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9,703,861 | 10 | 11 | 10. The method as claimed in claim 1 , further comprising: ranking said candidate answers based on their scores to determine one or more query answers, wherein an output candidate answer includes one of: a single query answer, or a ranked list of query answers. | 10. The method as claimed in claim 1 , further comprising: ranking said candidate answers based on their scores to determine one or more query answers, wherein an output candidate answer includes one of: a single query answer, or a ranked list of query answers. 11. The method as claimed in claim 10 , further comprising: generating an elaboration question for delivery to a user, and receiving information from a user responsive to said elaboration question for use in determining said query answer. | 0.906003 |
9,076,125 | 1 | 7 | 1. A system, comprising: one or more processors; memory communicatively coupled to the one or more processors; an analysis component that is stored in the memory and that is configured to evaluate an electronic message and identify participants to the electronic message; a database that is configured to organize contextual data for each of the participants, the contextual data of each of the participants being based at least in part on the evaluation of the electronic message, the contextual data of each of the participants indicating a preferred electronic messaging application that is utilized by the participant; and a graphics component that is stored in the memory and that is configured to transform the contextual data for each of the participants into a multi-dimensional visualization, the multi-dimensional visualization displaying visual representations that each correspond to one or more of the participants, the visual representations being organized into a first group and a second group, the first group of visual representations representing one or more of the participants that utilize a first type of electronic messaging application, the second group of visual representations representing one or more of the participants that utilize a second type of messaging application. | 1. A system, comprising: one or more processors; memory communicatively coupled to the one or more processors; an analysis component that is stored in the memory and that is configured to evaluate an electronic message and identify participants to the electronic message; a database that is configured to organize contextual data for each of the participants, the contextual data of each of the participants being based at least in part on the evaluation of the electronic message, the contextual data of each of the participants indicating a preferred electronic messaging application that is utilized by the participant; and a graphics component that is stored in the memory and that is configured to transform the contextual data for each of the participants into a multi-dimensional visualization, the multi-dimensional visualization displaying visual representations that each correspond to one or more of the participants, the visual representations being organized into a first group and a second group, the first group of visual representations representing one or more of the participants that utilize a first type of electronic messaging application, the second group of visual representations representing one or more of the participants that utilize a second type of messaging application. 7. The system of claim 1 , further comprising: a tracking component configured to monitor user response of at least one of the participants to the electronic message and compile or capture sentiment data based on the monitored user response, the sentiment data indicating a sentiment of the at least one of the participants toward the electronic message; and a data server configured to submit the sentiment data to the database. | 0.501163 |
7,548,847 | 33 | 36 | 33. A method of generating annotated training data for training a natural language understanding (NLU) system having at least one model, comprising: generating, with the NLU system, a proposed annotation for a unit of unannotated training data; displaying the proposed annotation with user actuable inputs for user correction or verification of the proposed annotation to obtain a user-confirmed annotation; training the model with the user-confirmed annotation; and checking for inconsistencies among user-confirmed annotation and data already used to train the model by determining whether the model accurately predicts the prior user-confirmed annotations; the user actuable inputs comprising: one or more user actuable node inputs for annotation alternatives; a user actuable delete node input which, when actuated, deletes a child node; and a user actuable add node input which, when actuated, adds a child node; the method further comprising: displaying a plurality of alternative proposed annotations to data portions associated with the child node in response to that child node being deleted, such that the user is enabled to select one of the alternative proposed annotations from among the plurality of alternative proposed annotations, and the user-selected alternative proposed annotation is incorporated into the annotated training data; and displaying an indication of a volume of training data used to train a plurality of different portions of the at least one model of the natural language understanding system. | 33. A method of generating annotated training data for training a natural language understanding (NLU) system having at least one model, comprising: generating, with the NLU system, a proposed annotation for a unit of unannotated training data; displaying the proposed annotation with user actuable inputs for user correction or verification of the proposed annotation to obtain a user-confirmed annotation; training the model with the user-confirmed annotation; and checking for inconsistencies among user-confirmed annotation and data already used to train the model by determining whether the model accurately predicts the prior user-confirmed annotations; the user actuable inputs comprising: one or more user actuable node inputs for annotation alternatives; a user actuable delete node input which, when actuated, deletes a child node; and a user actuable add node input which, when actuated, adds a child node; the method further comprising: displaying a plurality of alternative proposed annotations to data portions associated with the child node in response to that child node being deleted, such that the user is enabled to select one of the alternative proposed annotations from among the plurality of alternative proposed annotations, and the user-selected alternative proposed annotation is incorporated into the annotated training data; and displaying an indication of a volume of training data used to train a plurality of different portions of the at least one model of the natural language understanding system. 36. The method of claim 33 wherein checking comprises: re-generating annotations with the NLU system for the unit of unannotated training data and comparing the re-generated annotations with the user-confirmed annotations. | 0.913281 |
9,183,323 | 1 | 8 | 1. A computer-implemented method comprising: receiving a search query; obtaining one or more search results responsive to the search query, wherein a first search result of the one or more search results identifies a resource including text; computing a respective similarity measure between the search query and each of a plurality of contiguous portions of text of the resource; selecting a suggested query phrase from a first contiguous portion of text of the resource having a highest computed similarity measure with the search query; generating a first search result snippet to be presented as part of the first search result in a presentation of the one or more search results, wherein the first search result snippet presented as part of the first search result includes the suggested query phrase as a selectable user interface element for a user to submit the suggested query phrase as a new search query; and providing the presentation of the one or more search results, including the first search result snippet as part of the first search result, in response to the search query. | 1. A computer-implemented method comprising: receiving a search query; obtaining one or more search results responsive to the search query, wherein a first search result of the one or more search results identifies a resource including text; computing a respective similarity measure between the search query and each of a plurality of contiguous portions of text of the resource; selecting a suggested query phrase from a first contiguous portion of text of the resource having a highest computed similarity measure with the search query; generating a first search result snippet to be presented as part of the first search result in a presentation of the one or more search results, wherein the first search result snippet presented as part of the first search result includes the suggested query phrase as a selectable user interface element for a user to submit the suggested query phrase as a new search query; and providing the presentation of the one or more search results, including the first search result snippet as part of the first search result, in response to the search query. 8. The method of claim 1 , wherein computing a respective similarity measure between the search query and each of a plurality of contiguous sections of text of the resource comprises: identifying a plurality of sentences in the text of the resource; and computing a respective similarity measure between the search query and each sentence in the plurality of sentences. | 0.82462 |
7,552,381 | 34 | 38 | 34. A computer readable storage medium having instructions stored thereon which, when executed by a computer, cause the computer to perform operations comprising: scanning a coversheet having an overview of a collection; receiving an image of the overview of the collection that comprises a first plurality of indication areas associated with a plurality of documents and a second plurality of indication areas associated with a plurality of actions, wherein the plurality of actions includes printing, faxing, sending by electronic mail, and grouping; identifying at least one action from the plurality of actions set forth in the image; identifying at least one document from the plurality of document for the at least one action identified from the plurality of actions, wherein the identifying the at least one action is performed based on the second plurality of the indication areas in the image and the identifying the at least one document is performed based on the first plurality of the indication areas in the image, wherein the at least one action and the at least one document are identified by scanning the image; and performing the at least one action on the at least one document in response to the identifying the at least one action from the fourth plurality of actions set forth in the image and the identifying the at least one document from the third plurality of document from the image. | 34. A computer readable storage medium having instructions stored thereon which, when executed by a computer, cause the computer to perform operations comprising: scanning a coversheet having an overview of a collection; receiving an image of the overview of the collection that comprises a first plurality of indication areas associated with a plurality of documents and a second plurality of indication areas associated with a plurality of actions, wherein the plurality of actions includes printing, faxing, sending by electronic mail, and grouping; identifying at least one action from the plurality of actions set forth in the image; identifying at least one document from the plurality of document for the at least one action identified from the plurality of actions, wherein the identifying the at least one action is performed based on the second plurality of the indication areas in the image and the identifying the at least one document is performed based on the first plurality of the indication areas in the image, wherein the at least one action and the at least one document are identified by scanning the image; and performing the at least one action on the at least one document in response to the identifying the at least one action from the fourth plurality of actions set forth in the image and the identifying the at least one document from the third plurality of document from the image. 38. The computer readable storage medium defined in claim 34 wherein the indication area is located on top of a portion of a graphic representing at least one document in a collection. | 0.823755 |
8,327,255 | 16 | 29 | 16. A computer program product comprising electronic transcript and exhibit files, wherein the computer program product comprises a computer readable medium that stores: an importing module configured to import one or more electronic transcript files and one or more electronic exhibit files; an association module configured to establish an operable electronic link between the one or more electronic exhibit files and one or more entries in the one or more electronic transcript files that are associated with the one or more electronic exhibit files; and a bundle that comprises the one or more electronic transcript files, the one or more electronic exhibit files, the operable electronic link, and an executable viewer module configured to: provide the one or more electronic transcript files in the bundle; and provide the one or more electronic exhibit files in the bundle in response to an input activating the operable electronic link via the one or more entries in the one or more provided electronic transcript files, the computer readable medium comprising a portable device. | 16. A computer program product comprising electronic transcript and exhibit files, wherein the computer program product comprises a computer readable medium that stores: an importing module configured to import one or more electronic transcript files and one or more electronic exhibit files; an association module configured to establish an operable electronic link between the one or more electronic exhibit files and one or more entries in the one or more electronic transcript files that are associated with the one or more electronic exhibit files; and a bundle that comprises the one or more electronic transcript files, the one or more electronic exhibit files, the operable electronic link, and an executable viewer module configured to: provide the one or more electronic transcript files in the bundle; and provide the one or more electronic exhibit files in the bundle in response to an input activating the operable electronic link via the one or more entries in the one or more provided electronic transcript files, the computer readable medium comprising a portable device. 29. The computer program product of claim 16 , wherein the executable viewer file is further configured to: provide images associated with the one or more electronic transcript files in a transcript window that includes controls to view, search, and scroll through the images associated with one or more electronic transcript files; and provide images associated with the one or more electronic exhibit files in an exhibit window in response to the input activating the operable electronic link via searching or scrolling through the images associated with one or more electronic transcript files to view the one or more entries in the one or more electronic transcript files that are associated with the one or more electronic exhibit files. | 0.651316 |
8,626,768 | 1 | 4 | 1. One or more computer storage media storing computer-useable instructions that, when used by one or more computing devices, cause the one or more computing devices to perform a method comprising: receiving an initial set of search queries including one or more input search queries, the one or more input search queries having been manually determined to be relevant to a given subject area; generating an expanded set of search queries by analyzing search engine session data to identify a plurality of additional search queries related to the one or more input search queries, the expanded set of search queries including the one or more input search queries and the plurality of additional search queries; employing the expanded set of search queries to identify a plurality of URLs relevant to the given subject area, each URL in the plurality of URLs associated with a website, wherein employing the expanded set of search queries to identify the plurality of URLs relevant to the given subject area comprises analyzing search engine session data and user web browsing data based on the expanded set of search queries to identify the plurality of URLs; determining for each URL in the plurality of URLs a section of the website that is relevant to the given subject area; periodically crawling, based on the section identified for each URL, documents associated with the plurality of URLs, to provide a plurality of content items from the URLs; employing a classifier to identify relevant content items from the plurality of content items, the relevant content items being determined by the classifier to be relevant to the given subject area; clustering the relevant content items into a plurality of clusters, each cluster including a group of content items associated with a particular event or topic within the given subject area, wherein the particular event or topic is associated with a main content item identified based on the number of hyperlinks in the relevant content items to the main content item; ranking the plurality of clusters against one another, wherein ranking comprises: (1) retrieving content from social network sites; (2) counting within the content from social network sites the number of hyperlinks to URLs corresponding with the relevant content items within the plurality of clusters; and (3) ranking a first cluster higher than a second cluster when the first cluster has more hyperlinks than the second cluster; and generating a user interface allowing a user to view and interact with the plurality of clusters, wherein the user interface provides, for each cluster, the main content item and a plurality of related content items, wherein the plurality of related content items comprises the content from social network sites having hyperlinks to URLs corresponding with the relevant content items. | 1. One or more computer storage media storing computer-useable instructions that, when used by one or more computing devices, cause the one or more computing devices to perform a method comprising: receiving an initial set of search queries including one or more input search queries, the one or more input search queries having been manually determined to be relevant to a given subject area; generating an expanded set of search queries by analyzing search engine session data to identify a plurality of additional search queries related to the one or more input search queries, the expanded set of search queries including the one or more input search queries and the plurality of additional search queries; employing the expanded set of search queries to identify a plurality of URLs relevant to the given subject area, each URL in the plurality of URLs associated with a website, wherein employing the expanded set of search queries to identify the plurality of URLs relevant to the given subject area comprises analyzing search engine session data and user web browsing data based on the expanded set of search queries to identify the plurality of URLs; determining for each URL in the plurality of URLs a section of the website that is relevant to the given subject area; periodically crawling, based on the section identified for each URL, documents associated with the plurality of URLs, to provide a plurality of content items from the URLs; employing a classifier to identify relevant content items from the plurality of content items, the relevant content items being determined by the classifier to be relevant to the given subject area; clustering the relevant content items into a plurality of clusters, each cluster including a group of content items associated with a particular event or topic within the given subject area, wherein the particular event or topic is associated with a main content item identified based on the number of hyperlinks in the relevant content items to the main content item; ranking the plurality of clusters against one another, wherein ranking comprises: (1) retrieving content from social network sites; (2) counting within the content from social network sites the number of hyperlinks to URLs corresponding with the relevant content items within the plurality of clusters; and (3) ranking a first cluster higher than a second cluster when the first cluster has more hyperlinks than the second cluster; and generating a user interface allowing a user to view and interact with the plurality of clusters, wherein the user interface provides, for each cluster, the main content item and a plurality of related content items, wherein the plurality of related content items comprises the content from social network sites having hyperlinks to URLs corresponding with the relevant content items. 4. The one or more computer storage media of claim 1 , wherein the classifier is created by crawling at least a portion of the URLs to obtain content and generating a language model for the given subject area based on the content. | 0.700521 |
8,874,577 | 11 | 12 | 11. A method for triaging information feeds, comprising: receiving a plurality of information feeds, wherein each information feed comprises at one or more feed items; determining one or more facets for each feed item, comprising: generating the one or more facets comprising at least one of a creator facet comprising creators, a source facet comprising sources, and a time facet comprising times by directly extracting the creators, sources and times from each of the feed items; and generating a topic facet comprising at least one topic for each feed item based on at least one of nouns and noun phrases included in that feed item and designating the nouns and noun phrases as the topics for that feed item; displaying within a user interface, the creator, source, time and topic facets; receiving from the user a selection of one of the plurality of topics from the topic facet displayed within the user interface; displaying only the feed items that are associated with the selected topic within the user interface; updating the topics in the topic facet displayed within the user interface by presenting for display only those topics that are associated with the displayed feed items; and filtering in or filtering out one or more of the extracted creators, sources, and times within at least one of the creator, source and time facets in the display of the user interface based on the feed items associated with the selected topic. | 11. A method for triaging information feeds, comprising: receiving a plurality of information feeds, wherein each information feed comprises at one or more feed items; determining one or more facets for each feed item, comprising: generating the one or more facets comprising at least one of a creator facet comprising creators, a source facet comprising sources, and a time facet comprising times by directly extracting the creators, sources and times from each of the feed items; and generating a topic facet comprising at least one topic for each feed item based on at least one of nouns and noun phrases included in that feed item and designating the nouns and noun phrases as the topics for that feed item; displaying within a user interface, the creator, source, time and topic facets; receiving from the user a selection of one of the plurality of topics from the topic facet displayed within the user interface; displaying only the feed items that are associated with the selected topic within the user interface; updating the topics in the topic facet displayed within the user interface by presenting for display only those topics that are associated with the displayed feed items; and filtering in or filtering out one or more of the extracted creators, sources, and times within at least one of the creator, source and time facets in the display of the user interface based on the feed items associated with the selected topic. 12. A method according to claim 11 , further comprising: extracting at least one of creator, source, and time from the feed items of the information feed; and presenting the at least one of extracted creator, source, and time in a creator facet, source facet, and time facet respectively. | 0.797183 |
9,658,735 | 1 | 13 | 1. A system for user interface optimization, the system comprising: a rules base configured to store a plurality of rules that define an application having a user interface; a rules engine configured to execute at least one rule from the rules base; and a digital data processor in communication with the rules base and the rules engine, wherein the system is configured for: identifying one or more rules for execution by the rules engine; generating any of (a) a markup language page providing a user interface and (b) a markup language stream providing the user interface as a result of execution of the one or more rules; determining whether one or more aspects of the generated user interface is in conformity with one or more requirements based on comparing the one or more aspects of the user interface with the one or more requirements, the one or more aspects of the user interface pertaining to any of accessibility of the user interface by disabled users, amenability of the user interface to localization/globalization, and a need for localizing/globalizing the user interface, wherein the one or more requirements are defined relative to any of (a) one or more other rules and/or a user interface generated based thereon, (b) transactional data relating to the user interface, (c) a context in which the user interface is any of transmitted, displayed, and viewed by a user, and (d) a collection defining any of grammar, spelling, usage, punctuation, and style of the user interface; responding to a negative such determination by executing any of: i. generating a notification that identifies modifications to the one or more rules so as to generate at least one of the markup language page and the markup language stream providing a conforming user interface, the conforming user interface including a feature having a modified display characteristic vis-à-vis a non-conforming user interface, wherein the display characteristic is modified based on one or more of the requirements relating to any of (a) transactional data associated with the field having the modified display characteristic and (b) the context in which the user interface is any of transmitted, displayed, and viewed by the user, and wherein execution of the one or more rules would otherwise result in the non-conforming user interface, ii. modifying the one or more rules so as to generate the at least one of the markup language page and the markup language stream providing the conforming user interface, and iii. modifying the at least one of the markup language page and the markup language stream providing the conforming user interface, and any of storing to and generating as output from the system at least one of the generated notification, the modified one or more rules, the modified markup language page, and the modified markup language stream providing the conforming user interface. | 1. A system for user interface optimization, the system comprising: a rules base configured to store a plurality of rules that define an application having a user interface; a rules engine configured to execute at least one rule from the rules base; and a digital data processor in communication with the rules base and the rules engine, wherein the system is configured for: identifying one or more rules for execution by the rules engine; generating any of (a) a markup language page providing a user interface and (b) a markup language stream providing the user interface as a result of execution of the one or more rules; determining whether one or more aspects of the generated user interface is in conformity with one or more requirements based on comparing the one or more aspects of the user interface with the one or more requirements, the one or more aspects of the user interface pertaining to any of accessibility of the user interface by disabled users, amenability of the user interface to localization/globalization, and a need for localizing/globalizing the user interface, wherein the one or more requirements are defined relative to any of (a) one or more other rules and/or a user interface generated based thereon, (b) transactional data relating to the user interface, (c) a context in which the user interface is any of transmitted, displayed, and viewed by a user, and (d) a collection defining any of grammar, spelling, usage, punctuation, and style of the user interface; responding to a negative such determination by executing any of: i. generating a notification that identifies modifications to the one or more rules so as to generate at least one of the markup language page and the markup language stream providing a conforming user interface, the conforming user interface including a feature having a modified display characteristic vis-à-vis a non-conforming user interface, wherein the display characteristic is modified based on one or more of the requirements relating to any of (a) transactional data associated with the field having the modified display characteristic and (b) the context in which the user interface is any of transmitted, displayed, and viewed by the user, and wherein execution of the one or more rules would otherwise result in the non-conforming user interface, ii. modifying the one or more rules so as to generate the at least one of the markup language page and the markup language stream providing the conforming user interface, and iii. modifying the at least one of the markup language page and the markup language stream providing the conforming user interface, and any of storing to and generating as output from the system at least one of the generated notification, the modified one or more rules, the modified markup language page, and the modified markup language stream providing the conforming user interface. 13. The system of claim 1 , wherein the one or more requirements are defined by a further rule. | 0.921227 |
