongs to the candidate entity and the target data record of the target entity; and the data record matcher further configured to: store, in response to the first score exceeding a first pre-determined threshold, a combination of a portion of the target data record and a portion of the candidate data record as an expanded profile of the target entity. 14. The system of claim 10 , further an information analyzer executing on the processor and configured to: analyze the expanded profile to identify a network error; and applying a corrective action of the network error to both the target entity and the candidate entity. | 0.890155 |
8,939,837 | 11 | 15 | 11. A method comprising: determining, by a system including a processor, a first language of an intended recipient of user input based on a first identity of the intended recipient; determining, by the system, a second language of another intended recipient of the user input based on a second identity of the other intended recipient translating, by the system, the user input into a first translated message of the first language using a multi-lingual library, wherein the user input was inputted into a first accessory operably coupled with a first computing device, wherein the first computing device is programmed to present a video game; providing, by the system, the first translated message to a second accessory for presentation to the intended recipient; translating, by the system, the user input into a second translated message of the second language using the multi-lingual library; and providing, by the system, the second translated message to a third accessory for presentation to the other intended recipient. | 11. A method comprising: determining, by a system including a processor, a first language of an intended recipient of user input based on a first identity of the intended recipient; determining, by the system, a second language of another intended recipient of the user input based on a second identity of the other intended recipient translating, by the system, the user input into a first translated message of the first language using a multi-lingual library, wherein the user input was inputted into a first accessory operably coupled with a first computing device, wherein the first computing device is programmed to present a video game; providing, by the system, the first translated message to a second accessory for presentation to the intended recipient; translating, by the system, the user input into a second translated message of the second language using the multi-lingual library; and providing, by the system, the second translated message to a third accessory for presentation to the other intended recipient. 15. The method of claim 11 , wherein the second accessory is operably coupled with a second computing device, and wherein the user input is speech. | 0.788793 |
4,697,209 | 1 | 8 | 1. A method of identifying a signal received by an audience member's television receiver for determining audience ratings comprising the steps of: detecting the occurence of a first event in the functional operations of he audience namber's televison receiver; detecting the occurence of a second event after the detected first event in the signal to be identified; extracting a signature from a single frame of the video signal to be identified after the occurrence of the second event; storing the signature and the time of occurence thereof; and comparing the stored signature with reference signatures that occurred at approximately the same time as the stored signature. | 1. A method of identifying a signal received by an audience member's television receiver for determining audience ratings comprising the steps of: detecting the occurence of a first event in the functional operations of he audience namber's televison receiver; detecting the occurence of a second event after the detected first event in the signal to be identified; extracting a signature from a single frame of the video signal to be identified after the occurrence of the second event; storing the signature and the time of occurence thereof; and comparing the stored signature with reference signatures that occurred at approximately the same time as the stored signature. 8. The method recited in claim 1 wherein said first event is the elapse of a predetermined length of time after the occurrence of the previous first event. | 0.79712 |
9,910,831 | 1 | 8 | 1. A method of providing a font effect of a display apparatus, the method comprising: identifying a first attribute value of a font effect to be applied to a character according to at least one of a characteristic of the character and a characteristic of the display apparatus; rendering the font effect based on the first attribute value and applying the rendered font effect based on the first attribute value to the character, wherein the font effect comprises a glow effect for adjusting a filtering intensity of a border of the character; and outputting the character to which the font effect is applied, and wherein the identifying the first attribute value of the font effect comprises reducing the glow effect by reducing the filtering intensity of the glow effect such that the filtering intensity of the glow effect decreases as a screen resolution characteristic of the display apparatus decreases, and wherein in response to a size of the character to which the rendered font effect is applied being changed, identifying a second attribute value of the font effect according to the changed character, and applying the font effect based on the second attribute value to the character. | 1. A method of providing a font effect of a display apparatus, the method comprising: identifying a first attribute value of a font effect to be applied to a character according to at least one of a characteristic of the character and a characteristic of the display apparatus; rendering the