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9,864,802 | 1 | 3 | 1. A method for rules-based knowledge-driven search filters, the method performed by a data processing system and comprising: receiving metadata for a plurality of searchable objects, by the data processing system, the metadata including at least one of an object type definition and object properties; defining search filter rules based on user properties and data conditions, by the data processing system; performing a search according to a rule-based configuration, by the data processing system, the rule-based configuration including filters for object properties and filter ordering rules, the filter ordering rules specifying the order in which the filters are applied; and displaying search results according to the rule-based configuration, by the data processing system. | 1. A method for rules-based knowledge-driven search filters, the method performed by a data processing system and comprising: receiving metadata for a plurality of searchable objects, by the data processing system, the metadata including at least one of an object type definition and object properties; defining search filter rules based on user properties and data conditions, by the data processing system; performing a search according to a rule-based configuration, by the data processing system, the rule-based configuration including filters for object properties and filter ordering rules, the filter ordering rules specifying the order in which the filters are applied; and displaying search results according to the rule-based configuration, by the data processing system. 3. The method of claim 1 , wherein the data processing system defines a configuration definition for a specific search engine based on the search filter rules and the rule-based configuration. | 0.594937 |
10,108,680 | 1 | 11 | 1. A method of analyzing data, comprising: generating, by an entity, a query based at least in part on a topic of interest; executing the query on a plurality of data sources, at least one of the plurality of data sources comprising at least one of financial data, operational data, human resources data, production data, information technology data, or a combination thereof; selecting, by the entity, a data source from the plurality of data sources for monitoring based on a correlation between the data source and the topic of interest, the correlation determined based on results of the executed query; monitoring, based on a set schedule, the data source for matches to the query to extract data from the data source when at least an update to stored data matches the query, newly added data matches the query, or a combination thereof; extracting data from the data source when at least an update to stored data matches the query, newly added data matches the query, or a combination thereof; determining an extraction rate for extracting the data, the extraction rate indicating an amount of the data that is extracted over a first time period; determining a first processing rate for processing the extracted data with a number of parallel processors, the first processing rate indicating an amount of extracted data that is processed over a second time period; dynamically adjusting the number of parallel processors for analyzing the extracted data based on the extraction rate to obtain a second processing rate that is greater than the first processing rate; analyzing, with the parallel processors, the extracted data to determine at least one of a sentiment, an index, a pattern, or a combination thereof; establishing a two-way communication channel, between at least the entity that selected the data source for monitoring and a user device of a user that provided data to the data source, based on the analysis of the extracted data; transmitting, from the entity via the two-way communication channel, a first message directed to the user device based on the analysis of the extracted data; and receiving, from the user device via the two-way communication channel, a second message in response to the first message directed to the user device. | 1. A method of analyzing data, comprising: generating, by an entity, a query based at least in part on a topic of interest; executing the query on a plurality of data sources, at least one of the plurality of data sources comprising at least one of financial data, operational data, human resources data, production data, information technology data, or a combination thereof; selecting, by the entity, a data source from the plurality of data sources for monitoring based on a correlation between the data source and the topic of interest, the correlation determined based on results of the executed query; monitoring, based on a set schedule, the data source for matches to the query to extract data from the data source when at least an update to stored data matches the query, newly added data matches the query, or a combination thereof; extracting data from the data source when at least an update to stored data matches the query, newly added data matches the query, or a combination thereof; determining an extraction rate for extracting the data, the extraction rate indicating an amount of the data that is extracted over a first time period; determining a first processing rate for processing the extracted data with a number of parallel processors, the first processing rate indicating an amount of extracted data that is processed over a second time period; dynamically adjusting the number of parallel processors for analyzing the extracted data based on the extraction rate to obtain a second processing rate that is greater than the first processing rate; analyzing, with the parallel processors, the extracted data to determine at least one of a sentiment, an index, a pattern, or a combination thereof; establishing a two-way communication channel, between at least the entity that selected the data source for monitoring and a user device of a user that provided data to the data source, based on the analysis of the extracted data; transmitting, from the entity via the two-way communication channel, a first message directed to the user device based on the analysis of the extracted data; and receiving, from the user device via the two-way communication channel, a second message in response to the first message directed to the user device. 11. The method of claim 1 , further comprising analyzing, by an entity user corresponding to the entity, the results of the query in view of the topic of interest and a sub-topic of interest; and determining, by the entity user, the correlation between the data source and the topic of interest based on the analysis of the results of the query. | 0.5 |
8,571,047 | 9 | 10 | 9. The MG according to claim 8 , wherein: the context processing unit comprises one or more of the following modules: a creating module, configured to create a new packet filter rule context according to a message for creating the packet filter rule context sent by the MGC; a modifying module, configured to modify a created packet filter rule context according to a message for modifying the created packet filter rule context sent by the MGC, wherein the modification of the created packet filter rule context comprises adding, modifying or deleting the filter rule in the packet filter rule context; and a deleting module, configured to delete a created packet filter rule context according to a message for deleting the created packet filter rule context sent by the MGC; and the context processing unit further comprises: a replying module, configured to return a reply message to the MGC after the creating module completes creation by the creating module, or the modifying module completes modification, or the deleting module completes deletion. | 9. The MG according to claim 8 , wherein: the context processing unit comprises one or more of the following modules: a creating module, configured to create a new packet filter rule context according to a message for creating the packet filter rule context sent by the MGC; a modifying module, configured to modify a created packet filter rule context according to a message for modifying the created packet filter rule context sent by the MGC, wherein the modification of the created packet filter rule context comprises adding, modifying or deleting the filter rule in the packet filter rule context; and a deleting module, configured to delete a created packet filter rule context according to a message for deleting the created packet filter rule context sent by the MGC; and the context processing unit further comprises: a replying module, configured to return a reply message to the MGC after the creating module completes creation by the creating module, or the modifying module completes modification, or the deleting module completes deletion. 10. The MG according to claim 9 , wherein: the MG comprises a storing unit, and the storing unit comprises a rule module which is configured to store the filter rule. | 0.768802 |
8,312,110 | 1 | 3 | 1. An information processing device which transmits and receives information to and from other devices via a network, comprising: (a) a memory storing a plurality of objects as nodes of a hierarchical tree structure, wherein: (i) each object is denoted as a particular sub-tree of the hierarchical tree structure, each object comprising one or more application nodes, one or more data nodes, and zero or more objects as sub-nodes, (ii) each sub-tree is given a unique label that represents corresponding nodes of the hierarchical tree structure, the unique label including (A) name information showing node names from a highest order node of the hierarchical tree structure to a highest order node of the sub-tree, the name information being continuous and (B) a storing position indicating a location of the sub-tree on the network; (iii) each application node including application logic for calling-up, writing, deleting, or partially changing one or more data nodes of the respective application node's object; (b) a transmitting and receiving section transmitting and receiving information expressed in an external language to and from other devices via the network, the transmitting and receiving section receiving an HTTP request comprising: a particular label expressing a designated sub-tree, and an identification of an application node of the designated sub-tree; and (c) an engine section which functions as a universal interface and includes: a first translating section including a deserializing section which via the network acquires the designated sub-tree and the identified application node of the designated sub-tree from the HTTP request and translates the designated sub-tree into information expressed in an internal language while preserving a tree structure of the designated sub-tree, a control section executing the application logic of the identified application node of the designated sub-tree, and a second translating section including a serializing section translating a sub-tree stored in the memory into the external language while preserving a tree structure of the stored sub-tree; wherein the information processing device enables a user to change contents of the one or more data nodes during the execution of a program of the information processing device that accesses the plurality of objects. | 1. An information processing device which transmits and receives information to and from other devices via a network, comprising: (a) a memory storing a plurality of objects as nodes of a hierarchical tree structure, wherein: (i) each object is denoted as a particular sub-tree of the hierarchical tree structure, each object comprising one or more application nodes, one or more data nodes, and zero or more objects as sub-nodes, (ii) each sub-tree is given a unique label that represents corresponding nodes of the hierarchical tree structure, the unique label including (A) name information showing node names from a highest order node of the hierarchical tree structure to a highest order node of the sub-tree, the name information being continuous and (B) a storing position indicating a location of the sub-tree on the network; (iii) each application node including application logic for calling-up, writing, deleting, or partially changing one or more data nodes of the respective application node's object; (b) a transmitting and receiving section transmitting and receiving information expressed in an external language to and from other devices via the network, the transmitting and receiving section receiving an HTTP request comprising: a particular label expressing a designated sub-tree, and an identification of an application node of the designated sub-tree; and (c) an engine section which functions as a universal interface and includes: a first translating section including a deserializing section which via the network acquires the designated sub-tree and the identified application node of the designated sub-tree from the HTTP request and translates the designated sub-tree into information expressed in an internal language while preserving a tree structure of the designated sub-tree, a control section executing the application logic of the identified application node of the designated sub-tree, and a second translating section including a serializing section translating a sub-tree stored in the memory into the external language while preserving a tree structure of the stored sub-tree; wherein the information processing device enables a user to change contents of the one or more data nodes during the execution of a program of the information processing device that accesses the plurality of objects. 3. The information processing device of claim 1 , wherein the engine section includes a determining section which determines whether the designated sub-tree is stored internally or externally of the information processing device; and upon determining the designated sub-tree is stored in external storage, translating the request into the external language at the second translating section; and upon determining the designated sub-tree is stored in internal storage of the information processing device, outputting the request to the transmitting and receiving section. | 0.5 |
8,799,099 | 1 | 4 | 1. A method, comprising: receiving, by a system server and from a capture device, text captured by the capture device from a paper form of a published document, the document having a publisher; identifying, by the system server, the published document and the publisher from the captured text; retrieving, by the system server and from a repository of digital documents, a digital form of the document; identifying a first product, by the system server, the first product being related to the captured text or to the digital document; providing to the capture device, by the system server, a purchase option to purchase the first product from a seller server; receiving a request to purchase the first product from the capture device and providing the request to the seller server, by the system server; receiving, by the system server, a purchase notice confirming the purchase from the seller server; associating, by the system server, the purchase with the document and publisher; providing confirmation of the purchase to the capture device, by the system server; and providing a purchase and source notice to the publisher, by the system server, enabling the publisher or an author of the document to obtain compensation for the purchase. | 1. A method, comprising: receiving, by a system server and from a capture device, text captured by the capture device from a paper form of a published document, the document having a publisher; identifying, by the system server, the published document and the publisher from the captured text; retrieving, by the system server and from a repository of digital documents, a digital form of the document; identifying a first product, by the system server, the first product being related to the captured text or to the digital document; providing to the capture device, by the system server, a purchase option to purchase the first product from a seller server; receiving a request to purchase the first product from the capture device and providing the request to the seller server, by the system server; receiving, by the system server, a purchase notice confirming the purchase from the seller server; associating, by the system server, the purchase with the document and publisher; providing confirmation of the purchase to the capture device, by the system server; and providing a purchase and source notice to the publisher, by the system server, enabling the publisher or an author of the document to obtain compensation for the purchase. 4. The method of claim 1 , wherein providing the purchase option to purchase the first product comprises providing a context menu of actions comprising the purchase option to purchase the first product. | 0.811215 |
7,770,182 | 7 | 12 | 7. A system, comprising: a processor; and one or more computer-readable storage media having computer-executable instructions stored thereon that, when executed by the processor, implement: an extensible editor that processes editing events requesting manipulation of a document, the extensible editor having an event routing controller and a default event handler; and a first extension and a second extension coupled with the extensible editor for processing the editing events, wherein the event routing controller provides an editing event received by the extensible editor to the first extension prior to providing the editing event to be processed by the default event handler, wherein the event routing controller provides the editing event to the second extension prior to providing the editing event to be processed by the default event handler when the first extension does not consume the editing event, and wherein an order in which the editing event is routed to the first extension and the second extension is based on an order in which the first extension and the second extension were registered with the extensible editor. | 7. A system, comprising: a processor; and one or more computer-readable storage media having computer-executable instructions stored thereon that, when executed by the processor, implement: an extensible editor that processes editing events requesting manipulation of a document, the extensible editor having an event routing controller and a default event handler; and a first extension and a second extension coupled with the extensible editor for processing the editing events, wherein the event routing controller provides an editing event received by the extensible editor to the first extension prior to providing the editing event to be processed by the default event handler, wherein the event routing controller provides the editing event to the second extension prior to providing the editing event to be processed by the default event handler when the first extension does not consume the editing event, and wherein an order in which the editing event is routed to the first extension and the second extension is based on an order in which the first extension and the second extension were registered with the extensible editor. 12. The system as recited in claim 7 , wherein the extensible editor further comprises an edit designer interface that includes a pre-handle event method for providing the event to the first extension prior to the default event handler receiving the event. | 0.714922 |
8,712,828 | 1 | 7 | 1. A system for managing churn among customers of a business having a statistically large customer base, the system comprising: a memory device configured to store a data mart; a processor in communication with the memory device; a population architecture executable by the processor to receive customer data from one or more data sources stored in the data mart, the customer data defining a plurality of customer attributes for each customer in the customer base; a data manipulation module executable by the processor to: calculate derived variable values based on the customer data, wherein each of the derived variable values is indicative of at least one customer characteristic; select a subset of the derived variable values in response to a preselected data mining function; and generate at least one analytical record containing the subset of the derived variable values, wherein the at least one analytical record is associated with a plurality of customers; a data mining tool executable by the processor to perform the preselected data mining function, the preselected data mining function configured to: analyze the at least one analytical record; return results identifying clusters of customers sharing common customer attributes in response to the analysis of the at least one analytical record; and calculate, based on the at least one analytical record, individual customers' propensities to churn during a predefined period in the future, the data manipulation module storing the results in the data mart; and an end user access module executable by the processor to: access the results returned from the data mining tool; and present the results to a user. | 1. A system for managing churn among customers of a business having a statistically large customer base, the system comprising: a memory device configured to store a data mart; a processor in communication with the memory device; a population architecture executable by the processor to receive customer data from one or more data sources stored in the data mart, the customer data defining a plurality of customer attributes for each customer in the customer base; a data manipulation module executable by the processor to: calculate derived variable values based on the customer data, wherein each of the derived variable values is indicative of at least one customer characteristic; select a subset of the derived variable values in response to a preselected data mining function; and generate at least one analytical record containing the subset of the derived variable values, wherein the at least one analytical record is associated with a plurality of customers; a data mining tool executable by the processor to perform the preselected data mining function, the preselected data mining function configured to: analyze the at least one analytical record; return results identifying clusters of customers sharing common customer attributes in response to the analysis of the at least one analytical record; and calculate, based on the at least one analytical record, individual customers' propensities to churn during a predefined period in the future, the data manipulation module storing the results in the data mart; and an end user access module executable by the processor to: access the results returned from the data mining tool; and present the results to a user. 7. The system for managing churn of claim 1 wherein the end user access module is further executable by the processor to generate one or more reports configured to analyze churn based on customer data stored in the data mart. | 0.690083 |
5,426,700 | 15 | 23 | 15. An apparatus for verifying a document belonging to a particular class of documents, said particular class being one of a plurality of classes of document, each of said classes corresponding to a class encryption/decryption key pair CE,CD, said document incorporating encrypted information E comprising information M derived from said document and encrypted with an encryption key E selected from an encryption/decryption key pair E, D and said document further incorporating encrypted decryption, key CE comprising decryption key D for said key pair E, D encrypted with encryption key CE selected from class encryption/decryption key pair CE, CD associated with said particular class, comprising: a) means for scanning said document to input scanned information, said scanned information including said encrypted information E, said encrypted decryption key CE, and information identifying said particular class; b) means responsive to enabling information for enabling retrieval of a decryption key from any document in a selected group of said classes of documents and responsive said identifying information to determine if said document is in said selected group, and if so retrieving said decryption key D from said scanned information; c) means for decrypting said encrypted information E from said scanned information to obtain decrypted encrypted information D; and d) means for comparing said decrypted encrypted information D with said information M to verifying the information contained in said document as authentic and unchanged. | 15. An apparatus for verifying a document belonging to a particular class of documents, said particular class being one of a plurality of classes of document, each of said classes corresponding to a class encryption/decryption key pair CE,CD, said document incorporating encrypted information E comprising information M derived from said document and encrypted with an encryption key E selected from an encryption/decryption key pair E, D and said document further incorporating encrypted decryption, key CE comprising decryption key D for said key pair E, D encrypted with encryption key CE selected from class encryption/decryption key pair CE, CD associated with said particular class, comprising: a) means for scanning said document to input scanned information, said scanned information including said encrypted information E, said encrypted decryption key CE, and information identifying said particular class; b) means responsive to enabling information for enabling retrieval of a decryption key from any document in a selected group of said classes of documents and responsive said identifying information to determine if said document is in said selected group, and if so retrieving said decryption key D from said scanned information; c) means for decrypting said encrypted information E from said scanned information to obtain decrypted encrypted information D; and d) means for comparing said decrypted encrypted information D with said information M to verifying the information contained in said document as authentic and unchanged. 23. Am apparatus as described in claim 15 wherein said document further incorporates a second encrypted decryption key GE encrypted with a group encryption key GE selected from an encryption/decryption key pair GE,GD, and wherein documents in at least a kth class incorporate a third encrypted decryption key GE; and said enabling means further comprises memory means for storing a decryption key GD selected from said encryption/decryption key pair GE,GD, said decryption key GD enabling decryption of encrypted decryption keys on all documents comprised in said selected group; said apparatus further comprising means, responsive to said enabling information for storing said decryption key GD in said memory means. | 0.5 |
9,189,483 | 16 | 17 | 16. The non-transitory computer-readable storage medium of claim 15 , wherein the information comprises demographic features. | 16. The non-transitory computer-readable storage medium of claim 15 , wherein the information comprises demographic features. 17. The non-transitory computer-readable storage medium of claim 16 , wherein the demographic features comprise one of age, gender, socio-economic group, nationality, and origin. | 0.5 |
7,689,407 | 11 | 12 | 11. A computer product having a computer readable medium that contains a program to enable users to learn multiple languages through a computer, the computer readable medium comprising: a first code: providing a text input interface allowing a users to input a plurality of input words; a second code: searching for a plurality of pictures corresponding to the plurality of input words, wherein the plurality of pictures are used to express the plurality of input words; a third code: searching for a plurality of output words corresponding to the plurality of input words, wherein the plurality of output words are used to express the plurality of input words; and a fourth code: providing a picture/text text displaying: the plurality of input words; the plurality of output words; and the plurality of pictures. | 11. A computer product having a computer readable medium that contains a program to enable users to learn multiple languages through a computer, the computer readable medium comprising: a first code: providing a text input interface allowing a users to input a plurality of input words; a second code: searching for a plurality of pictures corresponding to the plurality of input words, wherein the plurality of pictures are used to express the plurality of input words; a third code: searching for a plurality of output words corresponding to the plurality of input words, wherein the plurality of output words are used to express the plurality of input words; and a fourth code: providing a picture/text text displaying: the plurality of input words; the plurality of output words; and the plurality of pictures. 12. The computer product as claimed in claim 11 , wherein said computer readable medium further comprising: a fifth code: providing an input language option interface capable of displaying a plurality of input languages from which users can choose one as an appointed input language of the input words; and a sixth code: providing an output language option interface capable of displaying a plurality of output languages from which users can choose one as an appointed output language of the output words. | 0.5 |
10,133,840 | 18 | 19 | 18. The computer system of claim 17 , wherein the program instructions further include instructions for causing the one or more computer processor(s) to perform the following operations: placing the logic circuit according to the set of circuit priority indicators; and routing the logic circuit according to the set of circuit priority indicators. | 18. The computer system of claim 17 , wherein the program instructions further include instructions for causing the one or more computer processor(s) to perform the following operations: placing the logic circuit according to the set of circuit priority indicators; and routing the logic circuit according to the set of circuit priority indicators. 19. The computer system of claim 18 , wherein synthesizing includes logical synthesis or physical synthesis. | 0.5 |