9,787,597 | 1 | 6 | 1. A machine implemented method for implementing model definition, constraint enforcement, and validation, the method being performed by at least a client computing device and comprising: a client computing device requesting a reference to one or more model definition resources for a representation of a model that pertains to a tax preparation or financial management software application at least by transmitting a request for the reference to a remote host computer hosting the tax preparation or financial management application via a network element; the client computing device executing a model definition resolver, residing on and stored at least partially in memory of the client computing device, that identifies or determines one or more actual locations for the one or more model definition resources at least by resolving the reference and retrieving the one or more model definition resources from the one or more actual locations, wherein the one or more model definition resources specify constraints for constraint enforcement or validation on the model; the client computing device reducing data processing and data to be populated into the model at least by removing a portion of the data and populating a remaining portion of the data into the model through using one or more validation modules and one or more formatting modules stored at least partially in the memory of the client computing device, the client computing device reducing the data processing and the data comprising: disabling the constraint enforcement or validation for a portion of a flow of the tax preparation or financial management application; reducing a total number of data elements and a total number of invalid data elements at least by formatting one or more user inputs for the tax preparation or financial management application at the one or more formatting modules; and performing the constraint enforcement or validation at the one or more validation modules on at least the one or more user inputs, which have been formatted, based in part or in whole upon at least the one or more model definition resources obtained by the model definition resolver residing on the client computing device from the remote host computer via the network element; and the client computing device performing data binding for the data to bind the data to the model by using a first application programming interface located on the client computing device, further comprising; the client computing device identifying a model definition for a requested key of a key-value pair for the data and obtaining an actual implementation of the one or more model definition resources from the remote host computer by processing the reference with a mapping module; the client computing device generating a first result at least by determining whether the requested key for the data is allowed in the model based at least in part upon the actual implementation of the one or more model definition resources and further generating a second result at least by determining whether a requested value of the key-value pair for the data matches the model definition; and the client computing device performing the data binding for the data and the model based at least in part upon the first result and the second result. | 1. A machine implemented method for implementing model definition, constraint enforcement, and validation, the method being performed by at least a client computing device and comprising: a client computing device requesting a reference to one or more model definition resources for a representation of a model that pertains to a tax preparation or financial management software application at least by transmitting a request for the reference to a remote host computer hosting the tax preparation or financial management application via a network element; the client computing device executing a model definition resolver, residing on and stored at least partially in memory of the client computing device, that identifies or determines one or more actual locations for the one or more model definition resources at least by resolving the reference and retrieving the one or more model definition resources from the one or more actual locations, wherein the one or more model definition resources specify constraints for constraint enforcement or validation on the model; the client computing device reducing data processing and data to be populated into the model at least by removing a portion of the data and populating a remaining portion of the data into the model through using one or more validation modules and one or more formatting modules stored at least partially in the memory of the client computing device, the client computing device reducing the data processing and the data comprising: disabling the constraint enforcement or validation for a portion of a flow of the tax preparation or financial management application; reducing a total number of data elements and a total number of invalid data elements at least by formatting one or more user inputs for the tax preparation or financial management application at the one or more formatting modules; and performing the constraint enforcement or validation at the one or more validation modules on at least the one or more user inputs, which have been formatted, based in part or in whole upon at least the one or more model definition resources obtained by the model definition resolver residing on the client computing device from the remote host computer via the network element; and the client computing device performing data binding for the data to bind the data to the model by using a first application programming interface located on the client computing device, further comprising; the client computing device identifying a model definition for a requested key of a key-value pair for the data and obtaining an actual implementation of the one or more model definition resources from the remote host computer by processing the reference with a mapping module; the client computing device generating a first result at least by determining whether the requested key for the data is allowed in the model based at least in part upon the actual implementation of the one or more model definition resources and further generating a second result at least by determining whether a requested value of the key-value pair for the data matches the model definition; and the client computing device performing the data binding for the data and the model based at least in part upon the first result and the second result. 6. The machine implemented method of claim 1 , further comprising the client computing device populating the key-value pair for the data into a repository for the model; and identifying or creating one or more error messages for the constraint enforcement or validation on the data. | 0.758148 |
9,552,130 | 1 | 11 | 1. A method, comprising: hosting an application for utilization by a remote computing platform; identifying a plurality of UI elements of a graphical user interface (UI) generated by the hosted application; generating a plurality of proxy UI elements, each of the plurality of proxy UI elements corresponding to one or more of the plurality of UI elements; transmitting, to the remote computing platform, the graphical UI generated by the hosted application and the plurality of proxy UI elements; processing a transcript of an audio sample, the audio sample comprising an utterance of a user of the remote computing platform, and the transcript of the audio sample comprising at least one word corresponding to one or more of the plurality of proxy UI elements; invoking a functionality of the hosted application, said functionality corresponding to one or more of the plurality of UI elements that correspond to the one or more of the plurality of proxy UI elements; identifying a plurality of properties of the plurality of UI elements and generating the plurality of proxy UI elements based on the identified plurality of properties, wherein each respective proxy UI element of the plurality of proxy UI elements is associated with one or more words corresponding to one or more of the plurality of properties, the one or more of the plurality of properties corresponding to one or more of the UI elements that correspond to the respective proxy UI element; and wherein the plurality of properties comprise one or more UI element labels of a labeled UI element of the plurality of UI elements, wherein the at least one word corresponding to one or more of the plurality of proxy UI elements comprises a word corresponding to at least one of the one or more UI element labels of the labeled UI element, and wherein invoking the functionality of the hosted application comprises changing a currently selected UI element of the hosted application from the currently selected UI element of the hosted application to the labeled UI element. | 1. A method, comprising: hosting an application for utilization by a remote computing platform; identifying a plurality of UI elements of a graphical user interface (UI) generated by the hosted application; generating a plurality of proxy UI elements, each of the plurality of proxy UI elements corresponding to one or more of the plurality of UI elements; transmitting, to the remote computing platform, the graphical UI generated by the hosted application and the plurality of proxy UI elements; processing a transcript of an audio sample, the audio sample comprising an utterance of a user of the remote computing platform, and the transcript of the audio sample comprising at least one word corresponding to one or more of the plurality of proxy UI elements; invoking a functionality of the hosted application, said functionality corresponding to one or more of the plurality of UI elements that correspond to the one or more of the plurality of proxy UI elements; identifying a plurality of properties of the plurality of UI elements and generating the plurality of proxy UI elements based on the identified plurality of properties, wherein each respective proxy UI element of the plurality of proxy UI elements is associated with one or more words corresponding to one or more of the plurality of properties, the one or more of the plurality of properties corresponding to one or more of the UI elements that correspond to the respective proxy UI element; and wherein the plurality of properties comprise one or more UI element labels of a labeled UI element of the plurality of UI elements, wherein the at least one word corresponding to one or more of the plurality of proxy UI elements comprises a word corresponding to at least one of the one or more UI element labels of the labeled UI element, and wherein invoking the functionality of the hosted application comprises changing a currently selected UI element of the hosted application from the currently selected UI element of the hosted application to the labeled UI element. 11. The method of claim 1 , wherein at least a portion of the plurality of proxy UI elements are configured to be hidden from view of the user of the remote computing platform, wherein one or more of the at least a portion of the plurality of proxy UI elements that are configured to be hidden from view of the user of the remote computing platform comprises a navigation menu option, the navigation menu option being configured to show one or more navigation options available from a UI state of the hosted application currently being displayed by the remote computing platform, wherein the at least one word corresponding to the one or more of the plurality of proxy UI elements comprises at least one word corresponding to the navigation menu option, and wherein invoking the functionality of the hosted application comprises navigating the hosted application to one of the one or more navigation options. | 0.50055 |
9,256,795 | 13 | 14 | 13. The computing device of claim 12 , wherein analyzing the output to isolate the character string indicative of the text entity includes performing one or more heuristic tests. | 13. The computing device of claim 12 , wherein analyzing the output to isolate the character string indicative of the text entity includes performing one or more heuristic tests. 14. The computing device of claim 13 , wherein the instructions, when executed by the processor, further enable the computing device to: convert the output into text lines; and omit characters that do not fit a pattern indicative of the text entity. | 0.937967 |
9,502,039 | 8 | 10 | 8. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving, by a computing device that uses voice-based speaker identification, audio data corresponding to an utterance by the user of a predefined hotword; in response to a false rejection of the audio data corresponding to the utterance, prompting the user to verify their identification using a technique other than voice-based speaker identification; in response to the user successfully verifying their identification using the technique other than voice-based speaker identification, prompting the user to confirm that the audio data corresponding to the utterance was falsely rejected; receiving data indicating that the user has confirmed that the audio data corresponding to the utterance was falsely rejected; and in response to receiving the data indicating that the user has confirmed that the audio data corresponding to the utterance was falsely rejected, using the audio data in determining whether audio data corresponding to subsequently received utterances by the user of the predefined hotword are to be accepted or rejected. | 8. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving, by a computing device that uses voice-based speaker identification, audio data corresponding to an utterance by the user of a predefined hotword; in response to a false rejection of the audio data corresponding to the utterance, prompting the user to verify their identification using a technique other than voice-based speaker identification; in response to the user successfully verifying their identification using the technique other than voice-based speaker identification, prompting the user to confirm that the audio data corresponding to the utterance was falsely rejected; receiving data indicating that the user has confirmed that the audio data corresponding to the utterance was falsely rejected; and in response to receiving the data indicating that the user has confirmed that the audio data corresponding to the utterance was falsely rejected, using the audio data in determining whether audio data corresponding to subsequently received utterances by the user of the predefined hotword are to be accepted or rejected. 10. The system of claim 8 , wherein the operations further comprise: in response to the user successfully verifying their identification using the technique other than voice-based speaker identification, prompting the user to confirm that additional, previously received audio data corresponding to an additional utterance by the user of the predefined hotword was properly accepted, properly rejected, falsely accepted, or falsely rejected. | 0.634328 |
8,595,220 | 12 | 13 | 12. The system of claim 11 , the method comprising: generating the second summary without input from a user. | 12. The system of claim 11 , the method comprising: generating the second summary without input from a user. 13. The system of claim 12 , the generating the second summary comprising: receiving a search query; identifying the document as pertaining to the search query; determining that no summaries indicative of the document are available; and generating the second summary upon determining that no summaries indicative of the document are available. | 0.877237 |
8,448,242 | 29 | 31 | 29. A non-transitory computer-readable medium containing computer-executable instructions that, when executed by a processor, cause the processor to perform a method for outputting data based on anomaly detection, the method comprising: receiving a shared binary anomaly detection model; comparing the shared binary anomaly detection model with a local anomaly detection model; combining the shared binary anomaly detection model with the local anomaly detection model to form a new binary anomaly detection model; using the new binary anomaly detection model to determine whether an input dataset contains an anomaly, wherein the new binary anomaly detection model is used to determine whether the input dataset contains an anomaly by checking the new binary anomaly detection model for an n-gram in the input dataset; and outputting the input dataset based on whether the input dataset contains an anomaly. | 29. A non-transitory computer-readable medium containing computer-executable instructions that, when executed by a processor, cause the processor to perform a method for outputting data based on anomaly detection, the method comprising: receiving a shared binary anomaly detection model; comparing the shared binary anomaly detection model with a local anomaly detection model; combining the shared binary anomaly detection model with the local anomaly detection model to form a new binary anomaly detection model; using the new binary anomaly detection model to determine whether an input dataset contains an anomaly, wherein the new binary anomaly detection model is used to determine whether the input dataset contains an anomaly by checking the new binary anomaly detection model for an n-gram in the input dataset; and outputting the input dataset based on whether the input dataset contains an anomaly. 31. The medium of claim 29 , wherein the local binary anomaly detection model is represented using a Bloom filter. | 0.764463 |
8,972,420 | 1 | 3 | 1. A system for enabling a user to associate a story with one or more referents, the system comprising: a data store that stores data associated with a plurality of stories; and a computing device in communication with the data store and that is configured to at least: cause display of information representing a first version of a story, said information stored in the data store; in the display of information representing the first version of the story, enable a user to select a first point in the first version of the story to associate with one or more referents, wherein the one or more referents each comprise at least one of an event, a character, an object, a subject, a time, a place or a person; receive a user request to associate a referent with the first point in the first version of the story, wherein the referent and the first point to associate with the referent are selected by the user, wherein the user request represents an indication from the user that the referent is related to the first point in the first version of the story; based at least in part on the user request, store information that associates the referent with the first point in the first version of the story; automatically determine a second point in a second version of the story based at least in part on a point map associated with the first version of the story and the second version of the story, wherein the point map relates each of a plurality of points in the first version with a point in the second version; automatically store information that associates the referent associated with the first point in the first version of the story with the second point in the second version of the story; and cause display of information representing (1) the second version of the story, and (2) the second point in the second version of the story that has been automatically associated with the referent. | 1. A system for enabling a user to associate a story with one or more referents, the system comprising: a data store that stores data associated with a plurality of stories; and a computing device in communication with the data store and that is configured to at least: cause display of information representing a first version of a story, said information stored in the data store; in the display of information representing the first version of the story, enable a user to select a first point in the first version of the story to associate with one or more referents, wherein the one or more referents each comprise at least one of an event, a character, an object, a subject, a time, a place or a person; receive a user request to associate a referent with the first point in the first version of the story, wherein the referent and the first point to associate with the referent are selected by the user, wherein the user request represents an indication from the user that the referent is related to the first point in the first version of the story; based at least in part on the user request, store information that associates the referent with the first point in the first version of the story; automatically determine a second point in a second version of the story based at least in part on a point map associated with the first version of the story and the second version of the story, wherein the point map relates each of a plurality of points in the first version with a point in the second version; automatically store information that associates the referent associated with the first point in the first version of the story with the second point in the second version of the story; and cause display of information representing (1) the second version of the story, and (2) the second point in the second version of the story that has been automatically associated with the referent. 3. The system of claim 1 , wherein the referent is related to the first point in the first version of the story by the referent being referenced in content of the first version of the story at the first point. | 0.573469 |
8,832,055 | 7 | 8 | 7. The method of claim 1 , wherein programmatically analyzing search activity data further comprises normalizing the search activity data for variations in traffic among the index-type search engines. | 7. The method of claim 1 , wherein programmatically analyzing search activity data further comprises normalizing the search activity data for variations in traffic among the index-type search engines. 8. The method of claim 7 , wherein programmatically analyzing search activity data further comprises unequally weighting the normalized comparisons of search engine data. | 0.937362 |
7,912,714 | 1 | 6 | 1. A method for forming discrete segment clusters of one or more sequential sentences from a corpus of communication transcripts of transactional communications, each communication transcript including a sequence of sentences spoken between a caller and a responder, the method comprising: dividing the communication transcripts of the corpus into a first set of sentences spoken by the caller and a second set of sentences spoken by the responder; using a processor, generating a set of sentence clusters by grouping the first and second sets of sentences according to a measure of lexical similarity using an unsupervised partitional clustering method; generating a collection of sequences of sentence types by assigning a distinct sentence type to each sentence cluster and representing each sentence of each communication transcript of the corpus with the sentence type assigned to the sentence cluster into which the sentence is grouped; and generating a specified number of discrete segment clusters of one or more sequential sentences by successively merging sentence clusters according to a proximity-based measure between the sentence types assigned to the sentence clusters within sequences of the collection. | 1. A method for forming discrete segment clusters of one or more sequential sentences from a corpus of communication transcripts of transactional communications, each communication transcript including a sequence of sentences spoken between a caller and a responder, the method comprising: dividing the communication transcripts of the corpus into a first set of sentences spoken by the caller and a second set of sentences spoken by the responder; using a processor, generating a set of sentence clusters by grouping the first and second sets of sentences according to a measure of lexical similarity using an unsupervised partitional clustering method; generating a collection of sequences of sentence types by assigning a distinct sentence type to each sentence cluster and representing each sentence of each communication transcript of the corpus with the sentence type assigned to the sentence cluster into which the sentence is grouped; and generating a specified number of discrete segment clusters of one or more sequential sentences by successively merging sentence clusters according to a proximity-based measure between the sentence types assigned to the sentence clusters within sequences of the collection. 6. The method of claim 1 , further comprising correcting the discrete segment clusters using semi-supervision by incorporating a distinct predetermined collection of key phrases for each of one or more segment types. | 0.834356 |
8,727,780 | 16 | 20 | 16. A method of creating and using a matrix for a mathematical research system, the method comprising: accessing a plurality of documents; analyzing each of the plurality of documents to identify any mathematical concepts expressed in each document, wherein the mathematical concepts are extracted from one or more algorithmic, linguistic, geometric, and graphic mathematical exercise representations; for each document including one or more mathematical concepts, tagging the documents with a corresponding one or more mathematical concept tags; creating two or more concept line items (CLI)s from the one or more mathematical concepts, wherein a CLI is a textual expression of a mathematical concept; defining interrelationships between the two or more CLIs, wherein each defined interrelationship includes one or more of a prerequisite to another CLI, a dependency on another CLI, or a lack of relationship to another CLI; assigning each defined interrelationship a relationship code; generating, by a computer processor, a matrix including the two or more CLIs and the relationship codes, wherein the relationship codes identify the defined interrelationships between the two or more CLIs, wherein the matrix includes one or more prerequisite and dependency interrelationships; storing the two or more CLIs and the generated mapping in one or more databases; creating an index relating each CLI to the corresponding documents; providing a search interface through which a user searches the database to identify documents related to one or more CLIs; and generating an educational curriculum from a search result, wherein the search interface accepts a search term provided by the user, and wherein the search interface accepts all of the following forms of search terms: text-based phrases, mathematical expressions expressed in documents, interrelationships, and concept ranges. | 16. A method of creating and using a matrix for a mathematical research system, the method comprising: accessing a plurality of documents; analyzing each of the plurality of documents to identify any mathematical concepts expressed in each document, wherein the mathematical concepts are extracted from one or more algorithmic, linguistic, geometric, and graphic mathematical exercise representations; for each document including one or more mathematical concepts, tagging the documents with a corresponding one or more mathematical concept tags; creating two or more concept line items (CLI)s from the one or more mathematical concepts, wherein a CLI is a textual expression of a mathematical concept; defining interrelationships between the two or more CLIs, wherein each defined interrelationship includes one or more of a prerequisite to another CLI, a dependency on another CLI, or a lack of relationship to another CLI; assigning each defined interrelationship a relationship code; generating, by a computer processor, a matrix including the two or more CLIs and the relationship codes, wherein the relationship codes identify the defined interrelationships between the two or more CLIs, wherein the matrix includes one or more prerequisite and dependency interrelationships; storing the two or more CLIs and the generated mapping in one or more databases; creating an index relating each CLI to the corresponding documents; providing a search interface through which a user searches the database to identify documents related to one or more CLIs; and generating an educational curriculum from a search result, wherein the search interface accepts a search term provided by the user, and wherein the search interface accepts all of the following forms of search terms: text-based phrases, mathematical expressions expressed in documents, interrelationships, and concept ranges. 20. The method of claim 16 wherein said CLIs are identified as nodes in a undirected graph. | 0.866176 |
8,532,333 | 1 | 4 | 1. A computer-implemented method, comprising: recognizing one or more text strings in each of a plurality of geo-tagged images, each of the plurality of geo-tagged images indicating a geographical location of its corresponding geo-tagged image; identifying one or more establishments near the geographical locations of the plurality of geo-tagged images; for each specific establishment of the one or more establishments: (i) extracting one or more phrases from information associated with the specific establishment; (ii) comparing the one or more text strings recognized in the plurality of geo-tagged images with the extracted one or more phrases to derive one or more matches; (iii) determining one or more image-establishment pairs based on the one or more matches, each image-establishment pair pairing a specific geo-tagged image with the specific establishment; and (iv) selecting a representative geo-tagged image for the specific establishment from among the plurality of geo-tagged images based on the one or more image-establishment pairs. | 1. A computer-implemented method, comprising: recognizing one or more text strings in each of a plurality of geo-tagged images, each of the plurality of geo-tagged images indicating a geographical location of its corresponding geo-tagged image; identifying one or more establishments near the geographical locations of the plurality of geo-tagged images; for each specific establishment of the one or more establishments: (i) extracting one or more phrases from information associated with the specific establishment; (ii) comparing the one or more text strings recognized in the plurality of geo-tagged images with the extracted one or more phrases to derive one or more matches; (iii) determining one or more image-establishment pairs based on the one or more matches, each image-establishment pair pairing a specific geo-tagged image with the specific establishment; and (iv) selecting a representative geo-tagged image for the specific establishment from among the plurality of geo-tagged images based on the one or more image-establishment pairs. 4. The method of claim 1 , wherein each of the one or more matches comprises an approximate match between a specific text string of the one or more text strings and a specific phrase of the extracted one or more phrases. | 0.884937 |
7,562,343 | 11 | 12 | 11. The method of claim 10 , wherein generating proposals comprises: generating proposals from the last matching token; and adding the proposals to a proposal vector. | 11. The method of claim 10 , wherein generating proposals comprises: generating proposals from the last matching token; and adding the proposals to a proposal vector. 12. The method or claim 11 , wherein proposal vectors are generated from multiple cursor engines parsing different parts of the program statements; concatenating the proposal vectors to create a combined proposal vector that is returned; matching the combined proposal vector to determine an image; displaying a window containing the determined image from which the user select a keyword, identifier or constant to continue entry of the partial program statement. | 0.866185 |
7,962,443 | 15 | 20 | 15. A design and data replacement system, comprising: a client computing device; a delivery server; and a repository including a plurality of ready-built design templates, wherein the delivery server comprises logic configured to: (A) receive, from the client computing device, a user data set, the user data set including a first plurality of fields, wherein the first plurality of fields comprises dynamic structured data; (B) determine whether the user data set matches one or more of the plurality of ready-built design templates stored in the repository, each of the plurality of ready-built design templates including a second plurality of fields comprising replaceable data, based on one or more of the group consisting of a comparison between the first plurality of fields and the second plurality of fields, a comparison between a first plurality of labels corresponding to the first plurality of fields and a second plurality of labels corresponding to the second plurality of fields, a comparison between the user data set and a data descriptor, and a comparison between a data type of at least one of the first plurality of fields and a data type of at least one of the second plurality of fields; (C) return, to the client computing device, an output representing a matching subset of the plurality of read-built design templates stored in the repository; (D) receive, from the client computing device, input representing a selection by a user of at least one ready-built design template from the matching subset; and (E) generate a graphical representation on the client computing device by replacing the replaceable data of the second plurality of fields of the selected at least one ready-built design template from the matching subset with the data set of the first plurality of fields. | 15. A design and data replacement system, comprising: a client computing device; a delivery server; and a repository including a plurality of ready-built design templates, wherein the delivery server comprises logic configured to: (A) receive, from the client computing device, a user data set, the user data set including a first plurality of fields, wherein the first plurality of fields comprises dynamic structured data; (B) determine whether the user data set matches one or more of the plurality of ready-built design templates stored in the repository, each of the plurality of ready-built design templates including a second plurality of fields comprising replaceable data, based on one or more of the group consisting of a comparison between the first plurality of fields and the second plurality of fields, a comparison between a first plurality of labels corresponding to the first plurality of fields and a second plurality of labels corresponding to the second plurality of fields, a comparison between the user data set and a data descriptor, and a comparison between a data type of at least one of the first plurality of fields and a data type of at least one of the second plurality of fields; (C) return, to the client computing device, an output representing a matching subset of the plurality of read-built design templates stored in the repository; (D) receive, from the client computing device, input representing a selection by a user of at least one ready-built design template from the matching subset; and (E) generate a graphical representation on the client computing device by replacing the replaceable data of the second plurality of fields of the selected at least one ready-built design template from the matching subset with the data set of the first plurality of fields. 20. The design and data replacement system of claim 15 , wherein the delivery server further comprises logic configured to: (F) receive, from a user, input representing a hint; and wherein (B) comprises determining whether the user data set matches the one or more of the plurality of ready-built design templates based on the hint. | 0.761494 |