font effect based on the first attribute value and applying the rendered font effect based on the first attribute value to the character, wherein the font effect comprises a glow effect for adjusting a filtering intensity of a border of the character; and outputting the character to which the font effect is applied, and wherein the identifying the first attribute value of the font effect comprises reducing the glow effect by reducing the filtering intensity of the glow effect such that the filtering intensity of the glow effect decreases as a screen resolution characteristic of the display apparatus decreases, and wherein in response to a size of the character to which the rendered font effect is applied being changed, identifying a second attribute value of the font effect according to the changed character, and applying the font effect based on the second attribute value to the character. 8. The method according to claim 1 , further comprising obtaining font effect information that includes a type of the font effect to be applied to the character, and the type of the font effect comprises at least one of a shadow effect, the glow effect, a bevel effect, an emboss effect, a color overlay effect, a stroke effect, a linear gradient effect, and a pattern effect. | 0.5 |
7,975,217 | 4 | 5 | 4. The method of claim 1 , wherein the metadata is an index identifying the displayable data. | 4. The method of claim 1 , wherein the metadata is an index identifying the displayable data. 5. The method of claim 4 , wherein the displayable data represents a translation of a source message, and further comprising: (d) receiving a request including the index; and (e) retrieving the translation identified by the index. | 0.53252 |
9,699,145 | 13 | 15 | 13. A method comprising using at least one hardware processor for: receiving a masking rule of a data element in one or more documents of a JSQN-type, said one or more documents each comprise a hierarchy of at least two levels identified by name-value pairs, wherein the masking rule is based on the hierarchy of the one or more documents of the JSON-type, wherein the masking rule is defined according to one or more constraints with respect to a level of the at least two levels; and enforcing the masking rule on a document of the one or more documents of the JSON-type; wherein the enforcing of the masking rule comprises, listening to traffic in a network; identifying the document as it passes through the network; and enforcing the masking rule on the document. | 13. A method comprising using at least one hardware processor for: receiving a masking rule of a data element in one or more documents of a JSQN-type, said one or more documents each comprise a hierarchy of at least two levels identified by name-value pairs, wherein the masking rule is based on the hierarchy of the one or more documents of the JSON-type, wherein the masking rule is defined according to one or more constraints with respect to a level of the at least two levels; and enforcing the masking rule on a document of the one or more documents of the JSON-type; wherein the enforcing of the masking rule comprises, listening to traffic in a network; identifying the document as it passes through the network; and enforcing the masking rule on the document. 15. The method of claim 13 , wherein the enforcing of the masking rule on the document is performed offline. | 0.5 |
9,405,186 | 5 | 6 | 5. The method of claim 1 , wherein in the case that the at least one relevancy criterion includes the printing difficulty, the plurality of clips includes a highest printing difficulty clip and a lowest printing difficulty clip, and the printing difficulty is calculated based on a critical dimension or a lithography difficulty estimator (LDE) of the clip. | 5. The method of claim 1 , wherein in the case that the at least one relevancy criterion includes the printing difficulty, the plurality of clips includes a highest printing difficulty clip and a lowest printing difficulty clip, and the printing difficulty is calculated based on a critical dimension or a lithography difficulty estimator (LDE) of the clip. 6. The method of claim 5 , further comprising multiplying the relevancy score for each of the plurality of clips by a respective scaling factor, wherein the scaling factor is based on the printing difficulty of the corresponding clip or a probability distribution of printing difficulties. | 0.5 |
9,798,748 | 25 | 30 | 25. A method of constructing a query with which to retrieve information from a database, the method comprising: representing graphically a first dataset of a database as a first icon and a second dataset of the database as a second icon in a canvas presented to a user on a display of a computer system; receiving an indication to combine the first dataset with the second dataset, wherein the indication is received in response to the first icon being graphically associated with the second icon in the canvas; based on the received indication and metadata associated with the first dataset and the second dataset, presenting to the user on the display of the computer system natural language query options for combining elements of the first and second datasets, the natural language query options including intuitive descriptions of the combining elements based on the metadata and an experience level of the user, the intuitive descriptions being different for users with different experience levels; in response to the user's selection of one of the presented natural language query options, generating a third icon in the canvas representing a combination dataset of elements of the first and second datasets; representing user-defined relationships between the first and second datasets and the combination dataset as a connected graph of the first, second and third icons, wherein the connected graph presents a graphical representation of the