9,165,043 | 9 | 16 | 9. A computer program product comprising a computer useable memory device having a computer readable program stored thereon, wherein the computer readable program when executed on a computer causes the computer to: provide a pre-defined logical schema to a user of a database system, wherein the pre-defined logical schema is mapped to at least two relational database entities of different databases storing data therein; receive a logical query for data stored in the databases from the user, wherein the logical query is written in an object-oriented query language utilizing the pre-defined logical schema, and comprises two or more predicates and an operator specifying an action to take with one or more of the predicates; in response to receiving the logical query, interpret the logical query using the pre-defined logical schema to determine which of the relational database entities is a subject of the logical query, and which of the relational database entities is associated with each predicate; request the database of each determined relational database entity of the logical query to: translate each of the associated predicates of that database in the logical query into a query language specific to that database, wherein at least two different databases translate an associated predicate; and apply an authorization rule with each of the associated predicates of that database in the logical query, wherein the authorization rule identifies unauthorized data; receive a translated predicate query for each determined predicate of the logical query from its associated database of the relational database entity, wherein each translated predicate query is written in a relational query language specific to the associated database and is a translation of one of the object-oriented predicates in the logical query; combine each translated predicate query received from the databases of the determined relational database entities into a master query using the operator; execute the master query against the databases of the relational database entities that are subjects of the logical query; in response to executing the master query, receive a query result set from the databases of the relational database entities that are subjects of the logical query; and provide the query result set to the user, wherein the query result set lacks the unauthorized data. | 9. A computer program product comprising a computer useable memory device having a computer readable program stored thereon, wherein the computer readable program when executed on a computer causes the computer to: provide a pre-defined logical schema to a user of a database system, wherein the pre-defined logical schema is mapped to at least two relational database entities of different databases storing data therein; receive a logical query for data stored in the databases from the user, wherein the logical query is written in an object-oriented query language utilizing the pre-defined logical schema, and comprises two or more predicates and an operator specifying an action to take with one or more of the predicates; in response to receiving the logical query, interpret the logical query using the pre-defined logical schema to determine which of the relational database entities is a subject of the logical query, and which of the relational database entities is associated with each predicate; request the database of each determined relational database entity of the logical query to: translate each of the associated predicates of that database in the logical query into a query language specific to that database, wherein at least two different databases translate an associated predicate; and apply an authorization rule with each of the associated predicates of that database in the logical query, wherein the authorization rule identifies unauthorized data; receive a translated predicate query for each determined predicate of the logical query from its associated database of the relational database entity, wherein each translated predicate query is written in a relational query language specific to the associated database and is a translation of one of the object-oriented predicates in the logical query; combine each translated predicate query received from the databases of the determined relational database entities into a master query using the operator; execute the master query against the databases of the relational database entities that are subjects of the logical query; in response to executing the master query, receive a query result set from the databases of the relational database entities that are subjects of the logical query; and provide the query result set to the user, wherein the query result set lacks the unauthorized data. 16. The computer program product of claim 9 , wherein the computer useable memory device is a computer useable optical storage medium. | 0.903874 |
7,725,308 | 11 | 12 | 11. The method of claim 1 wherein each bridging rule includes a device-object-independent non-terminal in a left-hand side and a set of expansions of the non-terminal in a right-hand side. | 11. The method of claim 1 wherein each bridging rule includes a device-object-independent non-terminal in a left-hand side and a set of expansions of the non-terminal in a right-hand side. 12. The method of claim 11 wherein each of the set of expansions is a device-specific instantiation of the broad category of queries. | 0.5 |
9,686,596 | 17 | 31 | 17. A system comprising: a networked device and a client device to apply an automatic content recognition algorithm to determine a content identifier of an audio-visual data and to associate the content identifier with an advertisement data based on a semantic correlation between a meta-data of the advertisement provided by a content provider and the content identifier; a capture infrastructure to annotate the audio-visual data with at least one of a brand name and a product name by comparing entries in the master database with at least one of a closed captioning data of the audio-visual data and through an application of an optical character recognition algorithm in the audio-visual data; and an advertising exchange server to generate an advertisement based on the content identifier of the audio-visual data and a public internet-protocol address associated with an application requesting the advertisement data, wherein advertisement targeting is improved when a script is embedded in at least one of the client device, a supply-side platform, and a data provider integrated with the supply side platform, to execute arbitrary cross-site scripts in a sandboxed application of the client device, wherein the content identifier is obfuscated in a manner that it is relevant to a particular demand-side platform to eliminate a need to query the provider of the content identifier on a per ad-spot basis, wherein the demand-side platform to submit requests to an advertising exchange based on a constraint type rather than through a bidding methodology on a per advertisement spot basis, and wherein at least one of: a provider of the content identifier receives a compensation when the advertisement data is associated with the audio-visual data based on the public internet protocol address associated with the application requesting the advertisement data, the provider of the content appends at least one of a set of content identifiers from associated clients and a viewing history from associated clients to a plurality of advertisements and resells the advertisement data back to the advertising exchange based on the appended content identifiers, a capture infrastructure annotates the audio-visual data with at least one of a brand name and a product name by comparing entries in the master database with at least one of a closed captioning data of the audio-visual data and through an application of an optical character recognition algorithm in the audio-visual data, the sandboxed application of the client device requests access to at least one of a microphone and a camera on the client device to capture a raw audio/video data, the capture infrastructure processes the raw audio/video data with at least one of the brand name and the product name by comparing entries in the master database with at least one of the raw audio/video data and through the application of a sensory recognition algorithm of the raw audio/video data, the content identifier is at least one of a music identification, an object identification, a facial identification, and a voice identification, a minimal functionality comprising accessing at least one of a tuner and a stream decoder that identifies at least one of a channel and a content is found in the networked media device, the networked media device produces at least one of an audio fingerprint and a video fingerprint that are communicated with a capture infrastructure, the capture infrastructure compares at least one of the audio fingerprint and the video fingerprint with a master database, the capture infrastructure annotates the audio-visual data with a logo name by comparing entries in the master database with a logo data of the audio-visual data identified using a logo detection algorithm, the capture infrastructure automatically divides the audio-visual data into a series of scenes based on a semantic grouping of actions in the audio-visual data, the audio-visual data is analyzed in advance of a broadcast to determine content identifiers associated with each commercial in the audio-visual data such that advertisements are pre-inserted into the audio-visual data prior to broadcast, the capture infrastructure applies a time-order algorithm to automatically match advertisements to the audio-visual data when a correlation pattern is identified by the capture infrastructure with other audio-visual content previously analyzed, the capture infrastructure includes a buffer that is saved to a persistent storage and for which a label is generated to facilitate identification of reoccurring sequences, a post-processing operation is at least one of automated through a post-processing algorithm and a crowd-sourced operation using a plurality of users in which a turing test is applied to determine a veracity of an input, a device pairing algorithm is used in which a cookie data associated with a web page visited by the user stored on a browser on the client device is paired with the networked media device when the client device is communicatively coupled with the networked media device, a transitive public IP matching algorithm is utilized in which at least one of the client device and the networked media device communicates each public IP address with any paired entity to the capture infrastructure, and a tag that is unconstrained from a same-origin policy is used to automatically load the advertisement in the browser, the tag being at least one of an image tag, a frame, an iframe, and a script tag. | 17. A system comprising: a networked device and a client device to apply an automatic content recognition algorithm to determine a content identifier of an audio-visual data and to associate the content identifier with an advertisement data based on a semantic correlation between a meta-data of the advertisement provided by a content provider and the content identifier; a capture infrastructure to annotate the audio-visual data with at least one of a brand name and a product name by comparing entries in the master database with at least one of a closed captioning data of the audio-visual data and through an application of an optical character recognition algorithm in the audio-visual data; and an advertising exchange server to generate an advertisement based on the content identifier of the audio-visual data and a public internet-protocol address associated with an application requesting the advertisement data, wherein advertisement targeting is improved when a script is embedded in at least one of the client device, a supply-side platform, and a data provider integrated with the supply side platform, to execute arbitrary cross-site scripts in a sandboxed application of the client device, wherein the content identifier is obfuscated in a manner that it is relevant to a particular demand-side platform to eliminate a need to query the provider of the content identifier on a per ad-spot basis, wherein the demand-side platform to submit requests to an advertising exchange based on a constraint type rather than through a bidding methodology on a per advertisement spot basis, and wherein at least one of: a provider of the content identifier receives a compensation when the advertisement data is associated with the audio-visual data based on the public internet protocol address associated with the application requesting the advertisement data, the provider of the content appends at least one of a set of content identifiers from associated clients and a viewing history from associated clients to a plurality of advertisements and resells the advertisement data back to the advertising exchange based on the appended content identifiers, a capture infrastructure annotates the audio-visual data with at least one of a brand name and a product name by comparing entries in the master database with at least one of a closed captioning data of the audio-visual data and through an application of an optical character recognition algorithm in the audio-visual data, the sandboxed application of the client device requests access to at least one of a microphone and a camera on the client device to capture a raw audio/video data, the capture infrastructure processes the raw audio/video data with at least one of the brand name and the product name by comparing entries in the master database with at least one of the raw audio/video data and through the application of a sensory recognition algorithm of the raw audio/video data, the content identifier is at least one of a music identification, an object identification, a facial identification, and a voice identification, a minimal functionality comprising accessing at least one of a tuner and a stream decoder that identifies at least one of a channel and a content is found in the networked media device, the networked media device produces at least one of an audio fingerprint and a video fingerprint that are communicated with a capture infrastructure, the capture infrastructure compares at least one of the audio fingerprint and the video fingerprint with a master database, the capture infrastructure annotates the audio-visual data with a logo name by comparing entries in the master database with a logo data of the audio-visual data identified using a logo detection algorithm, the capture infrastructure automatically divides the audio-visual data into a series of scenes based on a semantic grouping of actions in the audio-visual data, the audio-visual data is analyzed in advance of a broadcast to determine content identifiers associated with each commercial in the audio-visual data such that advertisements are pre-inserted into the audio-visual data prior to broadcast, the capture infrastructure applies a time-order algorithm to automatically match advertisements to the audio-visual data when a correlation pattern is identified by the capture infrastructure with other audio-visual content previously analyzed, the capture infrastructure includes a buffer that is saved to a persistent storage and for which a label is generated to facilitate identification of reoccurring sequences, a post-processing operation is at least one of automated through a post-processing algorithm and a crowd-sourced operation using a plurality of users in which a turing test is applied to determine a veracity of an input, a device pairing algorithm is used in which a cookie data associated with a web page visited by the user stored on a browser on the client device is paired with the networked media device when the client device is communicatively coupled with the networked media device, a transitive public IP matching algorithm is utilized in which at least one of the client device and the networked media device communicates each public IP address with any paired entity to the capture infrastructure, and a tag that is unconstrained from a same-origin policy is used to automatically load the advertisement in the browser, the tag being at least one of an image tag, a frame, an iframe, and a script tag. 31. The system of claim 17 : wherein the sandboxed application is at least one of a web page, a script, a binary executable, an intermediate bytecode, an abstract syntax tree, and an executable application in the security sandbox, wherein the sandboxed application comprises at least one of a markup language application such as a HyperText Markup Language 5 (HTML5) application, a Javascript® application, an Adobe® Flash® application, a Microsoft® Silverlight® application, a JQuery® application, and an Asynchronous Javascript® and a XML (AJAX) application, and wherein an access control algorithm governs a policy through which a secondary authentication is required when establishing a communication between the sandboxed application and the networked device. | 0.747855 |
9,418,316 | 7 | 11 | 7. A computer-implemented method comprising: binarizing a first image frame to generate a second binarized image frame; outputting the second binarized image frame; receiving a first indication indicating that the second binarized image frame contains at least one of: no text, text that is not recognizable, text that is partially recognizable, or text that is recognizable; determining a first feature metric characterizing image sharpness; generating, using the first feature metric, a first feature value associated with the first image frame; training an image classifier to select an input image frame for optical character recognition using at least the first feature metric and the first indication; receiving an order of sharpness indicating that a third image frame is sharper than a fourth image frame and the fourth image frame is sharper than a fifth image frame; generating, using the first feature metric, a second feature value associated with the third image frame; generating, using the first feature metric, a third feature value associated with the fourth image frame; generating, using the first feature metric, a fourth feature value associated with the fifth image frame; receiving a first score output by the image classifier based on the second feature value; receiving a second score output by the image classifier, based on the third feature value; receiving a third score output by the image classifier, based on the fourth feature value; determining a first correlation between the order of sharpness and the first score, the second score and the third score output by the image classifier; determining that the first correlation satisfies a selection criteria; and selecting the first feature metric for use in image frame selection. | 7. A computer-implemented method comprising: binarizing a first image frame to generate a second binarized image frame; outputting the second binarized image frame; receiving a first indication indicating that the second binarized image frame contains at least one of: no text, text that is not recognizable, text that is partially recognizable, or text that is recognizable; determining a first feature metric characterizing image sharpness; generating, using the first feature metric, a first feature value associated with the first image frame; training an image classifier to select an input image frame for optical character recognition using at least the first feature metric and the first indication; receiving an order of sharpness indicating that a third image frame is sharper than a fourth image frame and the fourth image frame is sharper than a fifth image frame; generating, using the first feature metric, a second feature value associated with the third image frame; generating, using the first feature metric, a third feature value associated with the fourth image frame; generating, using the first feature metric, a fourth feature value associated with the fifth image frame; receiving a first score output by the image classifier based on the second feature value; receiving a second score output by the image classifier, based on the third feature value; receiving a third score output by the image classifier, based on the fourth feature value; determining a first correlation between the order of sharpness and the first score, the second score and the third score output by the image classifier; determining that the first correlation satisfies a selection criteria; and selecting the first feature metric for use in image frame selection. 11. The computer-implemented method of claim 7 , further comprising: generating, using a second feature metric, a fifth feature value associated with the third image frame; generating, using the second feature metric, a sixth feature value associated with the fourth image frame; generating, using the second feature metric, a seventh feature value associated with the fifth image frame; receiving a fourth score output by the image classifier based on the fifth feature value; receiving a fifth score output by the image classifier, the fifth score based on the sixth feature value; receiving a sixth score output by the image classifier, the sixth score based on the seventh feature value; determining a second correlation between the order of sharpness and the fourth score, the fifth score and the sixth score output by the image classifier; determining that the second correlation does not satisfy the selection criteria; and retraining the image classifier using a plurality of feature metrics and the first indication, wherein the plurality of feature metrics comprises the first feature metric but omits the second feature metric. | 0.541499 |
8,285,719 | 9 | 10 | 9. A method for relational analysis of data, comprising: (a) receiving an input data set is represented as a set of exponential family distributions {{F (j) } j=1 m , {S (j) } j=1 m , {R (ij) } i,j=1 m }; (b) storing an initial estimate of a set of matrices {tilde over (Ω)}, comprising: membership matrices {Λ (j) } j=1 m , attribute expectation matrices {Θ (j) } j=1 m for attribute matrices F (j) , homogeneous relation expectation matrices {Γ (j) } j=1 m for homogeneous relation matrices S (j) , and heterogeneous relation expectation matrices {γ (ij) } i,j=1 m , for heterogeneous relation matrices R (ij) ; (c) with an automated processor, iteratively computing, for each respective value of i and j, a posterior function , wherein Pr({C (j) }|F (j) } j=1 m ,{S (j) } j=1 m ,{R (ij) } i,j=1 m ,{tilde over (Ω)}), wherein C (j) is a cluster indicator matrix: Λ ( j ) using Λ hp ( 1 ) = Pr ( C hp ( 1 ) = 1 ❘ F , S , R , Ω ~ ) , Θ ( j ) using Θ · g = ∑ p = 1 n 1 F · p Pr ( C gp ( 1 ) = 1 ❘ F , S , R , Ω ~ ) ∑ p = 1 n 1 Pr ( C gp ( 1 ) = 1 ❘ F , S , R , Ω ~ ) , Γ ( j ) using Γ gh = ∑ p , q = 1 n 1 S pq Pr ( C gp ( 1 ) = 1 , C hq ( 1 ) = 1 ❘ F , S , R , Ω ~ ) ∑ p , q = 1 n 1 Pr ( C gp ( 1 ) = 1 , C hq ( 1 ) = 1 ❘ F , S , R , Ω ~ ) , Υ ( ij ) using Υ gh = ∑ p = 1 n 1 ∑ q = 1 n 2 R pq Pr ( C gp ( 1 ) = 1 , C hq ( 2 ) = 1 ❘ F , S , R , Ω ~ ) ∑ p = 1 n 1 ∑ q = 1 n 2 Pr ( C gp ( 1 ) = 1 , C hq ( 2 ) = 1 ❘ F , S , R , Ω ~ ) ; (d) comparing the stored set of matrices {tilde over (Ω)} with a resulting set of matrices Ω of an iteration of said computing to determine a convergence; and (e) outputting a converged result Ω. | 9. A method for relational analysis of data, comprising: (a) receiving an input data set is represented as a set of exponential family distributions {{F (j) } j=1 m , {S (j) } j=1 m , {R (ij) } i,j=1 m }; (b) storing an initial estimate of a set of matrices {tilde over (Ω)}, comprising: membership matrices {Λ (j) } j=1 m , attribute expectation matrices {Θ (j) } j=1 m for attribute matrices F (j) , homogeneous relation expectation matrices {Γ (j) } j=1 m for homogeneous relation matrices S (j) , and heterogeneous relation expectation matrices {γ (ij) } i,j=1 m , for heterogeneous relation matrices R (ij) ; (c) with an automated processor, iteratively computing, for each respective value of i and j, a posterior function , wherein Pr({C (j) }|F (j) } j=1 m ,{S (j) } j=1 m ,{R (ij) } i,j=1 m ,{tilde over (Ω)}), wherein C (j) is a cluster indicator matrix: Λ ( j ) using Λ hp ( 1 ) = Pr ( C hp ( 1 ) = 1 ❘ F , S , R , Ω ~ ) , Θ ( j ) using Θ · g = ∑ p = 1 n 1 F · p Pr ( C gp ( 1 ) = 1 ❘ F , S , R , Ω ~ ) ∑ p = 1 n 1 Pr ( C gp ( 1 ) = 1 ❘ F , S , R , Ω ~ ) , Γ ( j ) using Γ gh = ∑ p , q = 1 n 1 S pq Pr ( C gp ( 1 ) = 1 , C hq ( 1 ) = 1 ❘ F , S , R , Ω ~ ) ∑ p , q = 1 n 1 Pr ( C gp ( 1 ) = 1 , C hq ( 1 ) = 1 ❘ F , S , R , Ω ~ ) , Υ ( ij ) using Υ gh = ∑ p = 1 n 1 ∑ q = 1 n 2 R pq Pr ( C gp ( 1 ) = 1 , C hq ( 2 ) = 1 ❘ F , S , R , Ω ~ ) ∑ p = 1 n 1 ∑ q = 1 n 2 Pr ( C gp ( 1 ) = 1 , C hq ( 2 ) = 1 ❘ F , S , R , Ω ~ ) ; (d) comparing the stored set of matrices {tilde over (Ω)} with a resulting set of matrices Ω of an iteration of said computing to determine a convergence; and (e) outputting a converged result Ω. 10. The method according to claim 9 , further comprising using at least the converged result Ω to partition a graph. | 0.650602 |
8,667,055 | 1 | 2 | 1. A method for implementing an online mutual aid question-answer service, the method comprising: a questioning user device uploading a question to an extensible markup language document management (XDM) server; after uploading the question to said XDM server, said questioning user device further choosing a frequency for sending an answer notification and a number of answers included in each answer notification, and uploading the frequency and the number to said XDM server; an answering user device subscribing for questions from said XDM server, said XDM server sending the question to said answering user device; after said answering user device receives the question, said answering user device answering the question, and informing said questioning user device about an answer to the question; after answering said question, said answering user device uploads the answer to the question to said XDM server, said XDM server sends down the answer to the question to said questioning user device through the answer notification; said XDM server sending down said answer notification to said questioning user device according to the frequency for sending said answer notification chosen by the questioning user device and the number of answers included in each answer notification; after said answering user device answers the question, said answering user device obtaining presence information of the questioning user device, choosing a synchronous communication mode available to the questioning user device according to said presence information, and sending a communication request with the synchronous communication mode; and the questioning user device receiving said communication request, and communicating with said answering user device after choosing to accept the communication request to receive the answer to said question. | 1. A method for implementing an online mutual aid question-answer service, the method comprising: a questioning user device uploading a question to an extensible markup language document management (XDM) server; after uploading the question to said XDM server, said questioning user device further choosing a frequency for sending an answer notification and a number of answers included in each answer notification, and uploading the frequency and the number to said XDM server; an answering user device subscribing for questions from said XDM server, said XDM server sending the question to said answering user device; after said answering user device receives the question, said answering user device answering the question, and informing said questioning user device about an answer to the question; after answering said question, said answering user device uploads the answer to the question to said XDM server, said XDM server sends down the answer to the question to said questioning user device through the answer notification; said XDM server sending down said answer notification to said questioning user device according to the frequency for sending said answer notification chosen by the questioning user device and the number of answers included in each answer notification; after said answering user device answers the question, said answering user device obtaining presence information of the questioning user device, choosing a synchronous communication mode available to the questioning user device according to said presence information, and sending a communication request with the synchronous communication mode; and the questioning user device receiving said communication request, and communicating with said answering user device after choosing to accept the communication request to receive the answer to said question. 2. The method as claimed in claim 1 , wherein said question uploaded to said XDM server by said questioning user device includes a field to which the question belongs, said answering user device subscribes for the questions of one or more fields from said XDM server, and said XDM server sends the questions of the one or more fields subscribed for by the answering user device to said answering user device. | 0.789907 |
9,443,511 | 1 | 12 | 1. A method for recognizing an environmental sound at a client device, the method comprising: accessing a client database including a plurality of sound models representing environmental sounds and a plurality of labels, wherein each of the plurality of labels identifies at least one of the plurality of sound models; receiving an input environmental sound and generating an input sound model based on the input environmental sound; determining similarity values between the input sound model and the plurality of sound models to identify one or more sound models of the plurality of sound models that are similar to the input sound model; selecting a first label from one or more labels, of the plurality of labels, associated with the one or more sound models; associating the first label with the input environmental sound based on a confidence level of the first label; and if the confidence level is less than a confidence threshold: transmitting the input sound model to a server; and receiving a second label identifying the input environmental sound from the server. | 1. A method for recognizing an environmental sound at a client device, the method comprising: accessing a client database including a plurality of sound models representing environmental sounds and a plurality of labels, wherein each of the plurality of labels identifies at least one of the plurality of sound models; receiving an input environmental sound and generating an input sound model based on the input environmental sound; determining similarity values between the input sound model and the plurality of sound models to identify one or more sound models of the plurality of sound models that are similar to the input sound model; selecting a first label from one or more labels, of the plurality of labels, associated with the one or more sound models; associating the first label with the input environmental sound based on a confidence level of the first label; and if the confidence level is less than a confidence threshold: transmitting the input sound model to a server; and receiving a second label identifying the input environmental sound from the server. 12. The method of claim 1 , further comprising: receiving location information associated with the input sound model, wherein the client database further includes information corresponding to at least one of a location or a time associated with each of the plurality of sound models. | 0.674713 |