8,880,515 | 1 | 2 | 1. A system for determining one or more concepts associated with a query, comprising: a processor configured to: receive a query; receive a list of result documents ordered by relevance to the query; receive a list of candidate concepts, the concepts being tags associated with the result documents, the concepts fitting within a concept hierarchy; use a density function to evaluate the received concepts by identifying the concept hierarchy within which the received concepts fit, identifying an affinity score for each of the concepts which measure how closely the concept match the query, and determining a density score for each of the concepts, wherein the density score relates affinity scores for children concepts located underneath one of the concepts to the number of children concepts beneath said one of the concepts; wherein the density score more specifically relates a summation of affinity scores for children concepts underneath said one of the concepts to a number of paths to the children concepts; wherein concepts in the concept hierarchy comprise branch nodes which are connected to lower level children concepts and leaf nodes which are not connected to lower children concepts, and wherein the density score for a branch node more specifically divides a summation of affinity scores for leaf nodes which are lower level children concepts associated with said branch node by a number of paths to the leaf nodes which are lower level children concepts associated with said branch node; and associate one or more concepts with the query based at least in part on the results of the density function; and a memory coupled to the processor and configured to provide the processor with instructions. | 1. A system for determining one or more concepts associated with a query, comprising: a processor configured to: receive a query; receive a list of result documents ordered by relevance to the query; receive a list of candidate concepts, the concepts being tags associated with the result documents, the concepts fitting within a concept hierarchy; use a density function to evaluate the received concepts by identifying the concept hierarchy within which the received concepts fit, identifying an affinity score for each of the concepts which measure how closely the concept match the query, and determining a density score for each of the concepts, wherein the density score relates affinity scores for children concepts located underneath one of the concepts to the number of children concepts beneath said one of the concepts; wherein the density score more specifically relates a summation of affinity scores for children concepts underneath said one of the concepts to a number of paths to the children concepts; wherein concepts in the concept hierarchy comprise branch nodes which are connected to lower level children concepts and leaf nodes which are not connected to lower children concepts, and wherein the density score for a branch node more specifically divides a summation of affinity scores for leaf nodes which are lower level children concepts associated with said branch node by a number of paths to the leaf nodes which are lower level children concepts associated with said branch node; and associate one or more concepts with the query based at least in part on the results of the density function; and a memory coupled to the processor and configured to provide the processor with instructions. 2. The system of claim 1 , wherein the density score more specifically relates a summation of affinity scores for children concepts underneath said one of the concepts to a number of children concepts underneath said one of the concepts. | 0.738411 |
7,617,222 | 4 | 5 | 4. The method of claim 1 , wherein the data structure of the first computer is different than the corresponding data structure in the second computer, the metadata representing the differences in the data structures. | 4. The method of claim 1 , wherein the data structure of the first computer is different than the corresponding data structure in the second computer, the metadata representing the differences in the data structures. 5. The method of claim 4 , wherein the first computer has a hierarchical file structure, and the second computer has a hierarchical file structure. | 0.92477 |
7,765,157 | 22 | 23 | 22. A method for searching for information contained in at least one database based upon input received from a user via an electronic device, the method comprising: presenting text to the user via at least one user interface of the electronic device; receiving input from the user via the at least one user interface to highlight a portion of the text; performing a database search of the at least one database using at least a portion of the highlighted portion of the text to produce search results; and presenting the search results to the user via the at least one user interface. | 22. A method for searching for information contained in at least one database based upon input received from a user via an electronic device, the method comprising: presenting text to the user via at least one user interface of the electronic device; receiving input from the user via the at least one user interface to highlight a portion of the text; performing a database search of the at least one database using at least a portion of the highlighted portion of the text to produce search results; and presenting the search results to the user via the at least one user interface. 23. The method of claim 22 , further comprising: receiving input from the user via the at least one user interface to initiate the database search; and forming a search argument based upon the highlighted portion of the text; and performing the database search using the search argument. | 0.635787 |
7,966,323 | 5 | 6 | 5. The method of claim 1 wherein said receiving a user search request comprises receiving an indication of a category to be searched as a selection from a master list of categories and further comprising adding a category to said master list of categories by: receiving a new category name; associating a plurality of elements with said new category name, said plurality of elements being derived in accordance with said second mapping function; and adding a reference to said new category name to said master list of categories. | 5. The method of claim 1 wherein said receiving a user search request comprises receiving an indication of a category to be searched as a selection from a master list of categories and further comprising adding a category to said master list of categories by: receiving a new category name; associating a plurality of elements with said new category name, said plurality of elements being derived in accordance with said second mapping function; and adding a reference to said new category name to said master list of categories. 6. The method of claim 5 wherein said plurality of elements associated with said new category name comprises, in a given element, a value indicating a quantity of elements in said plurality of elements associated with said new category name. | 0.946133 |
8,620,842 | 2 | 4 | 2. The system of claim 1 , wherein the processor is further adapted to receive or provide relevance rankings, wherein the relevance rankings are generated by one or more keyword searching algorithms or by a comparison with one or more exemplary documents. | 2. The system of claim 1 , wherein the processor is further adapted to receive or provide relevance rankings, wherein the relevance rankings are generated by one or more keyword searching algorithms or by a comparison with one or more exemplary documents. 4. The system of claim 2 , wherein the selected document is selected by choosing between utilizing the selected scores, the relevance rankings, or a combination of the selected scores and the relevance rankings. | 0.933897 |
8,010,527 | 1 | 13 | 1. A method implemented within a computer system comprising a central processing unit (CPU) and a random access memory (RAM), the method comprising: a. utilizing the CPU and RAM to obtain a history of online activities of a user; b. utilizing the CPU and RAM to receive user preference information from the user; c. utilizing the CPU and RAM to identify a plurality of online information resources linking to online resources viewed by the user, wherein each of the plurality of online information resources is associated with an online information source; d. utilizing the CPU and RAM to generate a plurality of relevance scores for each of the identified online information resource; and e. using the generated plurality of relevance scores to generate a ranked list of recommended online information sources, wherein a rank of each online information source is determined by aggregating at least some of the plurality of relevance scores of the identified online information resources according to the received user preference information, wherein: the ranked list comprises links to online sources that are not in the history of the online activities of the user, the online information resource is a blog post, the online resource viewed by the user is a visited web page, the online information source is a source blog web page, and the online source is a related web feed. | 1. A method implemented within a computer system comprising a central processing unit (CPU) and a random access memory (RAM), the method comprising: a. utilizing the CPU and RAM to obtain a history of online activities of a user; b. utilizing the CPU and RAM to receive user preference information from the user; c. utilizing the CPU and RAM to identify a plurality of online information resources linking to online resources viewed by the user, wherein each of the plurality of online information resources is associated with an online information source; d. utilizing the CPU and RAM to generate a plurality of relevance scores for each of the identified online information resource; and e. using the generated plurality of relevance scores to generate a ranked list of recommended online information sources, wherein a rank of each online information source is determined by aggregating at least some of the plurality of relevance scores of the identified online information resources according to the received user preference information, wherein: the ranked list comprises links to online sources that are not in the history of the online activities of the user, the online information resource is a blog post, the online resource viewed by the user is a visited web page, the online information source is a source blog web page, and the online source is a related web feed. 13. The method of claim 1 , wherein generating a ranked list of recommended online information sources comprises calculating an aggregate score for each online information source by combining at least some of the plurality of relevance scores corresponding to identified online information resources associated with the online information sources. | 0.501437 |
8,065,313 | 1 | 2 | 1. A method for automatically annotating a video in a computer system, comprising: receiving a video comprising a plurality of frames; obtaining images contained in two or more representative frames from the video; for each of the images, iteratively extracting image features from the image on different spatial scales, wherein the image features comprise visual characteristics associated with tiles of different sizes within the image; matching the extracted image features to known image features; identifying other images with similar image features using one or more combinations of the matched image features; obtaining text associated with the other images, wherein obtaining the text associated with the other images comprises obtaining text that surrounds each image in a web page in which the image is located; identifying one or more intersecting keywords in the text associated with the other images; and annotating the image with the intersecting keywords using the computer system; analyzing the keywords for the images to determine a common set of keywords; and annotating the video using the common set of keywords. | 1. A method for automatically annotating a video in a computer system, comprising: receiving a video comprising a plurality of frames; obtaining images contained in two or more representative frames from the video; for each of the images, iteratively extracting image features from the image on different spatial scales, wherein the image features comprise visual characteristics associated with tiles of different sizes within the image; matching the extracted image features to known image features; identifying other images with similar image features using one or more combinations of the matched image features; obtaining text associated with the other images, wherein obtaining the text associated with the other images comprises obtaining text that surrounds each image in a web page in which the image is located; identifying one or more intersecting keywords in the text associated with the other images; and annotating the image with the intersecting keywords using the computer system; analyzing the keywords for the images to determine a common set of keywords; and annotating the video using the common set of keywords. 2. The method of claim 1 , wherein iteratively extracting the image features from the image involves: partitioning the image into the tiles of different sizes; and extracting the image features from the tiles. | 0.801331 |
8,539,348 | 1 | 7 | 1. A method of enabling input into an electronic device that comprises a plurality of input members having a number of characters assigned thereto, and a memory having stored therein a plurality of words and a plurality of word frames, each word frame having associated therewith at least a first word comprising a plurality of characters that include a sequential plurality of characters that are all assigned to a particular input member, each word frame being representative of the at least first word and comprising, in place of the sequential plurality of characters of the at least first word, a contracted portion that is representative of any nonzero quantity of the characters assigned to the particular input member, the method comprising: detecting as an input a plurality of input member selections; identifying in the memory one or more words that correspond with the input for at least partial inclusion in an output set; identifying in the memory a particular word frame that corresponds with the input; adding to the output set at least a portion of at least a first word associated with the particular word frame; and outputting at least a portion of the output set. | 1. A method of enabling input into an electronic device that comprises a plurality of input members having a number of characters assigned thereto, and a memory having stored therein a plurality of words and a plurality of word frames, each word frame having associated therewith at least a first word comprising a plurality of characters that include a sequential plurality of characters that are all assigned to a particular input member, each word frame being representative of the at least first word and comprising, in place of the sequential plurality of characters of the at least first word, a contracted portion that is representative of any nonzero quantity of the characters assigned to the particular input member, the method comprising: detecting as an input a plurality of input member selections; identifying in the memory one or more words that correspond with the input for at least partial inclusion in an output set; identifying in the memory a particular word frame that corresponds with the input; adding to the output set at least a portion of at least a first word associated with the particular word frame; and outputting at least a portion of the output set. 7. The method of claim 1 , further comprising outputting as at least a portion of the at least portion of the output set at least a portion of the at least first word, the at least portion of the at least first word comprising a quantity of characters that is one of: one greater than the quantity of the plurality of input member selections of the input, and one lesser than the quantity of the plurality of input member selections of the input. | 0.739486 |
9,330,139 | 7 | 8 | 7. The method of claim 1 , wherein the first input, the second input and the search commands are received by a web browser application executing on the communication device. | 7. The method of claim 1 , wherein the first input, the second input and the search commands are received by a web browser application executing on the communication device. 8. The method of claim 7 , wherein receiving the second input comprises copying selected content displayed by the communication device, the selected content thus comprising the second search keyword; and generating the second query comprises automatically generating the query upon receipt of the further search command. | 0.927765 |
9,984,314 | 11 | 12 | 11. A classification system comprising: a general classifier configured to classify multiple aspects of content of an image stream; a template store including multiple different specialized classifier templates, each of the specialized classifier templates being configured to classify a subset of the multiple aspects; and a classifier selection system configured to re-train one of the multiple different specialized classifier templates to generate a specialized classifier for a particular subset of the multiple aspects, and to determine when to switch between using the general classifier to classify the multiple aspects of content of the image stream and using the specialized classifier to classify the multiple aspects of content of the image stream. | 11. A classification system comprising: a general classifier configured to classify multiple aspects of content of an image stream; a template store including multiple different specialized classifier templates, each of the specialized classifier templates being configured to classify a subset of the multiple aspects; and a classifier selection system configured to re-train one of the multiple different specialized classifier templates to generate a specialized classifier for a particular subset of the multiple aspects, and to determine when to switch between using the general classifier to classify the multiple aspects of content of the image stream and using the specialized classifier to classify the multiple aspects of content of the image stream. 12. The classification system as recited in claim 11 , the classifier selection system being further configured to: obtain a specialized classifier template from the template store, the obtained specialized classifier template including multiple layers; re-train a top layer of the obtained specialized classifier template using images including the particular subset of the multiple aspects; and use ones of the multiple layers below the top layer as they exist in the obtained specialized classifier template. | 0.774492 |
9,729,717 | 14 | 15 | 14. A system, comprising at least one automatic speech recognition component configured to analyze at least one voice interaction by at least one agent that follows at least one script in at least one of a plurality of panels and to determine whether the at least one agent has adequately followed the at least one script using confidence level thresholds assigned to each of the plurality of panels. | 14. A system, comprising at least one automatic speech recognition component configured to analyze at least one voice interaction by at least one agent that follows at least one script in at least one of a plurality of panels and to determine whether the at least one agent has adequately followed the at least one script using confidence level thresholds assigned to each of the plurality of panels. 15. The system of claim 14 , wherein each of the plurality of panels is related to the at least one automatic speech recognition component. | 0.844519 |
10,102,093 | 11 | 12 | 11. A non-transitory computer readable storage medium storing a program that, when executed by one or more hardware processors, causes the one or more hardware processors to perform a method for facilitating an operation of a device, the method comprising: receiving an indication of an operation problem for a first device; acquiring historical operation data of a plurality of devices including the first device, the historical operation data including structured data and unstructured data; determining at least a list of first entities and a list of second entities based on the structured data; determining, based on the structured and unstructured data, a frequency of association between each of the first entities and each of the second entities; determining, based on the frequency of association, a set of entity associations, each entity association including at least one of the first entities and at least one of the second entities; determining one or more relationships between each of the entity associations, based on: determination of a degree of association associated with a relationship between each of the entity associations and a direction associated with the relationship, wherein the direction reflects a causality relationship; and determination of a hypothesis for a reason for the operation problem based on a resultant matrix obtained by matrix manipulation of an adjacency matrix, wherein the adjacency matrix is determined based on the degree of association and the direction; and determining, based on the one or more determined relationships, an operation solution to solve the operation problem. | 11. A non-transitory computer readable storage medium storing a program that, when executed by one or more hardware processors, causes the one or more hardware processors to perform a method for facilitating an operation of a device, the method comprising: receiving an indication of an operation problem for a first device; acquiring historical operation data of a plurality of devices including the first device, the historical operation data including structured data and unstructured data; determining at least a list of first entities and a list of second entities based on the structured data; determining, based on the structured and unstructured data, a frequency of association between each of the first entities and each of the second entities; determining, based on the frequency of association, a set of entity associations, each entity association including at least one of the first entities and at least one of the second entities; determining one or more relationships between each of the entity associations, based on: determination of a degree of association associated with a relationship between each of the entity associations and a direction associated with the relationship, wherein the direction reflects a causality relationship; and determination of a hypothesis for a reason for the operation problem based on a resultant matrix obtained by matrix manipulation of an adjacency matrix, wherein the adjacency matrix is determined based on the degree of association and the direction; and determining, based on the one or more determined relationships, an operation solution to solve the operation problem. 12. The medium of claim 11 , wherein the structured data include a set of discrete data that are associated with specific fields configured to provide one or more meanings to the set of discrete data; wherein the first and second entities include the set of discrete data. | 0.709402 |
9,996,670 | 1 | 15 | 1. A computer-implemented method of analyzing a clinical decision support (CDS) document and improving content analyzer system accuracy, the method comprising: improving content analyzer system accuracy in identifying CDS document deficiencies and consistencies with respect to reference content, the reference content comprising clinical guidelines, by repeatedly training a machine learning module, hosted by a content analyzer system, based on new incoming data, wherein the machine learning module is configured to automatically determine which content features are to be used to determine whether content is to be designated as relevant and matching to the reference content and which content is to be designated as non-relevant and to construct or modify an electronic model accordingly, wherein the features comprise one or more of text length, presence of a medication term, medical intervention language, use of a negation, or context, wherein improving content analyzer system accuracy by training the machine learning module comprises repeatedly: collecting positive and negative cases from CDS documents, training the electronic model using the collected positive and negative cases from CDS documents, taking as input a text segment extracted from a CDS document and returning a likelihood that the text segment matches a reference checklist item, and wherein the new incoming data indicates whether the likelihood that the text segment matches a reference checklist item is correct or incorrect; receiving at a computer system, including hardware and comprising an analytics engine, a clinical decision support document from a medical service provider system; accessing over a network from a remote system, by the computer system, reference content corresponding at least in part to the clinical decision support document, the reference content comprising clinical guidelines; using, by the computer system, the electronic model to identify and extract medical intervention content from the clinical decision support document; using feedback with respect to the identification of the medical intervention content to refine the electronic model; segmenting, by the computer system, at least a portion of the extracted medical intervention content into a first plurality of segments including: at least a first segment, comprising a first set of text, and a second segment comprising a second set of text, wherein a given segment in the first plurality of segments is evaluated to identify a core concept, wherein identifying the core concept further comprises determining whether a given segment includes a plurality of medications, and determining which of the plurality of medications are part of the core concept and which of the plurality of medications are not part of the core concept, and if the core concept of the given segment comprises at least one a medical intervention, determining whether a negation is associated with the at least one medical intervention; determining, by the trained machine learning engine, if the first segment corresponds to at least a first item included in the reference content, the first item comprising a third set of text comprising terminology not present in the first and second sets of text; at least partly in response to determining that the first segment, comprising the first set of text, corresponds to the first item included in the reference content, the first item comprising the third set of text, causing a version of the clinical decision support document to be generated to include a visual indication that the first segment corresponds to the first item included in the reference content; determining, by the trained machine learning engine, if a second item included in the reference content corresponds to at least one of the first plurality of segments; at least partly in response to determining that the second item included in the reference content does not correspond to at least one of the first plurality of segments, causing the version of the clinical decision support document to be generated to include a visual indication that the first plurality of segments fails to include at least one segment that corresponds to the second item included in the reference content; and at least partly in response to determining that the second item included in the reference content does correspond to at least one of the first plurality of segments, causing the version of the clinical decision support document to include a visual indication that the second item corresponds to at least one segment in the first plurality of segments. | 1. A computer-implemented method of analyzing a clinical decision support (CDS) document and improving content analyzer system accuracy, the method comprising: improving content analyzer system accuracy in identifying CDS document deficiencies and consistencies with respect to reference content, the reference content comprising clinical guidelines, by repeatedly training a machine learning module, hosted by a content analyzer system, based on new incoming data, wherein the machine learning module is configured to automatically determine which content features are to be used to determine whether content is to be designated as relevant and matching to the reference content and which content is to be designated as non-relevant and to construct or modify an electronic model accordingly, wherein the features comprise one or more of text length, presence of a medication term, medical intervention language, use of a negation, or context, wherein improving content analyzer system accuracy by training the machine learning module comprises repeatedly: collecting positive and negative cases from CDS documents, training the electronic model using the collected positive and negative cases from CDS documents, taking as input a text segment extracted from a CDS document and returning a likelihood that the text segment matches a reference checklist item, and wherein the new incoming data indicates whether the likelihood that the text segment matches a reference checklist item is correct or incorrect; receiving at a computer system, including hardware and comprising an analytics engine, a clinical decision support document from a medical service provider system; accessing over a network from a remote system, by the computer system, reference content corresponding at least in part to the clinical decision support document, the reference content comprising clinical guidelines; using, by the computer system, the electronic model to identify and extract medical intervention content from the clinical decision support document; using feedback with respect to the identification of the medical intervention content to refine the electronic model; segmenting, by the computer system, at least a portion of the extracted medical intervention content into a first plurality of segments including: at least a first segment, comprising a first set of text, and a second segment comprising a second set of text, wherein a given segment in the first plurality of segments is evaluated to identify a core concept, wherein identifying the core concept further comprises determining whether a given segment includes a plurality of medications, and determining which of the plurality of medications are part of the core concept and which of the plurality of medications are not part of the core concept, and if the core concept of the given segment comprises at least one a medical intervention, determining whether a negation is associated with the at least one medical intervention; determining, by the trained machine learning engine, if the first segment corresponds to at least a first item included in the reference content, the first item comprising a third set of text comprising terminology not present in the first and second sets of text; at least partly in response to determining that the first segment, comprising the first set of text, corresponds to the first item included in the reference content, the first item comprising the third set of text, causing a version of the clinical decision support document to be generated to include a visual indication that the first segment corresponds to the first item included in the reference content; determining, by the trained machine learning engine, if a second item included in the reference content corresponds to at least one of the first plurality of segments; at least partly in response to determining that the second item included in the reference content does not correspond to at least one of the first plurality of segments, causing the version of the clinical decision support document to be generated to include a visual indication that the first plurality of segments fails to include at least one segment that corresponds to the second item included in the reference content; and at least partly in response to determining that the second item included in the reference content does correspond to at least one of the first plurality of segments, causing the version of the clinical decision support document to include a visual indication that the second item corresponds to at least one segment in the first plurality of segments. 15. The method as defined in claim 1 , wherein the first segment, corresponding to the first item, does not include any words in common with the first item. | 0.843058 |
7,881,934 | 1 | 11 | 1. A method of adjusting a voice prompt of a system based upon a state of a user of the system, the method comprising: receiving an utterance of the user; obtaining utterance parameters from the utterance, the utterance parameters indicating the state of the user; determining the state of the user based upon an utterance parameter vector; and adjusting the voice prompt by adjusting at least one of a tone of voice of the voice prompt, a content of the voice prompt, a prosody of the voice prompt, and a gender of the voice prompt based upon the determined state of the user, wherein obtaining utterance parameters comprises: partitioning the utterance into segments; assigning one of a plurality of classifications to each segment, each classification corresponding to at least one of a plurality of states of the user; and determining a total number of one of the plurality of classifications divided by a total number of segments of the utterance for each of the plurality of states of the user to create the utterance parameter vector. | 1. A method of adjusting a voice prompt of a system based upon a state of a user of the system, the method comprising: receiving an utterance of the user; obtaining utterance parameters from the utterance, the utterance parameters indicating the state of the user; determining the state of the user based upon an utterance parameter vector; and adjusting the voice prompt by adjusting at least one of a tone of voice of the voice prompt, a content of the voice prompt, a prosody of the voice prompt, and a gender of the voice prompt based upon the determined state of the user, wherein obtaining utterance parameters comprises: partitioning the utterance into segments; assigning one of a plurality of classifications to each segment, each classification corresponding to at least one of a plurality of states of the user; and determining a total number of one of the plurality of classifications divided by a total number of segments of the utterance for each of the plurality of states of the user to create the utterance parameter vector. 11. The method of claim 1 , further comprising adjusting a graphical character display corresponding to the voice prompt based upon the determined state of the user. | 0.852415 |