query to the user; constructing, by at least one processor of the computer system, a machine-readable structured query based on the connected graph; and returning data from the database, the returned data corresponding to an execution of the machine-readable structured query against the database. | 25. A method of constructing a query with which to retrieve information from a database, the method comprising: representing graphically a first dataset of a database as a first icon and a second dataset of the database as a second icon in a canvas presented to a user on a display of a computer system; receiving an indication to combine the first dataset with the second dataset, wherein the indication is received in response to the first icon being graphically associated with the second icon in the canvas; based on the received indication and metadata associated with the first dataset and the second dataset, presenting to the user on the display of the computer system natural language query options for combining elements of the first and second datasets, the natural language query options including intuitive descriptions of the combining elements based on the metadata and an experience level of the user, the intuitive descriptions being different for users with different experience levels; in response to the user's selection of one of the presented natural language query options, generating a third icon in the canvas representing a combination dataset of elements of the first and second datasets; representing user-defined relationships between the first and second datasets and the combination dataset as a connected graph of the first, second and third icons, wherein the connected graph presents a graphical representation of the query to the user; constructing, by at least one processor of the computer system, a machine-readable structured query based on the connected graph; and returning data from the database, the returned data corresponding to an execution of the machine-readable structured query against the database. 30. The method of claim 25 , wherein the user's selection of one of the presented natural language query options includes a selection to filter the second dataset through the first dataset to create the combination dataset, which includes only records from the first dataset having a corresponding record in the second dataset. | 0.614387 |
8,644,809 | 1 | 6 | 1. A method of transmitting an electronic voucher through a short message, the method which comprises: converting an electronic voucher to be transmitted into a bit stream; mapping each n-bits of the bit stream to any one text character of one of a plurality of text character groups, wherein text characters in each of the text character groups have at least one same or similar geometry or image feature, and a bit number n corresponding to each text character depends on a number m of the text character groups; arranging the text characters obtained by the mapping into a character sequence; and transmitting the character sequence through a short message; wherein, the plurality of text character groups are achieved by dividing a text character set in terms of the geometry or image features of the text characters of the text character set; and a criterion of dividing the text character set in terms of the geometry or image features of the text characters of the text character set includes: axial symmetry and rotational symmetry of the text characters. | 1. A method of transmitting an electronic voucher through a short message, the method which comprises: converting an electronic voucher to be transmitted into a bit stream; mapping each n-bits of the bit stream to any one text character of one of a plurality of text character groups, wherein text characters in each of the text character groups have at least one same or similar geometry or image feature, and a bit number n corresponding to each text character depends on a number m of the text character groups; arranging the text characters obtained by the mapping into a character sequence; and transmitting the character sequence through a short message; wherein, the plurality of text character groups are achieved by dividing a text character set in terms of the geometry or image features of the text characters of the text character set; and a criterion of dividing the text character set in terms of the geometry or image features of the text characters of the text character set includes: axial symmetry and rotational symmetry of the text characters. 6. The method according to claim 1 , wherein the text character set is divided into m text character groups in terms of a geometry or image features of the text characters of the text character set, and wherein m is not less than 2. | 0.681319 |
8,688,449 | 1 | 4 | 1. A speech recognition device comprising: a speech recognition dictionary in which a place name for use in an address having a hierarchical structure from a wide area to a narrow area is registered as a vocabulary to be recognized; a recognition likelihood calculation unit for extracting features from a speech, and calculating likelihood of the speech to each of the vocabularies, to be recognized, registered in the speech recognition dictionary based upon the features; an address database in which a tree structure indicating a hierarchical relationship of the place names of the vocabularies to be recognized; a weight coefficient calculation unit for calculating a weight coefficient of likelihood of the vocabulary to be recognized based on a number of geographic names belonging to a hierarchy lower than the vocabulary to be recognized, as the number of geographic names becomes larger, the weight coefficient becomes larger; and a recognition result output unit for outputting a speech recognition result based on weighted likelihood obtained by weighting the likelihood of the speech calculated by the recognition