7,516,397 | 1 | 2 | 1. A method for generating a representation of a Web-site comprising a plurality of Web-pages, said method comprising: identifying a set of hyperlinks associated with a first Web-site comprising a plurality of first Web-pages, wherein said identifying comprises searching for hyperlinks corresponding to a plurality of different types of interaction with said first Web-site, wherein said searching comprises searching for inbound hyperlinks to said first Web-site from Web-pages other than said first Web-pages, outbound hyperlinks from said first Web-site to a Web-site other than said first Web-site, and internal hyperlinks linking a plurality of said Web-pages within said first Web-site; identifying anchor text for each of the identified set of hyperlinks associated with said first Web-site, wherein said anchor text for each of the hyperlinks is associated with a node within a DOM tree that represents a structure of said Web-site, wherein said anchor text comprises a subset of words within neighboring nodes of said node within said DOM tree, and wherein said subset comprises words formatted within format tags; determining a location-specific context for each of the anchor texts, wherein said location-specific context comprises a location above and before said anchor text up to a root node of said DOM tree, wherein said root node of said DOM tree corresponds to a top of a particular Web page; and combining said anchor text with a respective location-specific context for each of said set of hyperlinks, to generate a representation of said first Web-site. | 1. A method for generating a representation of a Web-site comprising a plurality of Web-pages, said method comprising: identifying a set of hyperlinks associated with a first Web-site comprising a plurality of first Web-pages, wherein said identifying comprises searching for hyperlinks corresponding to a plurality of different types of interaction with said first Web-site, wherein said searching comprises searching for inbound hyperlinks to said first Web-site from Web-pages other than said first Web-pages, outbound hyperlinks from said first Web-site to a Web-site other than said first Web-site, and internal hyperlinks linking a plurality of said Web-pages within said first Web-site; identifying anchor text for each of the identified set of hyperlinks associated with said first Web-site, wherein said anchor text for each of the hyperlinks is associated with a node within a DOM tree that represents a structure of said Web-site, wherein said anchor text comprises a subset of words within neighboring nodes of said node within said DOM tree, and wherein said subset comprises words formatted within format tags; determining a location-specific context for each of the anchor texts, wherein said location-specific context comprises a location above and before said anchor text up to a root node of said DOM tree, wherein said root node of said DOM tree corresponds to a top of a particular Web page; and combining said anchor text with a respective location-specific context for each of said set of hyperlinks, to generate a representation of said first Web-site. 2. The method according to claim 1 , wherein said searching comprises a Web crawler visiting a plurality of Web sites and parsing a plurality of Web-sites to identify a set of Web-sites having hyperlinks pointing to said first Web-site. | 0.5 |
8,392,666 | 19 | 20 | 19. The method of claim 11 , wherein said using word masks to detect the load-store collision comprises: indicating a load-store collision if the word masks indicate a word overlap within the cache line specified by the load operation and the store operation; and otherwise indicating no load-store collision. | 19. The method of claim 11 , wherein said using word masks to detect the load-store collision comprises: indicating a load-store collision if the word masks indicate a word overlap within the cache line specified by the load operation and the store operation; and otherwise indicating no load-store collision. 20. The method of claim 19 , wherein said using word masks to detect the load-store collision further comprises shifting the word masks of the load operation and the store operation to their respective locations within the cache line prior to said indicating a load-store collision if the word masks indicate a word overlap within the cache line specified by the load operation and the store operation. | 0.5 |
7,921,100 | 1 | 5 | 1. A method for calculating a similarity score of a query set comprising a query set of tokens and a first database set comprising a first database set of tokens, wherein the first database set is one of a plurality of database sets in a data collection set stored on a non-transitory computer readable medium, comprising the steps of: for each specific token in the query set, determining the number of database sets that contain the specific token; for each specific token in the query set, calculating an inverse document frequency (idf) weight, based at least in part on the number of database sets that contain the specific token and on the total number of database sets in the data collection set; calculating a normalized length of the first database set; calculating a normalized length of the query set; and, calculating a similarity score based at least in part on the normalized length of the first database set, the normalized length of the query set, and the idf weight of each of the tokens in the query set. | 1. A method for calculating a similarity score of a query set comprising a query set of tokens and a first database set comprising a first database set of tokens, wherein the first database set is one of a plurality of database sets in a data collection set stored on a non-transitory computer readable medium, comprising the steps of: for each specific token in the query set, determining the number of database sets that contain the specific token; for each specific token in the query set, calculating an inverse document frequency (idf) weight, based at least in part on the number of database sets that contain the specific token and on the total number of database sets in the data collection set; calculating a normalized length of the first database set; calculating a normalized length of the query set; and, calculating a similarity score based at least in part on the normalized length of the first database set, the normalized length of the query set, and the idf weight of each of the tokens in the query set. 5. The method of claim 1 further comprising the step of determining that the first database set contains information relevant to the query set by performing the steps of: defining a threshold value; comparing the similarity score to the threshold value; and, determining that the first database set contains relevant information if the similarity score is greater than or equal to the threshold value. | 0.527123 |
8,601,024 | 10 | 11 | 10. In a computing environment, a system comprising: at least one processing unit; a transformation mechanism, implemented on the at least one processing unit, configured to process a search log, including by determining which queries from the search log correspond to information that is safe to publish, and for each of the queries having information that is safe to publish, configured to publish the information as output data, wherein determining which queries from the search log correspond to information that is safe to publish comprises limiting how many queries in the search log each user can contribute to a set of queries for processing, wherein publishing the information as output data comprises outputting a query-action graph having nodes representing queries and nodes representing actions taken, with each edge between a query node and an action node having a weight that indicates how many times that action was taken following that query, wherein the weight has zero noise, a negative noise or a positive noise added thereto, or outputting a query-inaction graph having nodes representing queries and nodes representing actions skipped, with each edge between a query node and an inaction node having a weight that indicates how many times that action was not taken following that query, and wherein the weight has zero noise, a negative noise or a positive noise added thereto. | 10. In a computing environment, a system comprising: at least one processing unit; a transformation mechanism, implemented on the at least one processing unit, configured to process a search log, including by determining which queries from the search log correspond to information that is safe to publish, and for each of the queries having information that is safe to publish, configured to publish the information as output data, wherein determining which queries from the search log correspond to information that is safe to publish comprises limiting how many queries in the search log each user can contribute to a set of queries for processing, wherein publishing the information as output data comprises outputting a query-action graph having nodes representing queries and nodes representing actions taken, with each edge between a query node and an action node having a weight that indicates how many times that action was taken following that query, wherein the weight has zero noise, a negative noise or a positive noise added thereto, or outputting a query-inaction graph having nodes representing queries and nodes representing actions skipped, with each edge between a query node and an inaction node having a weight that indicates how many times that action was not taken following that query, and wherein the weight has zero noise, a negative noise or a positive noise added thereto. 11. The system of claim 10 wherein the transformation mechanism is further configured to output at least one of: a query-action graph having nodes representing queries and nodes representing actions taken, wherein the actions taken comprise returned uniform resource locators, or a query reformulation graph having nodes representing queries and nodes representing reformulated queries, wherein the reformulated queries comprise those that are returned as the top reformulated queries for each query with respect to a search performed with the each query. | 0.5 |
10,049,482 | 5 | 6 | 5. The animation server system of claim 4 , wherein instructions, when executed by the at least one processor, cause the system to compare the similarity between the pose of the 3D character at the end of the first animation and the initial 3D motion model pose at the start of the animation of the one or more additional animations using a weighted least squares comparison. | 5. The animation server system of claim 4 , wherein instructions, when executed by the at least one processor, cause the system to compare the similarity between the pose of the 3D character at the end of the first animation and the initial 3D motion model pose at the start of the animation of the one or more additional animations using a weighted least squares comparison. 6. The animation server system of claim 5 , wherein instructions, when executed by the at least one processor, cause the system to determine weights in the weighted least squares comparison using a support vector machine. | 0.5 |
9,928,875 | 10 | 11 | 10. The computer-implemented method of claim 9 , wherein the Kalman filtering performs filtering using the set of already provided annotations and the optical flow information to output (i) a set of best estimated bounding boxes across each of the plurality of frames and (2) the annotation uncertainty measure for any of the plurality of frames that include one of the best estimated bounding boxes from the set. | 10. The computer-implemented method of claim 9 , wherein the Kalman filtering performs filtering using the set of already provided annotations and the optical flow information to output (i) a set of best estimated bounding boxes across each of the plurality of frames and (2) the annotation uncertainty measure for any of the plurality of frames that include one of the best estimated bounding boxes from the set. 11. The computer-implemented method of claim 10 , wherein the set of best estimated bounding boxes is determined so as to minimize an expected loss in annotation quality across the video sequence. | 0.66323 |
7,895,240 | 9 | 14 | 9. A system for managing information, said system comprising: a server configured to: provide, to a first user, a predefined plurality of questions including at least a first predefined question and a second predefined question and a predefined plurality of answer options; receive a plurality of answers from the first user including an answer to the first predefined question, wherein at least the second predefined question in the predefined plurality of questions provided to the user is modified based on the answer from the first predefined question chosen by the user, wherein modifying at least the second predefined question comprises at least one of adding at least one answer option associated with the at least second predefined question and removing at least one answer option associated with the at least second predefined question; generate a first database based on the predefined plurality of questions; pre-define, within the first database, a task to be performed based on each of the plurality of predefined answer options; associate, by an administrator via a server system, at least one of the plurality of redefined answer options with the task; determine the task to be performed based on the plurality of answers provided by the first user; receive an association of the task with a plurality of tools, wherein the plurality of tools includes at least one expert on the task; create at least one additional database based on the predefined plurality of answer options, wherein said server performs at least one of generating the first database and creating the at least one additional database without making a change to at least one source code; associate, the first database with the at least one additional database based on the plurality of answers; populate the at least one additional database with the task; and prompt the first user to customize the task within the at least one additional database, without a change to a source code, after the at least one additional database has been populated. | 9. A system for managing information, said system comprising: a server configured to: provide, to a first user, a predefined plurality of questions including at least a first predefined question and a second predefined question and a predefined plurality of answer options; receive a plurality of answers from the first user including an answer to the first predefined question, wherein at least the second predefined question in the predefined plurality of questions provided to the user is modified based on the answer from the first predefined question chosen by the user, wherein modifying at least the second predefined question comprises at least one of adding at least one answer option associated with the at least second predefined question and removing at least one answer option associated with the at least second predefined question; generate a first database based on the predefined plurality of questions; pre-define, within the first database, a task to be performed based on each of the plurality of predefined answer options; associate, by an administrator via a server system, at least one of the plurality of redefined answer options with the task; determine the task to be performed based on the plurality of answers provided by the first user; receive an association of the task with a plurality of tools, wherein the plurality of tools includes at least one expert on the task; create at least one additional database based on the predefined plurality of answer options, wherein said server performs at least one of generating the first database and creating the at least one additional database without making a change to at least one source code; associate, the first database with the at least one additional database based on the plurality of answers; populate the at least one additional database with the task; and prompt the first user to customize the task within the at least one additional database, without a change to a source code, after the at least one additional database has been populated. 14. The system in accordance with claim 9 wherein said server further configured to: migrate a plurality of tasks comprising the task into the first database; populate the at least one additional database by downloading the plurality of tasks from the first database into the at least one additional database, wherein said server populates the at least one additional database when the plurality of tasks are migrated into the first database; identify an additional task into the first database; and prompt the first user whether to export the additional task to a project database comprising the plurality of tasks. | 0.541667 |
9,032,361 | 8 | 14 | 8. A computerized system of unit and regression testing for domain specific languages, the system having at least a memory storing a plurality of modules and a processor for executing code of plurality of modules, the plurality of modules of the system comprising: an input module, stored in the memory, configured to generate an input XML and an expected output XML, wherein the input XML refers to a set of input test cases, and the expected output XML refers to results that are expected post running of the set of the test cases; a modeling unit, stored in the memory, adapted to accept input from a repository and generate an operation for testing in a test driven development process; a code generating module, stored in the memory, coupled with the repository and is configured to generate domain specific language code templates and an application library code for operations accessed from the repository; an executing module, stored in the memory, configured to invoke a function that reads the input XML and call an unit test load to write output to an output file; a comparing module, stored in the memory, adapted to compare a resulting actual output XML with the expected output XML, assign a flag to each compared test case in the set of test cases to indicate a state of the each compared test case in the set of the test cases, wherein the state of the each compared test case refers to either pass state or fail state; and a result generating module, stored in the memory, adapted to report defects unveiled during comparison, wherein the test cases with pass state are used to build regression test suites, wherein the regression test suites are used for performing regression testing, wherein regression testing comprises executing an entirety of the regression test suites to uncover unknown defects to all parts of an enterprise computer application due to a change at a unit level of the enterprise computer application. | 8. A computerized system of unit and regression testing for domain specific languages, the system having at least a memory storing a plurality of modules and a processor for executing code of plurality of modules, the plurality of modules of the system comprising: an input module, stored in the memory, configured to generate an input XML and an expected output XML, wherein the input XML refers to a set of input test cases, and the expected output XML refers to results that are expected post running of the set of the test cases; a modeling unit, stored in the memory, adapted to accept input from a repository and generate an operation for testing in a test driven development process; a code generating module, stored in the memory, coupled with the repository and is configured to generate domain specific language code templates and an application library code for operations accessed from the repository; an executing module, stored in the memory, configured to invoke a function that reads the input XML and call an unit test load to write output to an output file; a comparing module, stored in the memory, adapted to compare a resulting actual output XML with the expected output XML, assign a flag to each compared test case in the set of test cases to indicate a state of the each compared test case in the set of the test cases, wherein the state of the each compared test case refers to either pass state or fail state; and a result generating module, stored in the memory, adapted to report defects unveiled during comparison, wherein the test cases with pass state are used to build regression test suites, wherein the regression test suites are used for performing regression testing, wherein regression testing comprises executing an entirety of the regression test suites to uncover unknown defects to all parts of an enterprise computer application due to a change at a unit level of the enterprise computer application. 14. The system of claim 8 , wherein the blank operation template refers to a template generated by MasterCraft. | 0.707895 |
7,904,397 | 2 | 3 | 2. The method of claim 1 , wherein said examples in said dataset comprise feature vectors usable as inputs into an inductive learning model. | 2. The method of claim 1 , wherein said examples in said dataset comprise feature vectors usable as inputs into an inductive learning model. 3. The method of claim 2 , wherein said dataset of examples comprises historical data and each said feature vector comprises a value for each of one or more attributes potentially useful to make predictions of future events, in accordance with said processing of said inductive learning model. | 0.5 |
8,760,726 | 22 | 25 | 22. The system of claim 21 , wherein said image processing module additionally comprises means for identifying thin lines in said analysed files, said lookup table is additionally adapted to determine a percentage of polymer pixels reduction for identified thin lines and said raster image additionally comprising stochastically removed pixels from the overlay area concurrent with each said identified thin lines, according to said determined percentage. | 22. The system of claim 21 , wherein said image processing module additionally comprises means for identifying thin lines in said analysed files, said lookup table is additionally adapted to determine a percentage of polymer pixels reduction for identified thin lines and said raster image additionally comprising stochastically removed pixels from the overlay area concurrent with each said identified thin lines, according to said determined percentage. 25. The system of claim 22 , additionally comprising a second storage unit communicating with said raster image processor, for storing raster overlay images. | 0.5 |
8,239,189 | 1 | 3 | 1. A method for estimating a sentiment for an entity named in a search query, the method comprising: providing a system having a user interface, non-transitory memory, and a processing platform, the system being communicatively connectable to an information space, the memory having a sentiment dictionary, the sentiment dictionary comprising: a plurality of adjectives, a plurality of group contexts, each group context having at least one noun, each of the adjectives being associated with at least one respective group context, each of the adjectives being assigned a fixed value to represent an emotional sentiment for each group context that adjective is associated with such that that adjective is determined to have the assigned fixed value when that adjective is identified as describing the at least one noun of the group context, the system receiving a search query that comprises at least one noun or name associated with the entity; the system retrieving documents from the information space that correspond to the search query; the system searching the retrieved documents for the adjectives in the sentiment dictionary that are within a predetermined textual distance of the group contexts of the sentiment dictionary; the system calculating a sentiment score value for the search query, the calculating of the sentiment score value comprising: determining a number of occurrences of the adjectives of the sentiment dictionary for each of the retrieved documents, determining the fixed value for each of the adjectives for each of the occurrences such that the fixed value of the adjective for each occurrence is the fixed value assigned to the adjective for the group context to which the occurrence of the adjective is detected, and adding the fixed values for the adjectives found in the retrieved documents to determine a sentiment score value. | 1. A method for estimating a sentiment for an entity named in a search query, the method comprising: providing a system having a user interface, non-transitory memory, and a processing platform, the system being communicatively connectable to an information space, the memory having a sentiment dictionary, the sentiment dictionary comprising: a plurality of adjectives, a plurality of group contexts, each group context having at least one noun, each of the adjectives being associated with at least one respective group context, each of the adjectives being assigned a fixed value to represent an emotional sentiment for each group context that adjective is associated with such that that adjective is determined to have the assigned fixed value when that adjective is identified as describing the at least one noun of the group context, the system receiving a search query that comprises at least one noun or name associated with the entity; the system retrieving documents from the information space that correspond to the search query; the system searching the retrieved documents for the adjectives in the sentiment dictionary that are within a predetermined textual distance of the group contexts of the sentiment dictionary; the system calculating a sentiment score value for the search query, the calculating of the sentiment score value comprising: determining a number of occurrences of the adjectives of the sentiment dictionary for each of the retrieved documents, determining the fixed value for each of the adjectives for each of the occurrences such that the fixed value of the adjective for each occurrence is the fixed value assigned to the adjective for the group context to which the occurrence of the adjective is detected, and adding the fixed values for the adjectives found in the retrieved documents to determine a sentiment score value. 3. The method of claim 1 wherein the detection of the group context to which the occurrence of the adjective is performed such that if multiple context groups are determined to be applicable to a detected occurrence, the fixed value assigned to the adjective for the group context to which the occurrence of the adjective is detected is the fixed value for the group context having a highest priority relative to any other context group determined to be applicable to the occurrence. | 0.569519 |
9,799,312 | 17 | 22 | 17. A computer program product stored in a computer readable storage medium, comprising computer program code that, when executed by an information handling system, causes the information handling system to perform actions comprising: configuring a reinforcement learning model based on one or more inspiration selections received from a user, each of the one or more inspiration selections corresponding to one or more musical characteristics, wherein the configuring further comprises: determining one or more emotion characteristics of one or more songs corresponding to at least one of the one or more inspiration selections; displaying one or more emotion objects to the user on a display, each of the one or more emotion objects corresponding to one of the one or more emotion characteristics; receiving at least one emotion object adjustment from the user that adjusts a size of at least one of the one or more emotion objects; adjusting at least one of the one or more musical characteristics based on the emotion object adjustment; and loading the at least one adjusted musical characteristic into an environment in the reinforcement learning model to adjust a reward structure of the environment; performing a plurality of training iterations using the configured reinforcement learning model, wherein the plurality of training iterations generate a plurality of actions and the environment generates a plurality of rewards corresponding to the plurality of actions; and generating a musical composition based on the plurality of actions in response to determining that the plurality of rewards reach an empirical threshold. | 17. A computer program product stored in a computer readable storage medium, comprising computer program code that, when executed by an information handling system, causes the information handling system to perform actions comprising: configuring a reinforcement learning model based on one or more inspiration selections received from a user, each of the one or more inspiration selections corresponding to one or more musical characteristics, wherein the configuring further comprises: determining one or more emotion characteristics of one or more songs corresponding to at least one of the one or more inspiration selections; displaying one or more emotion objects to the user on a display, each of the one or more emotion objects corresponding to one of the one or more emotion characteristics; receiving at least one emotion object adjustment from the user that adjusts a size of at least one of the one or more emotion objects; adjusting at least one of the one or more musical characteristics based on the emotion object adjustment; and loading the at least one adjusted musical characteristic into an environment in the reinforcement learning model to adjust a reward structure of the environment; performing a plurality of training iterations using the configured reinforcement learning model, wherein the plurality of training iterations generate a plurality of actions and the environment generates a plurality of rewards corresponding to the plurality of actions; and generating a musical composition based on the plurality of actions in response to determining that the plurality of rewards reach an empirical threshold. 22. The computer program product of claim 17 wherein the information handling system performs additional actions comprising: receiving feedback from the user subsequent to the user listening to the musical composition; re-configuring the reinforcement learning model based on the feedback; and generating a subsequent musical composition using the reconfigured reinforcement learning model. | 0.756858 |