4,087,632 | 1 | 2 | 1. A speech recognition system responsive to sampled representations of applied speech comprising: an extended feature extractor, responsive to said sampled representations, for determining features of speech contained in said sampled representations, including a feature corresponding to the tongue body position and its direction of movement; and an acceptor, responsive to said feature extractor, for matching the sequence of said determined features to predetermined sequences of features corresponding to selected words. | 1. A speech recognition system responsive to sampled representations of applied speech comprising: an extended feature extractor, responsive to said sampled representations, for determining features of speech contained in said sampled representations, including a feature corresponding to the tongue body position and its direction of movement; and an acceptor, responsive to said feature extractor, for matching the sequence of said determined features to predetermined sequences of features corresponding to selected words. 2. The system of claim 1 wherein the said features determined by said feature extractor comprise a Silence token, a Burst token, a Fricative token, and a Tongue body trajectory token. | 0.838053 |
9,355,568 | 1 | 3 | 1. A method for utilizing an electronic reader to provide output by the electronic reader at a rate corresponding to a user in order to overcome a lack of user-specific focus, the method comprising: providing, by a processor of a computer system, a text for digital access within the computer system, the computer system comprising a tactile input device and a visually perceptible output device; displaying, by the processor, a first page of the text on the visually perceptible output device; providing, by the processor, a user-controlled visualization in which the first page is configured to be turned to reveal a second page, wherein as the first page turns, a first perimeter of the first page is configured to appear to be peeled away from its original position, such that a portion of the first page appears to pivot about an axis, and such that the first portion of the first page appears to be placed face down to reveal the second page; causing, by the processor, the first perimeter to appear as though it is curling back from its original position when a user passes a pointer over the first perimeter; causing, by the processor, the first perimeter of the first page to appear to be peeled away from its original position and the first page to be turned when the user hard selects the first perimeter, wherein the hard selection comprises an action that is different than passing the pointer over the first perimeter; and visually emphasizing, via the processor, a word of the text through the visually perceptible output device so as to distinguish the word from a surrounding word in the text when the pointer passes over or near the word from any direction, wherein the word is at least one of: (a) audibly and visually emphasized at a rate determined by the user when the pointer passes over or near the word in a reading direction, and wherein the pointer is required to pass over or near the word in the reading direction in order for the word to be both audibly and visually emphasized, and (b) visually distinguished on the visually perceptible output device from the surrounding word through a first selection process which occurs when the pointer passes over or near the word, and in which the word is audibly emphasized when the word is selected through a second selection process that comprises an action that is different from the first selection process, and wherein a report of words selected through the second selection process is generated. | 1. A method for utilizing an electronic reader to provide output by the electronic reader at a rate corresponding to a user in order to overcome a lack of user-specific focus, the method comprising: providing, by a processor of a computer system, a text for digital access within the computer system, the computer system comprising a tactile input device and a visually perceptible output device; displaying, by the processor, a first page of the text on the visually perceptible output device; providing, by the processor, a user-controlled visualization in which the first page is configured to be turned to reveal a second page, wherein as the first page turns, a first perimeter of the first page is configured to appear to be peeled away from its original position, such that a portion of the first page appears to pivot about an axis, and such that the first portion of the first page appears to be placed face down to reveal the second page; causing, by the processor, the first perimeter to appear as though it is curling back from its original position when a user passes a pointer over the first perimeter; causing, by the processor, the first perimeter of the first page to appear to be peeled away from its original position and the first page to be turned when the user hard selects the first perimeter, wherein the hard selection comprises an action that is different than passing the pointer over the first perimeter; and visually emphasizing, via the processor, a word of the text through the visually perceptible output device so as to distinguish the word from a surrounding word in the text when the pointer passes over or near the word from any direction, wherein the word is at least one of: (a) audibly and visually emphasized at a rate determined by the user when the pointer passes over or near the word in a reading direction, and wherein the pointer is required to pass over or near the word in the reading direction in order for the word to be both audibly and visually emphasized, and (b) visually distinguished on the visually perceptible output device from the surrounding word through a first selection process which occurs when the pointer passes over or near the word, and in which the word is audibly emphasized when the word is selected through a second selection process that comprises an action that is different from the first selection process, and wherein a report of words selected through the second selection process is generated. 3. The method of claim 1 , wherein the first perimeter of the first page comprises at least one of an upper-right-hand corner, a lower-right-hand corner, an upper-left-hand-corner, a lower-left hand corner, an upper edge, a lower edge, a right-hand edge, and a left-hand edge. | 0.601156 |
8,434,062 | 7 | 9 | 7. A system displaying computer source code text, the source code comprising one or more classes, methods, and variables having textual names, the system comprising: a processing element configured for assigning unique graphical symbols to a plurality of corresponding textual names of the classes, methods, and variables and replacing the plurality of textual names of the classes, methods, and variables in the source code text with the corresponding assigned unique graphical symbols; memory for storing the assigned unique graphical symbols; and a display element configured for displaying the source code with the assigned unique graphical symbols in place of the corresponding textual names of the classes, methods, and variables within the displayed source code text. | 7. A system displaying computer source code text, the source code comprising one or more classes, methods, and variables having textual names, the system comprising: a processing element configured for assigning unique graphical symbols to a plurality of corresponding textual names of the classes, methods, and variables and replacing the plurality of textual names of the classes, methods, and variables in the source code text with the corresponding assigned unique graphical symbols; memory for storing the assigned unique graphical symbols; and a display element configured for displaying the source code with the assigned unique graphical symbols in place of the corresponding textual names of the classes, methods, and variables within the displayed source code text. 9. The system of claim 7 , wherein the processing element is further configured for exporting a mapping of the assigned unique graphical symbols and each respective textual name to a central repository. | 0.880473 |
9,424,247 | 1 | 13 | 1. A method, comprising: identifying a task entry of a user utilizing one or more processors, wherein the task entry includes one or more information fields; identifying, utilizing one or more of the processors, one or more messages sent or received by the user, wherein each of the messages includes one or more terms; identifying, utilizing one or more of the processors, an association between the task entry and the one or more messages; identifying, utilizing one or more of the processors, that a new message is related to the one or more messages, wherein the new message is sent or received by the user after creation of the task entry of the user, and wherein the new message includes one or more new message terms; determining, utilizing one or more of the processors, an n-gram based on the one or more new message terms of the new message; determining, utilizing one or more of the processors, a similarity score between the n-gram and the task entry, wherein the similarity score is indicative of a likelihood that the user has interest in associating the n-gram with the task entry; and associating, utilizing one or more of the processors, the n-gram with the task entry based on the similarity score. | 1. A method, comprising: identifying a task entry of a user utilizing one or more processors, wherein the task entry includes one or more information fields; identifying, utilizing one or more of the processors, one or more messages sent or received by the user, wherein each of the messages includes one or more terms; identifying, utilizing one or more of the processors, an association between the task entry and the one or more messages; identifying, utilizing one or more of the processors, that a new message is related to the one or more messages, wherein the new message is sent or received by the user after creation of the task entry of the user, and wherein the new message includes one or more new message terms; determining, utilizing one or more of the processors, an n-gram based on the one or more new message terms of the new message; determining, utilizing one or more of the processors, a similarity score between the n-gram and the task entry, wherein the similarity score is indicative of a likelihood that the user has interest in associating the n-gram with the task entry; and associating, utilizing one or more of the processors, the n-gram with the task entry based on the similarity score. 13. The method of claim 1 , wherein at least one of the information fields of the task entry is associated with an entity, and wherein determining the similarity score includes determining an association between the n-gram and the entity. | 0.688482 |
7,779,002 | 3 | 7 | 3. An apparatus comprising: at least one processor; and at least one storage device storing processor executable instructions which, when executed by the at least one processor, processes search results by: receiving search results in response to a query, the query including one or more keywords, the search results including a first search result and a second search result; generating a set of final search results from the received search results, including: adding the first search result to the set of final search results: determining that a first document corresponding to the first search result and a second document corresponding to the second search result are query-specific duplicate documents from a comparison of one or more of the first query-relevant parts of the first document and one or more second query-relevant parts of the second document, where each query-relevant part includes at least one of the one or more keywords; and in response to the determination, not adding the second search result to the set of final search results; and presenting the set of final search results. | 3. An apparatus comprising: at least one processor; and at least one storage device storing processor executable instructions which, when executed by the at least one processor, processes search results by: receiving search results in response to a query, the query including one or more keywords, the search results including a first search result and a second search result; generating a set of final search results from the received search results, including: adding the first search result to the set of final search results: determining that a first document corresponding to the first search result and a second document corresponding to the second search result are query-specific duplicate documents from a comparison of one or more of the first query-relevant parts of the first document and one or more second query-relevant parts of the second document, where each query-relevant part includes at least one of the one or more keywords; and in response to the determination, not adding the second search result to the set of final search results; and presenting the set of final search results. 7. The apparatus of claim 3 wherein the set of final search results includes Web pages. | 0.923818 |
10,038,786 | 26 | 32 | 26. A non-transitory computer-readable medium storing a set of instructions that when executed cause a computer to perform a method comprising: determining one or more mood metrics in each of two or more chat stages of a real-time textual conversation between an agent and a customer, wherein said determining the one or more mood metrics for a chat stage of the real-time textual conversation, by the processor, further comprises determining an overall mood for the chat stage based on a polarity based approach by: assigning polarity labels to features present in the chat stage; assigning polarity strength scores for the polarity labels assigned to the features present in the chat stage; calculating weighted polarity scores for the features based on aggregation of the polarity labels and the polarity strength scores to determine the overall mood for the chat stage; and determining the overall mood, by the processor, based on a subjectivity-based approach by removing terms classified as objective from the real-time textual conversation prior to assigning the polarity labels and the polarity strength scores; tracking changes in the one or more mood metrics across the two or more chat stages of the real-time textual conversation between the agent and the customer; and determining at least one action associated with the real-time textual conversation based on the changes in the one or more mood metrics; and performing, by the processor, the at least one action associated with the real-time textual conversation, wherein performing the at least one action comprises any of: displaying, by the processor, information associated with the at least one action to a supervisor monitoring the real-time textual conversation and providing, by the processor, the information associated with the at least one action to the agent engaged in the real-time textual conversation based on an input received from the supervisor so as to enable the agent to perform the at least one action thereby causing a target outcome of the real-time textual conversation; monitoring an agent engagement score associated with the two or more chat stages of the real-time textual conversation; storing the real-time textual conversation with a timestamp of the real-time textual conversation; and displaying one or more textual recommendations to the agent so as to enable the agent to use the one or more textual recommendations in the real-time textual conversation to thereby cause a target outcome of the real-time textual conversation. | 26. A non-transitory computer-readable medium storing a set of instructions that when executed cause a computer to perform a method comprising: determining one or more mood metrics in each of two or more chat stages of a real-time textual conversation between an agent and a customer, wherein said determining the one or more mood metrics for a chat stage of the real-time textual conversation, by the processor, further comprises determining an overall mood for the chat stage based on a polarity based approach by: assigning polarity labels to features present in the chat stage; assigning polarity strength scores for the polarity labels assigned to the features present in the chat stage; calculating weighted polarity scores for the features based on aggregation of the polarity labels and the polarity strength scores to determine the overall mood for the chat stage; and determining the overall mood, by the processor, based on a subjectivity-based approach by removing terms classified as objective from the real-time textual conversation prior to assigning the polarity labels and the polarity strength scores; tracking changes in the one or more mood metrics across the two or more chat stages of the real-time textual conversation between the agent and the customer; and determining at least one action associated with the real-time textual conversation based on the changes in the one or more mood metrics; and performing, by the processor, the at least one action associated with the real-time textual conversation, wherein performing the at least one action comprises any of: displaying, by the processor, information associated with the at least one action to a supervisor monitoring the real-time textual conversation and providing, by the processor, the information associated with the at least one action to the agent engaged in the real-time textual conversation based on an input received from the supervisor so as to enable the agent to perform the at least one action thereby causing a target outcome of the real-time textual conversation; monitoring an agent engagement score associated with the two or more chat stages of the real-time textual conversation; storing the real-time textual conversation with a timestamp of the real-time textual conversation; and displaying one or more textual recommendations to the agent so as to enable the agent to use the one or more textual recommendations in the real-time textual conversation to thereby cause a target outcome of the real-time textual conversation. 32. The computer-readable medium of claim 26 , wherein said determining the one or more mood metrics comprises determining a customer engagement score based on at least one of a customer sentiment parameter, a customer response time, and a frequency of use of emoticons in the real-time textual conversation. | 0.834764 |
7,577,901 | 11 | 14 | 11. The method of claim 4 , further comprising: in response to a request to retrieve a multimedia document from among the plurality of multimedia documents stored in the storage, performing a content-based search on multimedia annotations of the plurality of multimedia documents for the requested multimedia document within the storage based on content of the multimedia annotation associated with the requested multimedia document. | 11. The method of claim 4 , further comprising: in response to a request to retrieve a multimedia document from among the plurality of multimedia documents stored in the storage, performing a content-based search on multimedia annotations of the plurality of multimedia documents for the requested multimedia document within the storage based on content of the multimedia annotation associated with the requested multimedia document. 14. The method of claim 11 , wherein the content-based search is performed on a video clip of the multimedia annotation based on an image of the user using face recognition techniques. | 0.922232 |
8,804,178 | 1 | 7 | 1. A method for routing a confirmation of receipt of a facsimile or portion thereof, comprising: analyzing text of a facsimile for at least one of a meaning and a context of the text; recognizing one or more global document features of the facsimile based at least in part on the analysis; comparing at least one recognized global document feature in the facsimile to a plurality of known document types; routing one or more confirmations to one or more destinations based at least in part on the analysis and the comparison; and determining a type of information contained within each physical area of significance and each logical area of significance; wherein the global document features comprise physical areas of significance and logical areas of significance, and wherein the determining does not utilize any OCR techniques. | 1. A method for routing a confirmation of receipt of a facsimile or portion thereof, comprising: analyzing text of a facsimile for at least one of a meaning and a context of the text; recognizing one or more global document features of the facsimile based at least in part on the analysis; comparing at least one recognized global document feature in the facsimile to a plurality of known document types; routing one or more confirmations to one or more destinations based at least in part on the analysis and the comparison; and determining a type of information contained within each physical area of significance and each logical area of significance; wherein the global document features comprise physical areas of significance and logical areas of significance, and wherein the determining does not utilize any OCR techniques. 7. A method as recited in claim 1 , further comprising determining a position of one or more of the global document features. | 0.885531 |
9,922,655 | 2 | 4 | 2. The system of claim 1 , wherein the interruption determining circuit determines an allowable time for causing the computer speech output unit to output the computer speech based on the priority setting and the status of the human conversation. | 2. The system of claim 1 , wherein the interruption determining circuit determines an allowable time for causing the computer speech output unit to output the computer speech based on the priority setting and the status of the human conversation. 4. The system of claim 2 , wherein the interruption determining circuit sets the allowable time for interruption as a pause time in the human conversation. | 0.943431 |
7,571,110 | 26 | 27 | 26. The method of claim 25 , said compensation report is constrained by at least one of: an attribute of said user profile; and a reporting goal. | 26. The method of claim 25 , said compensation report is constrained by at least one of: an attribute of said user profile; and a reporting goal. 27. The method of claim 26 , said goal comprising at least one of: a desired compensation; a desired range of compensation; a desired geographic location; a desired firm; and a desired range of firms. | 0.973836 |
8,645,388 | 11 | 17 | 11. A system for processing a query, the system comprising: a processor-based database management system executed on a computer system and configured to store a structured document in its native format and to provide an inverted multi-path index configured to store a plurality of path-value pairs and a selectivity factor for each of the path-value pairs based on a number of the structured documents that include one of the path-value pairs, wherein each path-value pair references at least one structured document stored in the database system and comprises an index path expression of an indexed element in an indexed structured document and an indexed value associated with the indexed element; and a processor-based indexing engine executed on the computer system and configured to receive a clause including a path expression-value pair comprising a path expression associated with an element of a structured document, wherein the clause is included in a query for at least one structured document satisfying the clause, to determine that the clause can be processed by the inverted multi-path index, to process the clause to identify a path-value pair in the inverted multi-path index matching the path expression-value pair of the clause, and to identify the at least one structured document referenced by the matching path-value pair. | 11. A system for processing a query, the system comprising: a processor-based database management system executed on a computer system and configured to store a structured document in its native format and to provide an inverted multi-path index configured to store a plurality of path-value pairs and a selectivity factor for each of the path-value pairs based on a number of the structured documents that include one of the path-value pairs, wherein each path-value pair references at least one structured document stored in the database system and comprises an index path expression of an indexed element in an indexed structured document and an indexed value associated with the indexed element; and a processor-based indexing engine executed on the computer system and configured to receive a clause including a path expression-value pair comprising a path expression associated with an element of a structured document, wherein the clause is included in a query for at least one structured document satisfying the clause, to determine that the clause can be processed by the inverted multi-path index, to process the clause to identify a path-value pair in the inverted multi-path index matching the path expression-value pair of the clause, and to identify the at least one structured document referenced by the matching path-value pair. 17. The system of claim 11 wherein the clause includes a first path expression-value pair joined to a second path expression-value pair, and wherein the indexing engine is configured to: identify a first path-value pair in the inverted multi-path index that matches the first path expression-value pair of the clause; identify a second path-value pair in the inverted multi-path index that matches the second path expression-value pair of the clause; intersect the at least one structured documents referenced by the first path-value pair with the at least one structured documents referenced by the second path-value pair; and identify at least one common structured document referenced by the first path-value pair and the second path-value pair. | 0.508541 |
8,775,158 | 1 | 2 | 1. A data processing device generating a graph which expresses an input data structure by a plurality of nodes having a single word as content thereof and by a dependency branch connecting two nodes in a dependent relationship within the plurality of nodes, and extracting a characteristic structure characterizing the input data from the graph, the device comprising: an association node extraction unit for extracting nodes semantically associated with each other, which are nodes corresponding to words representing same or similar content, from each of sentence structures in a given sentence structure collection, and outputting information on the sentence collection and association nodes in each of the sentence structures; an association node joint unit for joining the nodes semantically associated with each other in each of the sentence structures by a semantic association branch based on the information on the sentence structure collection to newly generate a structure that expresses a concept which is not present, but implied, in each original sentence structure, and the association nodes in each of the sentence structures received from the association node extraction unit so as to transform each of the sentence structures in the sentence structure collection, and outputting a structure collection obtained by the transformation; and a characteristic structure extraction unit for extracting a characteristic partial structure based on the sentence structure collection transformed by joining the semantic association branch received from the association node joint unit, wherein the characteristic structure extraction unit performs characteristic structure extraction processing by distinguishing a branch indicating a dependent relationship in the graph structure from the semantic association branch. | 1. A data processing device generating a graph which expresses an input data structure by a plurality of nodes having a single word as content thereof and by a dependency branch connecting two nodes in a dependent relationship within the plurality of nodes, and extracting a characteristic structure characterizing the input data from the graph, the device comprising: an association node extraction unit for extracting nodes semantically associated with each other, which are nodes corresponding to words representing same or similar content, from each of sentence structures in a given sentence structure collection, and outputting information on the sentence collection and association nodes in each of the sentence structures; an association node joint unit for joining the nodes semantically associated with each other in each of the sentence structures by a semantic association branch based on the information on the sentence structure collection to newly generate a structure that expresses a concept which is not present, but implied, in each original sentence structure, and the association nodes in each of the sentence structures received from the association node extraction unit so as to transform each of the sentence structures in the sentence structure collection, and outputting a structure collection obtained by the transformation; and a characteristic structure extraction unit for extracting a characteristic partial structure based on the sentence structure collection transformed by joining the semantic association branch received from the association node joint unit, wherein the characteristic structure extraction unit performs characteristic structure extraction processing by distinguishing a branch indicating a dependent relationship in the graph structure from the semantic association branch. 2. The data processing device, as claimed in claim 1 , wherein the association node joint unit has a function of categorizing the association nodes into strong association nodes which are in a strong semantic association and weak association nodes which are in a weak semantic association, and a function of joining the strong association nodes into one node. | 0.501389 |
8,458,115 | 15 | 18 | 15. A computer-implemented method comprising: in response to Internet browsing activities, identifying a collection of user generated content; searching an image library for images having associated descriptive data that is similar to text in the collection of user generated content; processing the descriptive data of the images to derive a topic for the collection of user generated content; selecting a recommendation based at least in part on the topic derived; and in further response to the Internet browsing activities, presenting the recommendation. | 15. A computer-implemented method comprising: in response to Internet browsing activities, identifying a collection of user generated content; searching an image library for images having associated descriptive data that is similar to text in the collection of user generated content; processing the descriptive data of the images to derive a topic for the collection of user generated content; selecting a recommendation based at least in part on the topic derived; and in further response to the Internet browsing activities, presenting the recommendation. 18. A method as recited in claim 15 , wherein processing the descriptive data comprises searching a vertical topic space based on the descriptive data. | 0.901307 |
7,627,882 | 5 | 6 | 5. The device of claim 1 , wherein the display further includes a first user-link, where selecting the link provides instructions to the display means. | 5. The device of claim 1 , wherein the display further includes a first user-link, where selecting the link provides instructions to the display means. 6. The device of claim 5 , wherein the instructions provided to the display means instruct the display means to display a television program. | 0.955577 |
8,369,627 | 1 | 2 | 1. A system for generating groups of cluster spines for display, comprising: a theme generator to generate one or more concepts for each cluster in a set of clusters; a spine generator to form spines from at least a portion of the clusters based on the concepts; a spine placement module to place at least one spine unique from all the other spines; a spine group generator to generate one or more spine groups by positioning at least one unplaced spine in relation to one of the placed unique spines; and a display to display the spine groups. | 1. A system for generating groups of cluster spines for display, comprising: a theme generator to generate one or more concepts for each cluster in a set of clusters; a spine generator to form spines from at least a portion of the clusters based on the concepts; a spine placement module to place at least one spine unique from all the other spines; a spine group generator to generate one or more spine groups by positioning at least one unplaced spine in relation to one of the placed unique spines; and a display to display the spine groups. 2. A system according to claim 1 , further comprising at least one of: a cluster positioning module to position one or more remaining clusters in the cluster set proximate to one of the spines in one of the spine groups when a sufficient similarity is determined between the remaining cluster and the spine; and a further cluster positioning module to position the cluster in a display area separately from the spine groups when a sufficient similarity between the remaining cluster and the spine is not met. | 0.500982 |
