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9,507,576 | 8 | 13 | 8. A computer-implemented method for optimizing conditional statements in a program, the method comprising: identifying a conditional expression having an inclusion relation corresponding to a plurality of conditional statements in the program, wherein a result of the conditional expression is representative of a result of the plurality of conditional statements respectively; and moving the conditional expression to a move destination in the program, wherein the move destination is determined by performing a data-flow analysis using expressions of positive conditions and negative conditions of the plurality of the conditional statements, and wherein moving the conditional expression comprises: in the case where a result of the conditional expression is not cached in a variable, generating an if statement for the conditional expression and caching a result of the conditional expression in a new variable, wherein the if statement is generated in increasing order of a cost of execution; and in the case where the result of the conditional expression is cached in the variable, replacing a conditional operator of the conditional expression with a logical operator, prior to caching the result in the new variable, in response to the result of the conditional expression being cached in a variable. | 8. A computer-implemented method for optimizing conditional statements in a program, the method comprising: identifying a conditional expression having an inclusion relation corresponding to a plurality of conditional statements in the program, wherein a result of the conditional expression is representative of a result of the plurality of conditional statements respectively; and moving the conditional expression to a move destination in the program, wherein the move destination is determined by performing a data-flow analysis using expressions of positive conditions and negative conditions of the plurality of the conditional statements, and wherein moving the conditional expression comprises: in the case where a result of the conditional expression is not cached in a variable, generating an if statement for the conditional expression and caching a result of the conditional expression in a new variable, wherein the if statement is generated in increasing order of a cost of execution; and in the case where the result of the conditional expression is cached in the variable, replacing a conditional operator of the conditional expression with a logical operator, prior to caching the result in the new variable, in response to the result of the conditional expression being cached in a variable. 13. The computer-implemented method of claim 8 , wherein the data-flow analysis comprises: in response to the conditional expression not affecting a part of the program beyond a scope of the conditional statement that includes the conditional expression, the move destination is an area in which the conditional expression has a possibility of being executed in the program based on the result of the conditional statement; and in response to the conditional expression affecting a part of the program beyond the scope of the conditional statement that includes the conditional expression, the move destination is an area in which the conditional expression is definitely executed. | 0.558366 |
8,209,333 | 23 | 40 | 23. A method for providing contextually related content to a user, the method comprising: detecting an occurrence of a key phrase on a page of a site; transforming the occurrence of the key phrase on the page, as loaded by a browser on a user computing device, into a user-selectable display element that is selectable by a user to initiate a display of contextually related content, wherein the key phrase is transformed into the user-selectable display element based on a score generated for said key phrase, said score based at least partly on view counts of social media content items associated with the key phrase; and responding to user selection of the display element by displaying contextually related content in a panel on the page, said contextually related content including an advertisement associated with the key phrase and further including non-advertisement social media content associated with the key phrase, said panel generated on the page by execution by the browser of a browser-executable component loaded by the browser. | 23. A method for providing contextually related content to a user, the method comprising: detecting an occurrence of a key phrase on a page of a site; transforming the occurrence of the key phrase on the page, as loaded by a browser on a user computing device, into a user-selectable display element that is selectable by a user to initiate a display of contextually related content, wherein the key phrase is transformed into the user-selectable display element based on a score generated for said key phrase, said score based at least partly on view counts of social media content items associated with the key phrase; and responding to user selection of the display element by displaying contextually related content in a panel on the page, said contextually related content including an advertisement associated with the key phrase and further including non-advertisement social media content associated with the key phrase, said panel generated on the page by execution by the browser of a browser-executable component loaded by the browser. 40. The method of claim 23 , further comprising loading the browser-executable component on the user computing device in response to a tag included in coding of the page. | 0.772118 |
9,690,847 | 1 | 2 | 1. A computer-implemented method performed by one or more processors comprising: receiving a first user query from a user device; processing the first user query including identifying one or more segments in the first user query, a segment representing a word or a phrase; and determining a stand-alone score for each segment of the first user query, wherein the stand-alone score is an indication of a likelihood that the segment represents a stand-alone query and that the segment represents a main topic of the first user query, and wherein determining includes: processing a historical log of queries to determine query-independent scores for segments that are included in queries represented by the historical log including: identifying an initial query-independent score for a given segment; processing a first query in the historical log and determining a query-dependent score for each segment in the first query in the historical log including normalizing the query-dependent scores for the first query; processing a plurality of other queries in the historical log and determining a query dependent score for each segment for a given query; adjusting the initial query-independent scores for segments associated with the first query based on the determined and normalized query-dependent scores for the first query and the plurality of other queries to create updated query-independent scores for a given segment, including applying a first function to the query-dependent scores for the segments determined by the processing of the first query and the plurality of other queries; saving the updated query-independent scores for the segments; and repeating the processing of the first query and the plurality of other queries, adjusting and saving using the updated query-independent scores; and using the updated query-independent scores to determine the stand-alone score for each segment of the first user query. | 1. A computer-implemented method performed by one or more processors comprising: receiving a first user query from a user device; processing the first user query including identifying one or more segments in the first user query, a segment representing a word or a phrase; and determining a stand-alone score for each segment of the first user query, wherein the stand-alone score is an indication of a likelihood that the segment represents a stand-alone query and that the segment represents a main topic of the first user query, and wherein determining includes: processing a historical log of queries to determine query-independent scores for segments that are included in queries represented by the historical log including: identifying an initial query-independent score for a given segment; processing a first query in the historical log and determining a query-dependent score for each segment in the first query in the historical log including normalizing the query-dependent scores for the first query; processing a plurality of other queries in the historical log and determining a query dependent score for each segment for a given query; adjusting the initial query-independent scores for segments associated with the first query based on the determined and normalized query-dependent scores for the first query and the plurality of other queries to create updated query-independent scores for a given segment, including applying a first function to the query-dependent scores for the segments determined by the processing of the first query and the plurality of other queries; saving the updated query-independent scores for the segments; and repeating the processing of the first query and the plurality of other queries, adjusting and saving using the updated query-independent scores; and using the updated query-independent scores to determine the stand-alone score for each segment of the first user query. 2. The computer-implemented method of claim 1 wherein at least one segment is a plurality of consecutive words in the first user query. | 0.925083 |
9,858,340 | 1 | 5 | 1. A computer-implemented method, comprising: receiving video data for a first video; deconstructing the video data of the first video into a plurality of context windows, wherein each of the context windows comprises at least one of: an image frame of a segment of the first video from the video data, and an audio frame of a segment the first video from the video data; performing, on each context window of the plurality of context windows that includes an image frame, a video analytic function on the image frame to identify one or more characteristics of the context window that are associated with image-related content of the first video, wherein performing the video analytic function on the image frame comprises utilizing a neural-network based analysis to perform at least one of object detection, object localization, caption generation, and segmentation; performing, on each context window of the plurality of context windows that includes an audio frame, a video analytic function on the audio frame to identify one or more characteristics of the context window that are associated with audio-related content of the first video, wherein performing the video analytic function on the audio frame comprises utilizing a neural-network based analysis to perform at least one of language detection, transcription, speaker diarization, and tonal analysis; generating, for each of the plurality of context windows, a respective local atomic unit comprising attributes derived from the identified one or more characteristics of the respective context window, to form a plurality of local atomic units; generating a local graph representation of the first video, comprising a plurality of nodes corresponding to the plurality of local atomic units, wherein generating the local graph representation comprises applying local graph edges connecting the plurality of nodes to each other, wherein the local graph edges represent relationships between the connected nodes based, at least in part, on the attributes of the corresponding local atomic units; generating a global graph representation of a plurality of videos that includes the first video; receiving a query of the global graph representation for information associated with content of the plurality of videos; and producing, in response to the query and by analyzing the global graph representation, a response including the information associated with the content of the plurality of videos. | 1. A computer-implemented method, comprising: receiving video data for a first video; deconstructing the video data of the first video into a plurality of context windows, wherein each of the context windows comprises at least one of: an image frame of a segment of the first video from the video data, and an audio frame of a segment the first video from the video data; performing, on each context window of the plurality of context windows that includes an image frame, a video analytic function on the image frame to identify one or more characteristics of the context window that are associated with image-related content of the first video, wherein performing the video analytic function on the image frame comprises utilizing a neural-network based analysis to perform at least one of object detection, object localization, caption generation, and segmentation; performing, on each context window of the plurality of context windows that includes an audio frame, a video analytic function on the audio frame to identify one or more characteristics of the context window that are associated with audio-related content of the first video, wherein performing the video analytic function on the audio frame comprises utilizing a neural-network based analysis to perform at least one of language detection, transcription, speaker diarization, and tonal analysis; generating, for each of the plurality of context windows, a respective local atomic unit comprising attributes derived from the identified one or more characteristics of the respective context window, to form a plurality of local atomic units; generating a local graph representation of the first video, comprising a plurality of nodes corresponding to the plurality of local atomic units, wherein generating the local graph representation comprises applying local graph edges connecting the plurality of nodes to each other, wherein the local graph edges represent relationships between the connected nodes based, at least in part, on the attributes of the corresponding local atomic units; generating a global graph representation of a plurality of videos that includes the first video; receiving a query of the global graph representation for information associated with content of the plurality of videos; and producing, in response to the query and by analyzing the global graph representation, a response including the information associated with the content of the plurality of videos. 5. The method of claim 1 , wherein at least one of the plurality of context windows that comprises an image frame is comprised of: a plurality of image frames from a continuous portion of the first video, or a plurality of image frames from discontinuous portions of the first video. | 0.819515 |
8,452,766 | 5 | 7 | 5. The method of claim 1 , further comprising determining that a pair of the documents are duplicates based on a comparison of the respective query-relevant parts of the pair of documents. | 5. The method of claim 1 , further comprising determining that a pair of the documents are duplicates based on a comparison of the respective query-relevant parts of the pair of documents. 7. The method of claim 5 wherein the respective query-relevant parts of the pair of documents are similar. | 0.96165 |
8,315,874 | 1 | 6 | 1. A method of generating a voice user interface implemented by a computing device, the method comprising: providing a response container for governing voice response processing for the voice user interface; providing a plurality of nodes associated with the response container and defining acts to be performed; providing a plurality of response classes defining types of voice input responses related to the plurality of nodes associated with the response container; automatically generating a flow path for each of the plurality of response classes in the response container, each flow path spanning from a first node defining a first act to be performed to a second node defining a second act to be performed, wherein the generating the flow path for each of the plurality of response classes determines a callflow sequence of execution of acts of the plurality of nodes according to a response class associated with a voice input response; receiving meanings of different possible answers expected in response to the plurality of nodes; receiving sample inputs associated with each of the received meanings; automatically generating control files to extract a meaning and to determine a response class associated with a received voice response based on the received meanings of different possible answers and the received sample inputs; and automatically assigning a flow path to the received voice response based on an extracted meaning and a determined response class for establishing a callflow sequence in response to the received voice response. | 1. A method of generating a voice user interface implemented by a computing device, the method comprising: providing a response container for governing voice response processing for the voice user interface; providing a plurality of nodes associated with the response container and defining acts to be performed; providing a plurality of response classes defining types of voice input responses related to the plurality of nodes associated with the response container; automatically generating a flow path for each of the plurality of response classes in the response container, each flow path spanning from a first node defining a first act to be performed to a second node defining a second act to be performed, wherein the generating the flow path for each of the plurality of response classes determines a callflow sequence of execution of acts of the plurality of nodes according to a response class associated with a voice input response; receiving meanings of different possible answers expected in response to the plurality of nodes; receiving sample inputs associated with each of the received meanings; automatically generating control files to extract a meaning and to determine a response class associated with a received voice response based on the received meanings of different possible answers and the received sample inputs; and automatically assigning a flow path to the received voice response based on an extracted meaning and a determined response class for establishing a callflow sequence in response to the received voice response. 6. The method of claim 1 , wherein the providing a plurality of nodes comprises providing at least one prompt node prompting a spoken response to be provided. | 0.708487 |
9,275,001 | 13 | 14 | 13. The computer program product of claim 12 , wherein the interaction is associated with an interaction strength, the interaction strength describing a degree of association of the user and the video. | 13. The computer program product of claim 12 , wherein the interaction is associated with an interaction strength, the interaction strength describing a degree of association of the user and the video. 14. The computer program product of claim 13 , wherein the interaction strength is based on implicit feedback from the user. | 0.966559 |