10,007,786 | 1 | 8 | 1. A computer-implemented method for detecting malware, at least a portion of the method being performed by a computing device comprising at least one processor, the method comprising: identifying a behavioral trace of a program, the behavioral trace comprising a sequence of runtime behaviors exhibited by the program; dividing the behavioral trace to identify a plurality of n-grams within the behavioral trace, each runtime behavior within the sequence of runtime behaviors corresponding to an n-gram token; analyzing the plurality of n-grams to generate a feature vector of the behavioral trace comprising: applying, for each given n-gram in the plurality of n-grams, a feature function to the behavioral trace that describes an occurrence characteristic of the given n-gram within the behavioral trace; and including a result of the feature function in the feature vector; and classifying the program based at least in part on the feature vector of the behavioral trace to determine whether the program is malicious; wherein: the feature vector comprises a plurality of dimensions, each n-gram within the plurality of n-grams corresponding to a dimension within the plurality of dimensions; the plurality of n-grams map to the plurality of dimensions according to a non-injective surjection; and including the result of the feature function in the feature vector comprises aggregating a subset of outputs of the feature function derived from a subset of the plurality of n-grams into a value and assigning the value to a dimension within the plurality of dimensions according to the non-injective surjection. | 1. A computer-implemented method for detecting malware, at least a portion of the method being performed by a computing device comprising at least one processor, the method comprising: identifying a behavioral trace of a program, the behavioral trace comprising a sequence of runtime behaviors exhibited by the program; dividing the behavioral trace to identify a plurality of n-grams within the behavioral trace, each runtime behavior within the sequence of runtime behaviors corresponding to an n-gram token; analyzing the plurality of n-grams to generate a feature vector of the behavioral trace comprising: applying, for each given n-gram in the plurality of n-grams, a feature function to the behavioral trace that describes an occurrence characteristic of the given n-gram within the behavioral trace; and including a result of the feature function in the feature vector; and classifying the program based at least in part on the feature vector of the behavioral trace to determine whether the program is malicious; wherein: the feature vector comprises a plurality of dimensions, each n-gram within the plurality of n-grams corresponding to a dimension within the plurality of dimensions; the plurality of n-grams map to the plurality of dimensions according to a non-injective surjection; and including the result of the feature function in the feature vector comprises aggregating a subset of outputs of the feature function derived from a subset of the plurality of n-grams into a value and assigning the value to a dimension within the plurality of dimensions according to the non-injective surjection. 8. The computer-implemented method of claim 1 , wherein: generating the feature vector of the behavioral trace comprises generating a plurality of feature vectors of the behavioral trace, the feature vectors within the plurality of feature vectors differing by at least one of: feature functions applied to n-grams sampled from the behavioral trace to generate respective feature vectors; subsets of n-grams selected from the behavioral trace to generate respective feature vectors; and classifying the program based at least in part on the feature vector of the behavioral trace comprises submitting each of the plurality of feature vectors to a machine learning classifier. | 0.5 |
9,355,084 | 1 | 7 | 1. A method of annotating an electronic text document, the method comprising: evaluating, by a computer, the electronic text document for one or more multi-word expressions comprising a first word and a second word; when a multi-word expression is found in the electronic text document, storing an abbreviation of the multi-word expression; evaluating the electronic text document for occurrences of the abbreviation after a location of the multi-word expression; when a competing term is found with respect to the abbreviation, annotating occurrences of the abbreviation following the competing term with the competing term; and when an occurrence of the abbreviation is found following the multi-word expression, annotating the occurrence of the abbreviation with the multi-word expression, wherein: the abbreviation comprises a first letter of the first word of the multi-word expression or the competing term, and the second word of the multi-word expression and the competing term, and the competing term comprises a first word having a first letter that is the same as the first letter of the first word of the multi-word expression, and a second word that is the same as the second word of the multi-word expression. | 1. A method of annotating an electronic text document, the method comprising: evaluating, by a computer, the electronic text document for one or more multi-word expressions comprising a first word and a second word; when a multi-word expression is found in the electronic text document, storing an abbreviation of the multi-word expression; evaluating the electronic text document for occurrences of the abbreviation after a location of the multi-word expression; when a competing term is found with respect to the abbreviation, annotating occurrences of the abbreviation following the competing term with the competing term; and when an occurrence of the abbreviation is found following the multi-word expression, annotating the occurrence of the abbreviation with the multi-word expression, wherein: the abbreviation comprises a first letter of the first word of the multi-word expression or the competing term, and the second word of the multi-word expression and the competing term, and the competing term comprises a first word having a first letter that is the same as the first letter of the first word of the multi-word expression, and a second word that is the same as the second word of the multi-word expression. 7. The method of claim 1 , wherein the-electronic text document comprises at least one of a scientific journal, an agricultural document, a news article, and a patent document. | 0.832061 |
10,079,792 | 8 | 9 | 8. The computer program product as recited in claim 7 , wherein the program code further comprises the programming instructions for: posting said composed message with said recommended hashtag on said social network system. | 8. The computer program product as recited in claim 7 , wherein the program code further comprises the programming instructions for: posting said composed message with said recommended hashtag on said social network system. 9. The computer program product as recited in claim 8 , wherein the program code further comprises the programming instructions for: monitoring said post with said recommended hashtag for changes to a sentiment, a trend and a propagation speed of said recommended hashtag; notifying said user regarding changes to said sentiment, said trend and said propagation speed of said recommend hashtag in response to changes in said sentiment, said trend and said propagation speed of said recommended hashtag; and recommending a new hashtag to replace said recommended hashtag to increase said propagation speed of said composed message and emphasize said sentiment of said composed message in response to changes in said sentiment, said trend and said propagation speed of said recommended hashtag. | 0.5 |
6,128,634 | 13 | 22 | 13. An apparatus for facilitating skimming of a document by a user, the document having a plurality of terms, the apparatus comprising: a processing system that determines a term-score for each of the plurality of terms and that maps the term-score of each of the plurality of terms onto one of at least three values of at least one variable emphasis attribute usable to present the document; and a presentation system that presents each of the plurality of terms of the document using the corresponding mapped values of the at least one variable emphasis attribute. | 13. An apparatus for facilitating skimming of a document by a user, the document having a plurality of terms, the apparatus comprising: a processing system that determines a term-score for each of the plurality of terms and that maps the term-score of each of the plurality of terms onto one of at least three values of at least one variable emphasis attribute usable to present the document; and a presentation system that presents each of the plurality of terms of the document using the corresponding mapped values of the at least one variable emphasis attribute. 22. The apparatus of claim 13, wherein the processing system further comprises a stop word assignment system that assigns a minimum value of the at least one variable emphasis attribute to stop words. | 0.528302 |
8,234,560 | 16 | 21 | 16. A computer-program product for use in conjunction with a computer system, the computer-program product comprising a computer-readable storage medium and a computer-program mechanism embedded therein for configuring the computer system to create documents in a hierarchy, the computer-program mechanism including: instructions for generating a root number which corresponds to a base level in the hierarchy; instructions for assigning document numbers to the documents, wherein the document numbers are generated based at least in part on the root number; instructions for assigning directory numbers to directories in the hierarchy, wherein a given directory number is generated based at least in part on a given document number, and wherein a given directory is in a branch that is coupled to the root level; instructions for determining paths in the hierarchy corresponding to the document numbers and the directory numbers, wherein a given path includes the base level and zero or more dependent branches; instructions for generating content numbers for the documents based at least in part on the corresponding paths through the hierarchy; instructions for creating the documents, wherein creating the documents comprises translating a content number for each document to determine content to be placed within the document; and instructions for storing, in a memory, the created documents in the directories in the hierarchy. | 16. A computer-program product for use in conjunction with a computer system, the computer-program product comprising a computer-readable storage medium and a computer-program mechanism embedded therein for configuring the computer system to create documents in a hierarchy, the computer-program mechanism including: instructions for generating a root number which corresponds to a base level in the hierarchy; instructions for assigning document numbers to the documents, wherein the document numbers are generated based at least in part on the root number; instructions for assigning directory numbers to directories in the hierarchy, wherein a given directory number is generated based at least in part on a given document number, and wherein a given directory is in a branch that is coupled to the root level; instructions for determining paths in the hierarchy corresponding to the document numbers and the directory numbers, wherein a given path includes the base level and zero or more dependent branches; instructions for generating content numbers for the documents based at least in part on the corresponding paths through the hierarchy; instructions for creating the documents, wherein creating the documents comprises translating a content number for each document to determine content to be placed within the document; and instructions for storing, in a memory, the created documents in the directories in the hierarchy. 21. The computer-program product of claim 16 , wherein the documents correspond to interconnected websites and web pages. | 0.804839 |
7,477,697 | 1 | 11 | 1. A method of multiplexing data words in a multicarrier transmit diversity system, comprising: a) generating a plurality of data blocks, each data block comprising data words and each data word containing data symbols derived from a data signal; b) determining for one or more data blocks in dependence on at least one transmission constraint if the data words of said one or more data blocks are to be multiplexed in space-time or in space-frequency, wherein the transmission constraint comprises a data-related transmission constraint relating to a pre-defined number of data symbols to be comprised within each data word which is to be multiplexed in space-time, and wherein the data words containing the predefined number of data symbols are multiplexed in space-time and the data words containing more or less data symbols are multiplexed in space-frequency; and c) multiplexing the data words of the data blocks in accordance with the determination in step b). | 1. A method of multiplexing data words in a multicarrier transmit diversity system, comprising: a) generating a plurality of data blocks, each data block comprising data words and each data word containing data symbols derived from a data signal; b) determining for one or more data blocks in dependence on at least one transmission constraint if the data words of said one or more data blocks are to be multiplexed in space-time or in space-frequency, wherein the transmission constraint comprises a data-related transmission constraint relating to a pre-defined number of data symbols to be comprised within each data word which is to be multiplexed in space-time, and wherein the data words containing the predefined number of data symbols are multiplexed in space-time and the data words containing more or less data symbols are multiplexed in space-frequency; and c) multiplexing the data words of the data blocks in accordance with the determination in step b). 11. The method according to claim 1 , wherein the at least one transmission constraint comprises a physical transmission constraint. | 0.823056 |
9,875,302 | 1 | 6 | 1. A method comprising: identifying a concept-unit from a multi-language document corpus, the concept-unit including a set of documents in different languages describing a particular concept; modeling the concept-unit identified from the multi-language document corpus to create a generative model, wherein the generative model represents at least: (a) a plurality of universal topics, individual ones of the plurality of universal topics being defined by a plurality of topic word distributions corresponding respectively to the different languages; and (b) a universal topic distribution associated with the concept-unit, the universal topic distribution identifying: (i) two or more universal topics for which documents describing the concept unit are to contain; and (ii) a relative significance of individual universal topics of the two or more universal topics within the concept unit; and classifying a set of documents of an unclassified document corpus using the generative model, the classifying comprising: obtaining the universal topic distribution for the set of documents of the generative model, obtaining a topic distribution of the set of documents of the unclassified document corpus, comparing the universal topic distribution to the topic distribution of the set of documents of the unclassified document corpus; and based on the comparing, classifying one or more documents of the set of documents of the unclassified document corpus. | 1. A method comprising: identifying a concept-unit from a multi-language document corpus, the concept-unit including a set of documents in different languages describing a particular concept; modeling the concept-unit identified from the multi-language document corpus to create a generative model, wherein the generative model represents at least: (a) a plurality of universal topics, individual ones of the plurality of universal topics being defined by a plurality of topic word distributions corresponding respectively to the different languages; and (b) a universal topic distribution associated with the concept-unit, the universal topic distribution identifying: (i) two or more universal topics for which documents describing the concept unit are to contain; and (ii) a relative significance of individual universal topics of the two or more universal topics within the concept unit; and classifying a set of documents of an unclassified document corpus using the generative model, the classifying comprising: obtaining the universal topic distribution for the set of documents of the generative model, obtaining a topic distribution of the set of documents of the unclassified document corpus, comparing the universal topic distribution to the topic distribution of the set of documents of the unclassified document corpus; and based on the comparing, classifying one or more documents of the set of documents of the unclassified document corpus. 6. A method as recited in claim 1 , further comprising comparing at least a first document and a second document to the plurality of topic word distributions to identify one or more groups of the documents sharing common topics, wherein the first document and the second document are not of the same language. | 0.755538 |
6,167,377 | 7 | 8 | 7. The method of claim 1, further comprising selecting a candidate by evaluating the candidates using the combined language model results. | 7. The method of claim 1, further comprising selecting a candidate by evaluating the candidates using the combined language model results. 8. The method of claim 7, further comprising selecting a candidate by evaluating the candidates using the combined language model results and acoustic information associated with the candidates. | 0.5 |
7,734,620 | 6 | 8 | 6. A method for optimizing a database query that includes a Select statement with a Fetch First n Rows Only clause, the method comprising the steps of: analyzing the query to determine the query is optimizable by determining that the query contains a Group By clause and that an index exists for a leftmost column but not all the columns of the Group By clause; generating for the query an optimized access plan that eliminates records defined by a Where clause prior to ordering records by creating an access plan that eliminates records prior to grouping by fetching n rows from the index over the leftmost column and remaining rows until a unique value of the index is encountered; and using the optimized access plan to retrieve data from the database table. | 6. A method for optimizing a database query that includes a Select statement with a Fetch First n Rows Only clause, the method comprising the steps of: analyzing the query to determine the query is optimizable by determining that the query contains a Group By clause and that an index exists for a leftmost column but not all the columns of the Group By clause; generating for the query an optimized access plan that eliminates records defined by a Where clause prior to ordering records by creating an access plan that eliminates records prior to grouping by fetching n rows from the index over the leftmost column and remaining rows until a unique value of the index is encountered; and using the optimized access plan to retrieve data from the database table. 8. The method of claim 6 further comprising the steps of: determining that the query contains an Order By clause, that an index exists for each predicate in the Where clause, that a field of the Order By clause exists in each index, and wherein the access plan eliminates records prior to a sort by fetching only n rows from each index; and returning n rows after sorting a set of records that includes the n rows from each index. | 0.5 |
8,041,701 | 17 | 19 | 17. The article of manufacture of claim 13 further comprising the steps of: displaying a placeholder image for said representation of said webpage; and replacing said placeholder image with said representation of said webpage after completion of said creating step. | 17. The article of manufacture of claim 13 further comprising the steps of: displaying a placeholder image for said representation of said webpage; and replacing said placeholder image with said representation of said webpage after completion of said creating step. 19. The article of manufacture of claim 17 , wherein the placeholder image is a server-generated thumbnail image, which is a reduced-sized presentation of the first webpage from an indeterminate point in the past. | 0.5 |
9,576,249 | 16 | 17 | 16. A computer-implemented method of constructing a numerical model to measure a user's comprehension of subject matter of a text as presented in a summary of the text constructed by a user, the method comprising: specifying a numerical model associated with a given text, the numerical model comprising a first variable and an associated first weighting factor, the first variable indicative of a similarity between a summary of the given text constructed by a user and a given reference summary, a second variable and an associated second weighting factor, the second variable indicative of a degree to which a single sentence of the summary summarizes an entirety of the given text, and a third variable and an associated third weighting factor, the third variable indicative of a degree of copying in the summary of multi-word sequences present in the given text; receiving a plurality of reference summaries for the given text, each reference summary having been given a reference score, the reference summaries spanning a range of reference scores reflecting varying degrees of comprehension of the subject matter of the given text, the reference summaries having been accepted as usable for training the numerical model; training the numerical model with a processing system using the reference summaries and the given reference scores to determine values for each of the first, second and third weighting factors; and configuring the numerical model with the determined values of the first, second and third weighting factors to receive a first numerical measure, a second numerical measure and a third numerical measure for the first variable, second variable and third variable, respectively, of an actual summary to be scored so as to generate a score for the actual summary that is indicative of the user's comprehension of the subject matter of the text as presented in a summary of the text. | 16. A computer-implemented method of constructing a numerical model to measure a user's comprehension of subject matter of a text as presented in a summary of the text constructed by a user, the method comprising: specifying a numerical model associated with a given text, the numerical model comprising a first variable and an associated first weighting factor, the first variable indicative of a similarity between a summary of the given text constructed by a user and a given reference summary, a second variable and an associated second weighting factor, the second variable indicative of a degree to which a single sentence of the summary summarizes an entirety of the given text, and a third variable and an associated third weighting factor, the third variable indicative of a degree of copying in the summary of multi-word sequences present in the given text; receiving a plurality of reference summaries for the given text, each reference summary having been given a reference score, the reference summaries spanning a range of reference scores reflecting varying degrees of comprehension of the subject matter of the given text, the reference summaries having been accepted as usable for training the numerical model; training the numerical model with a processing system using the reference summaries and the given reference scores to determine values for each of the first, second and third weighting factors; and configuring the numerical model with the determined values of the first, second and third weighting factors to receive a first numerical measure, a second numerical measure and a third numerical measure for the first variable, second variable and third variable, respectively, of an actual summary to be scored so as to generate a score for the actual summary that is indicative of the user's comprehension of the subject matter of the text as presented in a summary of the text. 17. The computer-implemented method of claim 16 , wherein the training comprises: processing each of the reference summaries to determine for each reference summary a first numerical measure indicative of a similarity between the summary and a particular reference summary, the particular reference summary having been designated as representative of the subject matter of the text, a second numerical measure indicative of a degree to which a single sentence of the reference summary summarizes an entirety of the text, and a third numerical measure indicative of a degree of copying in the reference summary of multi-word sequences present in the text; and conducting a numerical regression analysis based on the first, second and third numerical measures and reference score for each of the plurality of reference summaries to determine the first, second and third weighting factors. | 0.5 |
8,145,685 | 22 | 26 | 22. The integration server system of claim 20 wherein the integration server is capable of converting data objects between different formats specific to two or more client applications. | 22. The integration server system of claim 20 wherein the integration server is capable of converting data objects between different formats specific to two or more client applications. 26. The integration server system of claim 22 wherein metadata is stored both statically within the meta data model and within the database schema. | 0.543478 |
9,477,925 | 8 | 9 | 8. The system of claim 1 , wherein the DNNs include multiple layers, and wherein the pipelining includes streaming output data from a computation at a first processor of the one or more processors that processes an upper layer to a second processor of the one or more processors that processes a lower layer following a performance of an error back propagation step of a computation iteration, the streaming of the output data occurring at least partially in parallel with one or more of an model update or an input data forward propagation. | 8. The system of claim 1 , wherein the DNNs include multiple layers, and wherein the pipelining includes streaming output data from a computation at a first processor of the one or more processors that processes an upper layer to a second processor of the one or more processors that processes a lower layer following a performance of an error back propagation step of a computation iteration, the streaming of the output data occurring at least partially in parallel with one or more of an model update or an input data forward propagation. 9. The system of claim 8 , wherein the pipelining further includes streaming additional output data from a computation at the second processor of the one or more processors that processes the lower layer to the first processor of the one or more processors that processes the upper layer following the input data forward propagation, the streaming of the additional output data occurring at least partially in parallel with a computation of an error for another error back propagation. | 0.5 |
9,256,680 | 7 | 9 | 7. A method performed by a computer system executing machine-readable instructions, the method comprising acts of: receiving search results of a search related to a query; automatically associating at least one interactive relevance link with each result for indicating at least one of positive feedback or negative feedback of each result; automatically analyzing metadata of a given result, the metadata comprising at least one descriptive word from at least one target webpage; for each result, automatically reformulating the query into two new queries based respectively on the inclusion and exclusion of the at least one descriptive word of the metadata; and processing one of the two new queries of a given result to return new search results in response to interacting with the corresponding at least one interactive relevance link, where interacting to indicate positive feedback includes the descriptive word in one of the new queries, and interacting to indicate negative feedback negates the descriptive word from the respective other of the new queries. | 7. A method performed by a computer system executing machine-readable instructions, the method comprising acts of: receiving search results of a search related to a query; automatically associating at least one interactive relevance link with each result for indicating at least one of positive feedback or negative feedback of each result; automatically analyzing metadata of a given result, the metadata comprising at least one descriptive word from at least one target webpage; for each result, automatically reformulating the query into two new queries based respectively on the inclusion and exclusion of the at least one descriptive word of the metadata; and processing one of the two new queries of a given result to return new search results in response to interacting with the corresponding at least one interactive relevance link, where interacting to indicate positive feedback includes the descriptive word in one of the new queries, and interacting to indicate negative feedback negates the descriptive word from the respective other of the new queries. 9. The method of claim 7 , further comprising configuring the at least one interactive relevance link with a positive selection that when selected indicates positive feedback for the given result. | 0.5 |
8,615,664 | 20 | 27 | 20. The system of claim 1 , wherein said source identity acquisition module configured to acquire source identity data providing at least one identity of one or more sources that provide a basis, at least in part, for the inference data, the one or more sources including at least the one or more sensors comprises: a sensor identity (ID) acquisition module configured to acquire one or more identities of the one or more sensors used to sense the at least one physical characteristic of the authoring user. | 20. The system of claim 1 , wherein said source identity acquisition module configured to acquire source identity data providing at least one identity of one or more sources that provide a basis, at least in part, for the inference data, the one or more sources including at least the one or more sensors comprises: a sensor identity (ID) acquisition module configured to acquire one or more identities of the one or more sensors used to sense the at least one physical characteristic of the authoring user. 27. The system of claim 20 , wherein said sensor identity (ID) acquisition module configured to acquire one or more identities of the one or more sensors used to sense the at least one physical characteristic of the authoring user comprises: a sensor identity (ID) acquisition module configured to acquire an identity of a gaze tracking device or an iris response device used to sense the at least one physical characteristic of the authoring user. | 0.524416 |
8,065,606 | 2 | 3 | 2. The system according to claim 1 , wherein the content building application uses at least one content element stored in the memory to generate the user-modifiable document. | 2. The system according to claim 1 , wherein the content building application uses at least one content element stored in the memory to generate the user-modifiable document. 3. The system according to claim 2 , wherein the at least one content element includes any or all of a content element type, business data and a set of instructions for generating the user-modifiable document. | 0.5 |