likelihood calculation unit with the weight coefficient of the likelihood of the vocabulary to be recognized calculated by the weight coefficient calculation unit. | 1. A speech recognition device comprising: a speech recognition dictionary in which a place name for use in an address having a hierarchical structure from a wide area to a narrow area is registered as a vocabulary to be recognized; a recognition likelihood calculation unit for extracting features from a speech, and calculating likelihood of the speech to each of the vocabularies, to be recognized, registered in the speech recognition dictionary based upon the features; an address database in which a tree structure indicating a hierarchical relationship of the place names of the vocabularies to be recognized; a weight coefficient calculation unit for calculating a weight coefficient of likelihood of the vocabulary to be recognized based on a number of geographic names belonging to a hierarchy lower than the vocabulary to be recognized, as the number of geographic names becomes larger, the weight coefficient becomes larger; and a recognition result output unit for outputting a speech recognition result based on weighted likelihood obtained by weighting the likelihood of the speech calculated by the recognition likelihood calculation unit with the weight coefficient of the likelihood of the vocabulary to be recognized calculated by the weight coefficient calculation unit. 4. The speech recognition device according to claim 1 , wherein: the address database stores city names and facility names belonging to a lower hierarchy below the city names, and the number of geographic names belonging to the hierarchy lower than the vocabulary to be recognized in the address database is a number of telephone numbers of the facility names belonging to the lower hierarchy below the city names. | 0.5 |
8,166,003 | 12 | 13 | 12. The system of claim 8 , further comprising: an administration module, coupled with said document storage module, for setting said associated permission requirements for said plurality of document versions, such that the security level is one of a plurality of security levels and the authorized audience is a subset of authorized users. | 12. The system of claim 8 , further comprising: an administration module, coupled with said document storage module, for setting said associated permission requirements for said plurality of document versions, such that the security level is one of a plurality of security levels and the authorized audience is a subset of authorized users. 13. The system of claim 12 , wherein said associated permission requirements for said plurality of document versions comprise one of a plurality of access levels. | 0.5 |
7,603,330 | 1 | 3 | 1. A computer-implemented method for automatically classifying a first question, the method comprising: receiving unlabeled audio or digital text data from an input module, said unlabeled audio or digital text data comprising data that is not previously associated with an expected answer; automatically labeling said unlabeled audio or digital text data using a processor to produce first labeled audio or digital text data associating a first answer with the unlabeled audio or digital text data using a first artificial neural network, said first artificial neural network comprising a first set of weights, said first artificial neural network producing the first labeled audio or digital text data by performing one or more auxiliary tasks analyzing characteristics of said unlabeled audio or digital text data; transferring said first set of weights to a second artificial neural network; receiving second labeled audio or digital text data comprising a second question and a corresponding answer; training said second artificial neural network with the processor using said second labeled audio or digital text data by modifying a second set of weights associated with the second artificial neural network responsive to the second labeled audio or digital text data and freezing the first set of weights; receiving the first question from the input module; and associating a question category with the first question using said second artificial neural network, said question category identifying a source for retrieving text data or audio data describing an answer corresponding to the first question. | 1. A computer-implemented method for automatically classifying a first question, the method comprising: receiving unlabeled audio or digital text data from an input module, said unlabeled audio or digital text data comprising data that is not previously associated with an expected answer; automatically labeling said unlabeled audio or digital text data using a processor to produce first labeled audio or digital text data associating a first answer with the unlabeled audio or digital text data using a first artificial neural network, said first artificial neural network comprising a first set of weights, said first artificial neural network producing the first labeled audio or digital text data by performing one or more auxiliary tasks analyzing characteristics of said unlabeled audio or digital text data; transferring said first set of weights to a second artificial neural network; receiving second labeled audio or digital text data comprising a second question and a corresponding answer; training said second artificial neural network with the processor using said second labeled audio or digital text data by modifying a second set of weights associated with the second artificial neural network responsive to the second labeled audio or digital text data and freezing the first set of weights; receiving the first question from the input module; and associating a question category with the first question using said second artificial neural network, said question category identifying a source for retrieving text data or audio data describing an answer corresponding to the first question. 3. The method of claim 1 , wherein said automatically labeling comprises learning a predictive structure within said unlabeled audio or digital text data. | 0.834052 |