9,374,087 | 17 | 18 | 17. A virtual world processing method comprising: encoding information relating to sensor capability into first metadata based on predetermined representation syntax, wherein the predetermined representation syntax defines attributes and flags corresponding to the attributes, and wherein the first metadata includes the flags corresponding to the attributes, and at least one attribute corresponding to at least one flag having a predefined logic value; encoding information relating to a virtual world into second metadata, wherein the information relating to the virtual world comprises a virtual world object characteristic; generating information that is applied to the virtual world, based on the first metadata and the second metadata; and encoding the generated information into third metadata. | 17. A virtual world processing method comprising: encoding information relating to sensor capability into first metadata based on predetermined representation syntax, wherein the predetermined representation syntax defines attributes and flags corresponding to the attributes, and wherein the first metadata includes the flags corresponding to the attributes, and at least one attribute corresponding to at least one flag having a predefined logic value; encoding information relating to a virtual world into second metadata, wherein the information relating to the virtual world comprises a virtual world object characteristic; generating information that is applied to the virtual world, based on the first metadata and the second metadata; and encoding the generated information into third metadata. 18. The virtual world processing method of claim 17 , further comprising: encoding information collected from a real world into fourth metadata, wherein the generating comprises generating the information that is applied to the virtual world based on the first metadata, the second metadata, and the fourth metadata. | 0.50625 |
8,171,021 | 10 | 13 | 10. A computer-implemented method, comprising: identifying candidate queries from queries stored in a query log, wherein identifying the candidate queries comprises: identifying a query from the queries stored in a query log; determining whether the query was submitted at least a minimum number of times during a time period; determining whether the query was submitted less than a maximum number of times during the time period; and identifying, by one or more computer processors, the query as a candidate query if the query was submitted at least the minimum number of times and less than the maximum number of times during the time period; for each candidate query: receiving a relevancy scores for a collection of landing pages, the collection of landing pages being a proper subset of a plurality of landing pages, and each relevancy score being associated with a landing page and being a measure of the relevance of the candidate query to the landing page; identifying the landing pages having an associated relevancy score that exceeds a relevancy threshold; and associating the candidate query with identified landing pages. | 10. A computer-implemented method, comprising: identifying candidate queries from queries stored in a query log, wherein identifying the candidate queries comprises: identifying a query from the queries stored in a query log; determining whether the query was submitted at least a minimum number of times during a time period; determining whether the query was submitted less than a maximum number of times during the time period; and identifying, by one or more computer processors, the query as a candidate query if the query was submitted at least the minimum number of times and less than the maximum number of times during the time period; for each candidate query: receiving a relevancy scores for a collection of landing pages, the collection of landing pages being a proper subset of a plurality of landing pages, and each relevancy score being associated with a landing page and being a measure of the relevance of the candidate query to the landing page; identifying the landing pages having an associated relevancy score that exceeds a relevancy threshold; and associating the candidate query with identified landing pages. 13. The method of claim 10 , wherein associating the candidate query with identified landing pages comprises, for each candidate query: generating a first vector of terms from search results of a search of the collection of landing pages; determining an intent measure from the first vector of terms; and associating the candidate query with the landing page if the intent measure exceeds an intent threshold. | 0.701895 |
10,002,325 | 1 | 4 | 1. A method of modifying a computer-readable elemental data structure of a knowledge representation system, the method comprising: applying, using at least one processor executing stored program instructions, one or more rules of analysis to deconstruct a knowledge representation into one or more elemental components; adding, using at least one processor executing stored program instructions, data associated with the one or more elemental components to an elemental data structure, the elemental data structure storing data representing concepts and concept relationships; inferring, using at least one processor executing stored program instructions, candidate data associated with the elemental data structure, wherein the inferring comprises detecting language in reference data, wherein the language corresponds to a predetermined linguistic pattern expressing a relationship between at least a first concept and a second concept in natural language; modifying the elemental data structure to combine the candidate data and the data associated with the one or more elemental components, wherein the modifying comprises adding, to the elemental data structure, the first concept, the second concept, and/or the relationship between the first and second concepts expressed by the linguistic pattern detected in the reference data, wherein the reference data is obtained from a source other than the knowledge representation wherein the detected linguistic pattern expresses in natural language that the second concept subsumes the first concept; wherein detecting the language corresponding to the predetermined linguistic pattern comprises detecting in the reference data a first label associated with the first concept, followed by a subsumptive expression, followed by a second label associated with the second concept, wherein the linguistic pattern including the subsumptive expression expresses in natural language that the second concept subsumes the first concept; wherein the subsumptive expression comprises at least one of one or more predetermined words or one or more predetermined symbols; wherein detecting the predetermined linguistic pattern in the reference data comprises detecting in the reference data the first label associated with the first concept and the second label associated with the second concept, wherein a proximity of the first label to the second label is within a threshold proximity; wherein the one or more elemental components are encoded as one or more computer-readable data structures storing data associated with the one or more elemental components, and wherein the reference data is encoded as one or more computer-readable data structures storing data associated with reference communication. | 1. A method of modifying a computer-readable elemental data structure of a knowledge representation system, the method comprising: applying, using at least one processor executing stored program instructions, one or more rules of analysis to deconstruct a knowledge representation into one or more elemental components; adding, using at least one processor executing stored program instructions, data associated with the one or more elemental components to an elemental data structure, the elemental data structure storing data representing concepts and concept relationships; inferring, using at least one processor executing stored program instructions, candidate data associated with the elemental data structure, wherein the inferring comprises detecting language in reference data, wherein the language corresponds to a predetermined linguistic pattern expressing a relationship between at least a first concept and a second concept in natural language; modifying the elemental data structure to combine the candidate data and the data associated with the one or more elemental components, wherein the modifying comprises adding, to the elemental data structure, the first concept, the second concept, and/or the relationship between the first and second concepts expressed by the linguistic pattern detected in the reference data, wherein the reference data is obtained from a source other than the knowledge representation wherein the detected linguistic pattern expresses in natural language that the second concept subsumes the first concept; wherein detecting the language corresponding to the predetermined linguistic pattern comprises detecting in the reference data a first label associated with the first concept, followed by a subsumptive expression, followed by a second label associated with the second concept, wherein the linguistic pattern including the subsumptive expression expresses in natural language that the second concept subsumes the first concept; wherein the subsumptive expression comprises at least one of one or more predetermined words or one or more predetermined symbols; wherein detecting the predetermined linguistic pattern in the reference data comprises detecting in the reference data the first label associated with the first concept and the second label associated with the second concept, wherein a proximity of the first label to the second label is within a threshold proximity; wherein the one or more elemental components are encoded as one or more computer-readable data structures storing data associated with the one or more elemental components, and wherein the reference data is encoded as one or more computer-readable data structures storing data associated with reference communication. 4. The method of claim 1 , wherein the detected linguistic pattern expresses in natural language that the second concept defines the first concept. | 0.9 |
8,364,679 | 28 | 30 | 28. The article of claim 25 , further comprising instructions to compile a second compilation of documents as a subset of the first compilation based upon secondary criteria present in the search profile. | 28. The article of claim 25 , further comprising instructions to compile a second compilation of documents as a subset of the first compilation based upon secondary criteria present in the search profile. 30. The article of claim 28 , further comprising instructions to sort the second compilation of documents based upon the second relevancy score. | 0.965517 |
7,953,674 | 1 | 4 | 1. A computer implemented system comprising the following computer executable components: a processor; and a memory component communicatively coupled to the processor, the memory component having stored therein computer-executable instructions that when executed by the processor cause the processor to implement: a fuzzing system that receives a structured query language (SQL) statement, wherein the SQL statement includes actual grammar associated with the SQL statement and explicit user specified parameters associated with penetration testing of an SQL server; and a parsing component as part of the SQL server that separates the explicit user specified parameters from the actual grammar associated with the SQL statement, wherein the parsing component mitigates parsing errors by replacing the explicit user specified parameters with fuzz values generated within the SQL server that maintain conformance to syntactically correct SQL statements. | 1. A computer implemented system comprising the following computer executable components: a processor; and a memory component communicatively coupled to the processor, the memory component having stored therein computer-executable instructions that when executed by the processor cause the processor to implement: a fuzzing system that receives a structured query language (SQL) statement, wherein the SQL statement includes actual grammar associated with the SQL statement and explicit user specified parameters associated with penetration testing of an SQL server; and a parsing component as part of the SQL server that separates the explicit user specified parameters from the actual grammar associated with the SQL statement, wherein the parsing component mitigates parsing errors by replacing the explicit user specified parameters with fuzz values generated within the SQL server that maintain conformance to syntactically correct SQL statements. 4. The computer implemented system of claim 1 further comprising a switch that controls fuzzing capability during runtime. | 0.608974 |
9,542,489 | 21 | 22 | 21. A system for associating social media content items with a time-based media event, the system comprising: a computer processor; and a computer-readable storage medium storing computer program modules configured to execute on the computer processor, the computer program modules comprising: a data ingestion engine configured to access from a social networking system a plurality of candidate social media content items authored by users of the social networking system; a media/event alignment engine configured to determine a confidence score for each of the candidate social media content items indicative of a probability that the candidate social media content item is relevant to the event and to align with the event, based on their respective confidence scores, a subset of the plurality of the social media content items; and a data store for collecting the alignments between the event and the subset of the plurality of the social media content items. | 21. A system for associating social media content items with a time-based media event, the system comprising: a computer processor; and a computer-readable storage medium storing computer program modules configured to execute on the computer processor, the computer program modules comprising: a data ingestion engine configured to access from a social networking system a plurality of candidate social media content items authored by users of the social networking system; a media/event alignment engine configured to determine a confidence score for each of the candidate social media content items indicative of a probability that the candidate social media content item is relevant to the event and to align with the event, based on their respective confidence scores, a subset of the plurality of the social media content items; and a data store for collecting the alignments between the event and the subset of the plurality of the social media content items. 22. The system of claim 21 , the computer program modules further comprising: a comparative feature extraction engine configured to extract event features from annotations associated with the event and to extract social media features from the plurality of social media content items; and the media/event alignment engine further configured to map the event to the social media content items based on a relationship between the event features and social media features. | 0.596386 |
9,367,797 | 12 | 13 | 12. The apparatus of claim 9 , wherein the processing unit is further configured to: determine a scalar value based on the adjusted current delay, wherein the scalar value is an inverse of an exponential value of a coefficient of change of a membrane potential for the artificial neuron, the exponential raised to a power of the adjusted current delay before taking the inverse. | 12. The apparatus of claim 9 , wherein the processing unit is further configured to: determine a scalar value based on the adjusted current delay, wherein the scalar value is an inverse of an exponential value of a coefficient of change of a membrane potential for the artificial neuron, the exponential raised to a power of the adjusted current delay before taking the inverse. 13. The apparatus of claim 12 , wherein the scalar value is a coefficient of a linear transformation. | 0.943195 |
8,165,899 | 17 | 21 | 17. A method for managing form-generated data related to a patient encounter, the method comprising: receiving location information related to a form that has designated information fields in different locations on the form, wherein the location information is generated in response to a user writing on the form in one of the designated information fields; translating the location information to a contextualized data element, wherein the contextualized data element comprises contextual information that is associated with the user writing, wherein translating the location information to a contextualized data element comprises using the location information to identify a label by comparing the location information to a mapping data set that maps user areas on the form to labels that are associated with the designated information fields, and wherein the contextualized data element comprises the label; and utilizing the label in the contextualized data element to perform a function related to the user writing on the form, wherein the function is performed by an EMR/EHR application and wherein the contextualized data element is distributed to the EMR/EHR application via a publish/subscribe protocol in which the EMR/EHR application subscribes to a specific contextualized data element by identifying the label associated with the contextualized data element. | 17. A method for managing form-generated data related to a patient encounter, the method comprising: receiving location information related to a form that has designated information fields in different locations on the form, wherein the location information is generated in response to a user writing on the form in one of the designated information fields; translating the location information to a contextualized data element, wherein the contextualized data element comprises contextual information that is associated with the user writing, wherein translating the location information to a contextualized data element comprises using the location information to identify a label by comparing the location information to a mapping data set that maps user areas on the form to labels that are associated with the designated information fields, and wherein the contextualized data element comprises the label; and utilizing the label in the contextualized data element to perform a function related to the user writing on the form, wherein the function is performed by an EMR/EHR application and wherein the contextualized data element is distributed to the EMR/EHR application via a publish/subscribe protocol in which the EMR/EHR application subscribes to a specific contextualized data element by identifying the label associated with the contextualized data element. 21. The method of claim 17 further comprising generating an encounter data set comprising multiple contextualized data elements, which together characterize a patient encounter. | 0.754167 |