9,269,072 | 17 | 28 | 17. A system for facilitating navigation of previously presented screen data in an ongoing online meeting, the system comprising: a storage device for storing screen data representing a previously presented portion of an ongoing online meeting; image processing circuitry for capturing, in response to a trigger event, a screenshot of the screen data for the ongoing online meeting; display circuitry for causing the display, on a viewer computing device while the ongoing online meeting is still ongoing, of an image thumbnail generated from the screenshot, the image thumbnail facilitating navigation, on the viewer computing device, of the previously presented portion of the ongoing online meeting; a user interface for receiving, at the viewer computing device while the ongoing online meeting is still ongoing, a selection of the image thumbnail; and presentation circuitry for causing the display, in response to the selection of the image thumbnail, on the viewer computing device, simultaneously and while the ongoing online meeting is still ongoing, first screen data corresponding to a currently presented portion of the ongoing online meeting and second screen data corresponding to the screenshot, wherein the first screen data is presented picture-in-picture inside the second screen data or the second screen data is presented picture-in-picture inside the first screen data; wherein the trigger event comprises a size of an accumulative bounding box encapsulating changes to the stored screen data increasing past a threshold. | 17. A system for facilitating navigation of previously presented screen data in an ongoing online meeting, the system comprising: a storage device for storing screen data representing a previously presented portion of an ongoing online meeting; image processing circuitry for capturing, in response to a trigger event, a screenshot of the screen data for the ongoing online meeting; display circuitry for causing the display, on a viewer computing device while the ongoing online meeting is still ongoing, of an image thumbnail generated from the screenshot, the image thumbnail facilitating navigation, on the viewer computing device, of the previously presented portion of the ongoing online meeting; a user interface for receiving, at the viewer computing device while the ongoing online meeting is still ongoing, a selection of the image thumbnail; and presentation circuitry for causing the display, in response to the selection of the image thumbnail, on the viewer computing device, simultaneously and while the ongoing online meeting is still ongoing, first screen data corresponding to a currently presented portion of the ongoing online meeting and second screen data corresponding to the screenshot, wherein the first screen data is presented picture-in-picture inside the second screen data or the second screen data is presented picture-in-picture inside the first screen data; wherein the trigger event comprises a size of an accumulative bounding box encapsulating changes to the stored screen data increasing past a threshold. 28. The system of claim 17 , wherein the image processing circuitry suppresses the capture of a second screenshot if the screen data comprises video data. | 0.689516 |
9,632,748 | 9 | 10 | 9. The computing device of claim 7 , wherein the at least one module is further operable by the at least one processor to determine a current context of the first computing device, and wherein the at least one module is operable by the at least one processor to determine whether the first computing device should perform speech recognition on the spoken audio input further based on the current context of the first computing device. | 9. The computing device of claim 7 , wherein the at least one module is further operable by the at least one processor to determine a current context of the first computing device, and wherein the at least one module is operable by the at least one processor to determine whether the first computing device should perform speech recognition on the spoken audio input further based on the current context of the first computing device. 10. The computing device of claim 9 , wherein the at least one module operable to determine the current context of the first computing device is operable by the at least one processor to determine one or more of: a location of the first computing device, a current time as defined by the first computing device, one or more applications installed at the first computing device, one or more applications currently executing at the first computing device, one or more networks available to the first computing device, one or more other computing devices in proximity to the first computing device, an operating mode of the first computing device, an ambient temperature of the location of the first computing device, an ambient noise level of the location of the first computing device, an ambient light level of the location of the first computing device, a movement of the first computing device, a name of a user of the first computing device, a user identification (UID) of the user of the first computing device, a social media network service account associated with the user of the first computing device, one or more calendars associated with the user of the first computing device, or one or more social relationships of the user of the first computing device. | 0.5 |
8,918,323 | 1 | 7 | 1. A method, comprising: at a computer comprising a computer program to implement processing operations: receiving data related to content of a target; filtering the data to locate a target term; accessing one or more tables in a repository, the one or more tables comprising entries with a substitution unit corresponding to the target term, the entries arranged according to a prioritized scheme that defines a position for the substitution unit in the tables; and generating an output comprising data that represents the substitution unit to be utilized by a text-to-speech generator to generate spoken content, wherein the position of the substitution unit in the one or more tables is assigned based on a specificity characteristic that describes the relative inclusivity of the substitution unit as compared to other substitution units in the one or more tables. | 1. A method, comprising: at a computer comprising a computer program to implement processing operations: receiving data related to content of a target; filtering the data to locate a target term; accessing one or more tables in a repository, the one or more tables comprising entries with a substitution unit corresponding to the target term, the entries arranged according to a prioritized scheme that defines a position for the substitution unit in the tables; and generating an output comprising data that represents the substitution unit to be utilized by a text-to-speech generator to generate spoken content, wherein the position of the substitution unit in the one or more tables is assigned based on a specificity characteristic that describes the relative inclusivity of the substitution unit as compared to other substitution units in the one or more tables. 7. The method of claim 1 , further comprising: selecting a first entry from a first table of the one or more tables; and selecting a second entry from a second table of the one or more tables, wherein the data in the output comprises the substitution unit from the first entry and the second entry. | 0.5 |
8,250,072 | 1 | 3 | 1. A method for detecting real-word typos, the method comprising: receiving text designated for evaluation, the designated text comprising a plurality of words; executing instructions stored in memory, wherein execution of the instructions by a processor: parses the plurality of words into a plurality of word groups, each word group comprising a number of consecutive words found in the designated text, identifies a database for comparison to the text, the database comprising a plurality of word groups previously identified in one or more source texts, each word group in the database comprising the number of consecutive words found in the one or more source texts, and analyzes the word groups parsed from the designated text based on a comparison to the word groups in the identified database; and generating an indication that a word group from the designated text may include an error based on the analysis of the word group in comparison to the identified database, wherein the analysis comprises generating a typo likelihood value for each word group parsed from the designated text, and wherein the typo likelihood value of a word group parsed from the designated text is based on a product of a number of times each word in the word group appears in the database, the product divided by a number of times the word group comprising the consecutive words appears in the database. | 1. A method for detecting real-word typos, the method comprising: receiving text designated for evaluation, the designated text comprising a plurality of words; executing instructions stored in memory, wherein execution of the instructions by a processor: parses the plurality of words into a plurality of word groups, each word group comprising a number of consecutive words found in the designated text, identifies a database for comparison to the text, the database comprising a plurality of word groups previously identified in one or more source texts, each word group in the database comprising the number of consecutive words found in the one or more source texts, and analyzes the word groups parsed from the designated text based on a comparison to the word groups in the identified database; and generating an indication that a word group from the designated text may include an error based on the analysis of the word group in comparison to the identified database, wherein the analysis comprises generating a typo likelihood value for each word group parsed from the designated text, and wherein the typo likelihood value of a word group parsed from the designated text is based on a product of a number of times each word in the word group appears in the database, the product divided by a number of times the word group comprising the consecutive words appears in the database. 3. The method of claim 1 , further comprising building the database by: receiving a designated source text, the source text comprising a plurality of words; parsing the plurality of words into a plurality of word groups, each word group comprising the number of consecutive words found in the source text; and storing the parsed plurality of word groups in a database in memory. | 0.5 |
8,874,598 | 1 | 5 | 1. A computer-implemented method for verifying data structures, the method comprising: executing, by a processor, a software module to obtain a first reference set of data produced by the software module, wherein the first reference set of data comprising all data produced by the software module at a first time; introducing, by the processor, a change to the software module after the first time to generate a modified software module; executing, by the processor, the modified software module to obtain a second reference set of data produced by the modified software module, wherein the second reference set of data comprising all data including data related to at least one changed portion of the modified software module produced by the modified software module at a second time, and wherein the executing at the second time is response to the introduced change; transforming, by the processor, the first reference set of data produced by the software module and the second reference set of data produced by the modified software module into a first formal text form representation and a second formal text form representation, respectively; determining, by the processor, a plurality of differences between the first reference set of data produced by the software module and the second reference set of data produced by the modified software module by comparing the first formal text form representation and the second formal text form representation to each other; filtering, by the processor, the plurality of differences to obtain a sub-set of the plurality of differences based on at least one filter criteria; mapping, by the processor, each difference in the sub-set of the plurality of differences to a corresponding portion of the modified software module responsible for the difference to determine at which point in execution of the modified software module the difference occurred; generating, by the processor, a report of the sub-set of plurality of differences, the corresponding mapped portions of the modified software module, and root cause of the sub-set of plurality of differences; wherein the second reference set of data is further used, by the processor, to verify whether a functionality of the modified software module is retained after the introduced change; replacing, by the processor, the first reference set of data with the second set of second reference set of data upon verifying that the functionality is retained; and establishing, by the processor, the second reference set of data as a new first reference set of data to use in future software module verification. | 1. A computer-implemented method for verifying data structures, the method comprising: executing, by a processor, a software module to obtain a first reference set of data produced by the software module, wherein the first reference set of data comprising all data produced by the software module at a first time; introducing, by the processor, a change to the software module after the first time to generate a modified software module; executing, by the processor, the modified software module to obtain a second reference set of data produced by the modified software module, wherein the second reference set of data comprising all data including data related to at least one changed portion of the modified software module produced by the modified software module at a second time, and wherein the executing at the second time is response to the introduced change; transforming, by the processor, the first reference set of data produced by the software module and the second reference set of data produced by the modified software module into a first formal text form representation and a second formal text form representation, respectively; determining, by the processor, a plurality of differences between the first reference set of data produced by the software module and the second reference set of data produced by the modified software module by comparing the first formal text form representation and the second formal text form representation to each other; filtering, by the processor, the plurality of differences to obtain a sub-set of the plurality of differences based on at least one filter criteria; mapping, by the processor, each difference in the sub-set of the plurality of differences to a corresponding portion of the modified software module responsible for the difference to determine at which point in execution of the modified software module the difference occurred; generating, by the processor, a report of the sub-set of plurality of differences, the corresponding mapped portions of the modified software module, and root cause of the sub-set of plurality of differences; wherein the second reference set of data is further used, by the processor, to verify whether a functionality of the modified software module is retained after the introduced change; replacing, by the processor, the first reference set of data with the second set of second reference set of data upon verifying that the functionality is retained; and establishing, by the processor, the second reference set of data as a new first reference set of data to use in future software module verification. 5. The method of claim 1 , further comprising presenting the report of the sub-set of the plurality of differences in a graphical user interface. | 0.673423 |
9,324,113 | 8 | 9 | 8. The memory of claim 1 , wherein the method further comprises: presenting, in the specified area of the SERP that is outside of the domain associated with the social networking application, a selectable option for the user to send an electronic message to at least one of the one or more social network connections of the user. | 8. The memory of claim 1 , wherein the method further comprises: presenting, in the specified area of the SERP that is outside of the domain associated with the social networking application, a selectable option for the user to send an electronic message to at least one of the one or more social network connections of the user. 9. The memory of claim 8 , wherein the electronic message would appear to a recipient user as if the electronic message was sent directly from the social networking application. | 0.5 |
8,719,024 | 1 | 15 | 1. A method, comprising: receiving audio data and a textual transcript of the audio data to be aligned with the audio data; generating, from the textual transcript, a language model that represents a set of particular substrings of the textual transcript, the language model comprising allowed states of the language model and one or more transitions that link the allowed states; receiving, from a speech recognizer, recognized language elements from the received audio data and times at which the recognized language elements occur in the audio data; comparing the recognized language elements from the audio data to substrings represented by the language model to identify times at which particular ones of the substrings occur in the audio data; aligning a portion of the textual transcript with a portion of the audio data using the identified times; and outputting the aligned portion of the textual transcript. | 1. A method, comprising: receiving audio data and a textual transcript of the audio data to be aligned with the audio data; generating, from the textual transcript, a language model that represents a set of particular substrings of the textual transcript, the language model comprising allowed states of the language model and one or more transitions that link the allowed states; receiving, from a speech recognizer, recognized language elements from the received audio data and times at which the recognized language elements occur in the audio data; comparing the recognized language elements from the audio data to substrings represented by the language model to identify times at which particular ones of the substrings occur in the audio data; aligning a portion of the textual transcript with a portion of the audio data using the identified times; and outputting the aligned portion of the textual transcript. 15. The method of claim 1 , wherein the received textual transcript is based on closed captions, lyrics, or books. | 0.830357 |
6,076,182 | 12 | 15 | 12. A computer system for storing data, comprising: an input device that receives data from a user; a processor coupled to the input device, the processor being structured to process data according to programmed instructions; a memory module that stores a plurality of data words for use by the processor, each data word including a plurality of consecutive data bits divided into N subsets of data bits having data values, where N is an integer greater than 1, the data bits in each subset comprising every N.sup.th data bit in the data word; and a memory controller coupling the memory module to the processor, the memory controller being structured to create a separate error correction code for each of the subsets of each of the plurality of data words stored in the memory module, each error correction code including a plurality of check bits computed based on the data bits of the subset corresponding to the error correction code. | 12. A computer system for storing data, comprising: an input device that receives data from a user; a processor coupled to the input device, the processor being structured to process data according to programmed instructions; a memory module that stores a plurality of data words for use by the processor, each data word including a plurality of consecutive data bits divided into N subsets of data bits having data values, where N is an integer greater than 1, the data bits in each subset comprising every N.sup.th data bit in the data word; and a memory controller coupling the memory module to the processor, the memory controller being structured to create a separate error correction code for each of the subsets of each of the plurality of data words stored in the memory module, each error correction code including a plurality of check bits computed based on the data bits of the subset corresponding to the error correction code. 15. The computer system of claim 12 wherein the memory module includes a plurality of memory chips each having a plurality of data output pins structured to enable plural data bits to be output simultaneously from the memory chip, each of the plural data bits being from a different subset of a requested one of the data words. | 0.731086 |
9,922,631 | 1 | 2 | 1. A motor vehicle, comprising: a loudspeaker; a voice recognition module; a microphone configured to produce a microphone signal based upon lyrics of a song uttered by a human passenger within a passenger compartment of the motor vehicle; and an electronic processor communicatively coupled to the microphone, the loudspeaker, and the voice recognition module, the electronic processor being configured to: receive the microphone signal and communicate with the voice recognition module to thereby ascertain the lyrics uttered by the human passenger; retrieve Karaoke music corresponding to the ascertained lyrics uttered by the human passenger; continue to ascertain the lyrics being uttered by the human passenger while the Karaoke music is being retrieved; and begin playing the Karaoke music on the loudspeaker at a point in the music that corresponds to a point within the song at which the passenger is currently singing. | 1. A motor vehicle, comprising: a loudspeaker; a voice recognition module; a microphone configured to produce a microphone signal based upon lyrics of a song uttered by a human passenger within a passenger compartment of the motor vehicle; and an electronic processor communicatively coupled to the microphone, the loudspeaker, and the voice recognition module, the electronic processor being configured to: receive the microphone signal and communicate with the voice recognition module to thereby ascertain the lyrics uttered by the human passenger; retrieve Karaoke music corresponding to the ascertained lyrics uttered by the human passenger; continue to ascertain the lyrics being uttered by the human passenger while the Karaoke music is being retrieved; and begin playing the Karaoke music on the loudspeaker at a point in the music that corresponds to a point within the song at which the passenger is currently singing. 2. The motor vehicle of claim 1 wherein the Karaoke music is retrieved via the Internet. | 0.774359 |
7,899,827 | 24 | 26 | 24. A system comprising a plurality of computers at least two of which are coupled together through a data communications network, said system comprising: a tokenizer and a token processing module comprised of computer instructions in data storage distributed across the plurality of computers directing the plurality of computers to parse text of each of at least one text document into text tokens and assign semantic meaning to words of the parsed text tokens, where assigning comprises applying a plurality of regular expressions, rules and a set of dictionaries, where the set of dictionaries is selected from the group consisting of: a first collection of dictionaries consisting of a common chemical prefix dictionary and a common chemical suffix dictionary to recognize chemical name fragments and a second collection of dictionaries consisting of the common chemical prefix dictionary, the common chemical suffix dictionary to recognize chemical name fragments and a dictionary of stop words to eliminate erroneous chemical name fragments; the instructions of the token processing module directing the plurality of computers to recognize any substructures present in the chemical name fragments; the instructions of the token processing module directing the plurality of computers to extract keywords from the text document, where the keywords are associated with the recognized chemical name fragments and the substructures and to index the extracted keywords in a text index; the instructions of the token processing module directing the plurality of computers to add each of the recognized chemical name fragments and the substructures that do not contain a number to the text index; the instructions of the token processing module directing the plurality of computers to, for each of the at least one text document, determine structural connectivity information within each of the recognized chemical name fragments and the substructures that do not contain a number; the instructions of the token processing module directing the plurality of computers to index representations of the recognized chemical name fragments and the substructures in association with the determined structural connectivity information into a plurality of chemical connectivity tables of a chemical substructure index, where indexing the representations comprises: in a loop, testing each of the recognized chemical name fragments in the at least one text document to see if the recognized chemical name fragment occurs in a dictionary of SMILES fragments, where if it does then a SMILES expression for the recognized chemical name fragment is added to the chemical substructure index, then determining if the recognized chemical name fragment occurs in a MOL file dictionary, where if it does then a MOL file expression for the fragment token is added to the chemical substructure index, and storing the text index in association with the chemical substructure index; a searcher module comprised of computer instructions distributed across the plurality of computers and a graphical user interface comprised of a display configured to display a graphical list of substructures and a keyboard connected to a computer of the plurality of computers directing the plurality of computers to search the text index and the chemical substructure index, where the search comprises first entering search terms comprising one or more chemical fragment names and then selecting graphical representations of one or more substructures, where the selecting comprises using the graphical user interface as a pointer to the graphical list of substructures; and the graphical user interface configured to receive a search result, where the search result is an intersection of the chemical substructure index and the text index, identifying at least one text document where there are found chemical compounds that contain a reference to the search temis and the one or more substructures and where the search terms are found in the text index. | 24. A system comprising a plurality of computers at least two of which are coupled together through a data communications network, said system comprising: a tokenizer and a token processing module comprised of computer instructions in data storage distributed across the plurality of computers directing the plurality of computers to parse text of each of at least one text document into text tokens and assign semantic meaning to words of the parsed text tokens, where assigning comprises applying a plurality of regular expressions, rules and a set of dictionaries, where the set of dictionaries is selected from the group consisting of: a first collection of dictionaries consisting of a common chemical prefix dictionary and a common chemical suffix dictionary to recognize chemical name fragments and a second collection of dictionaries consisting of the common chemical prefix dictionary, the common chemical suffix dictionary to recognize chemical name fragments and a dictionary of stop words to eliminate erroneous chemical name fragments; the instructions of the token processing module directing the plurality of computers to recognize any substructures present in the chemical name fragments; the instructions of the token processing module directing the plurality of computers to extract keywords from the text document, where the keywords are associated with the recognized chemical name fragments and the substructures and to index the extracted keywords in a text index; the instructions of the token processing module directing the plurality of computers to add each of the recognized chemical name fragments and the substructures that do not contain a number to the text index; the instructions of the token processing module directing the plurality of computers to, for each of the at least one text document, determine structural connectivity information within each of the recognized chemical name fragments and the substructures that do not contain a number; the instructions of the token processing module directing the plurality of computers to index representations of the recognized chemical name fragments and the substructures in association with the determined structural connectivity information into a plurality of chemical connectivity tables of a chemical substructure index, where indexing the representations comprises: in a loop, testing each of the recognized chemical name fragments in the at least one text document to see if the recognized chemical name fragment occurs in a dictionary of SMILES fragments, where if it does then a SMILES expression for the recognized chemical name fragment is added to the chemical substructure index, then determining if the recognized chemical name fragment occurs in a MOL file dictionary, where if it does then a MOL file expression for the fragment token is added to the chemical substructure index, and storing the text index in association with the chemical substructure index; a searcher module comprised of computer instructions distributed across the plurality of computers and a graphical user interface comprised of a display configured to display a graphical list of substructures and a keyboard connected to a computer of the plurality of computers directing the plurality of computers to search the text index and the chemical substructure index, where the search comprises first entering search terms comprising one or more chemical fragment names and then selecting graphical representations of one or more substructures, where the selecting comprises using the graphical user interface as a pointer to the graphical list of substructures; and the graphical user interface configured to receive a search result, where the search result is an intersection of the chemical substructure index and the text index, identifying at least one text document where there are found chemical compounds that contain a reference to the search temis and the one or more substructures and where the search terms are found in the text index. 26. The system as in claim 24 , where the instructions of said token processing module further direct the plurality of computers to look up recognized fragments and substructures in a structure dictionary. | 0.776688 |
10,127,316 | 3 | 4 | 3. The method of claim 1 , wherein determining whether the unstructured text comprises a request for a recommendation comprises determining whether the unstructured text matches one or more predetermined words associated with requests for recommendation. | 3. The method of claim 1 , wherein determining whether the unstructured text comprises a request for a recommendation comprises determining whether the unstructured text matches one or more predetermined words associated with requests for recommendation. 4. The method of claim 3 , wherein determining whether the unstructured text comprises a request for a recommendation comprises: generating a score based on the unstructured text using a machine-learning model based on comparison of the unstructured text to the one or more predetermined words associated with requests for recommendation, wherein the unstructured text comprises a request for a recommendation when the score is greater than a threshold value. | 0.5 |