8,352,245 | 17 | 20 | 17. A non-transitory computer storage medium encoded with a computer program, the program comprising instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: accessing audio data; accessing information that indicates a first context, the first context comprising a first physical environment or physical state of a device that records the audio data; accessing at least one term; accessing information that indicates a second context, the second context comprising a second physical environment or physical state associated with the accessed term; determining a similarity score that indicates a degree of similarity between the first physical environment or physical state and the second physical environment or physical state; adjusting a language model based on the accessed term and the determined similarity score to generate an adjusted language model, wherein the adjusted language model includes the accessed term and a weighting value assigned to the accessed term based on the similarity score; and performing speech recognition on the audio data using the adjusted language model to select one or more candidate transcriptions for a portion of the audio data. | 17. A non-transitory computer storage medium encoded with a computer program, the program comprising instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: accessing audio data; accessing information that indicates a first context, the first context comprising a first physical environment or physical state of a device that records the audio data; accessing at least one term; accessing information that indicates a second context, the second context comprising a second physical environment or physical state associated with the accessed term; determining a similarity score that indicates a degree of similarity between the first physical environment or physical state and the second physical environment or physical state; adjusting a language model based on the accessed term and the determined similarity score to generate an adjusted language model, wherein the adjusted language model includes the accessed term and a weighting value assigned to the accessed term based on the similarity score; and performing speech recognition on the audio data using the adjusted language model to select one or more candidate transcriptions for a portion of the audio data. 20. The non-transitory computer storage medium of claim 17 , wherein the operations comprise determining that the accessed term was entered by a user, wherein: the audio data encodes speech of the user; the first context includes the environment in which the speech occurred; and the second context includes the environment in which the accessed term was previously entered by the user. | 0.5 |
9,406,030 | 1 | 4 | 1. A computerized method for electronic document classification, the method comprising: providing training documents sorted into a plurality of classes; using a processor to perform linear programming including selecting input values which maximize an output value, given specific constraints on the input values, wherein the output value maximized is a difference between: a. a first estimated probability that a document instance will be correctly classified, by a given classifier corresponding to given input values, as belonging to its own class, and b. a second estimated probability that the document instance will be classified, by the given classifier, as belonging to a class other than its own class; and classifying electronic document instances into the plurality of classes, using at least one preferred classifier corresponding to the input values selected by said linear programming including storing an indication of said classifying in computer memory, wherein some electronic document instances are classified as belonging to none of the plurality of classes. | 1. A computerized method for electronic document classification, the method comprising: providing training documents sorted into a plurality of classes; using a processor to perform linear programming including selecting input values which maximize an output value, given specific constraints on the input values, wherein the output value maximized is a difference between: a. a first estimated probability that a document instance will be correctly classified, by a given classifier corresponding to given input values, as belonging to its own class, and b. a second estimated probability that the document instance will be classified, by the given classifier, as belonging to a class other than its own class; and classifying electronic document instances into the plurality of classes, using at least one preferred classifier corresponding to the input values selected by said linear programming including storing an indication of said classifying in computer memory, wherein some electronic document instances are classified as belonging to none of the plurality of classes. 4. A method according to claim 1 , wherein said classifying uses said preferred classifier in conjunction with available partial information regarding correspondence between electronic document instances and the plurality of classes. | 0.825337 |
10,095,452 | 15 | 18 | 15. The system of claim 12 , further comprising generating, by said one or more processors in said MFD, said electronic document from said hand-filled document, wherein said hand-filled document is obtained from a user associated with said user-computing device. | 15. The system of claim 12 , further comprising generating, by said one or more processors in said MFD, said electronic document from said hand-filled document, wherein said hand-filled document is obtained from a user associated with said user-computing device. 18. The system of claim 15 , wherein said processing of said one or more portions in said generated electronic document to determine said second format of said character string is based on one or more handwriting recognition techniques. | 0.5 |