7,734,998 | 1 | 4 | 1. An apparatus comprising: at least one processor; a memory coupled to the at least one processor; a repository in the memory that includes a plurality of objects; and a content management system residing in the memory and executed by the at least one processor, the content management system comprising: a voidable link mechanism that upon initial reconstitution of a document in the repository, retrieves from the repository objects corresponding to original links in the document, stores values from the retrieved objects in corresponding fallback elements in the document, generates a list of original links and voidable links to objects referenced by the original links in the document, and replaces any original links in the document that are not voidable links with corresponding voidable links, wherein the voidable link mechanism, upon subsequent reconstitution of the document, determines the original links in the list, queries the repository to determine which of the objects corresponding to the original links in the list have not changed since the last reconstitution of the document, and for each object that has not changed since the last reconstitution of the document, the voidable link mechanism invalidates the corresponding voidable link. | 1. An apparatus comprising: at least one processor; a memory coupled to the at least one processor; a repository in the memory that includes a plurality of objects; and a content management system residing in the memory and executed by the at least one processor, the content management system comprising: a voidable link mechanism that upon initial reconstitution of a document in the repository, retrieves from the repository objects corresponding to original links in the document, stores values from the retrieved objects in corresponding fallback elements in the document, generates a list of original links and voidable links to objects referenced by the original links in the document, and replaces any original links in the document that are not voidable links with corresponding voidable links, wherein the voidable link mechanism, upon subsequent reconstitution of the document, determines the original links in the list, queries the repository to determine which of the objects corresponding to the original links in the list have not changed since the last reconstitution of the document, and for each object that has not changed since the last reconstitution of the document, the voidable link mechanism invalidates the corresponding voidable link. 4. The apparatus of claim 1 wherein, if a voidable link in the document is valid, the content management system accesses the corresponding object in the repository using the voidable link. | 0.658182 |
8,688,694 | 19 | 26 | 19. A computer readable storage medium having stored therein instructions, which when executed by a computer system cause the computer system to: receive user instructions to associate each of a first virtual channel and a second virtual channel with a first content provider, wherein the first virtual channel includes a first set of user-specified search criteria and the second virtual channel includes a second set of user-specified search criteria that is different from the first set of search criteria; continuously perform operations according to a predefined schedule, the operations including: receive a first set of information items from the first content provider, wherein each information item includes a document title, a document summary, and a document link to a document at a respective remote location; for each of the first set of information items, retrieve the document identified by the corresponding document link from the respective remote location; apply the first set of search criteria to each of the first set of information items and its associated document to generate a first set of search results, wherein the first set of search results includes a first set of chunks within a first document and a third set of chunks within a second document, and each of the first set and the third set of chunks satisfies the first set of search criteria; apply the second set of search criteria to each of the first set of information items and its associated document to generate a second set of search results, wherein the second set of search results includes a second set of chunks within the first document, and each of the second set of chunks satisfies the second set of search criteria, wherein there is at least one difference between the first set of chunks and the second set of chunks; associate the first virtual channel with the first set of search results and the second virtual channel with the second set of search results, wherein there is at least one search result associated with both the first virtual channel and the second virtual channel; display the first virtual channel and the second virtual channel on the client computer; in response to a user selection of the first virtual channel, display, at least partially, information items associated with the first set of search results and the first set of chunks within the first document and the third set of chunks within the second document to the user; and in response to a user selection of one of the first set of chunks, display, at least partially, the first document including the user-selected chunk to the user adjacent to the display of the first set of chunks and the third set of chunks, wherein the user-selected chunk is visually distinguished from the rest of the document. | 19. A computer readable storage medium having stored therein instructions, which when executed by a computer system cause the computer system to: receive user instructions to associate each of a first virtual channel and a second virtual channel with a first content provider, wherein the first virtual channel includes a first set of user-specified search criteria and the second virtual channel includes a second set of user-specified search criteria that is different from the first set of search criteria; continuously perform operations according to a predefined schedule, the operations including: receive a first set of information items from the first content provider, wherein each information item includes a document title, a document summary, and a document link to a document at a respective remote location; for each of the first set of information items, retrieve the document identified by the corresponding document link from the respective remote location; apply the first set of search criteria to each of the first set of information items and its associated document to generate a first set of search results, wherein the first set of search results includes a first set of chunks within a first document and a third set of chunks within a second document, and each of the first set and the third set of chunks satisfies the first set of search criteria; apply the second set of search criteria to each of the first set of information items and its associated document to generate a second set of search results, wherein the second set of search results includes a second set of chunks within the first document, and each of the second set of chunks satisfies the second set of search criteria, wherein there is at least one difference between the first set of chunks and the second set of chunks; associate the first virtual channel with the first set of search results and the second virtual channel with the second set of search results, wherein there is at least one search result associated with both the first virtual channel and the second virtual channel; display the first virtual channel and the second virtual channel on the client computer; in response to a user selection of the first virtual channel, display, at least partially, information items associated with the first set of search results and the first set of chunks within the first document and the third set of chunks within the second document to the user; and in response to a user selection of one of the first set of chunks, display, at least partially, the first document including the user-selected chunk to the user adjacent to the display of the first set of chunks and the third set of chunks, wherein the user-selected chunk is visually distinguished from the rest of the document. 26. The computer readable storage medium of claim 19 , wherein the first set of information items is an XML-based document. | 0.934295 |
6,122,658 | 22 | 23 | 22. The client computer of claim 19 wherein said global content includes a video stream. | 22. The client computer of claim 19 wherein said global content includes a video stream. 23. The client computer of claim 22 wherein said local content includes annotations associated with said video stream. | 0.921438 |
9,984,160 | 1 | 2 | 1. A method for determining a query answer selection, the method comprising: receiving, by one or more computer processors, one or more queries, wherein the one or more queries include one or more question types; retrieving, by one or more computer processors, one or more strong searcher answers and one or more weak searcher answers, wherein the one or more strong searcher answers are provided by one or more strong searcher engines and the one or more weak searcher answers are provided by one or more weak searcher engines; determining, by one or more computer processors, one or more pseudo-query answers, wherein determining the one or more pseudo-query answers includes utilizing the one or more strong searcher answers as a query to the one or more weak searcher engines, wherein determining includes generating a pseudo query using pseudo-relevance feedback, wherein pseudo-relevance feedback includes one or more query relevant terms determined from one or more retrieved documents, and wherein the one or more query relevant terms are clipped to a pre-defined number of terms; determining, by one or more computer processors, one or more quality predictors for the one or more pseudo-query answers and the one or more weak searcher answers; and responsive to determining the one or more quality predictors, determining, by one or more computer processors, whether at least one of the one or more quality predictors for the one or more pseudo-query answers exceeds at least one of the one or more quality predictors for the one or more weak searcher answers. | 1. A method for determining a query answer selection, the method comprising: receiving, by one or more computer processors, one or more queries, wherein the one or more queries include one or more question types; retrieving, by one or more computer processors, one or more strong searcher answers and one or more weak searcher answers, wherein the one or more strong searcher answers are provided by one or more strong searcher engines and the one or more weak searcher answers are provided by one or more weak searcher engines; determining, by one or more computer processors, one or more pseudo-query answers, wherein determining the one or more pseudo-query answers includes utilizing the one or more strong searcher answers as a query to the one or more weak searcher engines, wherein determining includes generating a pseudo query using pseudo-relevance feedback, wherein pseudo-relevance feedback includes one or more query relevant terms determined from one or more retrieved documents, and wherein the one or more query relevant terms are clipped to a pre-defined number of terms; determining, by one or more computer processors, one or more quality predictors for the one or more pseudo-query answers and the one or more weak searcher answers; and responsive to determining the one or more quality predictors, determining, by one or more computer processors, whether at least one of the one or more quality predictors for the one or more pseudo-query answers exceeds at least one of the one or more quality predictors for the one or more weak searcher answers. 2. The method of claim 1 , wherein the one or more question types includes one or more of: a fact question type; an item question type; a definition question type; a hypothetical question type; and a language translation question type. | 0.877477 |
10,083,690 | 7 | 10 | 7. The method of claim 1 , further comprising: identifying a second substring from the textual representation that corresponds to a second attribute of the primary domain; and parsing the identified second substring to determine a second secondary domain representing a user intent for the second substring, wherein performing the task flow is further based on the second secondary domain. | 7. The method of claim 1 , further comprising: identifying a second substring from the textual representation that corresponds to a second attribute of the primary domain; and parsing the identified second substring to determine a second secondary domain representing a user intent for the second substring, wherein performing the task flow is further based on the second secondary domain. 10. The method of claim 7 , wherein parsing the identified second substring comprises: determining a confidence score for a plurality of interpretations of the second substring; and determining the second secondary domain representing a user intent for the second substring based on an interpretation of the plurality of interpretations of the second substring having the highest confidence score. | 0.784239 |
9,886,726 | 6 | 7 | 6. A non-transitory computer-readable storage medium storing executable computer program instructions for detecting social networking spam, the computer program instructions comprising instructions for performing steps comprising: selecting a central member who is a user of a social networking environment; measuring degrees of association between the central member and other users of the social networking environment; defining a social networking group containing the central member and other members, where the other members are a subset of the other users of the social networking environment selected responsive to the other users' degrees of association with the central member; identifying that a new entry has been posted on a blog of the central member; analyzing the new entry in comparison to a group usage profile for the social networking group, the group usage profile indicating a pattern of publishing activity of the members of the social networking group in posting information on blogs of other members of the social networking group over a period of time; responsive to the analysis in comparison to the group usage profile indicating that the new entry deviates from the pattern of publishing activity, analyzing the new entry using a global usage profile comprising a spam usage profile to determine whether the new entry matches a spam signature representing known spam; detecting that the new entry is spam responsive to analyzing the new entry using the global usage profile; and determining a pattern of global publishing activity of users in posting information on blogs of other users in the social networking environment; wherein the global usage profile is based in part on the determined pattern of global publishing activity. | 6. A non-transitory computer-readable storage medium storing executable computer program instructions for detecting social networking spam, the computer program instructions comprising instructions for performing steps comprising: selecting a central member who is a user of a social networking environment; measuring degrees of association between the central member and other users of the social networking environment; defining a social networking group containing the central member and other members, where the other members are a subset of the other users of the social networking environment selected responsive to the other users' degrees of association with the central member; identifying that a new entry has been posted on a blog of the central member; analyzing the new entry in comparison to a group usage profile for the social networking group, the group usage profile indicating a pattern of publishing activity of the members of the social networking group in posting information on blogs of other members of the social networking group over a period of time; responsive to the analysis in comparison to the group usage profile indicating that the new entry deviates from the pattern of publishing activity, analyzing the new entry using a global usage profile comprising a spam usage profile to determine whether the new entry matches a spam signature representing known spam; detecting that the new entry is spam responsive to analyzing the new entry using the global usage profile; and determining a pattern of global publishing activity of users in posting information on blogs of other users in the social networking environment; wherein the global usage profile is based in part on the determined pattern of global publishing activity. 7. The computer-readable storage medium of claim 6 , wherein the global usage profile comprises a holiday usage profile, wherein the new entry deviating from the pattern of the holiday usage profile during a holiday season indicates that the new entry is spam. | 0.760589 |
7,696,427 | 1 | 8 | 1. A method for recommending music comprising: identifying a granularity of a plurality of genres based on a request for music similarity, wherein the request identifies a user; training a genre classifier, executing on a computer processor, based on the granularity to obtain a trained genre classifier; calculating, using the computer processor, a first profile by the trained genre classifier, wherein the first profile comprises, for each of the plurality of genres, a likelihood that a music selection associated with the user is in the genre; calculating, using the computer processor, a second profile by the trained genre classifier, wherein the second profile comprises, for each of the plurality of genres, a likelihood that an unknown music selection is in the genre; obtaining a first similarity score between the first profile and the second profile; and recommending the unknown music selection to the user based on the first similarity score. | 1. A method for recommending music comprising: identifying a granularity of a plurality of genres based on a request for music similarity, wherein the request identifies a user; training a genre classifier, executing on a computer processor, based on the granularity to obtain a trained genre classifier; calculating, using the computer processor, a first profile by the trained genre classifier, wherein the first profile comprises, for each of the plurality of genres, a likelihood that a music selection associated with the user is in the genre; calculating, using the computer processor, a second profile by the trained genre classifier, wherein the second profile comprises, for each of the plurality of genres, a likelihood that an unknown music selection is in the genre; obtaining a first similarity score between the first profile and the second profile; and recommending the unknown music selection to the user based on the first similarity score. 8. The method of claim 1 , further comprising: obtaining a second similarity score between the music selection of the user and the unknown music selection; and summing the first similarity score multiplied by a first weight and the second similarity score multiplied by a second weight to obtain a total similarity score, wherein recommending the unknown music selection is further performed based on the total similarity score. | 0.70564 |