9,733,716 | 24 | 28 | 24. An electronic device, comprising: a touch-sensitive surface; one or more processors; and memory storing one or more programs for execution by the one or more processors, the one or more programs including instructions for: displaying one or more views of a plurality of views, wherein: a first view of the one or more displayed views includes a plurality of gesture recognizers; the plurality of gesture recognizers in the first view includes one or more proxy gesture recognizers and one or more non-proxy gesture recognizers; each gesture recognizer indicates one of a plurality of predefined states; and a first proxy gesture recognizer in the first view has a state that corresponds to a state of a respective non-proxy gesture recognizer that is not in the first view, wherein the state of the first proxy gesture recognizer and the corresponding state of the respective non-proxy gesture recognizer are both selected from a same set of predefined states; detecting a sequence of one or more sub-events; delivering a respective sub-event to the respective non-proxy gesture recognizer that is not in the first view and at least a subset of the one or more non-proxy gesture recognizers in the first view; and processing the respective sub-event with at least one of the one or more non-proxy gesture recognizers in the first view in accordance with states of the first proxy gesture recognizer and at least the subset of the one or more non-proxy gesture recognizers in the first view. | 24. An electronic device, comprising: a touch-sensitive surface; one or more processors; and memory storing one or more programs for execution by the one or more processors, the one or more programs including instructions for: displaying one or more views of a plurality of views, wherein: a first view of the one or more displayed views includes a plurality of gesture recognizers; the plurality of gesture recognizers in the first view includes one or more proxy gesture recognizers and one or more non-proxy gesture recognizers; each gesture recognizer indicates one of a plurality of predefined states; and a first proxy gesture recognizer in the first view has a state that corresponds to a state of a respective non-proxy gesture recognizer that is not in the first view, wherein the state of the first proxy gesture recognizer and the corresponding state of the respective non-proxy gesture recognizer are both selected from a same set of predefined states; detecting a sequence of one or more sub-events; delivering a respective sub-event to the respective non-proxy gesture recognizer that is not in the first view and at least a subset of the one or more non-proxy gesture recognizers in the first view; and processing the respective sub-event with at least one of the one or more non-proxy gesture recognizers in the first view in accordance with states of the first proxy gesture recognizer and at least the subset of the one or more non-proxy gesture recognizers in the first view. 28. The device of claim 24 , wherein the one or more programs further include instructions for: delaying at least a first non-proxy gesture recognizer of the one or more non-proxy gesture recognizers in the first view from recognizing a gesture that corresponds to the sequence of one or more sub-events until after the first proxy gesture recognizer enters into a particular predefined state of the one or more predefined states. | 0.5 |
5,530,794 | 1 | 4 | 1. A method for properly displaying paragraphs of text that use a foreign paragraph delimiter, the foreign paragraph delimiter being different than a native paragraph delimiter of documents created on a word processing system, said method comprising the steps of: (a) producing a character position array in which each character of a document that is open on the word processing system is assigned a position, said character position array being divided into a plurality of pieces, each piece comprising a string of characters that are stored adjacent to one another in a file and which have identical format properties; (b) producing an array of data records including entries that correspond to each piece of the character position array, each entry including a file number and a file position within a file at which the string of characters comprising the piece are stored; (c) producing a file control block for each file storing text used in the document when the file is initially opened by the word processing system; (d) inserting delimiter identification data in the file control block of each file, said delimiter identification data indicating a type of paragraph delimiter used by the text stored in the file; (e) each time that a character of the document is displayed, referring to the character position array and to the array of data records to determine a specific file in which the character is stored, the delimiter identification data in the file control block for said specific file indicating the type of paragraph delimiter that is used for a paragraph containing the character; and (f) if the paragraph containing the character uses a foreign paragraph delimiter, translating the foreign paragraph delimiter to the native paragraph delimiter in a display buffer, so that the paragraph containing the character is properly displayed to the user. | 1. A method for properly displaying paragraphs of text that use a foreign paragraph delimiter, the foreign paragraph delimiter being different than a native paragraph delimiter of documents created on a word processing system, said method comprising the steps of: (a) producing a character position array in which each character of a document that is open on the word processing system is assigned a position, said character position array being divided into a plurality of pieces, each piece comprising a string of characters that are stored adjacent to one another in a file and which have identical format properties; (b) producing an array of data records including entries that correspond to each piece of the character position array, each entry including a file number and a file position within a file at which the string of characters comprising the piece are stored; (c) producing a file control block for each file storing text used in the document when the file is initially opened by the word processing system; (d) inserting delimiter identification data in the file control block of each file, said delimiter identification data indicating a type of paragraph delimiter used by the text stored in the file; (e) each time that a character of the document is displayed, referring to the character position array and to the array of data records to determine a specific file in which the character is stored, the delimiter identification data in the file control block for said specific file indicating the type of paragraph delimiter that is used for a paragraph containing the character; and (f) if the paragraph containing the character uses a foreign paragraph delimiter, translating the foreign paragraph delimiter to the native paragraph delimiter in a display buffer, so that the paragraph containing the character is properly displayed to the user. 4. The method of claim 1, further comprising the steps of scanning at least a portion of the text in a text file opened by the word processing system to identify a specific type of paragraph delimiter that is used in said portion of the text, and specifying the delimiter identification data corresponding to said specific type of paragraph delimiter in the file control block for said text file. | 0.62782 |
7,536,475 | 15 | 17 | 15. The ACM system in accordance with claim 14 wherein said web server configured to receive SOAP/XML requests from said network and transfer the SOAP/XML requests to said SOAP/XML server. | 15. The ACM system in accordance with claim 14 wherein said web server configured to receive SOAP/XML requests from said network and transfer the SOAP/XML requests to said SOAP/XML server. 17. The ACM system in accordance with claim 15 wherein said SOAP/XML server configured to transfer ACM data from said ACM CPU through said web server and said network to said computer. | 0.682759 |
9,607,216 | 1 | 5 | 1. A method for analyzing an image, the method comprising: identifying, by one or more processors, a document that includes (i) at least one image and (ii) unstructured text that is situated proximate to the image, wherein both the image and the unstructured text are concurrently displayed in the document; identifying, by one or more processors, one or more potential locations depicted in the image included in the identified document, based on an analysis of the unstructured text that is situated proximate to the image; identifying, by one or more processors, a set of images of a first potential location from the identified one or more potential locations; responsive to determining that an image in the set of images of the first potential location substantially matches the image included in the identified document, determining, by one or more processors, that the location depicted in the image that substantially matches the image included in the identified document is the location depicted in the image included in the identified document; identifying, by one or more processors, an updated image that includes the location depicted in the image included in the identified document, wherein the updated image is associated with a date that is more recent than a date associated with the image included in the identified document; and initiating, by one or more processors, display of the identified updated image within the identified document. | 1. A method for analyzing an image, the method comprising: identifying, by one or more processors, a document that includes (i) at least one image and (ii) unstructured text that is situated proximate to the image, wherein both the image and the unstructured text are concurrently displayed in the document; identifying, by one or more processors, one or more potential locations depicted in the image included in the identified document, based on an analysis of the unstructured text that is situated proximate to the image; identifying, by one or more processors, a set of images of a first potential location from the identified one or more potential locations; responsive to determining that an image in the set of images of the first potential location substantially matches the image included in the identified document, determining, by one or more processors, that the location depicted in the image that substantially matches the image included in the identified document is the location depicted in the image included in the identified document; identifying, by one or more processors, an updated image that includes the location depicted in the image included in the identified document, wherein the updated image is associated with a date that is more recent than a date associated with the image included in the identified document; and initiating, by one or more processors, display of the identified updated image within the identified document. 5. The method of claim 1 wherein, the identified updated image is displayed as a thumbnail image in proximity to the image included in the identified document and in proximity to the unstructured text that is situated proximate to the image. | 0.862912 |
8,533,178 | 10 | 14 | 10. A method of monitoring search data provided to users by multiple web pages that provide search functionality, the method comprising: accessing, by a monitoring application executing on a client system, requests issued by a web browser executing on the client system to a plurality of web pages hosted by one or more servers; applying, by the monitoring application executing on the client system, a set of rules to the accessed requests to detect from among the accessed requests those requests that are search suggestion requests resulting from the web browser rendering different web pages that are each configured to display search suggestions in response to character sequences entered by a user when using a search capability provided by the rendered web page, the search suggestion requests including character sequences; extracting, by the monitoring application executing on the client system, from the detected search suggestion requests, the character sequences included in the detected search suggestion requests; accessing, by the monitoring application executing on the client system, search suggestion responses received from one or more servers providing the search functionality, the search suggestion responses corresponding to the detected search suggestion requests and including search suggestions corresponding to the search suggestion requests; extracting, by the monitoring application executing on the client system, from the accessed search suggestion responses, the search suggestions included in the search suggestion responses; and sending, by the monitoring application executing on the client system, data including indications of the extracted character sequences and the corresponding extracted search suggestions to a collection server system such that the collection server system generates, based on the sent data, a data structure that correlates character sequences to lists of phrases. | 10. A method of monitoring search data provided to users by multiple web pages that provide search functionality, the method comprising: accessing, by a monitoring application executing on a client system, requests issued by a web browser executing on the client system to a plurality of web pages hosted by one or more servers; applying, by the monitoring application executing on the client system, a set of rules to the accessed requests to detect from among the accessed requests those requests that are search suggestion requests resulting from the web browser rendering different web pages that are each configured to display search suggestions in response to character sequences entered by a user when using a search capability provided by the rendered web page, the search suggestion requests including character sequences; extracting, by the monitoring application executing on the client system, from the detected search suggestion requests, the character sequences included in the detected search suggestion requests; accessing, by the monitoring application executing on the client system, search suggestion responses received from one or more servers providing the search functionality, the search suggestion responses corresponding to the detected search suggestion requests and including search suggestions corresponding to the search suggestion requests; extracting, by the monitoring application executing on the client system, from the accessed search suggestion responses, the search suggestions included in the search suggestion responses; and sending, by the monitoring application executing on the client system, data including indications of the extracted character sequences and the corresponding extracted search suggestions to a collection server system such that the collection server system generates, based on the sent data, a data structure that correlates character sequences to lists of phrases. 14. The method of claim 10 further comprising: determining an identifier corresponding to the one of the plurality of web pages, or provider of the one of the plurality of web pages, that provides the search suggestions; and sending to the server system the identifier such that the server system generates the data structure based on the identifier. | 0.566832 |
8,682,647 | 35 | 36 | 35. The system of claim 34 , wherein the score comprises a product of at least (1) the frequency of a plurality of n-grams appearing in the candidate, sentence, (2) the measure of commonality between the candidate sentence and the query, and (3) the rank of the candidate sentence according to the ranking of the plurality of documents. | 35. The system of claim 34 , wherein the score comprises a product of at least (1) the frequency of a plurality of n-grams appearing in the candidate, sentence, (2) the measure of commonality between the candidate sentence and the query, and (3) the rank of the candidate sentence according to the ranking of the plurality of documents. 36. The system of claim 35 , wherein the measure of commonality comprises a count of common words. | 0.5 |
8,375,017 | 1 | 5 | 1. A computerized method of automatically identifying keywords relevant to a document invisible to search engines comprising: analyzing at a computer the document invisible to search engine crawlers to obtain a keyword starter set from the document, the keyword starter set obtained by: (1) applying at said computer an automated parser to the document to obtain keywords; and (2) applying a frequency prominence analysis to the keywords to select one or more frequently occurring keywords to add to the keyword starter set; expanding at the computer the keyword starter set by applying a computerized taxonomy to the keyword starter set to form a keyword super set; applying at the computer a keyword stop list to keywords in the keyword super set to remove keywords included in the keyword stop list; refining at the computer the keyword super set to form a keyword final set by applying keyword demand data to the keyword super set to remove one or more additional keywords from the keyword super set, wherein the demand data reflects the frequency of use of the keywords as search terms in internet search engines; adding at the computer the keyword final set to a web page for accessing the document; storing the document invisible to search engines for retrieval via the web page for accessing the document; adding the web page with the keyword final set to a web site to facilitate location by internet search engines of the web page for accessing the document according to the keywords added to the web page; and providing internet users with access via the web page to the document invisible to search engines. | 1. A computerized method of automatically identifying keywords relevant to a document invisible to search engines comprising: analyzing at a computer the document invisible to search engine crawlers to obtain a keyword starter set from the document, the keyword starter set obtained by: (1) applying at said computer an automated parser to the document to obtain keywords; and (2) applying a frequency prominence analysis to the keywords to select one or more frequently occurring keywords to add to the keyword starter set; expanding at the computer the keyword starter set by applying a computerized taxonomy to the keyword starter set to form a keyword super set; applying at the computer a keyword stop list to keywords in the keyword super set to remove keywords included in the keyword stop list; refining at the computer the keyword super set to form a keyword final set by applying keyword demand data to the keyword super set to remove one or more additional keywords from the keyword super set, wherein the demand data reflects the frequency of use of the keywords as search terms in internet search engines; adding at the computer the keyword final set to a web page for accessing the document; storing the document invisible to search engines for retrieval via the web page for accessing the document; adding the web page with the keyword final set to a web site to facilitate location by internet search engines of the web page for accessing the document according to the keywords added to the web page; and providing internet users with access via the web page to the document invisible to search engines. 5. The method of claim 1 wherein obtaining a keyword starter set from the document comprises selecting a batch processor for batch processing the document to produce a keyword starter set. | 0.824953 |
4,435,617 | 24 | 25 | 24. In the system of claim 23, wherein said processing means groups the syllabits into syllabit groups, each syllabit group defining corresponding possible words, and wherein said processor means provides, for each of said possible words corresponding to each syllabit group, a skeletal sequence of phonemes comprising a corresponding grouping of phonemes. | 24. In the system of claim 23, wherein said processing means groups the syllabits into syllabit groups, each syllabit group defining corresponding possible words, and wherein said processor means provides, for each of said possible words corresponding to each syllabit group, a skeletal sequence of phonemes comprising a corresponding grouping of phonemes. 25. In the system of claim 24, wherein said processor means compares the input sequence of phonemes for each syllabit group with the respective skeletal sequences of phonemes of each of the corresponding possible words so as to determine, with reference to the phonemes in each grouping of phonemes, which possible word has a skeletal sequence of phonemes which contains, in a given sequence, phonemes all of which are found, in said given sequence, in the input sequence of phonemes, thereby identifying each of said words of said audio input, whereby to provide said identified words of said audio input in said visible form. | 0.5 |
8,447,702 | 1 | 2 | 1. A system, comprising: one or more server computers communicatively coupled to a network and running a domain name appraisal module configured to: A) receive a domain name comprising a domain name text string and a top level domain; B) calculate an appraisal value for said domain name, said appraisal value increasing or decreasing responsive to one or more variances in: i) a precision value; ii) a popularity value; iii) a presence value; iv) a pattern value; and v) a pay-per-click value; C) determine whether said top level domain comprises a .com top level domain; D) responsive to a determination that: i) said top level domain comprises said .com top level domain, assign a value of 1 to a domain scarcity multiplier; ii) said top level domain does not comprise said .com top level domain, calculate: a) a first sum of one or more domain name registrations for said top level domain; b) a second sum of one or more .com domain name registrations; c) said domain scarcity multiplier comprising a decimal value calculated according to said first sum divided by said second sum; name registrations); E) calculate a revised appraisal value for said domain name according to said appraisal value being multiplied by said domain scarcity multiplier; and F) transmit said revised appraisal value to a client computer communicatively coupled to said network. | 1. A system, comprising: one or more server computers communicatively coupled to a network and running a domain name appraisal module configured to: A) receive a domain name comprising a domain name text string and a top level domain; B) calculate an appraisal value for said domain name, said appraisal value increasing or decreasing responsive to one or more variances in: i) a precision value; ii) a popularity value; iii) a presence value; iv) a pattern value; and v) a pay-per-click value; C) determine whether said top level domain comprises a .com top level domain; D) responsive to a determination that: i) said top level domain comprises said .com top level domain, assign a value of 1 to a domain scarcity multiplier; ii) said top level domain does not comprise said .com top level domain, calculate: a) a first sum of one or more domain name registrations for said top level domain; b) a second sum of one or more .com domain name registrations; c) said domain scarcity multiplier comprising a decimal value calculated according to said first sum divided by said second sum; name registrations); E) calculate a revised appraisal value for said domain name according to said appraisal value being multiplied by said domain scarcity multiplier; and F) transmit said revised appraisal value to a client computer communicatively coupled to said network. 2. The system of claim 1 , wherein said domain name appraisal module is further configured to calculate said precision value for said domain name by: i) parsing one or more text strings from said domain name text string; ii) generating a keyword value comprising a numeric value for a quantity of said one or more text strings parsed from said domain name text string; iii) searching an electronic dictionary in a database communicatively coupled to said network for said one or more text strings; iv) generating a dictionary value comprising a true or false value reflecting whether one or more of said one or more text strings are found in said electronic dictionary; v) generating a length value comprising a number of characters in said domain name text string; and vi) generating a numerals value comprising a true or false value reflecting whether one or more numerals are found in said domain name text string. | 0.656811 |
8,863,081 | 2 | 4 | 2. The medium of claim 1 , wherein said additional command is in at least one of the following classes: file manipulation; data type determination; electronic mail transmission; typeB message component identification; typeB message manipulation; UNEdifact message manipulation; XML message manipulation; Character delimited (comma or other character); Positional character oriented; IP socket manipulation; printing; and database interaction and manipulation. | 2. The medium of claim 1 , wherein said additional command is in at least one of the following classes: file manipulation; data type determination; electronic mail transmission; typeB message component identification; typeB message manipulation; UNEdifact message manipulation; XML message manipulation; Character delimited (comma or other character); Positional character oriented; IP socket manipulation; printing; and database interaction and manipulation. 4. The medium of claim 2 , wherein said additional command is at least said file manipulation, and wherein said file manipulation includes at least one of: reading said file; saving said file; appending said file; retrieving a file name; retrieving a list of files; copying said file; moving said file; and deleting said file. | 0.526163 |
7,913,155 | 35 | 36 | 35. The process of claim 32 , wherein said text data is in a first language, wherein said audio/video data comprises a second language, and wherein said first language and said second language are different languages. | 35. The process of claim 32 , wherein said text data is in a first language, wherein said audio/video data comprises a second language, and wherein said first language and said second language are different languages. 36. The process of claim 35 , wherein said method further comprises: translating said text data into said second language. | 0.5 |
10,120,860 | 17 | 22 | 17. A non-transitory machine-readable medium comprising instructions which, when executed, cause at least one processor to at least: identify a token that frequently begins a suffix found in the corpus; identify first suffixes and second suffixes within the corpus; detect that the first suffixes begin with the token and that the second suffixes do not begin with the token; perform a first counting algorithm to identify a first count of n-grams in the first suffixes; and perform a second counting algorithm to identify a second count of n-grams in the second suffixes, the second counting algorithm different from the first counting algorithm, the second count of n-grams used as a statistic for computational linguistics. | 17. A non-transitory machine-readable medium comprising instructions which, when executed, cause at least one processor to at least: identify a token that frequently begins a suffix found in the corpus; identify first suffixes and second suffixes within the corpus; detect that the first suffixes begin with the token and that the second suffixes do not begin with the token; perform a first counting algorithm to identify a first count of n-grams in the first suffixes; and perform a second counting algorithm to identify a second count of n-grams in the second suffixes, the second counting algorithm different from the first counting algorithm, the second count of n-grams used as a statistic for computational linguistics. 22. The machine-readable medium of claim 17 , wherein the instructions, when executed, cause the at least one processor to perform the second counting algorithm by: sorting the second suffixes in a reverse lexicographical order; condensing the second suffixes into a list of suffixes and a number of occurrences of each suffix in the second suffixes; constructing n-grams that begin with a prefix of each suffix in the list of suffixes; and aggregating a count of the constructed n-grams to identify the second count of n-grams in the second suffixes. | 0.5 |
9,600,842 | 82 | 85 | 82. The computer program product of claim 29 , wherein the computer program product is configured for identifying taxonomy software elements in connection with at least one source data document. | 82. The computer program product of claim 29 , wherein the computer program product is configured for identifying taxonomy software elements in connection with at least one source data document. 85. The computer program product of claim 82 , wherein the computer program product is operable for allowing the taxonomy software elements to be modified to create a new combination of taxonomy software elements representative of a new text document. | 0.860089 |
8,660,838 | 15 | 19 | 15. The method according to claim 14 , wherein the object is an interactive advertisement. | 15. The method according to claim 14 , wherein the object is an interactive advertisement. 19. The method according to claim 15 , further comprising communicating with telephony equipment through the computer communication network. | 0.5 |