9,070,375 | 4 | 6 | 4. A speech processing method for processing a speech by a computer device, the method comprising the steps of: dividing an input speech signal into frames, wherein said input speech signal is received from a voice activity detection apparatus; converting said input speech signal to a logarithmic power spectrum; performing long-term spectrum variation component extraction to generate a first feature vector by steps of: transforming said logarithmic power spectrum to mel cepstrum coefficients; and extracting a long-term spectrum variation component from a sequence of said mel cepstrum coefficients by linear regression calculation using a longer delta window than an average phoneme duration of an utterance in said input speech signal to generate a first feature vector; performing harmonic structure feature extraction to generate a second feature vector by steps of: transforming said logarithmic power spectrum to cepstrum coefficients by a discrete cosine transform; cutting off upper and lower cepstrum components from said cepstrum coefficients; inverse discrete cosine transforming said cepstrum coefficients from which said upper and lower cepstrum components have been cut; converting an output of said inverse discrete cosine transform back to a power spectrum; mel filter bank processing said power spectrum to produce mel filter bank processed output; and transforming said mel filter bank processed output to a second feature vector comprising a harmonic structure feature by said discrete cosine transform, and determining a voiced segment by using said long-term spectrum variation component first feature vector concatenated with said harmonic structure feature second feature vector and comparing the concatenated feature vectors to a statistical model, wherein at least one of the steps is carried out using the computer device. | 4. A speech processing method for processing a speech by a computer device, the method comprising the steps of: dividing an input speech signal into frames, wherein said input speech signal is received from a voice activity detection apparatus; converting said input speech signal to a logarithmic power spectrum; performing long-term spectrum variation component extraction to generate a first feature vector by steps of: transforming said logarithmic power spectrum to mel cepstrum coefficients; and extracting a long-term spectrum variation component from a sequence of said mel cepstrum coefficients by linear regression calculation using a longer delta window than an average phoneme duration of an utterance in said input speech signal to generate a first feature vector; performing harmonic structure feature extraction to generate a second feature vector by steps of: transforming said logarithmic power spectrum to cepstrum coefficients by a discrete cosine transform; cutting off upper and lower cepstrum components from said cepstrum coefficients; inverse discrete cosine transforming said cepstrum coefficients from which said upper and lower cepstrum components have been cut; converting an output of said inverse discrete cosine transform back to a power spectrum; mel filter bank processing said power spectrum to produce mel filter bank processed output; and transforming said mel filter bank processed output to a second feature vector comprising a harmonic structure feature by said discrete cosine transform, and determining a voiced segment by using said long-term spectrum variation component first feature vector concatenated with said harmonic structure feature second feature vector and comparing the concatenated feature vectors to a statistical model, wherein at least one of the steps is carried out using the computer device. 6. The speech processing method according to claim 4 , wherein the step of cutting off upper and lower cepstrum components further comprises extracting components corresponding to said harmonic structure in a possible range as a human speech. | 0.5 |
9,514,195 | 12 | 13 | 12. The system of claim 9 , wherein inserting the search result identifying the second resource in the set of search results identifying the first resources comprises: determining an insertion score based, in part, on the search probability ratio, wherein the insertion score defines an ordinal insertion position at which a second resource search result referencing the second resource is to be inserted into a ranking of first resource search results referencing the first resources; and generating a search results resource for displaying the first resource search results according to their respective ordinal positions in the ranking and the second resource search results at the ordinal insertion position. | 12. The system of claim 9 , wherein inserting the search result identifying the second resource in the set of search results identifying the first resources comprises: determining an insertion score based, in part, on the search probability ratio, wherein the insertion score defines an ordinal insertion position at which a second resource search result referencing the second resource is to be inserted into a ranking of first resource search results referencing the first resources; and generating a search results resource for displaying the first resource search results according to their respective ordinal positions in the ranking and the second resource search results at the ordinal insertion position. 13. The system of claim 12 , wherein determining an insertion score based, in part, on the search probability ratio comprises: determining an insertion score corresponding to a first ordinal position when the search probability ratio meets a first insertion threshold; determining an insertion score corresponding to a second ordinal position when the search probability ratio meets a second insertion threshold but does not meet the first insertion threshold; and determining an insertion score corresponding to a third ordinal position when the search probability ratio meets a third insertion threshold but does not meet the second insertion threshold. | 0.5 |