7,703,037 | 7 | 14 | 7. In a computer system having a graphical user interface including a display, a method of providing a menu on the display, the method comprising the steps of: receiving a user search input in the form of a series of keystrokes as an indication of a particular task; retrieving a set of candidate tasks bearing a predetermined association with the indication of the particular task, wherein retrieving a set of candidate tasks comprises automatically retrieving a set of candidate tasks with each of the keystrokes; displaying in response to the user search input, on a display device associated with the computer system, each candidate task from said set in association with an indication of a corresponding applet, wherein each candidate task and its indication of a corresponding applet are simultaneously rendered on the display device, and wherein displaying in response to the user input further comprises displaying the set of candidate tasks grouped hierarchically beneath the corresponding applets, displaying a recent tasks access interface, displaying a search access interface, and displaying a plurality of selectable categories, and wherein displaying in response to the user search input still further comprises displaying in response to each of the keystrokes; receiving a menu entry selection signal indicative of a user interface selection device pointing at one of the candidate tasks included in the set; receiving an execution signal indicative of a user selecting said one of the candidate tasks, and in response to the execution signal, updating the graphical user interface with information pertinent to execution of a task associated with said one of the candidate tasks; wherein updating the graphical user interface comprises providing access to a guided help component that is a portion of a particular applet, the guided help component providing guidance for completing said one of the candidate task, the particular applet being an applet that corresponds to the applet indication with which the said one of the candidate tasks is displayed on the display device; wherein each step of the method is executed by a computer processor associated with the computing system, said execution being part of execution of a series of computer-readable instructions embedded on a computer-readable storage medium, and wherein the sequential order of the steps is first receiving a user search input, second retrieving a set of candidate tasks, third displaying in response to the user search input, fourth receiving a menu entry selection signal, and fifth receiving an execution signal. | 7. In a computer system having a graphical user interface including a display, a method of providing a menu on the display, the method comprising the steps of: receiving a user search input in the form of a series of keystrokes as an indication of a particular task; retrieving a set of candidate tasks bearing a predetermined association with the indication of the particular task, wherein retrieving a set of candidate tasks comprises automatically retrieving a set of candidate tasks with each of the keystrokes; displaying in response to the user search input, on a display device associated with the computer system, each candidate task from said set in association with an indication of a corresponding applet, wherein each candidate task and its indication of a corresponding applet are simultaneously rendered on the display device, and wherein displaying in response to the user input further comprises displaying the set of candidate tasks grouped hierarchically beneath the corresponding applets, displaying a recent tasks access interface, displaying a search access interface, and displaying a plurality of selectable categories, and wherein displaying in response to the user search input still further comprises displaying in response to each of the keystrokes; receiving a menu entry selection signal indicative of a user interface selection device pointing at one of the candidate tasks included in the set; receiving an execution signal indicative of a user selecting said one of the candidate tasks, and in response to the execution signal, updating the graphical user interface with information pertinent to execution of a task associated with said one of the candidate tasks; wherein updating the graphical user interface comprises providing access to a guided help component that is a portion of a particular applet, the guided help component providing guidance for completing said one of the candidate task, the particular applet being an applet that corresponds to the applet indication with which the said one of the candidate tasks is displayed on the display device; wherein each step of the method is executed by a computer processor associated with the computing system, said execution being part of execution of a series of computer-readable instructions embedded on a computer-readable storage medium, and wherein the sequential order of the steps is first receiving a user search input, second retrieving a set of candidate tasks, third displaying in response to the user search input, fourth receiving a menu entry selection signal, and fifth receiving an execution signal. 14. The method of claim 7 , wherein retrieving a set of candidate tasks comprises utilizing a stemming process to remove letters from the user search input. | 0.753943 |
7,526,424 | 9 | 18 | 9. The system of claim 8 wherein the flesh-out component is configured to insert function words in the ALR. | 9. The system of claim 8 wherein the flesh-out component is configured to insert function words in the ALR. 18. the system of claim 9 wherein the flesh-out component inserts adverbial Wh words. | 0.96949 |
8,200,584 | 1 | 14 | 1. A computer-implemented web-based system for advertising and promoting career opportunities, said system including a server computer communicatively coupled to at least one other computer, said server computer including multimedia transmission means, and said server computer providing to said at least one other computer, a computer displayable holistic multimedia overview of the career opportunity and life in the business location so that a job seeker may make an informed decision, said holistic multimedia overview comprising a menu of a plurality of user-selectable sections, in viewing order, said sections presenting multimedia, including streaming video, graphics and text concerning a service area topic and a career topic, the service area topic including multimedia relating to a geographic service area for the career opportunity, local schools, local businesses and local lifestyle attractions, including recreation, entertainment and shopping attractions, and the career topic including multimedia relating to facilities, and streaming video, photographs and testimonials of professionals associated with the business and in the service area and interviews with said professionals said system further comprising a subsystem for creating a posting for a career opportunity, said subsystem including a computer-executable interactive module adapted to request input of multimedia for the job posting, said multimedia including content concerning a geographic service area for the career, photographs of and information about service area leaders and information about service area organizations, said interactive module being further adapted to create the job posting using inputted multimedia, said interactive module being further adapted for network access. | 1. A computer-implemented web-based system for advertising and promoting career opportunities, said system including a server computer communicatively coupled to at least one other computer, said server computer including multimedia transmission means, and said server computer providing to said at least one other computer, a computer displayable holistic multimedia overview of the career opportunity and life in the business location so that a job seeker may make an informed decision, said holistic multimedia overview comprising a menu of a plurality of user-selectable sections, in viewing order, said sections presenting multimedia, including streaming video, graphics and text concerning a service area topic and a career topic, the service area topic including multimedia relating to a geographic service area for the career opportunity, local schools, local businesses and local lifestyle attractions, including recreation, entertainment and shopping attractions, and the career topic including multimedia relating to facilities, and streaming video, photographs and testimonials of professionals associated with the business and in the service area and interviews with said professionals said system further comprising a subsystem for creating a posting for a career opportunity, said subsystem including a computer-executable interactive module adapted to request input of multimedia for the job posting, said multimedia including content concerning a geographic service area for the career, photographs of and information about service area leaders and information about service area organizations, said interactive module being further adapted to create the job posting using inputted multimedia, said interactive module being further adapted for network access. 14. A system according to claim 1 , said information including content relating to a facilities and equipment for the career. | 0.948812 |
9,002,900 | 1 | 7 | 1. A method comprising: storing, in a first area of a memory of a first computer system, a hierarchical data structure including a plurality of activity data items each representing an activity and each having a description and a status, wherein: each activity data item other than a root activity data item is a child of a parent activity data item and represents a subtask of the task represented by the parent activity data item; and the status for each node is recorded in a data field associated with that node; storing, in a second area of the memory of the first computer system, an unstructured set of activity data items each having a description; receiving a user input instructing a processor of the first computer system to associate an activity data item from the unstructured set of activity data items with the hierarchical data structure as a child or parent of one of the activity data items of the hierarchical data structure; receiving from a second computer system an additional activity data item; and storing the received additional activity data item in the second area. | 1. A method comprising: storing, in a first area of a memory of a first computer system, a hierarchical data structure including a plurality of activity data items each representing an activity and each having a description and a status, wherein: each activity data item other than a root activity data item is a child of a parent activity data item and represents a subtask of the task represented by the parent activity data item; and the status for each node is recorded in a data field associated with that node; storing, in a second area of the memory of the first computer system, an unstructured set of activity data items each having a description; receiving a user input instructing a processor of the first computer system to associate an activity data item from the unstructured set of activity data items with the hierarchical data structure as a child or parent of one of the activity data items of the hierarchical data structure; receiving from a second computer system an additional activity data item; and storing the received additional activity data item in the second area. 7. The method of claim 1 , wherein receiving from the second computer system the additional activity data item comprises receiving the additional activity data item by way of a central server operable to translate at least one field of the received additional activity data item from a field that is not defined on the first computer system to a field that is defined on the first computer system. | 0.750628 |
8,990,235 | 7 | 8 | 7. A non-transitory computer-readable medium whose contents, when executed by a first computing device, cause the first computing device to perform operations comprising: receiving text; selecting a portion of the received text; forming a query based at least in part upon the selected portion of the received text, wherein the query includes a first portion that is associated with the selected portion of the text; selecting, from among multiple different indexes, at least one index to search based on the query; transmitting the query to at least one of one or more second computing devices based at least in part on the selected index; receiving information relevant to the query from at least one of the one or more second computing devices; and displaying the relevant information. | 7. A non-transitory computer-readable medium whose contents, when executed by a first computing device, cause the first computing device to perform operations comprising: receiving text; selecting a portion of the received text; forming a query based at least in part upon the selected portion of the received text, wherein the query includes a first portion that is associated with the selected portion of the text; selecting, from among multiple different indexes, at least one index to search based on the query; transmitting the query to at least one of one or more second computing devices based at least in part on the selected index; receiving information relevant to the query from at least one of the one or more second computing devices; and displaying the relevant information. 8. The non-transitory computer-readable medium of claim 7 , wherein the portion of text comprises text from an image optically captured by the first computing device. | 0.894937 |
10,140,279 | 1 | 17 | 1. A method, comprising: at one or more computing devices comprising one or more processors and storage media storing one or more computer programs executed by the one or more processors to perform the method, performing operations comprising: in response to detecting a semantic organization event in a first graphical user interface content-view of a spreadsheet, determining whether semantic organization data associated with a first set of data cells of the spreadsheet should be stored, wherein the semantic organization data describes how the spreadsheet is organized; wherein the first set of data cells includes a plurality of columns; wherein the plurality of columns of the first set of data cells includes a plurality of data cell values; wherein the spreadsheet includes a header that collectively groups the plurality of columns within the spreadsheet; wherein the header and the plurality of data cell values are content input into the spreadsheet; wherein the semantic organization data includes the header; in response to determining that semantic organization data should be stored, storing semantic organization data associated with the first set of data cells; generating a preview thumbnail image based on the semantic organization data; displaying a graphical user interface semantic-view of the spreadsheet, wherein the graphical user interface semantic-view comprises the preview thumbnail image corresponding to the first set of data cells; wherein the preview thumbnail image displayed includes display of the header that collectively groups the plurality of columns within the spreadsheet; and in response to detecting a selection of the preview thumbnail image corresponding to the first set of data cells, navigating to a display of the first set of data cells in a second graphical user interface content-view of the spreadsheet. | 1. A method, comprising: at one or more computing devices comprising one or more processors and storage media storing one or more computer programs executed by the one or more processors to perform the method, performing operations comprising: in response to detecting a semantic organization event in a first graphical user interface content-view of a spreadsheet, determining whether semantic organization data associated with a first set of data cells of the spreadsheet should be stored, wherein the semantic organization data describes how the spreadsheet is organized; wherein the first set of data cells includes a plurality of columns; wherein the plurality of columns of the first set of data cells includes a plurality of data cell values; wherein the spreadsheet includes a header that collectively groups the plurality of columns within the spreadsheet; wherein the header and the plurality of data cell values are content input into the spreadsheet; wherein the semantic organization data includes the header; in response to determining that semantic organization data should be stored, storing semantic organization data associated with the first set of data cells; generating a preview thumbnail image based on the semantic organization data; displaying a graphical user interface semantic-view of the spreadsheet, wherein the graphical user interface semantic-view comprises the preview thumbnail image corresponding to the first set of data cells; wherein the preview thumbnail image displayed includes display of the header that collectively groups the plurality of columns within the spreadsheet; and in response to detecting a selection of the preview thumbnail image corresponding to the first set of data cells, navigating to a display of the first set of data cells in a second graphical user interface content-view of the spreadsheet. 17. The method of claim 1 , wherein: the semantic view comprises a plurality of preview thumbnail images; each preview thumbnail image of the plurality of preview thumbnail images is selectable in the semantic view to navigate to a display of a respective set of data cells of the spreadsheet in the content-view of the spreadsheet. | 0.665996 |
9,355,093 | 1 | 2 | 1. A natural language generation method for generating a referring noun phrase for an intended referent found in one or more messages within a document plan, the method comprising: arranging, using a processor, one or more messages in a document plan, wherein messages represent a phrase or a simple sentence and are created in an instance in which an input data stream comprises data that satisfies one or more message requirements, and wherein at least a portion of the input data stream comprises non-linguistic data; identifying an intended referent in a message of the one or more messages to be referred to in a textual output; determining a lowest common ancestor for the intended referent and a previously referred-to entity within a part-of hierarchy; determining a salient ancestor of the intended referent within the part-of hierarchy; generating a referring noun phrase for the intended referent to be included in a textual output by traversing the part-of hierarchy from the salient ancestor to the lowest common ancestor such that a default descriptor is added to a queue for at least a portion of entities traversed in the part-of-hierarchy, wherein the reference noun phrase comprises a default descriptor of the intended referent and one or more default descriptors of one or more parts of the part-of hierarchy that are traversed; generating the textual output comprising the referring noun phrase such that it is displayable on a user interface, wherein the textual output linguistically describes at least a portion of the input data stream; and displaying the textual output via a display device. | 1. A natural language generation method for generating a referring noun phrase for an intended referent found in one or more messages within a document plan, the method comprising: arranging, using a processor, one or more messages in a document plan, wherein messages represent a phrase or a simple sentence and are created in an instance in which an input data stream comprises data that satisfies one or more message requirements, and wherein at least a portion of the input data stream comprises non-linguistic data; identifying an intended referent in a message of the one or more messages to be referred to in a textual output; determining a lowest common ancestor for the intended referent and a previously referred-to entity within a part-of hierarchy; determining a salient ancestor of the intended referent within the part-of hierarchy; generating a referring noun phrase for the intended referent to be included in a textual output by traversing the part-of hierarchy from the salient ancestor to the lowest common ancestor such that a default descriptor is added to a queue for at least a portion of entities traversed in the part-of-hierarchy, wherein the reference noun phrase comprises a default descriptor of the intended referent and one or more default descriptors of one or more parts of the part-of hierarchy that are traversed; generating the textual output comprising the referring noun phrase such that it is displayable on a user interface, wherein the textual output linguistically describes at least a portion of the input data stream; and displaying the textual output via a display device. 2. A method according to claim 1 , wherein the one or more parts of the part-of hierarchy that are traversed based on one or more salient ancestor links. | 0.803342 |