9,747,279 | 1 | 9 | 1. A system comprising: at least one processor; and a memory encoding computer executable instruction that, when executed by the at least one processor, cause the at least one processor to perform a method for contextual language understanding, the method comprising: receiving a first natural language input based on input from a first user; identifying a first set of entities in the first natural language input utilizing a schema; receiving a first response to the first natural language input based on the first set of entities, wherein the first response is generated by the system; identifying a second set of entities in the first response utilizing the schema; receiving a second natural language input; identifying a third set of entities in the second natural language input utilizing the schema; identifying a first set of carryover entities from any previous set of entities for carryover based on the third set of entities; determining a first user intent based on the third set of entities and the first set of carryover entities; and generating a second response based on the first user intent. | 1. A system comprising: at least one processor; and a memory encoding computer executable instruction that, when executed by the at least one processor, cause the at least one processor to perform a method for contextual language understanding, the method comprising: receiving a first natural language input based on input from a first user; identifying a first set of entities in the first natural language input utilizing a schema; receiving a first response to the first natural language input based on the first set of entities, wherein the first response is generated by the system; identifying a second set of entities in the first response utilizing the schema; receiving a second natural language input; identifying a third set of entities in the second natural language input utilizing the schema; identifying a first set of carryover entities from any previous set of entities for carryover based on the third set of entities; determining a first user intent based on the third set of entities and the first set of carryover entities; and generating a second response based on the first user intent. 9. The system of claim 1 , the method further comprising: identifying a fourth set of entities in the second response utilizing the schema; receiving a third natural language input, identifying a fifth set of entities in the third natural language input utilizing the schema, identifying a second set of carryover entities from any of the previous set of entities for, wherein the previous set of entities now include the third set of entities and the fourth set of entities, for carry over based on the fifth set of entities; determining a second user intent based on the fifth set of entities and the second set of carryover entities; and generating a third response based on the second user intent. | 0.500712 |
8,874,549 | 30 | 32 | 30. The system according to claim 23 , wherein the summarization engine is further adapted to cluster elements within the result set based on the measurement of distinctiveness. | 30. The system according to claim 23 , wherein the summarization engine is further adapted to cluster elements within the result set based on the measurement of distinctiveness. 32. The system according to claim 30 , wherein the summarization engine is further adapted to output the result set organized by at least one cluster of elements. | 0.958946 |
7,747,437 | 16 | 29 | 16. A speech recognition processing arrangement comprising: an automatic speech recognition engine having: means for producing an N-best list of recognition hypotheses corresponding to a spoken input; and means for rescoring the hypotheses to produce a rescored N-best list output; wherein the rescoring uses a plurality of rescoring categories based on position in the rescored N-best list such that some positions in the rescored N-best list are rescored based on a first combination of rescoring categories and other positions in the rescored N-best list are rescored based on a second combination of rescoring categories. | 16. A speech recognition processing arrangement comprising: an automatic speech recognition engine having: means for producing an N-best list of recognition hypotheses corresponding to a spoken input; and means for rescoring the hypotheses to produce a rescored N-best list output; wherein the rescoring uses a plurality of rescoring categories based on position in the rescored N-best list such that some positions in the rescored N-best list are rescored based on a first combination of rescoring categories and other positions in the rescored N-best list are rescored based on a second combination of rescoring categories. 29. A speech processing arrangement according to claim 16 , wherein the means for rescoring includes means for dividing the rescored N-best list into blocks, each block corresponding to a range of ranks in the rescored N-best list, the block boundaries varying depending on a metric corresponding to an expected recognition accuracy for the spoken input. | 0.566176 |
7,756,857 | 1 | 11 | 1. A method of querying one or more structured documents with an original query, the method comprising the steps of: providing a list of indexing components of the structured documents to be queried wherein said indexing components comprise at least markup tags; parsing at least one schema of the structured documents to determine predefined deterministic relationships between the indexing components; removing from the list those indexing components whose occurrences can be inferred, using said determined predefined deterministic relationships, from occurrences of another indexing component, to provide a reduced list of indexing components; indexing said structured documents by generating indices using said reduced list of indexing components, wherein the generated indices point to occurrences of the indexing components included in the reduced list of indexing components within the structured documents; generating a mapping list that maps the removed indexing components to indexing components in the reduced list of indexing components used to generate the indices; reformulating an original query by substituting references to one or more removed indexing components in the original query with references to indexing components in said reduced list of indexing components, using the generated mapping list; querying said one or more structured documents by using said generated indices and said reformulated query to provide one or more sets of intermediate results; and performing post-retrieval processing on the one or more sets of intermediate results to form a final result of said original query, wherein said post-retrieval processing comprises the sub-steps of: locating the removed indexing components existing in the one or more sets of intermediate results; and generating the final result set to satisfy the original query using the located removed indexing components. | 1. A method of querying one or more structured documents with an original query, the method comprising the steps of: providing a list of indexing components of the structured documents to be queried wherein said indexing components comprise at least markup tags; parsing at least one schema of the structured documents to determine predefined deterministic relationships between the indexing components; removing from the list those indexing components whose occurrences can be inferred, using said determined predefined deterministic relationships, from occurrences of another indexing component, to provide a reduced list of indexing components; indexing said structured documents by generating indices using said reduced list of indexing components, wherein the generated indices point to occurrences of the indexing components included in the reduced list of indexing components within the structured documents; generating a mapping list that maps the removed indexing components to indexing components in the reduced list of indexing components used to generate the indices; reformulating an original query by substituting references to one or more removed indexing components in the original query with references to indexing components in said reduced list of indexing components, using the generated mapping list; querying said one or more structured documents by using said generated indices and said reformulated query to provide one or more sets of intermediate results; and performing post-retrieval processing on the one or more sets of intermediate results to form a final result of said original query, wherein said post-retrieval processing comprises the sub-steps of: locating the removed indexing components existing in the one or more sets of intermediate results; and generating the final result set to satisfy the original query using the located removed indexing components. 11. A method as claimed in claim 1 , wherein the generated indices are generated by updating existing indices only for the indexing components that appear in the reduced list of indexing components. | 0.838762 |
9,558,747 | 1 | 4 | 1. A high intelligibility voice announcement system, comprising: an announcement station, including: a speech to text engine configured to convert a spoken announcement from a user to converted text data; an error recognition engine configured to identify any errors in the converted text data; a display on the announcement station configured to display the converted text data, wherein: the identified errors in the converted text data are identified for the user; and corrected text data is displayed, via the display on the announcement station, in a queue system including other announcements until corrected text data of the spoken announcement is ready to be sent; an input device allowing the user to correct any identified errors in the converted text data, resulting in the corrected text data; a number of zones each including one or more speakers; and a transmitter configured to send the corrected text data to the one or more speakers of the number of zones; and a text to speech engine configured to convert the corrected text data to an audible message in preparation for broadcast via the one or more speakers, wherein the broadcast of the audible message is delayed via a configurable delay that is different for the one or more speakers in different ones of the number of zones. | 1. A high intelligibility voice announcement system, comprising: an announcement station, including: a speech to text engine configured to convert a spoken announcement from a user to converted text data; an error recognition engine configured to identify any errors in the converted text data; a display on the announcement station configured to display the converted text data, wherein: the identified errors in the converted text data are identified for the user; and corrected text data is displayed, via the display on the announcement station, in a queue system including other announcements until corrected text data of the spoken announcement is ready to be sent; an input device allowing the user to correct any identified errors in the converted text data, resulting in the corrected text data; a number of zones each including one or more speakers; and a transmitter configured to send the corrected text data to the one or more speakers of the number of zones; and a text to speech engine configured to convert the corrected text data to an audible message in preparation for broadcast via the one or more speakers, wherein the broadcast of the audible message is delayed via a configurable delay that is different for the one or more speakers in different ones of the number of zones. 4. The high intelligibility voice announcement system of claim 1 , wherein at least one of the speech to text engine or the error recognition engine is located at one of the announcement station or externally at a server connected via the Internet. | 0.502008 |
9,805,024 | 1 | 5 | 1. A method comprising: tokenizing, by a computer-based system, a body of text by splitting the body of text into individual tokens; weighting, by the computer-based system and based on the tokenizing, the individual tokens having a pronoun grammatical role based on structured contextual information; analyzing, by the computer-based system, the structured contextual information to facilitate a homophora resolution; integrating, by the computer-based system and in response to the analyzing and in response to the weighting of the individual tokens, the homophora resolution into an anaphora resolution algorithm by substituting the structured contextual information into the body of text to create a substituted body of text; translating, by the computer-based system and based on the integrating, semantic concepts of the substituted body of text into one or more semantic tags; conducting, by the computer-based system, in response to the translating and using the one or more semantic tags, semantic reasoning to facilitate pattern identification within a group of documents, analyzing, by the computer-based system and based on semantic reasoning and using the one or more semantic tags, implied relationships of the text within the group of documents to identify a specific topic; and displaying, by the computer-based system to a user interface, the specific identified topic of the substituted body of text. | 1. A method comprising: tokenizing, by a computer-based system, a body of text by splitting the body of text into individual tokens; weighting, by the computer-based system and based on the tokenizing, the individual tokens having a pronoun grammatical role based on structured contextual information; analyzing, by the computer-based system, the structured contextual information to facilitate a homophora resolution; integrating, by the computer-based system and in response to the analyzing and in response to the weighting of the individual tokens, the homophora resolution into an anaphora resolution algorithm by substituting the structured contextual information into the body of text to create a substituted body of text; translating, by the computer-based system and based on the integrating, semantic concepts of the substituted body of text into one or more semantic tags; conducting, by the computer-based system, in response to the translating and using the one or more semantic tags, semantic reasoning to facilitate pattern identification within a group of documents, analyzing, by the computer-based system and based on semantic reasoning and using the one or more semantic tags, implied relationships of the text within the group of documents to identify a specific topic; and displaying, by the computer-based system to a user interface, the specific identified topic of the substituted body of text. 5. The method of claim 1 , wherein a known format of the body of text is based on a data source from which the body of text was received. | 0.707265 |
8,239,751 | 14 | 15 | 14. The non-transitory computer readable storage medium of claim 10 , wherein receiving the request to add the cell value comprises applying a request corresponding to an existing spreadsheet cell to at least one other spreadsheet cell. | 14. The non-transitory computer readable storage medium of claim 10 , wherein receiving the request to add the cell value comprises applying a request corresponding to an existing spreadsheet cell to at least one other spreadsheet cell. 15. The non-transitory computer readable storage medium of claim 14 , wherein applying the request to at least one other spreadsheet cell includes automatically adjusting cell references to correspond to the at least one other spreadsheet cell. | 0.915629 |
7,783,614 | 3 | 4 | 3. The method of claim 2 , further comprising associating the database query with a given markup language element in the document for writing the results of the database query into the document in a location in the document associated with the database query. | 3. The method of claim 2 , further comprising associating the database query with a given markup language element in the document for writing the results of the database query into the document in a location in the document associated with the database query. 4. The method of claim 3 , further comprising storing a programming procedure in the database for reading the data from the database and for writing the data to the document in a location in the document associated with the given markup language element. | 0.957582 |
9,652,453 | 2 | 3 | 2. The method of claim 1 , wherein the estimating of the parameters comprises: learning a function which maps values of at least one parameter of the translation scoring function to the computed measures of similarity for the comparative domains; and where the learned function indicates a correlation between the at least one parameter and the computed measures of similarity, estimating the at least one parameter for the target domain based on the learned function. | 2. The method of claim 1 , wherein the estimating of the parameters comprises: learning a function which maps values of at least one parameter of the translation scoring function to the computed measures of similarity for the comparative domains; and where the learned function indicates a correlation between the at least one parameter and the computed measures of similarity, estimating the at least one parameter for the target domain based on the learned function. 3. The method of claim 2 , where the learned function is a linear regression function. | 0.981577 |
7,711,573 | 436 | 439 | 436. The computer program product of claim 435 , wherein the required term of experience is rounded up to a unit of time. | 436. The computer program product of claim 435 , wherein the required term of experience is rounded up to a unit of time. 439. The computer program product of claim 436 , wherein each said at least one matching resume satisfies the job description when the parsed resume includes the required skill or experience-related phrase for any said at least one job requirement, and the term of experience for the required skill or experience-related phrase in the parsed resume is greater than or equal to the required term of experience. | 0.924539 |
6,032,116 | 7 | 8 | 7. The speech recognition system of claim 1 further comprising: a quantizer having a codebook having C codewords for generating the distance measure between each of the P line spectral pair frequencies of the speech input signal and each of a plurality of reference speech signals; and a second speech classifier to receive the output data based on the distance measures and generate speech classification output data to classify the speech input signal as one of u vocabulary words. | 7. The speech recognition system of claim 1 further comprising: a quantizer having a codebook having C codewords for generating the distance measure between each of the P line spectral pair frequencies of the speech input signal and each of a plurality of reference speech signals; and a second speech classifier to receive the output data based on the distance measures and generate speech classification output data to classify the speech input signal as one of u vocabulary words. 8. The speech recognition system of claim 7 wherein the quantizer is a single codebook quantizer having codewords representing a vocabulary of u words. | 0.941968 |
6,112,177 | 1 | 4 | 1. A method for generating a photo-realistic talking head for a text-to-speech synthesis application, comprising the steps of: sampling images of a subject; extracting a plurality of parameters from each image sample; storing the image sample parameters into an animation library; sampling multiphone images of the subject; sampling sounds associated with the multiphone images; extracting a plurality of parameters from each multiphone image sample; storing the multiphone image parameters and associated sound samples into a coarticulation library; reading, based on an input stimulus comprising one or more phoneme sequences, parameters from the coarticulation library corresponding to each phoneme sequence; generating, using parameters from the animation library corresponding to the read parameters, a sequence of animated frames, the sequence tracking the input stimulus. | 1. A method for generating a photo-realistic talking head for a text-to-speech synthesis application, comprising the steps of: sampling images of a subject; extracting a plurality of parameters from each image sample; storing the image sample parameters into an animation library; sampling multiphone images of the subject; sampling sounds associated with the multiphone images; extracting a plurality of parameters from each multiphone image sample; storing the multiphone image parameters and associated sound samples into a coarticulation library; reading, based on an input stimulus comprising one or more phoneme sequences, parameters from the coarticulation library corresponding to each phoneme sequence; generating, using parameters from the animation library corresponding to the read parameters, a sequence of animated frames, the sequence tracking the input stimulus. 4. The method of claim 1, further comprising the step of: timestamping the multiphone image samples and sound samples. | 0.781481 |
9,720,913 | 1 | 2 | 1. A system comprising: at least one processor; and a memory that stores instructions that, when executed by the at least one processor, cause the system to perform operations of: obtaining a document that is responsive to a user query, determining an interest of the user based on stored data associated with the user, determining that a portion of the document relates to the interest of the user; generating a first snippet for the document based on the portion of the document that relates to the interest of the user; generating a second snippet for the document; providing the first snippet for the document and the second snippet for the document as part of a result list; and providing a control that allows the user to toggle between a first view of the first snippet and a second view of the second snippet. | 1. A system comprising: at least one processor; and a memory that stores instructions that, when executed by the at least one processor, cause the system to perform operations of: obtaining a document that is responsive to a user query, determining an interest of the user based on stored data associated with the user, determining that a portion of the document relates to the interest of the user; generating a first snippet for the document based on the portion of the document that relates to the interest of the user; generating a second snippet for the document; providing the first snippet for the document and the second snippet for the document as part of a result list; and providing a control that allows the user to toggle between a first view of the first snippet and a second view of the second snippet. 2. The system of claim 1 , the instructions causing the system to further perform the operations of: refining the generated first snippet for the document prior to providing the first snippet. | 0.735537 |
8,599,419 | 1 | 9 | 1. A method for routing a facsimile, comprising: receiving or generating text of a facsimile in a computer-readable format; ascertaining one or more of a significance and a relevance of one or more words of the text by ascertaining a position of one or more keywords in the text, wherein at least two of the keywords are not adjacent in the text; routing the facsimile or text thereof to an intended recipient identified by recognizing at least one of a name, an email address and contact information of the intended recipient in the facsimile; analyzing the text of the facsimile for at least one of a meaning and a context of the text; and routing the facsimile or text thereof to one or more other destinations based on the analysis. | 1. A method for routing a facsimile, comprising: receiving or generating text of a facsimile in a computer-readable format; ascertaining one or more of a significance and a relevance of one or more words of the text by ascertaining a position of one or more keywords in the text, wherein at least two of the keywords are not adjacent in the text; routing the facsimile or text thereof to an intended recipient identified by recognizing at least one of a name, an email address and contact information of the intended recipient in the facsimile; analyzing the text of the facsimile for at least one of a meaning and a context of the text; and routing the facsimile or text thereof to one or more other destinations based on the analysis. 9. A method as recited in claim 1 , wherein the analysis further includes classifying the text as a specific document type, wherein the classifying utilizes one or more of: a naïve Bayes classifier, tf-idf weighting, latent semantic analysis, support vector machines, an artificial neural network, a k-nearest neighbor algorithm, decision trees, and concept mining. | 0.856975 |
8,185,377 | 7 | 16 | 7. A method of evaluating translation quality of a machine translator, comprising: intermittently generating and updating a bilingual corpus, augmented with linguistic information generated from performing semantic analysis of the bilingual corpus; when the bilingual corpus is generated, extracting evaluation examples, each evaluation example being extracted to test the machine translator in one of a plurality of predefined categories of evaluation; receiving translation results from the machine translator, for the evaluation examples; and utilizing a processor that is a component of a computing device to evaluate the translation results in each of the predefined categories of evaluation; and storing evaluation results for display to a user. | 7. A method of evaluating translation quality of a machine translator, comprising: intermittently generating and updating a bilingual corpus, augmented with linguistic information generated from performing semantic analysis of the bilingual corpus; when the bilingual corpus is generated, extracting evaluation examples, each evaluation example being extracted to test the machine translator in one of a plurality of predefined categories of evaluation; receiving translation results from the machine translator, for the evaluation examples; and utilizing a processor that is a component of a computing device to evaluate the translation results in each of the predefined categories of evaluation; and storing evaluation results for display to a user. 16. The method of claim 7 wherein intermittently generating and updating the bilingual corpus comprises: receiving from a data source a representation of textual data in a source language and a corresponding target language. | 0.776 |
8,046,353 | 11 | 12 | 11. A search database system, comprising: a hierarchical database to store a set of data in a hierarchical manner, wherein each of a plurality of points in the hierarchy has a unique identifier; a hierarchical database engine coupled with the hierarchical database, the hierarchical database engine to search the set of data stored in the hierarchical database; a document generator coupled with the hierarchical database engine, the document generator to create a different document from the data stored under each of the plurality of points in the hierarchical database; an inverted index; an inverted index engine coupled with the document generator, the inverted index engine to index each document and the associated unique identifiers in the inverted index, and search the inverted index; a search server user interface, coupled with the hierarchical database engine and the inverted index engine, the search server user interface to receive a single search query that has syntax identifying an unstructured search string within a structured search query to automatically cause a search of the inverted index and use of the result to automatically search the hierarchical database, the search server user interface including: a parser to extract the unstructured search string from the single search query and forward the extracted unstructured search string to the inverted index engine to cause a search of the inverted index; a structured query generator to receive a result of the inverted index search that includes the one or more unique identifiers of the documents that meet the search, and for each of the unique identifiers in the result, to generate a separate search query from the single search query by replacing the unstructured search string in the structured search query with that unique identifier, and forward the separate search query to the hierarchical database engine to cause a search of the hierarchical database according to the separate search query; wherein the hierarchical database includes one or more sub-trees branching from a tree root node, wherein each sub-tree includes one or more nodes starting at a sub-tree root node and includes at least one value, and wherein the unique identifier associated with each of the plurality of documents corresponds to the sub-tree root node; wherein each node existing directly below the tree root node represents a private sub-tree, wherein values and node information in the private sub-tree are private to an organization; and wherein the syntax for the single search query includes a SELECT clause and a FROM clause, wherein the SELECT clause includes syntax to identify a path in the hierarchical database starting at the tree root node, and wherein the FROM clause includes the unstructured search string. | 11. A search database system, comprising: a hierarchical database to store a set of data in a hierarchical manner, wherein each of a plurality of points in the hierarchy has a unique identifier; a hierarchical database engine coupled with the hierarchical database, the hierarchical database engine to search the set of data stored in the hierarchical database; a document generator coupled with the hierarchical database engine, the document generator to create a different document from the data stored under each of the plurality of points in the hierarchical database; an inverted index; an inverted index engine coupled with the document generator, the inverted index engine to index each document and the associated unique identifiers in the inverted index, and search the inverted index; a search server user interface, coupled with the hierarchical database engine and the inverted index engine, the search server user interface to receive a single search query that has syntax identifying an unstructured search string within a structured search query to automatically cause a search of the inverted index and use of the result to automatically search the hierarchical database, the search server user interface including: a parser to extract the unstructured search string from the single search query and forward the extracted unstructured search string to the inverted index engine to cause a search of the inverted index; a structured query generator to receive a result of the inverted index search that includes the one or more unique identifiers of the documents that meet the search, and for each of the unique identifiers in the result, to generate a separate search query from the single search query by replacing the unstructured search string in the structured search query with that unique identifier, and forward the separate search query to the hierarchical database engine to cause a search of the hierarchical database according to the separate search query; wherein the hierarchical database includes one or more sub-trees branching from a tree root node, wherein each sub-tree includes one or more nodes starting at a sub-tree root node and includes at least one value, and wherein the unique identifier associated with each of the plurality of documents corresponds to the sub-tree root node; wherein each node existing directly below the tree root node represents a private sub-tree, wherein values and node information in the private sub-tree are private to an organization; and wherein the syntax for the single search query includes a SELECT clause and a FROM clause, wherein the SELECT clause includes syntax to identify a path in the hierarchical database starting at the tree root node, and wherein the FROM clause includes the unstructured search string. 12. The search database system of claim 11 , wherein the result of the inverted index search includes a plurality of unique identifiers, where at least one unique identifier in the result corresponds to a virtual document that is associated to a first type of data in the hierarchical database, wherein the first type of data belongs to a first data domain, and where at least one other unique identifier in the result corresponds to a different virtual document that is associated to a second type of data in the hierarchical database, wherein the second type of data belongs to a different second data domain. | 0.500817 |
7,953,295 | 1 | 2 | 1. A computer-implemented method comprising: receiving, by a computing system, a plurality of images each image including a corresponding version of an identified candidate text region; aligning, by the computing system, each candidate text region from the plurality of images to a high resolution grid; compositing, by the computing system, the aligned candidate text regions to create a single superresolution image; and performing, by the computing system, character recognition on the superresolution image to identify text. | 1. A computer-implemented method comprising: receiving, by a computing system, a plurality of images each image including a corresponding version of an identified candidate text region; aligning, by the computing system, each candidate text region from the plurality of images to a high resolution grid; compositing, by the computing system, the aligned candidate text regions to create a single superresolution image; and performing, by the computing system, character recognition on the superresolution image to identify text. 2. The method of claim 1 , where receiving a plurality of images includes receiving images having features associated with text and features associated with non-text image content. | 0.754768 |