9,251,462 | 1 | 3 | 1. An emotion script generating apparatus, comprising: a sensor to receive a user's emotion data; a processor to generate emotion script using the emotion data based on a predefined template; the emotion script being computer readable program code, wherein the processor generates emotion script by: determining configurations of the predefined template, the configurations comprising a number of categories, one of the number of categories being a category of an emotion; and adding the values describing the emotion data into the number of categories of the predefined template according to the configurations of the predefined template; wherein the processor adjusts the generated emotion script according to the predefined template—by: providing the generated emotion script to an emotion data adjustment interface and providing the emotion data adjustment interface to the user; providing an emotion script experiencing apparatus to the user to experience an emotion corresponding to the emotion script by providing the generated emotion script to a number of devices that affect the user's environment; receiving the emotion script adjusted by the user after the user has experienced the emotion corresponding to the emotion script; wherein the processor replaces the stored emotion script with the adjusted emotion script; and wherein the processor associates the adjusted emotion script with a specific emotion felt by the user. | 1. An emotion script generating apparatus, comprising: a sensor to receive a user's emotion data; a processor to generate emotion script using the emotion data based on a predefined template; the emotion script being computer readable program code, wherein the processor generates emotion script by: determining configurations of the predefined template, the configurations comprising a number of categories, one of the number of categories being a category of an emotion; and adding the values describing the emotion data into the number of categories of the predefined template according to the configurations of the predefined template; wherein the processor adjusts the generated emotion script according to the predefined template—by: providing the generated emotion script to an emotion data adjustment interface and providing the emotion data adjustment interface to the user; providing an emotion script experiencing apparatus to the user to experience an emotion corresponding to the emotion script by providing the generated emotion script to a number of devices that affect the user's environment; receiving the emotion script adjusted by the user after the user has experienced the emotion corresponding to the emotion script; wherein the processor replaces the stored emotion script with the adjusted emotion script; and wherein the processor associates the adjusted emotion script with a specific emotion felt by the user. 3. The apparatus of claim 1 , wherein the sensor comprises: physiological parameter sensor or a biological signal detector. | 0.767045 |
8,788,480 | 10 | 11 | 10. The computer-implemented method of claim 9 , wherein the unsuitable ER candidate-building key is disqualified from ER candidate building, so as to facilitate removal of the unsuitable ER candidate-building key from one or more subsequent queries, prior to execution of the one or more subsequent queries, wherein one or more modified subsequent queries are generated, wherein execution of the query is aborted upon determining that the first ER candidate-building key is unsuitable comprises determining that a predefined unsuitability condition is satisfied. | 10. The computer-implemented method of claim 9 , wherein the unsuitable ER candidate-building key is disqualified from ER candidate building, so as to facilitate removal of the unsuitable ER candidate-building key from one or more subsequent queries, prior to execution of the one or more subsequent queries, wherein one or more modified subsequent queries are generated, wherein execution of the query is aborted upon determining that the first ER candidate-building key is unsuitable comprises determining that a predefined unsuitability condition is satisfied. 11. The computer-implemented method of claim 10 , wherein the method is performed by an identity resolution application, wherein the identity resolution application is configured to determine that the first ER candidate-building key is unsuitable based on each predefined unsuitability condition of:: (i) a first unsuitability condition comprising the first ER candidate-building key retrieving a count of candidate entities beyond a first predefined threshold count; (ii) a second unsuitability condition comprising the first ER candidate-building key retrieving a count of candidate entities beyond a second threshold count subsequent to retrieving the first predefined threshold count of candidate entities; and (iii) a third unsuitability condition comprising the first ER candidate-building key and a second ER candidate-building key each retrieving a respective count of candidate entities beyond a respective predefined threshold count. | 0.5 |
8,713,054 | 72 | 75 | 72. The system of claim 65 , wherein said at least one software engine further comprising: establish a hierarchal algorithm associated with said at least one criterion of said electronic document security regimen, establish an overall document mark for said electronic document from said hierarchal algorithm, where said hierarchal algorithm uses said classification mark existing for said at least one portion of said electronic document, and insert said overall document mark into said electronic document. | 72. The system of claim 65 , wherein said at least one software engine further comprising: establish a hierarchal algorithm associated with said at least one criterion of said electronic document security regimen, establish an overall document mark for said electronic document from said hierarchal algorithm, where said hierarchal algorithm uses said classification mark existing for said at least one portion of said electronic document, and insert said overall document mark into said electronic document. 75. The system of claim 72 , wherein said at least one software engine further comprising: a. establish a document identification string that incorporates said overall document mark, and b. insert said document identification string into a visible display space of a document development application for said electronic document. | 0.593827 |
10,126,909 | 1 | 5 | 1. An electronic device for managing electronic health records, comprising a processor, a display unit and a graphical user interface: wherein the graphical user interface is configured to: download a plurality of templates from a remote storage medium; store the plurality of downloaded templates at a storage medium local to the electronic device; display a plurality of first icons presenting templates of clinical notes; display a symbol adjacent to a template icon indicating whether that template has been downloaded or not; display a search box for searching the plurality of templates; generate a clinical note of a patient based on a template selected by a user; automatic bookmark predetermined information of a patient, the predetermined information including age, sex and patient number; automatically insert bookmarked information in the generated clinical note; store the generated clinical note in the remote storage medium; and generate and store, after the clinical note is finalized, a subnote file having additional information associated with the clinical note and display the subnote file together with the clinical note. | 1. An electronic device for managing electronic health records, comprising a processor, a display unit and a graphical user interface: wherein the graphical user interface is configured to: download a plurality of templates from a remote storage medium; store the plurality of downloaded templates at a storage medium local to the electronic device; display a plurality of first icons presenting templates of clinical notes; display a symbol adjacent to a template icon indicating whether that template has been downloaded or not; display a search box for searching the plurality of templates; generate a clinical note of a patient based on a template selected by a user; automatic bookmark predetermined information of a patient, the predetermined information including age, sex and patient number; automatically insert bookmarked information in the generated clinical note; store the generated clinical note in the remote storage medium; and generate and store, after the clinical note is finalized, a subnote file having additional information associated with the clinical note and display the subnote file together with the clinical note. 5. The electronic device of claim 1 , wherein the graphical user interface is configured to display a grid view icon and a list view icon that are used to switch between a grid view and a list view of the plurality of the first icons. | 0.625 |
8,832,525 | 12 | 13 | 12. The memory controller of claim 11 , wherein the LDPC decoding circuit generate a plurality of check node messages and a plurality of variable node messages according to the hard information of the first code word to determine whether the LDPC decoding circuit decodes the first code word successfully. | 12. The memory controller of claim 11 , wherein the LDPC decoding circuit generate a plurality of check node messages and a plurality of variable node messages according to the hard information of the first code word to determine whether the LDPC decoding circuit decodes the first code word successfully. 13. The memory controller of claim 12 , wherein the LDPC decoding circuit determines the first code word is not decoded successfully when a sum of the variable node messages is greater than a first predetermined value, when the sum of the variable node messages is less than a second predetermined value, or a difference between the sum of the variable node message and a previous sum of the variable node messages is less than a third predetermined value. | 0.5 |
10,140,976 | 3 | 4 | 3. The method for language processing of claim 2 , wherein determining the first natural language processing dictionary comprises concatenating all words in the natural language processing training data to generate raw natural language processing text. | 3. The method for language processing of claim 2 , wherein determining the first natural language processing dictionary comprises concatenating all words in the natural language processing training data to generate raw natural language processing text. 4. The method for language processing of claim 3 , further comprising tokenizing the raw natural language processing text using the automatic speech recognition dictionary. | 0.5 |
10,140,333 | 22 | 23 | 22. The computer program product according to claim 14 , wherein a search engine flattens the database entries, the search engine supporting a SPLIT operation, and is configured to search within sections of a document contained in the database. | 22. The computer program product according to claim 14 , wherein a search engine flattens the database entries, the search engine supporting a SPLIT operation, and is configured to search within sections of a document contained in the database. 23. The computer program product according to claim 22 , wherein the search engine performs the functions selected from the group consisting of a spell-checking function, thesaurus function, stemming function, lemmatizing function, tokenization function, and normalization function. | 0.5 |
8,601,079 | 3 | 5 | 3. The network device of claim 1 , wherein the processor enables further actions, the actions comprising: receiving, from the user, a request to attach a file to the message; displaying at least a first level of tags within the PHST to the user; enabling the user to expand or collapse at least the first level of tags to display at least a second level of tags within the PHST to the user; receiving, from the user, a tag selection of a tag from within the PHST; and attaching, to the message, at least one file associated with the selected tag. | 3. The network device of claim 1 , wherein the processor enables further actions, the actions comprising: receiving, from the user, a request to attach a file to the message; displaying at least a first level of tags within the PHST to the user; enabling the user to expand or collapse at least the first level of tags to display at least a second level of tags within the PHST to the user; receiving, from the user, a tag selection of a tag from within the PHST; and attaching, to the message, at least one file associated with the selected tag. 5. The network device of claim 3 , wherein attaching the at least one file further comprises attaching a plurality of files associated with the selected tag and one or more sublevel tags of the selected tag. | 0.5 |
8,266,131 | 1 | 8 | 1. In a computing system having a content corpus server storing data corresponding to content-entities, the content corpus server being adaptively coupled to an information device, a method for searching information associated with the content-entities stored on the content corpus server using the information device, the method comprising: a. generating at least one question corresponding to a search query for searching the information on the information device, wherein the at least one question is generated based on a predetermined ordering criterion when a predefined condition is true and on the data corresponding to the content-entities relating to the search query, the at least one question selected to reduce the number of content-entities that have information relevant to the search query, wherein the predefined condition is defined by the output interface characteristics of the information device and wherein the predetermined ordering criterion is utilized to minimize an expected number of user inputs that will lead the predefined condition to not be true; b. receiving at least one response to the at least one question; and c. rendering the information on the information device, wherein the information is rendered based on the at least one of the search query and a response, wherein the content corpus server organizes the content-entities into a plurality of cubes, each cube comprises entities having a similar set of dimensions, a dimension of a content-entity corresponds to at least one feature of the entity, dimensions corresponding to the content-entities differentiate the content-entities in the corpus content server. | 1. In a computing system having a content corpus server storing data corresponding to content-entities, the content corpus server being adaptively coupled to an information device, a method for searching information associated with the content-entities stored on the content corpus server using the information device, the method comprising: a. generating at least one question corresponding to a search query for searching the information on the information device, wherein the at least one question is generated based on a predetermined ordering criterion when a predefined condition is true and on the data corresponding to the content-entities relating to the search query, the at least one question selected to reduce the number of content-entities that have information relevant to the search query, wherein the predefined condition is defined by the output interface characteristics of the information device and wherein the predetermined ordering criterion is utilized to minimize an expected number of user inputs that will lead the predefined condition to not be true; b. receiving at least one response to the at least one question; and c. rendering the information on the information device, wherein the information is rendered based on the at least one of the search query and a response, wherein the content corpus server organizes the content-entities into a plurality of cubes, each cube comprises entities having a similar set of dimensions, a dimension of a content-entity corresponds to at least one feature of the entity, dimensions corresponding to the content-entities differentiate the content-entities in the corpus content server. 8. The method of claim 1 , wherein the predetermined ordering criterion corresponds to one or more of: a. selectivity function; b. information retrieval rank; c. personalization rules; and d. business rules. | 0.809743 |
8,064,576 | 18 | 19 | 18. The integrated messaging system of claim 17 , wherein the messaging server hosts an enterprise groupware application. | 18. The integrated messaging system of claim 17 , wherein the messaging server hosts an enterprise groupware application. 19. The integrated messaging system of claim 18 , wherein the groupware application comprises a directory service that includes user information for members of the enterprise. | 0.5 |
9,734,839 | 14 | 18 | 14. A computer-implemented method comprising: receiving, at a command router, a natural language input captured at a microphone; identifying first data from the natural language input, the first data including text representing one or more words; determining that the one or more words may be associated with a command; determining, using second data that is different from the first data, a first application score, the first application score indicating a likelihood that the command is associated with a first application; determining, using the second data that is different from the first data, a second application score, the second application score indicating a likelihood that the command is associated with a second application, wherein the second data is available to access prior to identification of the first data; providing the text to the first application; receiving, from the first application, a first matching probability score indicating a degree of matching between the one or more words of the natural language input and the command associated with the first application; providing the text to the second application; receiving, from the second application, a second matching probability score indicating a degree of matching between the one or more words of the natural language input and the command associated with the second application; and causing the first application to process the command based at least partly on the first application score, the second application score, the first matching probability score and the second matching probability score. | 14. A computer-implemented method comprising: receiving, at a command router, a natural language input captured at a microphone; identifying first data from the natural language input, the first data including text representing one or more words; determining that the one or more words may be associated with a command; determining, using second data that is different from the first data, a first application score, the first application score indicating a likelihood that the command is associated with a first application; determining, using the second data that is different from the first data, a second application score, the second application score indicating a likelihood that the command is associated with a second application, wherein the second data is available to access prior to identification of the first data; providing the text to the first application; receiving, from the first application, a first matching probability score indicating a degree of matching between the one or more words of the natural language input and the command associated with the first application; providing the text to the second application; receiving, from the second application, a second matching probability score indicating a degree of matching between the one or more words of the natural language input and the command associated with the second application; and causing the first application to process the command based at least partly on the first application score, the second application score, the first matching probability score and the second matching probability score. 18. The computer-implemented method of claim 14 , wherein the first matching probability score is determined at least in part by a statistical parser. | 0.777448 |
9,213,707 | 16 | 18 | 16. A computer implemented system for interrelating a plurality of source data files and providing access to said interrelated source data files, comprising: at least one processor; a non-transitory computer readable storage medium communicatively coupled to said at least one processor, said non-transitory computer readable storage medium configured to store a parsing component and an interrelated data integration application; said parsing component executable by said at least one processor, said parsing component configured to compile a configuration language and generate file descriptors usable by said interrelated data integration application, said configuration language configured to define: a lineage relationship between said source data files, each of said source data files containing one or more of a plurality of records, wherein said source data files are graphically related to each other in a tree structure using an array of symbols; one or more adopt key fields, wherein a common one of said one or more adopt key fields is configured to relate each child file containing one or more child file records to a corresponding parent file containing one or more parent file records in said tree structure; one or more order key fields configured to define ordering criteria for said records of one or more of said source data files; and one or more predetermined subprograms configured to process instances of one or more of said records from said source data files, a start point of one of a sequence and a subsequence of said records of said source data files, and an end point of said one of said sequence and said subsequence of said records of said source data files; said interrelated data integration application executable by said at least one processor, said interrelated data integration application configured to sort and access said records in said source data files according to a graphical representation of said lineage relationship between said source data files defined in said configuration language, wherein said interrelated data integration application comprises: an interlinear sort component configured to sort said each of said source data files based on one or more of said lineage relationship, said one or more order key fields, and said one or more adopt key fields defined in said configuration language, and attach a position number to each of said records of said each of said source data files; and an interrelated data access component configured to access said records in said source data files reordered by said interlinear sort component based on said lineage relationship between said source data files, and use said position number to determine access of a subsequent one of said records; and a display, outputting on a user device responsive to a user request at runtime a set of records comprising a common lineage relationship. | 16. A computer implemented system for interrelating a plurality of source data files and providing access to said interrelated source data files, comprising: at least one processor; a non-transitory computer readable storage medium communicatively coupled to said at least one processor, said non-transitory computer readable storage medium configured to store a parsing component and an interrelated data integration application; said parsing component executable by said at least one processor, said parsing component configured to compile a configuration language and generate file descriptors usable by said interrelated data integration application, said configuration language configured to define: a lineage relationship between said source data files, each of said source data files containing one or more of a plurality of records, wherein said source data files are graphically related to each other in a tree structure using an array of symbols; one or more adopt key fields, wherein a common one of said one or more adopt key fields is configured to relate each child file containing one or more child file records to a corresponding parent file containing one or more parent file records in said tree structure; one or more order key fields configured to define ordering criteria for said records of one or more of said source data files; and one or more predetermined subprograms configured to process instances of one or more of said records from said source data files, a start point of one of a sequence and a subsequence of said records of said source data files, and an end point of said one of said sequence and said subsequence of said records of said source data files; said interrelated data integration application executable by said at least one processor, said interrelated data integration application configured to sort and access said records in said source data files according to a graphical representation of said lineage relationship between said source data files defined in said configuration language, wherein said interrelated data integration application comprises: an interlinear sort component configured to sort said each of said source data files based on one or more of said lineage relationship, said one or more order key fields, and said one or more adopt key fields defined in said configuration language, and attach a position number to each of said records of said each of said source data files; and an interrelated data access component configured to access said records in said source data files reordered by said interlinear sort component based on said lineage relationship between said source data files, and use said position number to determine access of a subsequent one of said records; and a display, outputting on a user device responsive to a user request at runtime a set of records comprising a common lineage relationship. 18. The computer implemented system of claim 16 , wherein said interrelated data access component is further configured to determine: a position of each of said one or more child file records of said each of said source data files using a final position of a parent file record in said each of said source data files; a subsequent one of said records from one of said source data files for said processing of said instances of said one or more of said records from said source data files using a position of said parent file record of a current one of said records; said start point of said one of said sequence and said subsequence of said records of said source data files using said position of said parent file record; and said end point of said one of said sequence and said subsequence of said records of said source data files using said position of said parent file record. | 0.5 |
8,332,221 | 27 | 28 | 27. The system of claim 10 , wherein the plurality of text sections is a first plurality of text sections, and wherein the at least one processor is further programmed to, in response to the at least one modification indicated by the user: re-segment at least one portion of the first structured text into a second plurality of text sections; and assign a topic to each of the second plurality of text sections. | 27. The system of claim 10 , wherein the plurality of text sections is a first plurality of text sections, and wherein the at least one processor is further programmed to, in response to the at least one modification indicated by the user: re-segment at least one portion of the first structured text into a second plurality of text sections; and assign a topic to each of the second plurality of text sections. 28. The system of claim 27 , wherein the input from the user is a first input, and wherein the at least one processor is further programmed to: receive second input from the user indicating which one or more portions of the first structured text are to be re-segmented. | 0.5 |
9,432,368 | 9 | 13 | 9. An electronic signature system that comprises: a document repository storing a document comprising a plurality of document terms; an authentication data repository storing a plurality of reference ocular image datasets for a corresponding plurality of users, wherein each reference ocular image dataset is associated with user identification information that identifies one of the users; a configuration module adapted to generate instructions for processing an ocular image in accordance with an ocular recognition technology, and to associate the instructions with the document; an interactivity module configured to (a) receive, from a document recipient, an assent to the document terms and an ocular image dataset that is acquired in accordance with the instructions, and (b) authenticate the assent by matching the received ocular image dataset with one of the reference ocular image datasets stored in the authentication data repository; and a document status module configured to (a) update audit data associated with the document to indicate that the document recipient has assented to the document terms, and (b) add a visual indicium to the document in response to authentication of the asset, wherein the visual indicium comprises a link to the audit data and a graphical representation of the received ocular image dataset. | 9. An electronic signature system that comprises: a document repository storing a document comprising a plurality of document terms; an authentication data repository storing a plurality of reference ocular image datasets for a corresponding plurality of users, wherein each reference ocular image dataset is associated with user identification information that identifies one of the users; a configuration module adapted to generate instructions for processing an ocular image in accordance with an ocular recognition technology, and to associate the instructions with the document; an interactivity module configured to (a) receive, from a document recipient, an assent to the document terms and an ocular image dataset that is acquired in accordance with the instructions, and (b) authenticate the assent by matching the received ocular image dataset with one of the reference ocular image datasets stored in the authentication data repository; and a document status module configured to (a) update audit data associated with the document to indicate that the document recipient has assented to the document terms, and (b) add a visual indicium to the document in response to authentication of the asset, wherein the visual indicium comprises a link to the audit data and a graphical representation of the received ocular image dataset. 13. The electronic signature system of claim 9 , wherein: the received ocular image dataset comprises a greyscale ocular image; and the interactivity module is further configured to generate an encrypted version of the received ocular image dataset using a one-way cryptographic hashing function. | 0.522581 |
9,891,808 | 14 | 15 | 14. The computer-implemented method of claim 11 , wherein the first depth graph comprises a geological layer at a first depth level and the second depth graph comprises a geological layer at a second depth level that is different than the first depth level such that the geological layer in the first depth graph and the geological layer in the second depth graph are depicted in a different horizontal plane in the interactive user interface. | 14. The computer-implemented method of claim 11 , wherein the first depth graph comprises a geological layer at a first depth level and the second depth graph comprises a geological layer at a second depth level that is different than the first depth level such that the geological layer in the first depth graph and the geological layer in the second depth graph are depicted in a different horizontal plane in the interactive user interface. 15. The computer-implemented method of claim 14 further comprising: by the one or more processors executing program instructions: receiving a selection of the geological layer in the first depth graph; and updating the user interface data such that the geological layer in the first depth graph and the geological layer in the second depth graph are depicted in a same horizontal plane in the interactive user interface. | 0.5 |