9,946,813 | 6 | 11 | 6. A search support method performed by a computer, the search support method comprising: receiving a question containing a character string; identifying a question phrase for specifying a question type of the received question by comparing the character string contained in the question and a question phrase that is included in information that is stored in a storage unit, in the stored information a question phrase, a display mode and an extraction rule being associated one another, and identifying, based on the extraction rule that is associated with the identified question phrase, a search query used for searching in a database for a response to the received question, the database storing information published on Web sites; determining a display mode of an output of the response to the received question in accordance with the identified question phrase for specifying the question type by referring to the stored information; and outputting a search result of searching in the database using the identified search query, in the display mode of the output, wherein the process further comprises specifying, when the identified question phrase indicates that selection from objects that are included in the question is requested, the objects for selection and information related thereto from the search result; outputting each of the specified objects and information, in a display mode that enables comparison therebetween; extracting, when the identified question phrase indicates that objects of an action that is specified in the question are requested to be searched, the objects from the search result; and outputting the extracted objects in a display mode in which images of the extracted objects are listed. | 6. A search support method performed by a computer, the search support method comprising: receiving a question containing a character string; identifying a question phrase for specifying a question type of the received question by comparing the character string contained in the question and a question phrase that is included in information that is stored in a storage unit, in the stored information a question phrase, a display mode and an extraction rule being associated one another, and identifying, based on the extraction rule that is associated with the identified question phrase, a search query used for searching in a database for a response to the received question, the database storing information published on Web sites; determining a display mode of an output of the response to the received question in accordance with the identified question phrase for specifying the question type by referring to the stored information; and outputting a search result of searching in the database using the identified search query, in the display mode of the output, wherein the process further comprises specifying, when the identified question phrase indicates that selection from objects that are included in the question is requested, the objects for selection and information related thereto from the search result; outputting each of the specified objects and information, in a display mode that enables comparison therebetween; extracting, when the identified question phrase indicates that objects of an action that is specified in the question are requested to be searched, the objects from the search result; and outputting the extracted objects in a display mode in which images of the extracted objects are listed. 11. The search support method according to claim 6 , further comprising: extracting, when the identified question phrase indicates that comparison between a plurality of objects included in the question is requested, the objects and information related thereto from the search result, and performing a predetermined common analysis on each of the extracted objects based on the information; and outputting a result of the performed analysis in a display mode in which comparison is possible. | 0.501016 |
8,781,839 | 1 | 18 | 1. Computerized apparatus useful for locating an organization or entity, the organization or entity being disposed within a building or structure, the apparatus comprising: a wireless interface; data processing apparatus; a touch-screen input and display device; a speech digitization apparatus in data communication with the data processing apparatus; and a storage apparatus in data communication with the data processing apparatus, said storage apparatus comprising at least one computer program, said at least one program being configured to: receive a digitized speech input via the speech digitization apparatus, the input relating to an organization or entity which a user wishes to locate; based at least in part on the input, causing recognition of at least one word therein relating to the organization or entity, and identification of a location associated with the organization or entity based at least in part on the at least one recognized word, the location being inside of the building or structure; and provide a graphical or visual representation of the location on the touch screen input and display device in order to aid a user in finding the organization or entity, the graphical or visual representation of the location also comprising a graphical or visual representation of at least the immediate surroundings of the organization or entity, the immediate surroundings being inside the building or structure. | 1. Computerized apparatus useful for locating an organization or entity, the organization or entity being disposed within a building or structure, the apparatus comprising: a wireless interface; data processing apparatus; a touch-screen input and display device; a speech digitization apparatus in data communication with the data processing apparatus; and a storage apparatus in data communication with the data processing apparatus, said storage apparatus comprising at least one computer program, said at least one program being configured to: receive a digitized speech input via the speech digitization apparatus, the input relating to an organization or entity which a user wishes to locate; based at least in part on the input, causing recognition of at least one word therein relating to the organization or entity, and identification of a location associated with the organization or entity based at least in part on the at least one recognized word, the location being inside of the building or structure; and provide a graphical or visual representation of the location on the touch screen input and display device in order to aid a user in finding the organization or entity, the graphical or visual representation of the location also comprising a graphical or visual representation of at least the immediate surroundings of the organization or entity, the immediate surroundings being inside the building or structure. 18. The apparatus of claim 1 , where the at least one computer program is further configured to: generate a listing of a plurality of possible matches to said input, thereby creating an ambiguity; and receive further input regarding at least one of the listed plurality of possible matches to resolve the ambiguity. | 0.85 |
7,849,148 | 5 | 19 | 5. The method of claim 4 , and further comprising, utilizing the at least one window, displaying, in response to a first user interaction, the first additional information associated with the first message. | 5. The method of claim 4 , and further comprising, utilizing the at least one window, displaying, in response to a first user interaction, the first additional information associated with the first message. 19. The method of claim 5 , wherein the first message includes the first additional information. | 0.977143 |
8,793,199 | 1 | 6 | 1. A computer program product, comprising: a computer readable storage medium to store a computer readable program, wherein the computer readable program, when executed by a processor within a computer, causes the computer to perform operations for extracting information from electronic documents, the operations comprising: learning terms and term variants from a training corpus, wherein the terms and the term variants correspond to a specialized dictionary related to the training corpus; generating a list of negative indicators found in the training corpus; performing a partial match of the terms and the term variants in a set of electronic documents to create initial match results; and performing a negation test using the negative indicators and a positive terms test using the terms and the term variants on the initial match results to remove matches from the initial match results that fail either the negation test or the positive terms test, resulting in final match results. | 1. A computer program product, comprising: a computer readable storage medium to store a computer readable program, wherein the computer readable program, when executed by a processor within a computer, causes the computer to perform operations for extracting information from electronic documents, the operations comprising: learning terms and term variants from a training corpus, wherein the terms and the term variants correspond to a specialized dictionary related to the training corpus; generating a list of negative indicators found in the training corpus; performing a partial match of the terms and the term variants in a set of electronic documents to create initial match results; and performing a negation test using the negative indicators and a positive terms test using the terms and the term variants on the initial match results to remove matches from the initial match results that fail either the negation test or the positive terms test, resulting in final match results. 6. The computer program product of claim 1 , wherein performing the partial prefix match further comprises: segmenting the set of electronic documents into sentences; determining a longest common subfix score for each sentence, wherein the longest common subfix score corresponds to a maximum subset of term and term variant matches for each sentence; and retaining sentences that have the longest common subfix score above a predetermined threshold. | 0.501109 |
9,110,957 | 1 | 2 | 1. A method comprising: offloading data computation to a remote system in a business intelligence environment; receiving, from a user interface displaying data of a business intelligence document, a data mining assertion associated with the business intelligence document to identify relationships within the data, wherein the user interface displays the data of the business document in a tabular form comprising a plurality of cells displaying one or more data values and expressions disposed in a plurality of rows and columns, and wherein the data mining assertion is received in one or more cells of a row displaying at least one data value or at least one expression of the business intelligence document, wherein each cell of the row is capable of specifying a different component of the data mining assertion and each cell of a column is capable of specifying only a same component of the data mining assertion, the business intelligence document specifying a directed acyclic graph connected of entities in a pipeline to produce a complex and arbitrary sequence of expressions designated for the computation; varying, in the same user interface, the at least one data value and the at least one expression of the one or more cells of the row subject to the data mining assertion; solving the data mining assertion within a specified constraint based on the varying to identify individual data values that cause the data mining assertion received from the user interface to evaluate the data value and expressions variations available within the scope of the business intelligence document to iterate through the variations to determine the data and transformations that make the assertion true; presenting a solution of the solving in the same user interface by replacing the plurality of cells with the solution, the solution comprising a second plurality of cells displaying one or more data values or expressions disposed in the plurality of rows and columns; and receiving the data mining assertion by invoking an assertion mode on the row using a toolbar button or a menu item of the user interface associated with the business intelligence document, wherein the assertion mode provides a control on each cell of the row. | 1. A method comprising: offloading data computation to a remote system in a business intelligence environment; receiving, from a user interface displaying data of a business intelligence document, a data mining assertion associated with the business intelligence document to identify relationships within the data, wherein the user interface displays the data of the business document in a tabular form comprising a plurality of cells displaying one or more data values and expressions disposed in a plurality of rows and columns, and wherein the data mining assertion is received in one or more cells of a row displaying at least one data value or at least one expression of the business intelligence document, wherein each cell of the row is capable of specifying a different component of the data mining assertion and each cell of a column is capable of specifying only a same component of the data mining assertion, the business intelligence document specifying a directed acyclic graph connected of entities in a pipeline to produce a complex and arbitrary sequence of expressions designated for the computation; varying, in the same user interface, the at least one data value and the at least one expression of the one or more cells of the row subject to the data mining assertion; solving the data mining assertion within a specified constraint based on the varying to identify individual data values that cause the data mining assertion received from the user interface to evaluate the data value and expressions variations available within the scope of the business intelligence document to iterate through the variations to determine the data and transformations that make the assertion true; presenting a solution of the solving in the same user interface by replacing the plurality of cells with the solution, the solution comprising a second plurality of cells displaying one or more data values or expressions disposed in the plurality of rows and columns; and receiving the data mining assertion by invoking an assertion mode on the row using a toolbar button or a menu item of the user interface associated with the business intelligence document, wherein the assertion mode provides a control on each cell of the row. 2. The method of claim 1 wherein a constraint restricts the varying of the at least one data value or the at least one expression within a range. | 0.712302 |
7,539,669 | 13 | 17 | 13. The computer-implemented method of claim 9 , wherein the object types include a first object type that is in a structured format and a second object type that is in an unstructured format. | 13. The computer-implemented method of claim 9 , wherein the object types include a first object type that is in a structured format and a second object type that is in an unstructured format. 17. The computer-implemented method of claim 13 , wherein the metadata includes: first metadata corresponding to individual attributes associated with the first object type and each of the generic attributes; and second metadata corresponding to individual attributes associated with the second object type and each of the generic attributes. | 0.891083 |
7,805,398 | 10 | 11 | 10. A tangible computer-readable storage medium storing computer-executable instructions for execution by a processor, the medium storing one or more instructions for: defining one or more rules for validating or inferring a value of an attribute associated with an entity of a model, or registering one or more rules with a unit that validates or infers the value; and validating or inferring the value based on the one or more defined or registered rules. | 10. A tangible computer-readable storage medium storing computer-executable instructions for execution by a processor, the medium storing one or more instructions for: defining one or more rules for validating or inferring a value of an attribute associated with an entity of a model, or registering one or more rules with a unit that validates or infers the value; and validating or inferring the value based on the one or more defined or registered rules. 11. The medium of claim 10 , wherein at least one of the defined or registered rules includes a constraint on the value. | 0.820896 |
9,916,306 | 1 | 7 | 1. A method for providing dynamic feedback within an authoring environment to improve overall quality of text in real time, the method comprising: receiving in the authoring environment a first source text from an author; displaying, via a processor communicatively coupled with a memory, the first source text in the authoring environment; calculating a first numerical value representing translatability of the displayed first source text, the calculation of the first numerical value performed in real time based on a first trust score generated using a statistical machine translation engine for translation of the displayed first source text; displaying in the authoring environment a second source text related to the first source text in meaning and differing from the first source text; calculating a second numerical value representing translatability of the second source text, the second numerical value calculated in real time based on a second trust score generated using the statistical machine translation engine for translation of the second source text; visually enhancing the display of the first and second source text in the authoring environment in real time based on the first and second calculated numerical values to indicate to the author translatability of the respective visually enhanced first and second source text; and replacing the first source text with the second source text to improve the overall quality of text in the authoring environment. | 1. A method for providing dynamic feedback within an authoring environment to improve overall quality of text in real time, the method comprising: receiving in the authoring environment a first source text from an author; displaying, via a processor communicatively coupled with a memory, the first source text in the authoring environment; calculating a first numerical value representing translatability of the displayed first source text, the calculation of the first numerical value performed in real time based on a first trust score generated using a statistical machine translation engine for translation of the displayed first source text; displaying in the authoring environment a second source text related to the first source text in meaning and differing from the first source text; calculating a second numerical value representing translatability of the second source text, the second numerical value calculated in real time based on a second trust score generated using the statistical machine translation engine for translation of the second source text; visually enhancing the display of the first and second source text in the authoring environment in real time based on the first and second calculated numerical values to indicate to the author translatability of the respective visually enhanced first and second source text; and replacing the first source text with the second source text to improve the overall quality of text in the authoring environment. 7. The method according to claim 1 , further comprising: determining a domain for the first and second source text; receiving a first plurality of trust scores for the first source text from a plurality statistical machine translation engines that each utilize a unique domain-based parallel corpus; calculating the first numerical value representing translatability of the displayed first source text based on the first plurality of trust scores; receiving a second plurality of trust scores for the second source text from the plurality statistical machine translation engines that each utilize the unique domain-based parallel corpus; and calculating the second numerical value representing translatability of the displayed second source text based on the second plurality of trust scores. | 0.500631 |