8,271,473 | 1 | 3 | 1. A computer-implemented method for search engine optimization of career listings on a network, the method comprising: establishing base information for each of a plurality of careers at a company; creating at least one category webpage based on the base information for display over the network, the category webpage associated with a career listing category identifying the category webpage; creating a job listing page for each of the plurality of careers based on the respective base information for each career; associating at least one job listing page with a category webpage, the category webpage including a network link to each associated job listing page; and displaying the category webpage over the network to an end user job searcher; wherein the category webpage is persistent, such that at least one characteristic of the category webpage remains static on the network regardless of any change in job listing pages associated with the category webpage. | 1. A computer-implemented method for search engine optimization of career listings on a network, the method comprising: establishing base information for each of a plurality of careers at a company; creating at least one category webpage based on the base information for display over the network, the category webpage associated with a career listing category identifying the category webpage; creating a job listing page for each of the plurality of careers based on the respective base information for each career; associating at least one job listing page with a category webpage, the category webpage including a network link to each associated job listing page; and displaying the category webpage over the network to an end user job searcher; wherein the category webpage is persistent, such that at least one characteristic of the category webpage remains static on the network regardless of any change in job listing pages associated with the category webpage. 3. The computer-implemented method of claim 1 , wherein the at least one characteristic comprises information associated with a location. | 0.752708 |
8,086,600 | 13 | 16 | 13. A system comprising: one or more computers; and a computer-readable storage device having stored thereon instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising: receiving a plurality of first search results in a first presentation format, the first search results received from a first search engine, the first search results identified for a search query directed to the first search engine, the first search results having an associated order indicative of respective first quality scores that are used to rank the first search results; receiving one or more second search results in a second presentation format different from the first presentation format, the second search results received from a second search engine, the second search results identified for the search query directed to the second search engine, wherein the first search engine searches a first corpus of first resources, wherein the second search engine searches a second corpus of second resources, and wherein the first search engine and the second search engines are distinct from each other; obtaining a respective first quality score for a plurality of the first search results, the respective first quality score determined in relation to the corpus of first resources and obtaining a respective second quality score for each of the one or more second search results, each respective second quality score determined in relation to the corpus of second resources; and inserting one or more of the second search results into the order including decreasing one or more of the respective first quality scores by reducing a contribution of a scoring feature unique to the first search results and distinct from scoring features of the second search results so that the inserted second search results occur within a number of top- ranked search results in the order. | 13. A system comprising: one or more computers; and a computer-readable storage device having stored thereon instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising: receiving a plurality of first search results in a first presentation format, the first search results received from a first search engine, the first search results identified for a search query directed to the first search engine, the first search results having an associated order indicative of respective first quality scores that are used to rank the first search results; receiving one or more second search results in a second presentation format different from the first presentation format, the second search results received from a second search engine, the second search results identified for the search query directed to the second search engine, wherein the first search engine searches a first corpus of first resources, wherein the second search engine searches a second corpus of second resources, and wherein the first search engine and the second search engines are distinct from each other; obtaining a respective first quality score for a plurality of the first search results, the respective first quality score determined in relation to the corpus of first resources and obtaining a respective second quality score for each of the one or more second search results, each respective second quality score determined in relation to the corpus of second resources; and inserting one or more of the second search results into the order including decreasing one or more of the respective first quality scores by reducing a contribution of a scoring feature unique to the first search results and distinct from scoring features of the second search results so that the inserted second search results occur within a number of top- ranked search results in the order. 16. The system of claim 13 , wherein: the first resources are generic web pages and the second resources are video resources. | 0.841772 |
8,504,489 | 19 | 20 | 19. A computer program product comprising: a non-transitory computer-readable medium comprising: a first set of codes for causing a computer to receive a plurality of document coding inputs that each assign a review code to one of a plurality of first documents, which are associated with a case within an electronic discovery system and have been previously collected from one of a plurality of custodians associated with the case, wherein the review code indicates a level of relevancy or importance in relation to the case and at least one document coding input codes a first document as privileged; a second set of codes for causing a computer to (1), in response to receiving each of the plurality of document coding inputs, determine if one or more second documents, which are associated with the case, have been previously collected from the plurality of custodians and are pending review, are similar to or same as the first document and (2) in response to receiving the at least one document coding input that codes the first document as privileged determine if one or more third documents which are associated with other cases in the electronic discovery system, have been collected from the plurality of custodians and are pending review are same as the first document; third set of codes for causing a computer to (1), in response to determining that the second documents are same as the first document, automatically assign a review code assigned to the first document to the one or more second documents that are the same as the first document and (2), in response to determining that the third documents are same as the first document, automatically assign the privilege code to the one or more third documents that are the same as the first document; a fourth set of codes for causing a computer to remove the one or more second documents from a plurality of pending review documents based on the assignment of the review code, wherein the pending review documents are included in a document review assignment currently being reviewed by a reviewer; and a fifth set of codes for causing a computer to present, on a computing device display, the one or more second documents that are similar to the first document and a confidence indicator that indicates a level of similarity between the first document and a presented second document, wherein the reviewer makes a determination based on the confidence indicator as to whether the presented second document reaches a level of similarity to the first document to justify assigning the review code to the presented document. | 19. A computer program product comprising: a non-transitory computer-readable medium comprising: a first set of codes for causing a computer to receive a plurality of document coding inputs that each assign a review code to one of a plurality of first documents, which are associated with a case within an electronic discovery system and have been previously collected from one of a plurality of custodians associated with the case, wherein the review code indicates a level of relevancy or importance in relation to the case and at least one document coding input codes a first document as privileged; a second set of codes for causing a computer to (1), in response to receiving each of the plurality of document coding inputs, determine if one or more second documents, which are associated with the case, have been previously collected from the plurality of custodians and are pending review, are similar to or same as the first document and (2) in response to receiving the at least one document coding input that codes the first document as privileged determine if one or more third documents which are associated with other cases in the electronic discovery system, have been collected from the plurality of custodians and are pending review are same as the first document; third set of codes for causing a computer to (1), in response to determining that the second documents are same as the first document, automatically assign a review code assigned to the first document to the one or more second documents that are the same as the first document and (2), in response to determining that the third documents are same as the first document, automatically assign the privilege code to the one or more third documents that are the same as the first document; a fourth set of codes for causing a computer to remove the one or more second documents from a plurality of pending review documents based on the assignment of the review code, wherein the pending review documents are included in a document review assignment currently being reviewed by a reviewer; and a fifth set of codes for causing a computer to present, on a computing device display, the one or more second documents that are similar to the first document and a confidence indicator that indicates a level of similarity between the first document and a presented second document, wherein the reviewer makes a determination based on the confidence indicator as to whether the presented second document reaches a level of similarity to the first document to justify assigning the review code to the presented document. 20. The computer program product of claim 19 , wherein the fourth set of codes is further configured to cause the computer to remove, in near real-time to receiving the document coding input, the one or more second documents that are the same as the first document from the plurality of pending review documents, wherein removing occurs in near real-time to receiving the document coding input. | 0.717765 |
9,646,061 | 8 | 9 | 8. The method according to claim 4 , wherein said creating of substrings corresponding to the query string comprises: partitioning the query string into substrings; iteratively padding the query string with at least one dummy character up to the predetermined maximum number of dummy characters, to produce one or more padded strings of the query string; and partitioning each of the one or more padded strings of the query string into substrings. | 8. The method according to claim 4 , wherein said creating of substrings corresponding to the query string comprises: partitioning the query string into substrings; iteratively padding the query string with at least one dummy character up to the predetermined maximum number of dummy characters, to produce one or more padded strings of the query string; and partitioning each of the one or more padded strings of the query string into substrings. 9. The method according to claim 8 , wherein: the predetermined maximum number of dummy characters is equal to the edit distance threshold; said creating of substrings corresponding to the query string comprises discarding any substring of the query string which contains at least one dummy character; and said partitioning of the query string into substrings comprises determining the length of substrings of the query as a rounded-down integer m/(λ+1), where m is the length of the query string and λ is the edit distance threshold. | 0.849408 |
9,792,563 | 3 | 5 | 3. A system for defining a human resources system, comprising: a processor; a storage module for storing data associated with the human resources system; and a memory coupled with the processor, wherein the memory is configured to provide the processor with instructions which when executed cause the processor to: receive at least one new metadata model that defines at least one new object class in the human resource system, wherein the new metadata model includes one or more attributes, one or more relationships, and one or more methods associated with the new object class; receive process definitions; store the new metadata model including data associated with the new metadata model, and the process definitions in a minimalistic metamodel for persistence, wherein the minimalistic metamodel for persistence comprises three tables comprising an instance table, an attribute table, and a reference table for all of the objects in the human resources system, wherein the new metadata model including data associated with the new metadata model is stored by storing one or more instances of the new object class in the instance table, the one or more attributes in the attribute table, and the one or more relationships in the reference table, wherein: the instance table stores all instances of object classes in the human resource system as defined by a plurality of metadata models; the attribute table stores attribute data associated with all the instances of the object classes as defined by the plurality of metadata models; the reference table stores relationship data associated with all the instances of the object classes as defined by the plurality of metadata models; the instance table, the attribute table, and the reference table store data that has been specified; metadata model definitions and the process definitions are able to be interpreted using an interpretive engine; and the interpretive engine is configured to process the metadata model definitions and process definitions without compilation of any code; at a time of execution by the interpretive engine, all the objects specified in the instance table, the attribute table, and the reference table and processes are loaded into the memory for easy modification of instances of objects defined by the plurality of metadata models and the new metadata model; and for a process of one or more processes defined by the process definitions: defining an element to which the process responds; defining one or more process steps in response to the element; and defining an output response, wherein the process when interpreted by the interpretive engine are sufficient to define a fully functional human resource system; receive an update, wherein the update includes a change to an existing instance of an object class in the human resource system; update the human resource system by adding, removing, or changing a plurality of entries associated with the existing instance of the object class in at least one of the instance table, the attribute table, and the reference table, comprising: validate a transaction request relating to the existing instance of the object class, comprising: ensure a requestor has privileges to perform a requested transaction; check whether the transaction request corresponds to a metadata definition of an element of the transaction request; and ensure that data in the requested transaction is of a correct type and in a correct range of values; determine whether a controlling object to be updated exists, wherein the instances of the object class are organized in a tree structure, the controlling object relating to a trunk of the tree structure; in the event that the controlling object to be updated does not exist create the controlling object; and in the event that the controlling object to be updated exists, locate the controlling object associated with an instance of the object class; transfer the plurality of entries associated with the existing instance of the object class in the at least one of the instance table, the attribute table, and the reference table to the storage module after the updating of the human resource system is performed, wherein the transferring of the plurality of entries to the storage module is performed after each of the adding, removing, or changing to the plurality of entries have been completed to avoid inconsistencies in the human resource system the storage module including permanent storage; and execute the updated human resources system by interpreting the stored metadata model definitions and process definitions using the interpretive engine. | 3. A system for defining a human resources system, comprising: a processor; a storage module for storing data associated with the human resources system; and a memory coupled with the processor, wherein the memory is configured to provide the processor with instructions which when executed cause the processor to: receive at least one new metadata model that defines at least one new object class in the human resource system, wherein the new metadata model includes one or more attributes, one or more relationships, and one or more methods associated with the new object class; receive process definitions; store the new metadata model including data associated with the new metadata model, and the process definitions in a minimalistic metamodel for persistence, wherein the minimalistic metamodel for persistence comprises three tables comprising an instance table, an attribute table, and a reference table for all of the objects in the human resources system, wherein the new metadata model including data associated with the new metadata model is stored by storing one or more instances of the new object class in the instance table, the one or more attributes in the attribute table, and the one or more relationships in the reference table, wherein: the instance table stores all instances of object classes in the human resource system as defined by a plurality of metadata models; the attribute table stores attribute data associated with all the instances of the object classes as defined by the plurality of metadata models; the reference table stores relationship data associated with all the instances of the object classes as defined by the plurality of metadata models; the instance table, the attribute table, and the reference table store data that has been specified; metadata model definitions and the process definitions are able to be interpreted using an interpretive engine; and the interpretive engine is configured to process the metadata model definitions and process definitions without compilation of any code; at a time of execution by the interpretive engine, all the objects specified in the instance table, the attribute table, and the reference table and processes are loaded into the memory for easy modification of instances of objects defined by the plurality of metadata models and the new metadata model; and for a process of one or more processes defined by the process definitions: defining an element to which the process responds; defining one or more process steps in response to the element; and defining an output response, wherein the process when interpreted by the interpretive engine are sufficient to define a fully functional human resource system; receive an update, wherein the update includes a change to an existing instance of an object class in the human resource system; update the human resource system by adding, removing, or changing a plurality of entries associated with the existing instance of the object class in at least one of the instance table, the attribute table, and the reference table, comprising: validate a transaction request relating to the existing instance of the object class, comprising: ensure a requestor has privileges to perform a requested transaction; check whether the transaction request corresponds to a metadata definition of an element of the transaction request; and ensure that data in the requested transaction is of a correct type and in a correct range of values; determine whether a controlling object to be updated exists, wherein the instances of the object class are organized in a tree structure, the controlling object relating to a trunk of the tree structure; in the event that the controlling object to be updated does not exist create the controlling object; and in the event that the controlling object to be updated exists, locate the controlling object associated with an instance of the object class; transfer the plurality of entries associated with the existing instance of the object class in the at least one of the instance table, the attribute table, and the reference table to the storage module after the updating of the human resource system is performed, wherein the transferring of the plurality of entries to the storage module is performed after each of the adding, removing, or changing to the plurality of entries have been completed to avoid inconsistencies in the human resource system the storage module including permanent storage; and execute the updated human resources system by interpreting the stored metadata model definitions and process definitions using the interpretive engine. 5. A system as in claim 3 , wherein the one or more attributes include at least one of the following: description field-ethnicity descriptor, gender descriptor, hair color descriptor, amount field-compensation element amount, employee count, total base pay for employees, date-date of birth, company seniority hire date, Area Code, Name, Description, and/or Email Address. | 0.801706 |
9,079,098 | 28 | 36 | 28. A wagering game system comprising: a plurality of electronic gaming machines configured to provide a selection of wagering games to players having wagering game system registrations; a recommender processor configured to: generate a gaming and play behavior model by partitioning play data into a plurality of game session patterns, each game session pattern corresponds to a time period and indicates occurrence of game factors over the time period; detect in real time or near real time that a current player begins to play a game or indicates a desire to play the game using a first electronic gaming machine of the plurality of electronic gaming machines; collect, from the electronic gaming machine using a communication link, at the moment of the detection that the current player begins to play the game or indicates a desire to play the game, a first set of real time game data related to game factors for game play in an ongoing live game; determine that the first set of real-time game data has been collected for a minimum length of time of game play required to categorize game behaviour using the respective game session patterns; match the first set of real-time game data to a first set of game session patterns of the plurality of game sessions; determine at least one game player type from among a set of predefined game player types for a game player based on the matched game sessions, transmit, to the first electronic gaming machine using the communication link, the selection of wagering games which are identified based on the determined at least one game player type; and the communication link, the communication link having network infrastructure to link the central recommender processor to the plurality of electronic gaming machines. | 28. A wagering game system comprising: a plurality of electronic gaming machines configured to provide a selection of wagering games to players having wagering game system registrations; a recommender processor configured to: generate a gaming and play behavior model by partitioning play data into a plurality of game session patterns, each game session pattern corresponds to a time period and indicates occurrence of game factors over the time period; detect in real time or near real time that a current player begins to play a game or indicates a desire to play the game using a first electronic gaming machine of the plurality of electronic gaming machines; collect, from the electronic gaming machine using a communication link, at the moment of the detection that the current player begins to play the game or indicates a desire to play the game, a first set of real time game data related to game factors for game play in an ongoing live game; determine that the first set of real-time game data has been collected for a minimum length of time of game play required to categorize game behaviour using the respective game session patterns; match the first set of real-time game data to a first set of game session patterns of the plurality of game sessions; determine at least one game player type from among a set of predefined game player types for a game player based on the matched game sessions, transmit, to the first electronic gaming machine using the communication link, the selection of wagering games which are identified based on the determined at least one game player type; and the communication link, the communication link having network infrastructure to link the central recommender processor to the plurality of electronic gaming machines. 36. The system of claim 28 , wherein the first set of real time game data and the additional set of real time game data are related to distinct time periods. | 0.957636 |
8,843,495 | 26 | 27 | 26. The computer program product of claim 23 , wherein a first fielded query of said set of fielded queries is associated with said input query and with said first inverted index, wherein said first fielded query comprises a first conditioned subset of concept tokens of said plurality of tokens, a second conditioned subset of term tokens of said plurality of tokens, and a third conditioned subset of variable tokens of said plurality of tokens, wherein said first conditioned subset is a subset of a set of concept tokens comprised by an intersection of said input query and said first inverted index, wherein said second conditioned subset is a subset of a set of term tokens comprised by an intersection of said input query and said first inverted index, and wherein said third conditioned subset is a subset of a set of variable tokens comprised by said first inverted index. | 26. The computer program product of claim 23 , wherein a first fielded query of said set of fielded queries is associated with said input query and with said first inverted index, wherein said first fielded query comprises a first conditioned subset of concept tokens of said plurality of tokens, a second conditioned subset of term tokens of said plurality of tokens, and a third conditioned subset of variable tokens of said plurality of tokens, wherein said first conditioned subset is a subset of a set of concept tokens comprised by an intersection of said input query and said first inverted index, wherein said second conditioned subset is a subset of a set of term tokens comprised by an intersection of said input query and said first inverted index, and wherein said third conditioned subset is a subset of a set of variable tokens comprised by said first inverted index. 27. The computer program product of claim 26 , wherein said set of conditions require that a first sum of a number of tokens in said first conditioned subset plus a number of tokens in said second conditioned subset plus a number of tokens in said third conditioned subset be no less than a second sum of a number of concept tokens in a first minimum fielded document of said first cluster plus a number of term tokens in a second minimum fielded document of said first cluster plus a number of variable term tokens in a third minimum fielded document of said first cluster, wherein no fielded document of said first cluster contains fewer concept tokens than does said first minimum fielded document, no fielded document of said first cluster contains fewer term tokens than does said second minimum fielded document, and no fielded document of said first cluster contains fewer variable tokens than does said third minimum fielded document, and wherein said first sum be no greater than a total number of tokens in a fourth minimum fielded document, wherein no fielded document of said first cluster contains fewer tokens than does said fourth minimum fielded document. | 0.694575 |
10,002,301 | 1 | 7 | 1. A method for Arabic handwriting recognition, the method comprising: acquiring, an input image representative of a handwritten Arabic text from a user; partitioning, using processing circuitry of a server, the input image into a plurality of regions; determining, using the processing circuitry, a bag of features representation for each region of the plurality of regions; modeling, using the processing circuitry, each region independently by multi stream discrete Hidden Markov Model (HMM); and identifying, using processing circuitry, a recognized text based on the HMM models. | 1. A method for Arabic handwriting recognition, the method comprising: acquiring, an input image representative of a handwritten Arabic text from a user; partitioning, using processing circuitry of a server, the input image into a plurality of regions; determining, using the processing circuitry, a bag of features representation for each region of the plurality of regions; modeling, using the processing circuitry, each region independently by multi stream discrete Hidden Markov Model (HMM); and identifying, using processing circuitry, a recognized text based on the HMM models. 7. The method of claim 1 , wherein the plurality of regions includes a middle region, an upper region, and a lower region. | 0.748971 |
8,402,019 | 1 | 2 | 1. A method for determining a particular document that initiated a topic of interest in a collection of documents, each of the documents having contents and a time it was created, comprising: ranking the documents in the collection based on the respective times that the documents were created; ranking the documents based on how similar their respective contents are to the topic of interest; ranking the documents based on originality of their respective contents; ranking the documents based on a type of source each respective document originated from; producing a composite ranking of the documents based on the time, the contents, the originality rankings, and the type of source; and determining the particular document that initiated the topic of interest from the composite ranking. | 1. A method for determining a particular document that initiated a topic of interest in a collection of documents, each of the documents having contents and a time it was created, comprising: ranking the documents in the collection based on the respective times that the documents were created; ranking the documents based on how similar their respective contents are to the topic of interest; ranking the documents based on originality of their respective contents; ranking the documents based on a type of source each respective document originated from; producing a composite ranking of the documents based on the time, the contents, the originality rankings, and the type of source; and determining the particular document that initiated the topic of interest from the composite ranking. 2. The method of claim 1 , wherein the collection of documents is generated by a query request. | 0.886091 |
10,073,913 | 24 | 25 | 24. The system of claim 23 , wherein the vertical search result is represented as a widget application within said ranked result set. | 24. The system of claim 23 , wherein the vertical search result is represented as a widget application within said ranked result set. 25. The system of claim 24 , wherein to determine the confidence level, the server is configured to analyze one or more of: (i) the data maintained within the widget application, (ii) hierarchical position of the widget application within the ranked result set, and (iii) the intent weight associated with the search query; (iv) determinative words associated with the search query; (v) stop words associated with the search query and (vi) frequency of the search query and a response from the widget application combination when other users perform similar search queries. | 0.804703 |