9,036,083 | 4 | 7 | 4. The method of claim 1 , further comprising determining additional information corresponding to the video content based on the identified text and the determined category. | 4. The method of claim 1 , further comprising determining additional information corresponding to the video content based on the identified text and the determined category. 7. The method of claim 4 , further comprising providing the additional information to a software application on a media content device. | 0.633152 |
9,646,627 | 1 | 6 | 1. A speech processing device comprising: a processor; and a sound collection unit configured to record speech from a sound source, a distance detection unit comprising a sensor configured to detect a distance between the sound collection unit and the sound source, the distance detection unit configured to output distance data indicating the detected distance, wherein the processor is configured to acquire, from the distance detection unit, the detected distance between the sound collection unit and the sound source; measure a reverberation characteristic with respect to a predetermined distance in advance; estimate a reverberation characteristic with respect to the detected distance based on the detected distance and the measured reverberation characteristic with respect to the predetermined distance; generate correction data indicating a contribution of a reverberation component from the estimated reverberation characteristic; remove the reverberation component from the speech by correcting the amplitude of the speech based on the correction data, to produce de-reverbed speech signals in which the reverberation component is removed from the speech; and perform a speech recognizing process on the de-reverbed speech signals to recognize at least one word. | 1. A speech processing device comprising: a processor; and a sound collection unit configured to record speech from a sound source, a distance detection unit comprising a sensor configured to detect a distance between the sound collection unit and the sound source, the distance detection unit configured to output distance data indicating the detected distance, wherein the processor is configured to acquire, from the distance detection unit, the detected distance between the sound collection unit and the sound source; measure a reverberation characteristic with respect to a predetermined distance in advance; estimate a reverberation characteristic with respect to the detected distance based on the detected distance and the measured reverberation characteristic with respect to the predetermined distance; generate correction data indicating a contribution of a reverberation component from the estimated reverberation characteristic; remove the reverberation component from the speech by correcting the amplitude of the speech based on the correction data, to produce de-reverbed speech signals in which the reverberation component is removed from the speech; and perform a speech recognizing process on the de-reverbed speech signals to recognize at least one word. 6. The speech processing device according to claim 1 , further comprising: an acoustic model prediction unit configured to predict an acoustic model corresponding to the distance acquired by the processor from a first acoustic model trained using speech based on the predetermined distances and having a reverberation added thereto and the second acoustic model trained using speech under an environment in which a reverberation is negligible; and a speech recognition unit configured to perform a speech recognizing process using the first acoustic model and the second acoustic model. | 0.5 |
8,805,765 | 11 | 12 | 11. A system for analyzing a business rule for bill payment timeliness, comprising: an event handler configured to determine that a payment has been made for a bill; a triggering component configured to, responsive to determining that the payment has been made for the bill, trigger a rule to determine timeliness of the payment, wherein the rule is expressed in a goal-oriented manner; and a forward chaining rule engine configure to execute the rule to determine if the bill payment is timely. | 11. A system for analyzing a business rule for bill payment timeliness, comprising: an event handler configured to determine that a payment has been made for a bill; a triggering component configured to, responsive to determining that the payment has been made for the bill, trigger a rule to determine timeliness of the payment, wherein the rule is expressed in a goal-oriented manner; and a forward chaining rule engine configure to execute the rule to determine if the bill payment is timely. 12. The system of claim 11 , further comprising a graphical user interface configured to receive the rule. | 0.5 |
8,781,971 | 3 | 4 | 3. The method of claim 1 , wherein determining that the software application is permitted to access the API comprises utilizing a function, a key, and a text string associated with the software application. | 3. The method of claim 1 , wherein determining that the software application is permitted to access the API comprises utilizing a function, a key, and a text string associated with the software application. 4. The method of claim 3 , wherein the text string includes one or more use terms, and the key is unique to the software application and the use terms. | 0.5 |
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