9,188,456 | 1 | 4 | 1. A computer based method of fixing user input errors in a full string destination address provided to an in-vehicle computing system, the method comprising: interpreting the full string destination address received in a first user input; receiving contextual information corresponding to the full string destination address in an additional user input, the contextual information comprising a commercial category associated with a particular point of interest of a plurality of points of interest, the commercial category further specifying a type of the particular point of interest; presenting the full string destination address; receiving a second user input indicating an error in the presented full string destination address; identifying, by the in-vehicle computing system, a first portion of the full string destination address that is most likely incorrect using the contextual information, wherein the full string destination address includes the first portion and a second portion; requesting a user to re-enter the first portion of the full string destination address; receiving a spoken third user input of the first portion of the full string destination address; and identifying a full string destination address based on the spoken third user input of the first portion of the full string destination address, the contextual information, and the first user input of the second portion of the full string destination address. | 1. A computer based method of fixing user input errors in a full string destination address provided to an in-vehicle computing system, the method comprising: interpreting the full string destination address received in a first user input; receiving contextual information corresponding to the full string destination address in an additional user input, the contextual information comprising a commercial category associated with a particular point of interest of a plurality of points of interest, the commercial category further specifying a type of the particular point of interest; presenting the full string destination address; receiving a second user input indicating an error in the presented full string destination address; identifying, by the in-vehicle computing system, a first portion of the full string destination address that is most likely incorrect using the contextual information, wherein the full string destination address includes the first portion and a second portion; requesting a user to re-enter the first portion of the full string destination address; receiving a spoken third user input of the first portion of the full string destination address; and identifying a full string destination address based on the spoken third user input of the first portion of the full string destination address, the contextual information, and the first user input of the second portion of the full string destination address. 4. The method of claim 1 , wherein the first portion of the full string destination address comprises a street number and name of the destination address. | 0.931004 |
10,095,748 | 13 | 16 | 13. The computer readable storage device of claim 12 , further comprising providing an actor-action query suggestion for querying information about a content item action associated with a person corresponding to the content item action. | 13. The computer readable storage device of claim 12 , further comprising providing an actor-action query suggestion for querying information about a content item action associated with a person corresponding to the content item action. 16. The computer readable storage device of claim 13 , further comprising at least one of: receiving a selection of the people query suggestion and automatically providing information about the corresponding person in a computer-enabled user interface; or receiving a selection of the actor-action and automatically navigating to a content item associated with the content item action. | 0.844758 |
8,046,221 | 16 | 19 | 16. A spoken dialog system that applies a multi-state barge-in acoustic model, the system comprising: a module configured to present prompt to a user; a module configured to receive an audio speech input from the user during a presentation prompt; a module configured to accumulate the audio speech input from the user; a module configured to apply a non-speech component having at least two one-state Hidden Markov Models (HMMs) and two three-state left-to-right HMMs; and a module configured to apply a non-speech component having at least two one-state Hidden Markov Models (HMMs) to the audio speech input from the user; a module configured to apply a speech component having at least five three-state HMMs to the audio speech input from the user, wherein each of the five three-state HMMs represents a different phonetic category; a module configured to determine whether the audio speech input is a barge-in-speech input from the user; and a module configured, if the audio speech input is determined to be the barge-in-speech input from the user, to terminate the presentation of the prompt. | 16. A spoken dialog system that applies a multi-state barge-in acoustic model, the system comprising: a module configured to present prompt to a user; a module configured to receive an audio speech input from the user during a presentation prompt; a module configured to accumulate the audio speech input from the user; a module configured to apply a non-speech component having at least two one-state Hidden Markov Models (HMMs) and two three-state left-to-right HMMs; and a module configured to apply a non-speech component having at least two one-state Hidden Markov Models (HMMs) to the audio speech input from the user; a module configured to apply a speech component having at least five three-state HMMs to the audio speech input from the user, wherein each of the five three-state HMMs represents a different phonetic category; a module configured to determine whether the audio speech input is a barge-in-speech input from the user; and a module configured, if the audio speech input is determined to be the barge-in-speech input from the user, to terminate the presentation of the prompt. 19. The system of claim 16 , wherein the multi-state barge-in acoustic model is trained using a maximum likelihood (ML) training to detect speech during non-speech segments and to detect failure of speech when present. | 0.502283 |
8,554,716 | 11 | 15 | 11. The method of claim 2 , further comprising applying targeted document identification to the plurality of documents. | 11. The method of claim 2 , further comprising applying targeted document identification to the plurality of documents. 15. The method of claim 11 , wherein applying further comprises identifying a candidate key document based on sampling and extrapolation. | 0.958535 |
9,069,567 | 1 | 7 | 1. A computer-implemented method for accessing a native application programming interface (API) of a computing device, the method comprising: providing on the computing device, and from a first application written in a device-independent programming language, one or more control objects that include (a) state information that defines a context for accessing the native API and (b) at least one control script; compiling the control script on the computing device into a second application that is native to the operating system of the computing device; executing the second application on the computing device, wherein the executed second application accesses the native API of the computing device to generate an output through a hardware interface of the computing device based on the context; and accessing the native API by the second application, based on information about the state information in the control objects from the first application that is provided as a result of user input received by the first application, to generate one or more additional outputs through the hardware interface of the computing device. | 1. A computer-implemented method for accessing a native application programming interface (API) of a computing device, the method comprising: providing on the computing device, and from a first application written in a device-independent programming language, one or more control objects that include (a) state information that defines a context for accessing the native API and (b) at least one control script; compiling the control script on the computing device into a second application that is native to the operating system of the computing device; executing the second application on the computing device, wherein the executed second application accesses the native API of the computing device to generate an output through a hardware interface of the computing device based on the context; and accessing the native API by the second application, based on information about the state information in the control objects from the first application that is provided as a result of user input received by the first application, to generate one or more additional outputs through the hardware interface of the computing device. 7. The computer-implemented method of claim 1 , wherein the native API comprises a native rendering API, the hardware interface comprises a display screen, and the second application accesses the native rendering API to render drawing objects on the display screen. | 0.747137 |
7,840,460 | 15 | 18 | 15. A system for evaluating a patent portfolio, comprising patent documents, by one or more experts, the system comprising: a computer having a processor, and a computer readable medium having computer readable instructions stored thereon for execution by the processor, causing the processor to: (a) determine a bias of each said expert by comparing results of evaluation of at least one test patent document performed by each said expert with a predetermined evaluation of said test patent document; and (b) evaluate the patent documents from the patent portfolio by said experts according to an evaluation method, which is bias corrected for each said expert so that to compensate for the bias associated with each said expert; further comprising computer readable instructions stored in the computer readable medium, causing the processor to: evaluate a patent document from the patent portfolio according to an evaluation method, which processes a number of patent indices, characterizing different aspects of the patent document, into a Patent Quality index (PQ), characterizing value of the patent document, according to a non-linear function of said patent indices; select the non-linear function, which is characterized by a parameter of non-linearity, corresponding to a bias of an expert, the function being selected so that to compensate for the bias of the expert; select the non-linear function, which is continuous monotonously increasing or decreasing, bounded on the interval of variations of the patent indices, and processes the patent indices into the PQ as follows: when a value of any patent index lends substantially to its minimal value, then the PQ also tends substantially to its minimal value for the increasing function, and to its maximal value for the decreasing function. | 15. A system for evaluating a patent portfolio, comprising patent documents, by one or more experts, the system comprising: a computer having a processor, and a computer readable medium having computer readable instructions stored thereon for execution by the processor, causing the processor to: (a) determine a bias of each said expert by comparing results of evaluation of at least one test patent document performed by each said expert with a predetermined evaluation of said test patent document; and (b) evaluate the patent documents from the patent portfolio by said experts according to an evaluation method, which is bias corrected for each said expert so that to compensate for the bias associated with each said expert; further comprising computer readable instructions stored in the computer readable medium, causing the processor to: evaluate a patent document from the patent portfolio according to an evaluation method, which processes a number of patent indices, characterizing different aspects of the patent document, into a Patent Quality index (PQ), characterizing value of the patent document, according to a non-linear function of said patent indices; select the non-linear function, which is characterized by a parameter of non-linearity, corresponding to a bias of an expert, the function being selected so that to compensate for the bias of the expert; select the non-linear function, which is continuous monotonously increasing or decreasing, bounded on the interval of variations of the patent indices, and processes the patent indices into the PQ as follows: when a value of any patent index lends substantially to its minimal value, then the PQ also tends substantially to its minimal value for the increasing function, and to its maximal value for the decreasing function. 18. The system as described in claim 15 , wherein the non-linear transformation is as follows: PQ = 1 1 - b + b · ( K 1 x 1 + K 2 x 2 + … + K n x n ) , wherein b is the parameter of non-linearity, x 1 , x 2 , . . . , x n are patent indices, and K i , i=1, . . . n is a coefficient indicating relative contribution of the i-th patent index into the PQ, where K 1 +K 2 + . . . +K n =1. | 0.569663 |
9,959,559 | 11 | 14 | 11. A machine-readable storage medium storing an instruction that, when executed by a processor, causes the processor to perform a method of ranking search results in an electronic environment in response to a search query received from a searching party located in a geographic region, the method comprising: conducting a search and generating search results based on the search query received from a computing device used by the searching party to access an online marketplace: retrieving an IP address or a browser language setting of the computing device of the searching party; identifying the geographic region of the searching party based at least in part on the retrieved IP address or the retrieved browser language setting of the computing device; identifying a language associated with the identified geographic region of the searching party based on the identified geographic region of the searching party; for a seller associated with at least one of the search results, determining a proficiency in the language associated with the geographic region of the searching party; ranking the search results based at least on the identified language and the determined proficiency, wherein search results associated with a higher proficiency in the identified language are ranked higher relative to search results associated with a lower proficiency in the identified language: and providing the ranked search results to the searching party at the online marketplace. | 11. A machine-readable storage medium storing an instruction that, when executed by a processor, causes the processor to perform a method of ranking search results in an electronic environment in response to a search query received from a searching party located in a geographic region, the method comprising: conducting a search and generating search results based on the search query received from a computing device used by the searching party to access an online marketplace: retrieving an IP address or a browser language setting of the computing device of the searching party; identifying the geographic region of the searching party based at least in part on the retrieved IP address or the retrieved browser language setting of the computing device; identifying a language associated with the identified geographic region of the searching party based on the identified geographic region of the searching party; for a seller associated with at least one of the search results, determining a proficiency in the language associated with the geographic region of the searching party; ranking the search results based at least on the identified language and the determined proficiency, wherein search results associated with a higher proficiency in the identified language are ranked higher relative to search results associated with a lower proficiency in the identified language: and providing the ranked search results to the searching party at the online marketplace. 14. The machine-readable storage medium of claim 11 , wherein identifying the geographic region of the searching party is additionally based on one of more geographic indicators selected from: an account-specific data associated with the searching party, a shipping address associated with the searching party, a billing address associated with the searching party, a language setting associated with the searching party, and a payment service setting associated with the searching party. | 0.663912 |
8,214,319 | 1 | 4 | 1. A computerized system configured to enhance data originating from a distributed data set comprising an ontology and individual data elements, the individual data elements having a plurality of formats, the computerized system comprising: a plurality of computerized processing cells, each computerized processing cell configured to add inferences to individual data elements from the distributed data set by translating a portion of the individual data elements from the distributed data set into a common semantic information representation shared among the plurality of computerized processing cells, wherein at least a portion of the plurality of computerized processing cells are arranged in a first computerized data cell graph configured to progressively build a first semantic knowledge model from the individual data elements in the distributed data set in the common semantic information representation. | 1. A computerized system configured to enhance data originating from a distributed data set comprising an ontology and individual data elements, the individual data elements having a plurality of formats, the computerized system comprising: a plurality of computerized processing cells, each computerized processing cell configured to add inferences to individual data elements from the distributed data set by translating a portion of the individual data elements from the distributed data set into a common semantic information representation shared among the plurality of computerized processing cells, wherein at least a portion of the plurality of computerized processing cells are arranged in a first computerized data cell graph configured to progressively build a first semantic knowledge model from the individual data elements in the distributed data set in the common semantic information representation. 4. The computer system of claim 1 wherein a second portion of the plurality of computerized processing cells are arranged in a second computerized data cell graph to progressively build a second semantic knowledge model from the distributed data set in the common semantic information representation. | 0.848024 |