8,099,430 | 1 | 8 | 1. A computer method of navigating information comprising: receiving a first source of information and one or more second sources of information, each second source having a parent-child relationship with the first source, the first source being the parent; automatically extracting keywords from the first source and each of the second sources in a manner such that, for each extracted keyword, the keyword correlates the first source and at least one second source, resulting in a respective set of second sources for each keyword and resulting in precise keywords that enhance retrieval of second sources of information; and displaying to a user a listing of the keywords resulting from the automatic extracting, the displayed listing enabling the user to navigate the one or more second sources, different keywords in the displayed listing effectively referencing the different respective sets of second sources and the different respective sets of second sources having subject matter of the first source of information shown to the user, wherein the automatic extracting utilizes a semantic lexicon tool, and includes: extracting initial keywords from the first source; forming an initial taxonomy from the extracted initial keywords; detecting in the second sources words that match the initial taxonomy but that do not duplicate the extracted initial keywords of the first source; and combining the extracted initial keywords from the first source and the detected words from the second sources, said combining forming the listing of keywords. | 1. A computer method of navigating information comprising: receiving a first source of information and one or more second sources of information, each second source having a parent-child relationship with the first source, the first source being the parent; automatically extracting keywords from the first source and each of the second sources in a manner such that, for each extracted keyword, the keyword correlates the first source and at least one second source, resulting in a respective set of second sources for each keyword and resulting in precise keywords that enhance retrieval of second sources of information; and displaying to a user a listing of the keywords resulting from the automatic extracting, the displayed listing enabling the user to navigate the one or more second sources, different keywords in the displayed listing effectively referencing the different respective sets of second sources and the different respective sets of second sources having subject matter of the first source of information shown to the user, wherein the automatic extracting utilizes a semantic lexicon tool, and includes: extracting initial keywords from the first source; forming an initial taxonomy from the extracted initial keywords; detecting in the second sources words that match the initial taxonomy but that do not duplicate the extracted initial keywords of the first source; and combining the extracted initial keywords from the first source and the detected words from the second sources, said combining forming the listing of keywords. 8. A method as claimed in claim 1 wherein the step of automatically extracting keywords includes: extracting from a second source, nouns relating to nouns from the first source; and eliminating extracted nouns that are duplicates of extracted keywords from the first source, remaining extracted nouns being keywords that correlate the first source and the second source. | 0.775758 |
7,996,246 | 31 | 32 | 31. The method of claim 7 , further comprising: formulating a resource allocation determination as a function of the likelihood of citation. | 31. The method of claim 7 , further comprising: formulating a resource allocation determination as a function of the likelihood of citation. 32. The method of claim 31 , wherein the resource allocation determination is indicative of an allocation of resources operable to reduce the likelihood of citation in the resident care area associated with the quality score. | 0.933393 |
10,044,752 | 10 | 11 | 10. The method of claim 1 , wherein each one of the directed graphs in the set of directed graphs comprises an input finite-state machine that models computation for decoding a singly encoded null-byte that is encoded according to at least one of the encoding methods in the set of encoding methods. | 10. The method of claim 1 , wherein each one of the directed graphs in the set of directed graphs comprises an input finite-state machine that models computation for decoding a singly encoded null-byte that is encoded according to at least one of the encoding methods in the set of encoding methods. 11. The method of claim 10 , wherein generating the output finite-state machine includes combining the input finite-state machines into a total finite-state machine that represents all permutations of the encoding methods in the set of encoding methods. | 0.903582 |
8,856,107 | 1 | 10 | 1. A method of representing data comprising: in a processing system that tracks multiple participants, collecting pieces of communication data from a plurality of sources; normalizing the pieces of communication data, such that each piece of communication data includes multiple common fields; identifying participants related to each piece of communication data; and displaying a representation of multiple pieces of the communication data as a three dimensional collection of cubes, where each cube represents a subset of the multiple pieces of communication data, and wherein a first axis for each cube represents communication threads, a second axis represents participants and a third axis represents time; and generating a differential set of terms for each 3D slice, wherein the differential set of terms indicates a subtraction of one or more terms of a previous 3D slice from one or more terms in a current 3D slice. | 1. A method of representing data comprising: in a processing system that tracks multiple participants, collecting pieces of communication data from a plurality of sources; normalizing the pieces of communication data, such that each piece of communication data includes multiple common fields; identifying participants related to each piece of communication data; and displaying a representation of multiple pieces of the communication data as a three dimensional collection of cubes, where each cube represents a subset of the multiple pieces of communication data, and wherein a first axis for each cube represents communication threads, a second axis represents participants and a third axis represents time; and generating a differential set of terms for each 3D slice, wherein the differential set of terms indicates a subtraction of one or more terms of a previous 3D slice from one or more terms in a current 3D slice. 10. The method of claim 1 , comprising allowing a user to select or alter data filter parameters, wherein displaying the representation of multiple pieces of the communication data includes considering the data filter parameters to determine which pieces of the communications data will be represented. | 0.682105 |
9,015,803 | 1 | 8 | 1. A server computer implemented method of online document collaboration by authorized users, the method comprising the following steps performed by a server computer system connected to the Internet: establishing an account for each of a plurality of users, wherein each account is associated with storage space to store one or more documents; storing a first document in the server computer system in a first account, the first document capable of being modified by a plurality of authorized users; enabling access to the first document via a browser-controlled window executing on a client computer by one or more authorized users; associating a set of restrictions with the first document, the restrictions including an ability to modify the first document in one or more permitted ways by one of a first group of users, the first group of users being users whose identities are known to the server computer system; receiving a request to access the first document from a first user, wherein the first user is a member of the first group of users, wherein the request to access accompanies the first user's identification information and authorization information; verifying the identity of the first user; if the first user is authorized to access the first document, then permitting the first user to access the first document via a first browser-controlled window executing on a client computer; and if the first user is authorized to modify the first document, then: (a) applying one or more modifications to the first document, the one or more modifications having been received from the first user; (b) electronically notifying one or more of a second group of users that the first user modified the first document, the second group of users being users whose identities are known to the server computer system; and (c) enabling a second user to further modify the first document, wherein the second user is a member of the second group of users who are notified of the one or more modifications made by the first user to the first document, and wherein the second user is not the same as the first user. | 1. A server computer implemented method of online document collaboration by authorized users, the method comprising the following steps performed by a server computer system connected to the Internet: establishing an account for each of a plurality of users, wherein each account is associated with storage space to store one or more documents; storing a first document in the server computer system in a first account, the first document capable of being modified by a plurality of authorized users; enabling access to the first document via a browser-controlled window executing on a client computer by one or more authorized users; associating a set of restrictions with the first document, the restrictions including an ability to modify the first document in one or more permitted ways by one of a first group of users, the first group of users being users whose identities are known to the server computer system; receiving a request to access the first document from a first user, wherein the first user is a member of the first group of users, wherein the request to access accompanies the first user's identification information and authorization information; verifying the identity of the first user; if the first user is authorized to access the first document, then permitting the first user to access the first document via a first browser-controlled window executing on a client computer; and if the first user is authorized to modify the first document, then: (a) applying one or more modifications to the first document, the one or more modifications having been received from the first user; (b) electronically notifying one or more of a second group of users that the first user modified the first document, the second group of users being users whose identities are known to the server computer system; and (c) enabling a second user to further modify the first document, wherein the second user is a member of the second group of users who are notified of the one or more modifications made by the first user to the first document, and wherein the second user is not the same as the first user. 8. The server computer implemented method of claim 1 , wherein the one or more modifications made to the first document includes adding material to the first document, deleting material from the first document, adding a digital signature to the first document, underlining material in the first document, or highlighting material in the first document. | 0.864407 |
7,818,117 | 8 | 20 | 8. A method in a travel system of integrating advices and warnings from a plurality of individual travel advice documents, comprising: receiving, via a computer, a query for advices and warnings for a travel destination from and end-user; getting, via a computer, individual travel advice documents including text portions from a database of the travel system; providing a common formatting structure including sections, sections headers and paragraphs; parsing, via a computer, the individual travel advice documents, to obtain cleaned documents sharing the common formatting structure, said parsing step comprising for each individual travel advice document, the definition of tags and the extraction of text portions between the tags; integrating, via a computer, the cleaned documents into a merged report of travel advices and warnings for the travel destination, the integrating step including the further step of: i) choosing, among all the cleaned documents, a base document from which integration is performed; ii) creating sections in the merged report using the section headers of the base document as section headers of the merged report; iii) for each section of the base document, extracting paragraphs from the base document and inserting said paragraphs extracted from the base document in the merged report under the section header corresponding to the section header of the section of the base document; iv) comparing section headers of the cleaned documents other than the base document with the section headers of the base document to determine similarity values; v) defining a similarity value threshold and defining pairs of comparable sections each made of a section of a cleaned document other than the base document and of a section of the base document and for which the similarity value is above the similarity value threshold; vi) for each pair of comparable sections: comparing the paragraphs of the section of the cleaned document other than the base document with the paragraphs of the section of the base document to determine similarity scores; inserting the paragraphs of the section of the cleaned document other than the base document in the merged report, next to the paragraphs of the section of the base document returning the best similarity score; delivering, via a computer, the merged report of travel advices and warnings for the travel destination to the end-user of the travel system. | 8. A method in a travel system of integrating advices and warnings from a plurality of individual travel advice documents, comprising: receiving, via a computer, a query for advices and warnings for a travel destination from and end-user; getting, via a computer, individual travel advice documents including text portions from a database of the travel system; providing a common formatting structure including sections, sections headers and paragraphs; parsing, via a computer, the individual travel advice documents, to obtain cleaned documents sharing the common formatting structure, said parsing step comprising for each individual travel advice document, the definition of tags and the extraction of text portions between the tags; integrating, via a computer, the cleaned documents into a merged report of travel advices and warnings for the travel destination, the integrating step including the further step of: i) choosing, among all the cleaned documents, a base document from which integration is performed; ii) creating sections in the merged report using the section headers of the base document as section headers of the merged report; iii) for each section of the base document, extracting paragraphs from the base document and inserting said paragraphs extracted from the base document in the merged report under the section header corresponding to the section header of the section of the base document; iv) comparing section headers of the cleaned documents other than the base document with the section headers of the base document to determine similarity values; v) defining a similarity value threshold and defining pairs of comparable sections each made of a section of a cleaned document other than the base document and of a section of the base document and for which the similarity value is above the similarity value threshold; vi) for each pair of comparable sections: comparing the paragraphs of the section of the cleaned document other than the base document with the paragraphs of the section of the base document to determine similarity scores; inserting the paragraphs of the section of the cleaned document other than the base document in the merged report, next to the paragraphs of the section of the base document returning the best similarity score; delivering, via a computer, the merged report of travel advices and warnings for the travel destination to the end-user of the travel system. 20. The method of claim 8 , wherein the base document is a document supplied by an embassy of a country of citizenship of the end user. | 0.907787 |
7,747,619 | 8 | 29 | 8. A client computer system directly accessed by a user, the system comprising: a display; a gateway operable to connect the client computer system to a server; a local database, located on the client computer system and comprising at least preferences information of the user; and a processing module, located on the client computer system and operable to: i. obtain at least preferences information of the user by monitoring online activities of the user, wherein the preferences information of the user is based on a history of the online activities of the user; ii. store the obtained preferences information of the user in the database; iii. process information received from the server after the user submits a query; iv. determine likely preferences of the user based on contents of the database and the information received from the server; v. provide to the server decision information based, at least in part, on the determined likely preferences of the user and, in response, receive from the server targeted information based on the provided decision information; and vi. display the targeted information to the user on the display wherein the preferences information of the user is not accessible to any other entity except for the client computer system; wherein the server comprises a search engine, wherein the query is a search engine query and wherein the information received from the server comprises search results responsive to the query submitted by the user and a plurality of advertisements related to the search results, wherein the processing module is further operable to select at least one of the plurality of advertisements for displaying to the user based on the determined likely preferences of the user, wherein the selection is performed on the client computer system. | 8. A client computer system directly accessed by a user, the system comprising: a display; a gateway operable to connect the client computer system to a server; a local database, located on the client computer system and comprising at least preferences information of the user; and a processing module, located on the client computer system and operable to: i. obtain at least preferences information of the user by monitoring online activities of the user, wherein the preferences information of the user is based on a history of the online activities of the user; ii. store the obtained preferences information of the user in the database; iii. process information received from the server after the user submits a query; iv. determine likely preferences of the user based on contents of the database and the information received from the server; v. provide to the server decision information based, at least in part, on the determined likely preferences of the user and, in response, receive from the server targeted information based on the provided decision information; and vi. display the targeted information to the user on the display wherein the preferences information of the user is not accessible to any other entity except for the client computer system; wherein the server comprises a search engine, wherein the query is a search engine query and wherein the information received from the server comprises search results responsive to the query submitted by the user and a plurality of advertisements related to the search results, wherein the processing module is further operable to select at least one of the plurality of advertisements for displaying to the user based on the determined likely preferences of the user, wherein the selection is performed on the client computer system. 29. The client computer system of claim 8 , wherein determining the likely preferences of the user comprises matching the contents of the database and the information received from the server and, if a match is found, using the match as indication of the likely preferences of the user and wherein the decision information provided to the server is based on the found match. | 0.79696 |
9,386,037 | 18 | 20 | 18. A system comprising: a web server computer configured to serve website information associated with a website; and a similarity analysis computer communicatively coupled to the web server computer through a network connection, the similarity analysis computer configured to: receive website information from the web server computer corresponding to the website; render a document object model (DOM) object of the website using the website information; separate content within the DOM object into a plurality of data portions, each of the plurality of data portions having a fixed length; generate a hash signature of the DOM object by: apply a predetermined number of hashing functions to each of the plurality of data portions, wherein the predetermined number of hashing functions are generated using a common seed value, and wherein applying the predetermined number of hashing functions results in a predetermined number of values for each of the plurality of data portions; and select, using a selection policy, a predetermined number of hashed data portions of the plurality of hashed data portions, wherein the predetermined number of hashed data portions are selected to create a hash signature of the DOM object; compare the hash signature of the DOM object to a known hash signature of a DOM object associated with a known website having a first classification, wherein comparing the hash signature of the DOM object to the known hash signature of the DOM object associated with the known website includes comparing each of the plurality of hashed data portions to a plurality of known hashed data portions of the known hash signature; calculate a similarity measurement between the hash signature of the DOM object and the known hash signature of the DOM object associated with the known website; compare the similarity measurement to a threshold; and determine that the website has the first classification based on the similarity measurement exceeding the threshold. | 18. A system comprising: a web server computer configured to serve website information associated with a website; and a similarity analysis computer communicatively coupled to the web server computer through a network connection, the similarity analysis computer configured to: receive website information from the web server computer corresponding to the website; render a document object model (DOM) object of the website using the website information; separate content within the DOM object into a plurality of data portions, each of the plurality of data portions having a fixed length; generate a hash signature of the DOM object by: apply a predetermined number of hashing functions to each of the plurality of data portions, wherein the predetermined number of hashing functions are generated using a common seed value, and wherein applying the predetermined number of hashing functions results in a predetermined number of values for each of the plurality of data portions; and select, using a selection policy, a predetermined number of hashed data portions of the plurality of hashed data portions, wherein the predetermined number of hashed data portions are selected to create a hash signature of the DOM object; compare the hash signature of the DOM object to a known hash signature of a DOM object associated with a known website having a first classification, wherein comparing the hash signature of the DOM object to the known hash signature of the DOM object associated with the known website includes comparing each of the plurality of hashed data portions to a plurality of known hashed data portions of the known hash signature; calculate a similarity measurement between the hash signature of the DOM object and the known hash signature of the DOM object associated with the known website; compare the similarity measurement to a threshold; and determine that the website has the first classification based on the similarity measurement exceeding the threshold. 20. The system of claim 18 , wherein selecting the predetermined number of hashed data portions using the selection policy to create the hash signature of the DOM object further comprises: applying the selection policy to each of the predetermined number of values for each of the plurality of data portions to select a single value for each of the plurality of data portions. | 0.825603 |
8,589,457 | 9 | 14 | 9. A computer-readable storage device storing instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising: storing data identifying a plurality of training images for a query, wherein each of the training images is classified as being in a positive group of images for the query or a negative group of images for the query according to a respective query specific preference measure for the image; selecting a first image from either the positive group of images or the negative group of images, and applying a scoring model to the first image to determine a score for the first image; selecting a plurality of candidate images from the other group of images; applying the scoring model to each of the candidate images to determine a respective score for each candidate image, and then selecting a second image from the candidate images, the selection based on a probability that is proportional to a cardinality of the plurality of candidate images and a ranking of the second image relative to each other image in the candidate images based on the respective scores of the images; and determining that the scores for the first image and the second image fail to satisfy a criterion, wherein the criterion requires that a result of the score of the image selected from the positive group of images minus the score of the image selected from the negative group of images exceeds a threshold, updating the scoring model, and storing the updated scoring model. | 9. A computer-readable storage device storing instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising: storing data identifying a plurality of training images for a query, wherein each of the training images is classified as being in a positive group of images for the query or a negative group of images for the query according to a respective query specific preference measure for the image; selecting a first image from either the positive group of images or the negative group of images, and applying a scoring model to the first image to determine a score for the first image; selecting a plurality of candidate images from the other group of images; applying the scoring model to each of the candidate images to determine a respective score for each candidate image, and then selecting a second image from the candidate images, the selection based on a probability that is proportional to a cardinality of the plurality of candidate images and a ranking of the second image relative to each other image in the candidate images based on the respective scores of the images; and determining that the scores for the first image and the second image fail to satisfy a criterion, wherein the criterion requires that a result of the score of the image selected from the positive group of images minus the score of the image selected from the negative group of images exceeds a threshold, updating the scoring model, and storing the updated scoring model. 14. The computer-readable storage device of claim 9 , wherein the operations further comprise: in response to receiving the query through a search interface, identifying a plurality of images responsive to the query; applying the scoring model to each of the plurality of images to determine a respective score for each image; and presenting images from the plurality of images in the search interface, wherein the images are presented in an order according to the respective score for each image. | 0.623485 |
9,002,772 | 1 | 3 | 1. A system for scalable, rule-based processing, the system comprising: a processor; and storage coupled to the processor, wherein the storage stores computer program instructions, and wherein the computer program instructions are executed by the processor to: construct a plurality of automatons corresponding to a plurality of trigger rules and a plurality of word lists that are employed by the trigger rules; evaluate any of the plurality of trigger rules with respect to an input document by: selecting any of the automatons to evaluate a given one of the plurality of trigger rules, parsing the input document using the selected automatons, determining whether conditions of the given trigger rule are met, identifying any actions that are associated with the given trigger rule, and display in a rule tracing any of the plurality of trigger rules that are evaluated, together with indicia for different portions of the displayed trigger rules indicating an evaluation result of each of the different portions. | 1. A system for scalable, rule-based processing, the system comprising: a processor; and storage coupled to the processor, wherein the storage stores computer program instructions, and wherein the computer program instructions are executed by the processor to: construct a plurality of automatons corresponding to a plurality of trigger rules and a plurality of word lists that are employed by the trigger rules; evaluate any of the plurality of trigger rules with respect to an input document by: selecting any of the automatons to evaluate a given one of the plurality of trigger rules, parsing the input document using the selected automatons, determining whether conditions of the given trigger rule are met, identifying any actions that are associated with the given trigger rule, and display in a rule tracing any of the plurality of trigger rules that are evaluated, together with indicia for different portions of the displayed trigger rules indicating an evaluation result of each of the different portions. 3. The system of claim 1 where each of the trigger rules includes a trigger and at least one action. | 0.933244 |
9,990,223 | 1 | 4 | 1. A method of improving parallel functional processing, the method including: performing data transformations in a functional processing pipeline running on multiple processors using at least one instance or composition combining each of categorical functions in a group including PairMaker, FreeMonoidReduce, PairABtoAFMB, ReducePairs, and MonoidReduce, wherein: the PairMaker categorical function transforms a free monoid over strings into a free monoid over tuples including at least one key-value pair in each tuple; the FreeMonoidReduce categorical function merges a nested free monoid over tuples into one free monoid over tuples, thereby reducing a nesting depth of the nested free monoid over tuples; the PairABtoAFMB categorical function transforms one element in each tuple of the one free monoid over tuples into list of one element in a free monoid over tuples with an embedded list element; the ReducePairs categorical function merges consecutive tuples of the free monoid over tuples with the embedded list element; and the MonoidReduce categorical function transforms a plurality of list values into a single value based on a parameterized operation. | 1. A method of improving parallel functional processing, the method including: performing data transformations in a functional processing pipeline running on multiple processors using at least one instance or composition combining each of categorical functions in a group including PairMaker, FreeMonoidReduce, PairABtoAFMB, ReducePairs, and MonoidReduce, wherein: the PairMaker categorical function transforms a free monoid over strings into a free monoid over tuples including at least one key-value pair in each tuple; the FreeMonoidReduce categorical function merges a nested free monoid over tuples into one free monoid over tuples, thereby reducing a nesting depth of the nested free monoid over tuples; the PairABtoAFMB categorical function transforms one element in each tuple of the one free monoid over tuples into list of one element in a free monoid over tuples with an embedded list element; the ReducePairs categorical function merges consecutive tuples of the free monoid over tuples with the embedded list element; and the MonoidReduce categorical function transforms a plurality of list values into a single value based on a parameterized operation. 4. The method of claim 1 , wherein the PairMaker creates at least part of the tuples by combining consecutive elements of the free monoid over strings into the tuples. | 0.907016 |
9,223,933 | 17 | 18 | 17. A non-transitory computer readable storage medium including computer program code to be executed by a processor, the computer program code, when executed, to implement a formlet generation method comprising: receiving one or more query criterion to retrieve data and generating a query result of data from one or more data stores based on the one or more query criterion; applying a transform to the query result data, the transform applying analytics to the query result data to produce transformed query result data consumable by a graphical user interface, the transformed query result data modeled as an object; applying a data template and binding instructions to the transformed query result data to generate a formlet including data and associated relationship and functionality information for display of and interaction with the data, the binding instructions specifying how the transformed query result data is to be bound to the data template, the data template specifying visual components and visual styling instructions for the transformed query result data object to form the formlet; facilitating runtime configurability of the formlet by a user; and displaying the formlet via the graphical user interface, the formlet forming part of an application for user review and interaction. | 17. A non-transitory computer readable storage medium including computer program code to be executed by a processor, the computer program code, when executed, to implement a formlet generation method comprising: receiving one or more query criterion to retrieve data and generating a query result of data from one or more data stores based on the one or more query criterion; applying a transform to the query result data, the transform applying analytics to the query result data to produce transformed query result data consumable by a graphical user interface, the transformed query result data modeled as an object; applying a data template and binding instructions to the transformed query result data to generate a formlet including data and associated relationship and functionality information for display of and interaction with the data, the binding instructions specifying how the transformed query result data is to be bound to the data template, the data template specifying visual components and visual styling instructions for the transformed query result data object to form the formlet; facilitating runtime configurability of the formlet by a user; and displaying the formlet via the graphical user interface, the formlet forming part of an application for user review and interaction. 18. The computer readable storage medium of claim 17 , wherein the formlet forms part of a clinical application telling a story regarding one or more patients for user review and interaction. | 0.573661 |