8,180,800 | 12 | 17 | 12. A system, comprising: a processor; a memory coupled to the processor for storing context data; a context module to receive from a client, context data associated with a context and a user, the context data including information indicative of a category of offerings in a network-based marketplace, the context data identifying at least one category of products or services offered to purchasers in the network-based marketplace, the context module further to automatically discover context attributes associated with the context, the context attributes being automatically discovered by processing attribute data received from a plurality of other users, the attribute data being related to the at least one category of products or services, and the context module to associate the context data and the context attributes with a user identifier corresponding to the user; and a results module to create result data relevant to the user identified by the user identifier and to communicate the result data to the client, the context module and the results module being executable by the processor. | 12. A system, comprising: a processor; a memory coupled to the processor for storing context data; a context module to receive from a client, context data associated with a context and a user, the context data including information indicative of a category of offerings in a network-based marketplace, the context data identifying at least one category of products or services offered to purchasers in the network-based marketplace, the context module further to automatically discover context attributes associated with the context, the context attributes being automatically discovered by processing attribute data received from a plurality of other users, the attribute data being related to the at least one category of products or services, and the context module to associate the context data and the context attributes with a user identifier corresponding to the user; and a results module to create result data relevant to the user identified by the user identifier and to communicate the result data to the client, the context module and the results module being executable by the processor. 17. The system of claim 12 , wherein the results module to filter the result data according to geographic data associated with the user when the context is a service. | 0.63913 |
8,667,004 | 9 | 14 | 9. One or more computer-readable non-volatile storage embodying computer-readable instructions which, responsive to execution by at least one processor, are configured to: receive, via a search box comprising a native part of a Web browser, a text string associated with a user's search query, the search box not provided by a separately-installed tool bar mechanism; communicate the text string to a search provider via the Internet; receive, from the search provider, a response file that includes one or more parameters that describe information to be displayed in a search box drop down menu, the one or more parameters describing one or more of: sections to display, labels for a particular section, or an order of sections; process the response file and render the information in the search box drop down menu, wherein the computer-executable instructions to render are further configured to render in the search drop down menu at least some locally acquired information, wherein the locally acquired information comprises links associated with the user's search query; receive a text string entered in a third-party search provider search box; replicate the text string entered in a third-party search provider search box in the search box comprising the native part of the Web browser; and provide, via the search box comprising the native part of the Web browser, one or more suggestions associated with said replicated text string. | 9. One or more computer-readable non-volatile storage embodying computer-readable instructions which, responsive to execution by at least one processor, are configured to: receive, via a search box comprising a native part of a Web browser, a text string associated with a user's search query, the search box not provided by a separately-installed tool bar mechanism; communicate the text string to a search provider via the Internet; receive, from the search provider, a response file that includes one or more parameters that describe information to be displayed in a search box drop down menu, the one or more parameters describing one or more of: sections to display, labels for a particular section, or an order of sections; process the response file and render the information in the search box drop down menu, wherein the computer-executable instructions to render are further configured to render in the search drop down menu at least some locally acquired information, wherein the locally acquired information comprises links associated with the user's search query; receive a text string entered in a third-party search provider search box; replicate the text string entered in a third-party search provider search box in the search box comprising the native part of the Web browser; and provide, via the search box comprising the native part of the Web browser, one or more suggestions associated with said replicated text string. 14. The one or more computer-readable non-volatile storage of claim 9 , wherein the one or more parameters comprise a parameter associated with an image that can be displayed in the search box drop down menu. | 0.537778 |
7,870,495 | 1 | 8 | 1. A method of offering a service provided by a server computer in a communication network, comprising: sending, from the server computer that provides the service to a client computer, a service description document in a language for describing web services, which is independent of any client or user characteristic, the service description document comprising a description defining the type, content and sequencing of data exchanged between said server and any client when said service is executed, and comprising a description of a processing functionality implemented during a preprocessing or post-processing of data in XML format of a message exchanged during the execution of said service on the communication network, wherein the description of said processing functionality comprises a list of properties supported by said processing functionality, said properties defining at least respectively, the node in the communication network adapted to execute said processing, and the type of processing, wherein the description of said processing functionality comprises a property adapted to specify whether the processing to be carried out is obligatory or optional, and wherein said processing functionality also comprises a property adapted to specify whether said pre-processing is carried out on the reception of said message before executing said service or whether said post-processing is carried out on the sending of said message after executing said service. | 1. A method of offering a service provided by a server computer in a communication network, comprising: sending, from the server computer that provides the service to a client computer, a service description document in a language for describing web services, which is independent of any client or user characteristic, the service description document comprising a description defining the type, content and sequencing of data exchanged between said server and any client when said service is executed, and comprising a description of a processing functionality implemented during a preprocessing or post-processing of data in XML format of a message exchanged during the execution of said service on the communication network, wherein the description of said processing functionality comprises a list of properties supported by said processing functionality, said properties defining at least respectively, the node in the communication network adapted to execute said processing, and the type of processing, wherein the description of said processing functionality comprises a property adapted to specify whether the processing to be carried out is obligatory or optional, and wherein said processing functionality also comprises a property adapted to specify whether said pre-processing is carried out on the reception of said message before executing said service or whether said post-processing is carried out on the sending of said message after executing said service. 8. The method according to claim 1 , wherein said processing functionality also comprises a property adapted to define the data produced or used by said preprocessing or post-processing, and may also include the type of said data. | 0.5 |
8,527,524 | 15 | 20 | 15. A non-transitory computer-readable memory device comprising: one or more instructions which, when executed by at least one processor, cause the at least one processor to: determine, at a first time, a first set of topics relating to content within a document; determine, at a second time, a second set of topics relating to the content within the document, the second time being different than the first time; identify a change in a quantity of topics between the first set of topics and the second set of topics; generate a score for the document based on the change in the quantity of topics; and rank the document with regard to at least one other document based on the score. | 15. A non-transitory computer-readable memory device comprising: one or more instructions which, when executed by at least one processor, cause the at least one processor to: determine, at a first time, a first set of topics relating to content within a document; determine, at a second time, a second set of topics relating to the content within the document, the second time being different than the first time; identify a change in a quantity of topics between the first set of topics and the second set of topics; generate a score for the document based on the change in the quantity of topics; and rank the document with regard to at least one other document based on the score. 20. The computer-readable memory device of claim 15 , where the generated score is a first score and the one or more instructions further cause the at least one processor to: generate a second score, for the document, that is based on a relevance of the document to a search query; and combine the first and second scores to generate an overall score, where the one or more instructions to rank the document further cause the at least one processor to: rank the document with regard to the at least one other document based on the overall score. | 0.5 |
8,287,436 | 17 | 20 | 17. A memory storing instructions that, when executed, cause an apparatus at least to perform operations comprising: prompting for first input to identify a plurality of activities and a number of repetitions of each of the activities; for each of the activities, prompting for second input specifying a duration and intensity, and of whether to automatically proceed to a subsequent one of the activities upon determining completion of a previous one of the activities; receiving the first input and the second input; and generating an electronic training script defining a sequence in which to perform the plurality of activities. | 17. A memory storing instructions that, when executed, cause an apparatus at least to perform operations comprising: prompting for first input to identify a plurality of activities and a number of repetitions of each of the activities; for each of the activities, prompting for second input specifying a duration and intensity, and of whether to automatically proceed to a subsequent one of the activities upon determining completion of a previous one of the activities; receiving the first input and the second input; and generating an electronic training script defining a sequence in which to perform the plurality of activities. 20. The memory recited in claim 17 , wherein the instructions, when executed, cause the apparatus to communicate the electronic training script to a plurality of devices. | 0.752187 |
9,621,725 | 1 | 26 | 1. A computer implemented method for analyzing chat leakage, comprising: providing a processor configured for obtaining chat-related information from at least one chat session conducted over a computer network via a chat communications channel between a customer and an agent; said processor configured for identifying customer leakage information from said chat session to another communications channel; said processor configured for building a model based on said chat-related information and said leakage information; said processor building said model by: using a chat text to build an anchor; identifying one or more filters by extracting channel names referred to in the chat text that is used to build said anchor by using a window of words around the anchor to identify a type of channel; and once the anchors and filters are identified, using a priority matrix to identify an exact channel, wherein said processor is configured for applying said model to identifying a communications channel to which leakage occurs; and said processor configured for summarizing and passing contextual information of said chat session from said chat communications channel to said another communications channel to avoid repeating collection of said information and to allow said agents to communicate intuitively with said customers to improve the customer experience. | 1. A computer implemented method for analyzing chat leakage, comprising: providing a processor configured for obtaining chat-related information from at least one chat session conducted over a computer network via a chat communications channel between a customer and an agent; said processor configured for identifying customer leakage information from said chat session to another communications channel; said processor configured for building a model based on said chat-related information and said leakage information; said processor building said model by: using a chat text to build an anchor; identifying one or more filters by extracting channel names referred to in the chat text that is used to build said anchor by using a window of words around the anchor to identify a type of channel; and once the anchors and filters are identified, using a priority matrix to identify an exact channel, wherein said processor is configured for applying said model to identifying a communications channel to which leakage occurs; and said processor configured for summarizing and passing contextual information of said chat session from said chat communications channel to said another communications channel to avoid repeating collection of said information and to allow said agents to communicate intuitively with said customers to improve the customer experience. 26. The method of claim 1 , further comprising: said processor configured for applying said model to provide off line training analysis. | 0.830424 |
8,745,581 | 1 | 6 | 1. A method enabling a computer system to enable a user to select and copy text portions in a document and selectively replace text characters in copied text by configuring one or more processors in a computer system to: provide a text modification command set for identifying an existing text snippet associated with the user's current context within a text editing environment and duplicating and inserting the text snippet into a text editor and for automatically customizing the text snippet by replacing one or more characters in the snippet with user indicated characters; determine a start and stop position of a text snippet in a document; determine text characters to be modified in said text snippet using input received from a user interface; receive user input indicating replacement text characters to replace into copies of said text snippet using said user interface and the text modification command set; receive user input indicating an insertion point for modified copies of said text snippet using said user interface; activate a smart copy paste command in response to user input; said smart copy paste command inserting one or more modified copies of said text snippet, each modified copy containing character modifications indicated by the text modification command set and user input; store in electronic memory textual information including existing text snippets and modified text snippets; and display textual information including existing text snippets and modified text snippets. | 1. A method enabling a computer system to enable a user to select and copy text portions in a document and selectively replace text characters in copied text by configuring one or more processors in a computer system to: provide a text modification command set for identifying an existing text snippet associated with the user's current context within a text editing environment and duplicating and inserting the text snippet into a text editor and for automatically customizing the text snippet by replacing one or more characters in the snippet with user indicated characters; determine a start and stop position of a text snippet in a document; determine text characters to be modified in said text snippet using input received from a user interface; receive user input indicating replacement text characters to replace into copies of said text snippet using said user interface and the text modification command set; receive user input indicating an insertion point for modified copies of said text snippet using said user interface; activate a smart copy paste command in response to user input; said smart copy paste command inserting one or more modified copies of said text snippet, each modified copy containing character modifications indicated by the text modification command set and user input; store in electronic memory textual information including existing text snippets and modified text snippets; and display textual information including existing text snippets and modified text snippets. 6. The method of claim 1 further comprising configuring the one or more processors to receive user input indicating replacement text by one or more of: an input keyboard used to enter a text string to be used for replacement; an input keyboard used to enter a text string to be used for replacement, said text string including one or more wildcard characters or regular expressions; a pointing device used to indicate a string to be used for replacement; a touch screen keyboard. | 0.524802 |
8,380,731 | 13 | 17 | 13. A computer comprising at least a processing device and a memory operable to store executable instructions to be executed by said processing device, said computer programmed to: receive, via a computer interface, a search concept query that includes a plurality of principal words; extract the principal words from the search concept query using a natural language processing utility; amplify at least one of the principal words to a set of semantically similar words; receive, via the computer interface, user input for improving the set of semantically similar words and a degree of membership for individual word within the set of semantically similar words to generate improvement in search results, wherein the user input includes an addition of one or more words to the set of semantically similar words; calculate a degree of membership for each word that reflects a similarity in meaning to the principal word of the set with which each word is associated; perform a document search utilizing each of the set of semantically similar words; compute a score for each search result document at least in part by: determining the degree of membership corresponding to each of the semantically similar words included in the search result document and the at least one of the principal words to create a set of membership weights; and determining which membership weight among the determined set of membership weights is a minimum membership weight; selecting the minimum membership weight among the determined set of membership weights to be the score for the search result; use the computed scores to rank the search result documents; and display the search result documents. | 13. A computer comprising at least a processing device and a memory operable to store executable instructions to be executed by said processing device, said computer programmed to: receive, via a computer interface, a search concept query that includes a plurality of principal words; extract the principal words from the search concept query using a natural language processing utility; amplify at least one of the principal words to a set of semantically similar words; receive, via the computer interface, user input for improving the set of semantically similar words and a degree of membership for individual word within the set of semantically similar words to generate improvement in search results, wherein the user input includes an addition of one or more words to the set of semantically similar words; calculate a degree of membership for each word that reflects a similarity in meaning to the principal word of the set with which each word is associated; perform a document search utilizing each of the set of semantically similar words; compute a score for each search result document at least in part by: determining the degree of membership corresponding to each of the semantically similar words included in the search result document and the at least one of the principal words to create a set of membership weights; and determining which membership weight among the determined set of membership weights is a minimum membership weight; selecting the minimum membership weight among the determined set of membership weights to be the score for the search result; use the computed scores to rank the search result documents; and display the search result documents. 17. The computer according to claim 13 further programmed to calculate the degree of membership for selected semantically similar words. | 0.797015 |
8,275,784 | 1 | 3 | 1. A computer-implemented method for translating a SPARQL query, comprising the steps of: parsing said SPARQL query, wherein said parsing step is performed using a parser/lexer; detecting a hidden negative pattern in said parsed SPARQL query, wherein said detecting step is performed using a hidden negative pattern detector; translating said detected hidden negative pattern into an explicit negative pattern, wherein said translating step is performed using a hidden negative pattern translator; separating said explicit negative pattern from a positive pattern in said parsed SPARQL query, wherein said separating step is performed using a separator; translating said explicit negative pattern into a negative portion of an SQL statement, wherein said step of translating said explicit negative pattern is performed using a negative pattern translator; translating said positive pattern into a positive portion of said SQL statement, wherein said step of translating said positive pattern is performed using a positive pattern translator; and combining said negative portion of said SQL statement with said positive portion of said SQL statement, wherein said combining step is performed using a combiner. | 1. A computer-implemented method for translating a SPARQL query, comprising the steps of: parsing said SPARQL query, wherein said parsing step is performed using a parser/lexer; detecting a hidden negative pattern in said parsed SPARQL query, wherein said detecting step is performed using a hidden negative pattern detector; translating said detected hidden negative pattern into an explicit negative pattern, wherein said translating step is performed using a hidden negative pattern translator; separating said explicit negative pattern from a positive pattern in said parsed SPARQL query, wherein said separating step is performed using a separator; translating said explicit negative pattern into a negative portion of an SQL statement, wherein said step of translating said explicit negative pattern is performed using a negative pattern translator; translating said positive pattern into a positive portion of said SQL statement, wherein said step of translating said positive pattern is performed using a positive pattern translator; and combining said negative portion of said SQL statement with said positive portion of said SQL statement, wherein said combining step is performed using a combiner. 3. The method according to claim 1 , wherein said hidden negative pattern translation step further comprises the steps of: removing a triple in said hidden negative pattern duplicated with those in said positive pattern, wherein said removing step is performed using a triplet removing unit; replacing variables in an additional triple in said hidden negative pattern by corresponding variables in said positive pattern, wherein said replacing step is performed using a variable replacing unit; and appending a flag representing a “NOT” logic to said translated additional triple, wherein said appending step is performed using an appending unit. | 0.675703 |
5,441,277 | 13 | 14 | 13. A method of playing an educational, creativity-enhancing game including means for directing a player to a word selected at random, and instruction means for providing the player with instructions for creating or reciting a rhyme containing the word selected at random, within a designated period of time, said instruction means including instructions selected from the group consisting of: (a) reciting a word that rhymes with a word selected at random; (b) creating or reciting a multi-line rhyme, each line containing a plurality of words, one line ending with a word selected at random, and the remaining lines ending with a word that rhymes with the word selected at random; and (c) creating or reciting a multi-line rhyme, each line containing a plurality of words, a plurality of said lines ending with a word that rhymes with a word selected at random. | 13. A method of playing an educational, creativity-enhancing game including means for directing a player to a word selected at random, and instruction means for providing the player with instructions for creating or reciting a rhyme containing the word selected at random, within a designated period of time, said instruction means including instructions selected from the group consisting of: (a) reciting a word that rhymes with a word selected at random; (b) creating or reciting a multi-line rhyme, each line containing a plurality of words, one line ending with a word selected at random, and the remaining lines ending with a word that rhymes with the word selected at random; and (c) creating or reciting a multi-line rhyme, each line containing a plurality of words, a plurality of said lines ending with a word that rhymes with a word selected at random. 14. The method of claim 13, wherein the means for selecting a word at random comprises a plurality of cards, each having printed thereon a different word. | 0.5 |
9,798,391 | 11 | 24 | 11. A method of operating a device, said method comprising: displaying, by a display device supported by a housing of the device, a play of a game; detecting, by at least one gyroscope supported by the housing, motion of the housing during the play of the game; analyzing, by a controller, the detected motion of the housing and determining, by the controller, whether the detected motion of the housing corresponds to any of a plurality of different designated gestures; and responsive to determining that the detected motion of the housing corresponds to one of the plurality of designated gestures: determining, by the controller, a game input associated with said designated gesture, said determined game input being one of a plurality of different game inputs; determining, by the controller, at least one aspect of the play of the game to modify based on the determined game input; and causing, by the controller, a modification of the determined at least one aspect of the play of the game. | 11. A method of operating a device, said method comprising: displaying, by a display device supported by a housing of the device, a play of a game; detecting, by at least one gyroscope supported by the housing, motion of the housing during the play of the game; analyzing, by a controller, the detected motion of the housing and determining, by the controller, whether the detected motion of the housing corresponds to any of a plurality of different designated gestures; and responsive to determining that the detected motion of the housing corresponds to one of the plurality of designated gestures: determining, by the controller, a game input associated with said designated gesture, said determined game input being one of a plurality of different game inputs; determining, by the controller, at least one aspect of the play of the game to modify based on the determined game input; and causing, by the controller, a modification of the determined at least one aspect of the play of the game. 24. The method of claim 11 , wherein the game is a wagering game. | 0.974268 |
8,873,858 | 20 | 24 | 20. The apparatus of claim 19 , where the first display location is associated with a first application, and where the second display location is associated with a second application. | 20. The apparatus of claim 19 , where the first display location is associated with a first application, and where the second display location is associated with a second application. 24. The apparatus of claim 20 , where the first location is associated with an application that displays a list of candidate Chinese characters. | 0.55 |
9,075,760 | 2 | 7 | 2. The computer-implemented method of claim 1 further comprising: identifying at least one other narration settings file using the catalog information assigned to each of the plurality of narration settings files; and sending, to the user computing device, a notification indicating an availability of the at least one other narration settings file. | 2. The computer-implemented method of claim 1 further comprising: identifying at least one other narration settings file using the catalog information assigned to each of the plurality of narration settings files; and sending, to the user computing device, a notification indicating an availability of the at least one other narration settings file. 7. The computer-implemented method of claim 2 , wherein the requested narration settings file and the at least one other narration settings file have an identical creator. | 0.705172 |
6,134,540 | 1 | 5 | 1. A query system comprising: a query engine having a capability to build objects in a memory based upon a view type referenced in a query received from an application; and means for applying query rewrite optimizations to the query referencing the view type, wherein the query rewrite optimizations determine which portions of the query to push down to a database at a second tier for resolution and which portions of the query are to be processed by the query engine at a first tier to build objects from the view types. | 1. A query system comprising: a query engine having a capability to build objects in a memory based upon a view type referenced in a query received from an application; and means for applying query rewrite optimizations to the query referencing the view type, wherein the query rewrite optimizations determine which portions of the query to push down to a database at a second tier for resolution and which portions of the query are to be processed by the query engine at a first tier to build objects from the view types. 5. The system of claim 1 wherein the means for applying query rewrite optimizations further comprises: means for analyzing the query to determine if the query requests a handle on an object, or if the query references a method, or if the query raises a collation sequence issue. | 0.669834 |
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