8,554,618 | 16 | 18 | 16. A system, comprising: one or more computer processors; and memory having instructions stored thereon, the instructions, when executed by the one or more computer processors, causing the processors to perform operations comprising: identifying an original ad campaign including two or more creative items and a group of positive key terms, each positive key term comprising a keyword or a key phrase; dividing the group of positive key terms associated with the original ad campaign into two or more sub-groups of positive key terms; associating each sub-group of key terms with a new ad campaign; for each creative item: identifying a first set of topic clusters that are associated with text in the creative item; identifying second sets of topic clusters that are each associated with one of the sub-groups of positive key terms; and comparing the first set of topic cluster with each of the second set of topic clusters; individually determining, for each of the two or more creative items of the original ad campaign, which respective one of the new ad campaigns is more relevant to the creative item; and associating each of the two or more creative items with the respective new ad campaign that is individually determined to be more relevant to the creative item. | 16. A system, comprising: one or more computer processors; and memory having instructions stored thereon, the instructions, when executed by the one or more computer processors, causing the processors to perform operations comprising: identifying an original ad campaign including two or more creative items and a group of positive key terms, each positive key term comprising a keyword or a key phrase; dividing the group of positive key terms associated with the original ad campaign into two or more sub-groups of positive key terms; associating each sub-group of key terms with a new ad campaign; for each creative item: identifying a first set of topic clusters that are associated with text in the creative item; identifying second sets of topic clusters that are each associated with one of the sub-groups of positive key terms; and comparing the first set of topic cluster with each of the second set of topic clusters; individually determining, for each of the two or more creative items of the original ad campaign, which respective one of the new ad campaigns is more relevant to the creative item; and associating each of the two or more creative items with the respective new ad campaign that is individually determined to be more relevant to the creative item. 18. The system of claim 16 , wherein at least the first set of topic clusters or the second sets of topic clusters are identified using a semantic database. | 0.765766 |
8,392,397 | 3 | 4 | 3. The method as recited in claim 1 , wherein the aggregating relevancy results of the dependency functions and ordering the dependency functions according to a criterion includes computing a relevancy ratio. | 3. The method as recited in claim 1 , wherein the aggregating relevancy results of the dependency functions and ordering the dependency functions according to a criterion includes computing a relevancy ratio. 4. The method as recited in claim 3 , further comprising counting relevant inputs and counting input events, the method further comprising computing the relevancy ratio as a ratio of: relevant input counts/input event counts. | 0.927653 |
9,552,055 | 1 | 12 | 1. A method comprising, by one or more processors of a social-networking system of an online social network: accessing, by one or more of the processors, a social graph comprising a plurality of nodes and a plurality of edges connecting the nodes, each of the edges between two of the nodes representing a single degree of separation between them, the nodes comprising: a plurality of user nodes corresponding to a plurality of users associated with the online social network, respectively; and a plurality of concept nodes corresponding to a plurality of concepts associated with the online social network, respectively; accessing, by one or more of the processors, user information associated with each of the plurality of users relating to interactions with one or more of the plurality of concept nodes to generate a plurality of user-concept pairs; accessing, by one or more of the processors, user-concept scores for a first set of user nodes of the plurality of nodes, respectively, each user-concept score being with respect to particular user-concept pairs of the plurality of user-concept pairs, the particular user-concept pairs comprising a user node from the first set of user nodes that is connected by an edge to a concept node from the plurality of concept nodes; generating, by one or more of the processors, a recommendation-algorithm for estimating recommended user-concept scores for all user-concept pairs in the first set of user nodes and the plurality of concept nodes, the recommended user-concept scores being based on the accessed user-concept scores; calculating, by one or more of the processors, recommended user-concept scores for a random subset of user-concept pairs in a second set of user nodes of the plurality of user nodes and the plurality of concept nodes, the first set of user nodes being discrete from the second set of user nodes, the second set of user nodes comprising substantially all remaining user nodes of the plurality of user nodes, wherein the recommendation-algorithm computes the recommended user-concept scores by optimizing an objective function comprising a plurality of predicted rating functions, wherein each predicted rating function for a user-concept pair (u,i) comprises: a dot product of a user-score vector P(u) and concept-score vector Q(i); and bias values associated with user node u and concept node i; and sending, to one or more client systems of one or more users corresponding to user nodes of the second set of user nodes, recommendations for one or more concept nodes based on the calculated recommended user-concept scores for the second set of user nodes. | 1. A method comprising, by one or more processors of a social-networking system of an online social network: accessing, by one or more of the processors, a social graph comprising a plurality of nodes and a plurality of edges connecting the nodes, each of the edges between two of the nodes representing a single degree of separation between them, the nodes comprising: a plurality of user nodes corresponding to a plurality of users associated with the online social network, respectively; and a plurality of concept nodes corresponding to a plurality of concepts associated with the online social network, respectively; accessing, by one or more of the processors, user information associated with each of the plurality of users relating to interactions with one or more of the plurality of concept nodes to generate a plurality of user-concept pairs; accessing, by one or more of the processors, user-concept scores for a first set of user nodes of the plurality of nodes, respectively, each user-concept score being with respect to particular user-concept pairs of the plurality of user-concept pairs, the particular user-concept pairs comprising a user node from the first set of user nodes that is connected by an edge to a concept node from the plurality of concept nodes; generating, by one or more of the processors, a recommendation-algorithm for estimating recommended user-concept scores for all user-concept pairs in the first set of user nodes and the plurality of concept nodes, the recommended user-concept scores being based on the accessed user-concept scores; calculating, by one or more of the processors, recommended user-concept scores for a random subset of user-concept pairs in a second set of user nodes of the plurality of user nodes and the plurality of concept nodes, the first set of user nodes being discrete from the second set of user nodes, the second set of user nodes comprising substantially all remaining user nodes of the plurality of user nodes, wherein the recommendation-algorithm computes the recommended user-concept scores by optimizing an objective function comprising a plurality of predicted rating functions, wherein each predicted rating function for a user-concept pair (u,i) comprises: a dot product of a user-score vector P(u) and concept-score vector Q(i); and bias values associated with user node u and concept node i; and sending, to one or more client systems of one or more users corresponding to user nodes of the second set of user nodes, recommendations for one or more concept nodes based on the calculated recommended user-concept scores for the second set of user nodes. 12. The method of claim 1 , further comprising: projecting, by a random projection process, the calculated user-concept scores for a random subset of user-concept pairs onto all user-concept pairs in the second set of user nodes; and estimating, based on the projection, user-concept scores for substantially all remaining user-concept pairs in the second set of user nodes of the plurality of user nodes and the plurality of concept nodes. | 0.771547 |
9,875,121 | 15 | 16 | 15. The server of claim 14 , wherein the program further comprises a set of instructions for specifying a connector that connects the interface with the set of data storages by implementing a data exchange logic for the set of data storages. | 15. The server of claim 14 , wherein the program further comprises a set of instructions for specifying a connector that connects the interface with the set of data storages by implementing a data exchange logic for the set of data storages. 16. The server of claim 15 , wherein the set of instructions for specifying the connector comprises a set of instructions for using the set of data storages' drivers to implement the data exchange logic. | 0.944777 |
9,639,522 | 1 | 9 | 1. A computer implemented method, comprising: identifying that each of a plurality of documents are mature based on one or more signals associated with the documents; in response to identifying the documents as mature: storing, via one or more networks, edits to the documents in at least one database for defining edit rules based on the edits, wherein the edits are from a plurality of users and are made via user interface input provided via applications executing on computing devices of the users, wherein each of the edits identifies one of a plurality of pre-edit phrases and an associated one of a plurality of post-edit phrases, and wherein each of a plurality of the edits is based on a user implemented change of the one of the pre-edit phrases to the one of the post-edit phrases in one of the plurality of mature documents; determining an edit rule pre-edit phrase based on a set of one or more of the pre-edit phrases identified by the edits; determining one or more edit rule post-edit phrases based on one or more of the post-edit phrases associated with the set of the one or more pre-edit phrases; defining an edit rule that associates the edit rule pre-edit phrase with the edit rule post-edit phrases; storing the edit rule for automatically determining, for a future phrase conforming to the edit rule pre-edit phrase, a rephrasing of the future phrase based on at least one of the edit rule post-edit phrases; after storing the edit rule: identifying a current document being edited by a given user via a given application of a given computing device of the given user, identifying that a given phrase in the current document conforms to the edit rule pre-edit phrase of the edit rule, in response to identifying that the given phrase conforms to the edit rule pre-edit phrase, determining a candidate rephrasing of the given phrase based on the edit rule, the determining comprising determining the candidate rephrasing based on a given one of the edit rule post-edit phrases of the edit rule, in response to user interface input provided at the given computing device, providing the candidate rephrasing of the given phrase for presentation to the user. | 1. A computer implemented method, comprising: identifying that each of a plurality of documents are mature based on one or more signals associated with the documents; in response to identifying the documents as mature: storing, via one or more networks, edits to the documents in at least one database for defining edit rules based on the edits, wherein the edits are from a plurality of users and are made via user interface input provided via applications executing on computing devices of the users, wherein each of the edits identifies one of a plurality of pre-edit phrases and an associated one of a plurality of post-edit phrases, and wherein each of a plurality of the edits is based on a user implemented change of the one of the pre-edit phrases to the one of the post-edit phrases in one of the plurality of mature documents; determining an edit rule pre-edit phrase based on a set of one or more of the pre-edit phrases identified by the edits; determining one or more edit rule post-edit phrases based on one or more of the post-edit phrases associated with the set of the one or more pre-edit phrases; defining an edit rule that associates the edit rule pre-edit phrase with the edit rule post-edit phrases; storing the edit rule for automatically determining, for a future phrase conforming to the edit rule pre-edit phrase, a rephrasing of the future phrase based on at least one of the edit rule post-edit phrases; after storing the edit rule: identifying a current document being edited by a given user via a given application of a given computing device of the given user, identifying that a given phrase in the current document conforms to the edit rule pre-edit phrase of the edit rule, in response to identifying that the given phrase conforms to the edit rule pre-edit phrase, determining a candidate rephrasing of the given phrase based on the edit rule, the determining comprising determining the candidate rephrasing based on a given one of the edit rule post-edit phrases of the edit rule, in response to user interface input provided at the given computing device, providing the candidate rephrasing of the given phrase for presentation to the user. 9. The method of claim 1 , wherein determining the one or more edit rule post-edit phrases comprises: identifying a first post-edit phrase of the post-edit phrases associated with the set of one or more pre-edit phrases; determining a first count of associations of the first post-edit phrase with the one or more pre-edit phrases of the set; and based on the first count of associations satisfying a threshold, determining at least one of the edit rule post-edit phrases based on the first post-edit phrase. | 0.603744 |
9,785,678 | 14 | 18 | 14. A method to improve results of a search query, comprising: selecting, by at least one computing device, as at least one utility node, a first subset of a plurality of taxonomy nodes of a catalog of items based at least in part on a plurality of utility scores corresponding to the plurality of taxonomy nodes, the plurality of utility scores being based at least in part on a user interaction history of an electronic commerce system with respect to the search query; selecting, by the at least one computing device, as at least one relevant node, a second subset of the plurality of taxonomy nodes based at least in part on a plurality of relevance scores corresponding to the plurality of taxonomy nodes, the plurality of relevance scores being based at least in part on the search query; selecting, by the at least one computing device, at least one of the plurality of taxonomy nodes according to a convergence of the at least one utility node and the at least one relevant node as the improved results of the search query; and communicating, by the at least one computing device, an indication of the at least one of the plurality of taxonomy nodes to a client device facilitating a browsing of the catalog of items responsive to the search query and a selected one of the at least one of the plurality of taxonomy nodes. | 14. A method to improve results of a search query, comprising: selecting, by at least one computing device, as at least one utility node, a first subset of a plurality of taxonomy nodes of a catalog of items based at least in part on a plurality of utility scores corresponding to the plurality of taxonomy nodes, the plurality of utility scores being based at least in part on a user interaction history of an electronic commerce system with respect to the search query; selecting, by the at least one computing device, as at least one relevant node, a second subset of the plurality of taxonomy nodes based at least in part on a plurality of relevance scores corresponding to the plurality of taxonomy nodes, the plurality of relevance scores being based at least in part on the search query; selecting, by the at least one computing device, at least one of the plurality of taxonomy nodes according to a convergence of the at least one utility node and the at least one relevant node as the improved results of the search query; and communicating, by the at least one computing device, an indication of the at least one of the plurality of taxonomy nodes to a client device facilitating a browsing of the catalog of items responsive to the search query and a selected one of the at least one of the plurality of taxonomy nodes. 18. The method of claim 14 , further comprising applying a graph balancing approach to at least a portion of the convergence until a size of the convergence meets or falls below a predefined size threshold. | 0.869949 |
7,711,573 | 425 | 426 | 425. The computer program product of claim 424 , wherein the program code for setting the term of experience to the time difference further comprises: program code for computing a repeated entry time difference for each of the searchable phrases that is a repeated entry and is associated with an other experience range; and program code for adding to the time difference each repeated entry time difference, wherein the other experience range includes an other start time and an other end time, and wherein the other start time and the start time are different, or the other end time and the end time are different. | 425. The computer program product of claim 424 , wherein the program code for setting the term of experience to the time difference further comprises: program code for computing a repeated entry time difference for each of the searchable phrases that is a repeated entry and is associated with an other experience range; and program code for adding to the time difference each repeated entry time difference, wherein the other experience range includes an other start time and an other end time, and wherein the other start time and the start time are different, or the other end time and the end time are different. 426. The computer program product of claim 425 , wherein the program code for storing the parsed resume further comprises: program code for storing each of the searchable phrases having an identical term of experience as an element defined by a markup language, the element comprising a start tag, content data, and an end tag, wherein the start tag and the end tag include the identical term of experience, and wherein the content data includes each of the searchable phrases having the identical term of experience. | 0.883296 |