8,369,627 | 1 | 4 | 1. A system for generating groups of cluster spines for display, comprising: a theme generator to generate one or more concepts for each cluster in a set of clusters; a spine generator to form spines from at least a portion of the clusters based on the concepts; a spine placement module to place at least one spine unique from all the other spines; a spine group generator to generate one or more spine groups by positioning at least one unplaced spine in relation to one of the placed unique spines; and a display to display the spine groups. | 1. A system for generating groups of cluster spines for display, comprising: a theme generator to generate one or more concepts for each cluster in a set of clusters; a spine generator to form spines from at least a portion of the clusters based on the concepts; a spine placement module to place at least one spine unique from all the other spines; a spine group generator to generate one or more spine groups by positioning at least one unplaced spine in relation to one of the placed unique spines; and a display to display the spine groups. 4. A system according to claim 1 , further comprising: a spine selection module to first select the unique spines that are the longest by ordering all the spines by spine length and by selecting the unique spines from longest to shortest from the ordered spines. | 0.742633 |
9,134,966 | 10 | 11 | 10. A method of simulating an execution of an application object file having a high-level programming language element and an assembly programming language element, comprising: loading the application object file and a shared library onto a simulated memory, the application object file contains a simulated memory address for a first program written in assembly programming language and the shared library contains a routine with a second program written in a high-level programming language; extracting the routine containing the second program into an extracted routine file using an extraction script; receiving, as part of executing the first program and at a simulator that is communicatively coupled to a processor and a memory, the simulated memory address associated with at least one first program, the simulated memory address indicates the first program on the object file; accessing an annotation table that associates an annotation table address with the extracted routine file in the shared library; comparing, by the simulator, the received simulated memory address to the annotation table address in the annotation table; accessing, in response to the comparison, the routine; and executing the routine from the shared library. | 10. A method of simulating an execution of an application object file having a high-level programming language element and an assembly programming language element, comprising: loading the application object file and a shared library onto a simulated memory, the application object file contains a simulated memory address for a first program written in assembly programming language and the shared library contains a routine with a second program written in a high-level programming language; extracting the routine containing the second program into an extracted routine file using an extraction script; receiving, as part of executing the first program and at a simulator that is communicatively coupled to a processor and a memory, the simulated memory address associated with at least one first program, the simulated memory address indicates the first program on the object file; accessing an annotation table that associates an annotation table address with the extracted routine file in the shared library; comparing, by the simulator, the received simulated memory address to the annotation table address in the annotation table; accessing, in response to the comparison, the routine; and executing the routine from the shared library. 11. The method of claim 10 , wherein accessing the annotation table includes: accessing an extracted routine file within the shared library referenced by the annotation table; and locating the routine within the extracted routine file. | 0.784007 |
9,213,937 | 14 | 17 | 14. The neural network of claim 10 , wherein the node output is configured based on a reception of at least one of the plurality of spiking input signals and at least one of the plurality of non-spiking input signals at least one of the plurality of nodes. | 14. The neural network of claim 10 , wherein the node output is configured based on a reception of at least one of the plurality of spiking input signals and at least one of the plurality of non-spiking input signals at least one of the plurality of nodes. 17. The neural network of claim 14 , wherein: the node output comprises a tonic spiking output; and the at least one of the plurality of spiking input signals or at least one of the plurality of non-spiking input signals comprises a teaching signal configured to cause the node to cease output generation for the duration. | 0.840278 |
8,352,856 | 1 | 2 | 1. A system that resizes content within a document comprising: a computing device comprising memory which stores instructions for resizing the content and a processor, in communication with the memory, for executing the instructions, the computing device including: a document segmenter that receives a document that contains disparate content, the document segmenter analyzes the content within the document and segments the content into a plurality of objects and categorizes each of the plurality of objects as one of a plurality of object types such that similar content is categorized as a same object type; an object priority applicator assigns image objects of an image object type to a first class level and text objects of text object type to a second class level, the object priority applicator identifies a predetermined importance value for each of the plurality of objects in the document based on the class level assigned to each of the plurality of objects; a location scaler that identifies a datum point for each of the plurality of objects within the document for maintaining a relative location of each of the plurality of objects relative to one another; and an object sizing component that disparately resizes each of the plurality of objects at the datum point of each of the plurality of objects, the image objects assigned to the first class level being resized a first predetermined amount and the text objects assigned to the second class level being resized a second predetermined amount different from the first predetermined amount, the first and second amounts each based on the importance value, wherein the image objects in the document are resized equally in the first amount and the text objects in the document are resized equally in the second amount. | 1. A system that resizes content within a document comprising: a computing device comprising memory which stores instructions for resizing the content and a processor, in communication with the memory, for executing the instructions, the computing device including: a document segmenter that receives a document that contains disparate content, the document segmenter analyzes the content within the document and segments the content into a plurality of objects and categorizes each of the plurality of objects as one of a plurality of object types such that similar content is categorized as a same object type; an object priority applicator assigns image objects of an image object type to a first class level and text objects of text object type to a second class level, the object priority applicator identifies a predetermined importance value for each of the plurality of objects in the document based on the class level assigned to each of the plurality of objects; a location scaler that identifies a datum point for each of the plurality of objects within the document for maintaining a relative location of each of the plurality of objects relative to one another; and an object sizing component that disparately resizes each of the plurality of objects at the datum point of each of the plurality of objects, the image objects assigned to the first class level being resized a first predetermined amount and the text objects assigned to the second class level being resized a second predetermined amount different from the first predetermined amount, the first and second amounts each based on the importance value, wherein the image objects in the document are resized equally in the first amount and the text objects in the document are resized equally in the second amount. 2. A system according to claim 1 , wherein the document is segmented via an algorithm, metadata within the document and/or imaging software. | 0.756944 |
8,935,166 | 1 | 6 | 1. A system comprising: a processor, and one or more memory components that stores a target application and a dictation application, wherein the dictation application provides a dictation application interface, to be displayed on a user computing device's display, which is: automatically sized and positioned over the target application interface to fully cover a text area of the target application interface so as to appear as if the dictation application interface is part of the target application interface; and adjusted to match the target application interface if the target application interface is moved, minimized, maximized, or resized; and wherein the dictation application further causes the system to, in response to receiving a user command to complete a dictation, automatically copy a converted audio dictation from the dictation application interface and insert it into the target application interface. | 1. A system comprising: a processor, and one or more memory components that stores a target application and a dictation application, wherein the dictation application provides a dictation application interface, to be displayed on a user computing device's display, which is: automatically sized and positioned over the target application interface to fully cover a text area of the target application interface so as to appear as if the dictation application interface is part of the target application interface; and adjusted to match the target application interface if the target application interface is moved, minimized, maximized, or resized; and wherein the dictation application further causes the system to, in response to receiving a user command to complete a dictation, automatically copy a converted audio dictation from the dictation application interface and insert it into the target application interface. 6. A system as claimed in claim 1 further comprising: a remote computing device for converting an audio dictation; and means to update a voice recognition logic. | 0.881965 |
8,768,708 | 1 | 8 | 1. A method for analyzing a voice of a speaker, comprising: specifically programming at least one computer machine to at least perform the following: receiving data indicative of speech from the speaker; storing the received data in at least one database; calculating, based upon the received data, an average intensity value for each of a plurality of frequencies, wherein the calculation of the average intensity value for each frequency is based on: i) dividing the received data into a number of time periods; ii) obtaining an intensity of the speaker's speech for each frequency during each time period; iii) obtaining a sum of intensity values for each frequency during all time periods; and iv) dividing the sum of intensity values for each frequency by the number of time periods; calculating, based upon the received data, a maximum intensity value for each of the plurality of frequencies, wherein the maximum intensity value for each frequency is the highest intensity of the speaker's speech for each frequency during all time periods; calculating a level of a survival element of a personality profile of the speaker based upon at least one of: (a) a rapid change in the average intensity value between at least a portion of the plurality of frequencies and (b) a rapid change in the maximum intensity value between at least a portion of the plurality of frequencies; calculating a level of a homeostasis element of the personality profile of the speaker by measuring a distance between the average intensity value and the maximum intensity for each frequency of at least a portion of the plurality of frequencies; calculating a level of a growth element of the personality profile of the speaker based upon at least one of: (a) determining a frequency range within at least a portion of the plurality of frequencies in which the average intensity value of each frequency within the frequency range is higher than a value that is equal to a predetermined percent of the highest average intensity value within the frequency range of the at least a portion of the plurality of frequencies, (b) determining at least one frequency within at least a portion of the plurality of frequencies that has the highest maximum intensity value among the at least a portion of the plurality of frequencies, and (c) determining a level of correlation between changes in intensity values during the time periods of a first frequency and changes in intensity values during the time periods of a second frequency; and outputting an indicator of the personality profile of the speaker based upon a combination of the calculated level of the survival element of the speaker, the calculated level of the homeostasis element of the speaker, and the calculated level of the growth element of the speaker, wherein the indicator of the personal profile at least inform that the speaker exhibits at least one the following personality characteristics: i) a strive to innovate when the calculated level of the growth element is high, ii) a strive for personal enrichment when the calculated level of the growth element is high, iii) a tendency to seek isolation when the calculated level of the growth element is low, iv) a tendency for mental depression when the calculated level of the growth element is low, v) a tendency to engage in high-risk behavior when the calculated level of the survival element is high, vi) a tendency for aggressiveness when the calculated level of the survival element is high, vii) a tendency for possessiveness when the calculated level of the survival element is high, viii) a tendency for being indecisive when the calculated level of the survival element is low, ix) a tendency for being resistant to changing opinions when the calculated level of the homeostasis element is high, x) a tendency for being resistant to changing habits when the calculated level of the homeostasis element is high, xi) a tendency to frequently change opinions when the calculated level of the homeostasis element is low, and xii) a tendency to frequently change habits when the calculated level of the homeostasis element is low. | 1. A method for analyzing a voice of a speaker, comprising: specifically programming at least one computer machine to at least perform the following: receiving data indicative of speech from the speaker; storing the received data in at least one database; calculating, based upon the received data, an average intensity value for each of a plurality of frequencies, wherein the calculation of the average intensity value for each frequency is based on: i) dividing the received data into a number of time periods; ii) obtaining an intensity of the speaker's speech for each frequency during each time period; iii) obtaining a sum of intensity values for each frequency during all time periods; and iv) dividing the sum of intensity values for each frequency by the number of time periods; calculating, based upon the received data, a maximum intensity value for each of the plurality of frequencies, wherein the maximum intensity value for each frequency is the highest intensity of the speaker's speech for each frequency during all time periods; calculating a level of a survival element of a personality profile of the speaker based upon at least one of: (a) a rapid change in the average intensity value between at least a portion of the plurality of frequencies and (b) a rapid change in the maximum intensity value between at least a portion of the plurality of frequencies; calculating a level of a homeostasis element of the personality profile of the speaker by measuring a distance between the average intensity value and the maximum intensity for each frequency of at least a portion of the plurality of frequencies; calculating a level of a growth element of the personality profile of the speaker based upon at least one of: (a) determining a frequency range within at least a portion of the plurality of frequencies in which the average intensity value of each frequency within the frequency range is higher than a value that is equal to a predetermined percent of the highest average intensity value within the frequency range of the at least a portion of the plurality of frequencies, (b) determining at least one frequency within at least a portion of the plurality of frequencies that has the highest maximum intensity value among the at least a portion of the plurality of frequencies, and (c) determining a level of correlation between changes in intensity values during the time periods of a first frequency and changes in intensity values during the time periods of a second frequency; and outputting an indicator of the personality profile of the speaker based upon a combination of the calculated level of the survival element of the speaker, the calculated level of the homeostasis element of the speaker, and the calculated level of the growth element of the speaker, wherein the indicator of the personal profile at least inform that the speaker exhibits at least one the following personality characteristics: i) a strive to innovate when the calculated level of the growth element is high, ii) a strive for personal enrichment when the calculated level of the growth element is high, iii) a tendency to seek isolation when the calculated level of the growth element is low, iv) a tendency for mental depression when the calculated level of the growth element is low, v) a tendency to engage in high-risk behavior when the calculated level of the survival element is high, vi) a tendency for aggressiveness when the calculated level of the survival element is high, vii) a tendency for possessiveness when the calculated level of the survival element is high, viii) a tendency for being indecisive when the calculated level of the survival element is low, ix) a tendency for being resistant to changing opinions when the calculated level of the homeostasis element is high, x) a tendency for being resistant to changing habits when the calculated level of the homeostasis element is high, xi) a tendency to frequently change opinions when the calculated level of the homeostasis element is low, and xii) a tendency to frequently change habits when the calculated level of the homeostasis element is low. 8. The method of claim 1 , wherein the output indicator of the personality profile of the speaker is compared to results of a questionnaire. | 0.893617 |
8,145,681 | 19 | 20 | 19. A computer-readable storage medium including instructions which, when executed on a processor, cause the processor to perform a method of generating a data object, the method including steps, performed by a computer, of: generating, using a processor of the computer system, a definition file of a first format for the data object; generating a database table; generating a mapping between the definition file and the database table; linking the definition file to a data source by including a path of the data source in the definition file, the data source including an attribute, wherein the data source is stored separately from the database table; executing, using the processor, a query to extract the attribute from the data source; importing the attribute into the database table using the mapping between the definition file and the database table; storing the definition file, the database table, and the attribute for generation of the data object with the attribute; sending an instruction to edit the database table based on a name-value pair; selecting, from the data source, a property of the attribute; customizing the property; and setting a lifecycle of the data object, the lifecycle indicating a predetermined period of time, wherein the attribute is deleted from the database table when the period of time lapses. | 19. A computer-readable storage medium including instructions which, when executed on a processor, cause the processor to perform a method of generating a data object, the method including steps, performed by a computer, of: generating, using a processor of the computer system, a definition file of a first format for the data object; generating a database table; generating a mapping between the definition file and the database table; linking the definition file to a data source by including a path of the data source in the definition file, the data source including an attribute, wherein the data source is stored separately from the database table; executing, using the processor, a query to extract the attribute from the data source; importing the attribute into the database table using the mapping between the definition file and the database table; storing the definition file, the database table, and the attribute for generation of the data object with the attribute; sending an instruction to edit the database table based on a name-value pair; selecting, from the data source, a property of the attribute; customizing the property; and setting a lifecycle of the data object, the lifecycle indicating a predetermined period of time, wherein the attribute is deleted from the database table when the period of time lapses. 20. The computer-readable storage medium of claim 19 , wherein the method further comprises the steps of: converting the definition file from the first format to a second format for generation of the data object; naming the data object, wherein the query is of a predetermined syntax and includes the name of the data object and a name of the attribute; configuring a display setting of the data object; and limiting an ability of a user to edit the data object by assigning a security setting. | 0.50101 |
9,972,309 | 7 | 14 | 7. A system comprising: a processor configured to perform speech analysis; and a computer-readable storage medium having instructions stored which, when executed by the processor, cause the processor to perform operations comprising: identifying an identity of a user based on characteristics of received speech during a dialog between the user and a dialog system, to yield a user identification; generating a personalized natural language generation model based on a stylistic analysis on a literary narrative and the user identification, wherein the stylistic analysis identifies connections between two or more of a personality independent quotation lattice, personality independent attributes, personality dependent attributes, and speakers within the literary narrative; and applying the personalized natural language generation model while performing, as part of the dialog, one of automatic speech recognition and natural language generation. | 7. A system comprising: a processor configured to perform speech analysis; and a computer-readable storage medium having instructions stored which, when executed by the processor, cause the processor to perform operations comprising: identifying an identity of a user based on characteristics of received speech during a dialog between the user and a dialog system, to yield a user identification; generating a personalized natural language generation model based on a stylistic analysis on a literary narrative and the user identification, wherein the stylistic analysis identifies connections between two or more of a personality independent quotation lattice, personality independent attributes, personality dependent attributes, and speakers within the literary narrative; and applying the personalized natural language generation model while performing, as part of the dialog, one of automatic speech recognition and natural language generation. 14. The system of claim 7 , wherein the dialog system does not use templates when using the personalized natural language generation model. | 0.871771 |
8,301,616 | 1 | 13 | 1. A method for processing query data, comprising: in response to each character of a plurality of characters being entered into a client, and without activation in said client of a button control whose activation causes a currently entered query comprising said plurality of characters to be submitted over a network to a search engine, receiving a separate query portion from said client over said network, thereby receiving a plurality of query portions; wherein each query portion of said plurality of query portions is a portion of a same query; for each received query portion of said plurality of query portions, determining, for each search context of a plurality of search contexts, a relevance score based on one or more suggested queries derived from said received query portion, thereby determining a separate plurality of relevance scores for each received query portion of the plurality of query portions; wherein each search context of the plurality of search contexts is a different set of information that has been previously searched using the one or more suggested queries to obtain links relevant to the one or more suggested queries; wherein the one or more suggested queries derived from said received query portion include at least one query that is not the same as the received query portion; and in response to each character of said plurality of characters being entered into said client, and for each particular query portion of said plurality of query portions, providing to said client over said network an indication of multiple relevance scores that were determined for said particular query portion, each relevance score of said multiple relevance scores being associated with a different search context in the plurality of search contexts; wherein the step of providing the indication of relevance scores is performed by one or more processors in a computer system. | 1. A method for processing query data, comprising: in response to each character of a plurality of characters being entered into a client, and without activation in said client of a button control whose activation causes a currently entered query comprising said plurality of characters to be submitted over a network to a search engine, receiving a separate query portion from said client over said network, thereby receiving a plurality of query portions; wherein each query portion of said plurality of query portions is a portion of a same query; for each received query portion of said plurality of query portions, determining, for each search context of a plurality of search contexts, a relevance score based on one or more suggested queries derived from said received query portion, thereby determining a separate plurality of relevance scores for each received query portion of the plurality of query portions; wherein each search context of the plurality of search contexts is a different set of information that has been previously searched using the one or more suggested queries to obtain links relevant to the one or more suggested queries; wherein the one or more suggested queries derived from said received query portion include at least one query that is not the same as the received query portion; and in response to each character of said plurality of characters being entered into said client, and for each particular query portion of said plurality of query portions, providing to said client over said network an indication of multiple relevance scores that were determined for said particular query portion, each relevance score of said multiple relevance scores being associated with a different search context in the plurality of search contexts; wherein the step of providing the indication of relevance scores is performed by one or more processors in a computer system. 13. The method of claim 1 , wherein the plurality of search contexts include a plurality of collections of information, each collection being tailored to a specific topic or a specific demographic group. | 0.954464 |
9,041,736 | 1 | 6 | 1. A method, comprising: performing, by a computing device: obtaining from a server, by a mapping application, one or more map tiles, wherein each of said map tiles comprises one or more features, wherein one or more of said features comprise one or more style identifiers; rendering the obtained one or more map tiles for display on the computing device, wherein each feature comprising one or more style identifiers is rendered according to a previously obtained stylesheet at the computing device, wherein the stylesheet comprises one or more styles, wherein each style comprises rendering instructions for the style, wherein the rendering instructions comprise a plurality of values for the style each associated with rendering the style at a respective zoom level of a plurality of zoom levels, wherein each style is linked to one or more style identifiers, wherein a comparison of the one or more style identifiers of the feature to the one or more style identifiers linked to the one or more styles of the previously obtained stylesheet determines a style for the feature, and wherein the feature is rendered at a particular zoom level according to a corresponding one of the plurality of values from the rendering instructions for the determined style; changing the particular zoom level of the one or more rendered map tiles to a different zoom level; and rendering at least one of the previously obtained one or more map tiles for display on the computing device at the different zoom level according to the previously obtained stylesheet, wherein the feature is rendered at the different zoom level according to a different value from the previously obtained stylesheet associated with the different zoom level than the value associated with the particular zoom level. | 1. A method, comprising: performing, by a computing device: obtaining from a server, by a mapping application, one or more map tiles, wherein each of said map tiles comprises one or more features, wherein one or more of said features comprise one or more style identifiers; rendering the obtained one or more map tiles for display on the computing device, wherein each feature comprising one or more style identifiers is rendered according to a previously obtained stylesheet at the computing device, wherein the stylesheet comprises one or more styles, wherein each style comprises rendering instructions for the style, wherein the rendering instructions comprise a plurality of values for the style each associated with rendering the style at a respective zoom level of a plurality of zoom levels, wherein each style is linked to one or more style identifiers, wherein a comparison of the one or more style identifiers of the feature to the one or more style identifiers linked to the one or more styles of the previously obtained stylesheet determines a style for the feature, and wherein the feature is rendered at a particular zoom level according to a corresponding one of the plurality of values from the rendering instructions for the determined style; changing the particular zoom level of the one or more rendered map tiles to a different zoom level; and rendering at least one of the previously obtained one or more map tiles for display on the computing device at the different zoom level according to the previously obtained stylesheet, wherein the feature is rendered at the different zoom level according to a different value from the previously obtained stylesheet associated with the different zoom level than the value associated with the particular zoom level. 6. The method of claim 1 , further comprising: wherein the mapping application comprises one or more display modes; wherein the computing device previously obtained one or more stylesheets, and wherein each stylesheet is linked to the one or more display modes of the mapping application; obtaining input selecting a particular display mode of the one or more display modes of the mapping application; and in response to obtaining input selecting the particular display mode of the one or more display modes of the mapping application, rendering the one or more obtained map tiles, wherein each feature comprising one or more style identifiers is rendered according to the stylesheet linked to the particular display mode, and wherein a comparison of the one or more style identifiers of the feature to the one or more style identifiers linked to the one or more styles of the stylesheet linked to the particular display mode determines a style for the feature. | 0.67881 |
Subsets and Splits