6,038,527 | 1 | 7 | 1. A method of generating descriptors for natural language texts, using a plurality of training texts having a plurality of words, comprising the steps of: extracting words from a text during a training phase on the basis of the training texts; predetermining a minimum structure of said descriptors; breaking down words in the text into shorter word segments, wherein each shorter word segment within a longer word segment must meet said minimum structure for said breaking down to be permitted; and matching said word segments that remain in the text against each other to generate a list of descriptors. | 1. A method of generating descriptors for natural language texts, using a plurality of training texts having a plurality of words, comprising the steps of: extracting words from a text during a training phase on the basis of the training texts; predetermining a minimum structure of said descriptors; breaking down words in the text into shorter word segments, wherein each shorter word segment within a longer word segment must meet said minimum structure for said breaking down to be permitted; and matching said word segments that remain in the text against each other to generate a list of descriptors. 7. A method according to claim 1, wherein a frequency threshold is predetermined for the descriptors and only those descriptors are used whose frequency in the training texts exceeds the threshold. | 0.689274 |
5,412,567 | 1 | 2 | 1. A method for storing a word in a database in a computer system, the computer system including a user input device, a storage device and a processor, wherein the database is stored in the storage device and includes a model word represented as a first path within the database, wherein a path includes states joined by arcs, wherein each arc is associated with upper and lower symbols, wherein each symbol may be a letter, tag or null, the first path thereby defining an upper sequence of ordered upper symbols and a lower sequence of ordered lower symbols, wherein the upper sequence defines a lexical form of the model word and the lower sequence defines a surface form of the model word, the method comprising the steps of: (a) accepting signals from the user input device to specify a new word as one or more new symbols to be added to the database; (b) using the processor to identify at least one arc in the first path, wherein a symbol associated with the identified arc matches a new symbol in the new word; and (c) using the processor to add states and arcs to the database to represent the new word as a new path, wherein the new path includes the identified arc. | 1. A method for storing a word in a database in a computer system, the computer system including a user input device, a storage device and a processor, wherein the database is stored in the storage device and includes a model word represented as a first path within the database, wherein a path includes states joined by arcs, wherein each arc is associated with upper and lower symbols, wherein each symbol may be a letter, tag or null, the first path thereby defining an upper sequence of ordered upper symbols and a lower sequence of ordered lower symbols, wherein the upper sequence defines a lexical form of the model word and the lower sequence defines a surface form of the model word, the method comprising the steps of: (a) accepting signals from the user input device to specify a new word as one or more new symbols to be added to the database; (b) using the processor to identify at least one arc in the first path, wherein a symbol associated with the identified arc matches a new symbol in the new word; and (c) using the processor to add states and arcs to the database to represent the new word as a new path, wherein the new path includes the identified arc. 2. The method of claim 1 wherein: in step (a), the new symbols are letters that spell the surface form of the new word; in step (b), at least one letter in the new word is matched with a letter in the lower sequence of the first path; and in step (c), the new path defines a new upper sequence that represents a lexical form of the new word, and the new path defines a new lower sequence that represents the surface form of the new word. | 0.501142 |
8,527,874 | 11 | 14 | 11. A system for grouping search results using information representations, the system comprising: a server computer having a processor, memory, and a graphical user interface for displaying at least one tile that graphically represents at least one user in response to an input for selecting an object associated with the at least one user, wherein the selected object has a first object type; the server computer configured to perform operations for: a correlation determining system, the operations performed by the server computer for the correlation determining system including: determining a chronological timeline that represents a plurality of actions performed by the at least one user wherein the plurality of actions performed by the at least one user have a plurality of chronological positions in the chronological timeline; and inferring one or more relationships between the selected object and one or more other objects associated with the at least one user from the plurality of actions represented in the chronological timeline, wherein inferring the one or more relationships comprises: identifies identifying one or more of the plurality of actions that are related to the selected object from the plurality of chronological positions that the plurality of actions have in the chronological timeline, wherein the at least one user performed the identified one or more actions on the one or more other objects, and wherein the one or more other objects have a second object type different from the first object type; and determining that the one or more other objects are related to the selected object from a strength of the one or more inferred relationships, wherein the strength of the one or more inferred relationships is derived from the chronological positions that the identified one or more actions have in the chronological timeline; and a tile generator, the operations performed by the server computer for the tile generator including: generating one or more additional tiles that graphically represent the one or more other objects related to the selected object and performed by the at least one user in response to a selection of the at least one tile that graphically represents the at least one user; and causing the graphical user interface to simultaneously display the one or more additional tiles that graphically represent the one or more other objects related to the selected object and performed by the at least one user, wherein the graphical user interface further organizes the at least one tile and the one or more additional tiles based on the first object type for the selected object and the second object type for the one or more other objects. | 11. A system for grouping search results using information representations, the system comprising: a server computer having a processor, memory, and a graphical user interface for displaying at least one tile that graphically represents at least one user in response to an input for selecting an object associated with the at least one user, wherein the selected object has a first object type; the server computer configured to perform operations for: a correlation determining system, the operations performed by the server computer for the correlation determining system including: determining a chronological timeline that represents a plurality of actions performed by the at least one user wherein the plurality of actions performed by the at least one user have a plurality of chronological positions in the chronological timeline; and inferring one or more relationships between the selected object and one or more other objects associated with the at least one user from the plurality of actions represented in the chronological timeline, wherein inferring the one or more relationships comprises: identifies identifying one or more of the plurality of actions that are related to the selected object from the plurality of chronological positions that the plurality of actions have in the chronological timeline, wherein the at least one user performed the identified one or more actions on the one or more other objects, and wherein the one or more other objects have a second object type different from the first object type; and determining that the one or more other objects are related to the selected object from a strength of the one or more inferred relationships, wherein the strength of the one or more inferred relationships is derived from the chronological positions that the identified one or more actions have in the chronological timeline; and a tile generator, the operations performed by the server computer for the tile generator including: generating one or more additional tiles that graphically represent the one or more other objects related to the selected object and performed by the at least one user in response to a selection of the at least one tile that graphically represents the at least one user; and causing the graphical user interface to simultaneously display the one or more additional tiles that graphically represent the one or more other objects related to the selected object and performed by the at least one user, wherein the graphical user interface further organizes the at least one tile and the one or more additional tiles based on the first object type for the selected object and the second object type for the one or more other objects. 14. The system of claim 11 , wherein the graphical user interface organizes the one or more additional tiles in a predetermined arrangement providing one or more icons that illustrate the second object type for the one or more other objects, one or more titles that include descriptive content for the one or more other objects, one or more dates when the one or more other objects were created, and one or more available actions for interacting with the one or more other objects. | 0.574336 |
8,145,624 | 1 | 2 | 1. A method for associating a query expression, comprising: adding a metadata table to a database, wherein a portion of the metadata table comprises the query expression; and associating the query expression to a value from a tuple belonging to an existing data table of the database, wherein the existing data table is unmodified, wherein a second portion of the metadata table comprises an element of a tuple that is not listed in the existing data table, wherein the element of the tuple that is not listed in the existing data table comprises an additional query expression, and wherein the metadata table is configured to be queried independently from the existing data table by a user. | 1. A method for associating a query expression, comprising: adding a metadata table to a database, wherein a portion of the metadata table comprises the query expression; and associating the query expression to a value from a tuple belonging to an existing data table of the database, wherein the existing data table is unmodified, wherein a second portion of the metadata table comprises an element of a tuple that is not listed in the existing data table, wherein the element of the tuple that is not listed in the existing data table comprises an additional query expression, and wherein the metadata table is configured to be queried independently from the existing data table by a user. 2. The method of claim 1 , wherein the query expression is associated to the value by a relationship defined by a select clause corresponding to the query expression. | 0.784974 |
9,471,581 | 11 | 19 | 11. A non-transitory computer-readable medium containing instructions for suggesting one or more autocompletions to a file name for a file to save, the instructions for execution by a computer system, the non-transitory computer-readable medium comprising: instructions for building an autocomplete dictionary based on text in a file by adding at least some text from the file to the autocomplete dictionary; instructions for receiving a request from a user to save the file; instructions for, in response to the request from the user to save the file, the computer system presenting a user interface element for specifying a name for the file to be saved, the user interface element configured to receive text entry; instructions for receiving text entry from the user in the user interface element; instructions for submitting at least a portion of the text entry of the user to the autocomplete dictionary in order to search the autocomplete dictionary; instructions for, in response to the search based on the portion of the text entry of the user, receiving from the autocomplete dictionary one or more proposed autocompletions, each of the one or more proposed autocompletions containing the portion of the text entry of the user as a prefix and at least one of the one or more proposed autocompletions containing text from the file; instructions for presenting, by the computer system, the one or more proposed autocompletions to the user. | 11. A non-transitory computer-readable medium containing instructions for suggesting one or more autocompletions to a file name for a file to save, the instructions for execution by a computer system, the non-transitory computer-readable medium comprising: instructions for building an autocomplete dictionary based on text in a file by adding at least some text from the file to the autocomplete dictionary; instructions for receiving a request from a user to save the file; instructions for, in response to the request from the user to save the file, the computer system presenting a user interface element for specifying a name for the file to be saved, the user interface element configured to receive text entry; instructions for receiving text entry from the user in the user interface element; instructions for submitting at least a portion of the text entry of the user to the autocomplete dictionary in order to search the autocomplete dictionary; instructions for, in response to the search based on the portion of the text entry of the user, receiving from the autocomplete dictionary one or more proposed autocompletions, each of the one or more proposed autocompletions containing the portion of the text entry of the user as a prefix and at least one of the one or more proposed autocompletions containing text from the file; instructions for presenting, by the computer system, the one or more proposed autocompletions to the user. 19. The non-transitory computer-readable medium of claim 11 , wherein the autocomplete dictionary stores single words from the text of the file and phrases of multiple words from the text of the file. | 0.777283 |
6,085,162 | 1 | 2 | 1. A method for translating information from a source language to a target language based on the selection of a topic by a user, said method comprising: selection of said topic by said user; inputting said information in said source language to produce input words; automatically processing said information based on said topic selection, said processing comprising: classifying said input words; applying said input words against a topical dictionary comprising at least one layer of bi-directional translations formed via a frequency-of-association comparison of a word file in said source language and a word file in said target language based on said topic selected by said user, said source and target language word files each being formed from a plurality of materials specific to said selected topic, said application of said input words comprising producing one or more translations of said input words into said target language and outputting one or more untranslated words; forming sentences from said input words based on a set of syntax rules for said source language; applying a conventional dual-language dictionary to said untranslated words to provide at least one translation of said untranslated words into said target language, said at least one translation being provided only if said topical dictionary failed to provide one or more translations for said untranslated words; processing said input words, said at least one translation, and said one or more translations against a set of syntax rules for said target language to produce a translated message; and outputting said translated message. | 1. A method for translating information from a source language to a target language based on the selection of a topic by a user, said method comprising: selection of said topic by said user; inputting said information in said source language to produce input words; automatically processing said information based on said topic selection, said processing comprising: classifying said input words; applying said input words against a topical dictionary comprising at least one layer of bi-directional translations formed via a frequency-of-association comparison of a word file in said source language and a word file in said target language based on said topic selected by said user, said source and target language word files each being formed from a plurality of materials specific to said selected topic, said application of said input words comprising producing one or more translations of said input words into said target language and outputting one or more untranslated words; forming sentences from said input words based on a set of syntax rules for said source language; applying a conventional dual-language dictionary to said untranslated words to provide at least one translation of said untranslated words into said target language, said at least one translation being provided only if said topical dictionary failed to provide one or more translations for said untranslated words; processing said input words, said at least one translation, and said one or more translations against a set of syntax rules for said target language to produce a translated message; and outputting said translated message. 2. The method of claim 1, wherein said step of outputting said translated message includes the step of: displaying the translated message on a computer screen. | 0.698864 |
7,848,573 | 1 | 2 | 1. A method, comprising: receiving electronic ink input; converting the electronic ink input to one or more machine-generated objects; determining a size of the one or more machine-generated objects by calculating an average height of the corresponding electronic ink input and setting the size of the one or more machine-generated objects to be equivalent in scale for the calculated average height; rendering the one or more machine-generated objects using the determined size for the machine-generated object or objects and an original inter-word spacing of the electronic ink input; receiving input selecting an object from the rendered machine-generated object or objects; and displaying the electronic ink input corresponding to the selected object in place of the selected object. | 1. A method, comprising: receiving electronic ink input; converting the electronic ink input to one or more machine-generated objects; determining a size of the one or more machine-generated objects by calculating an average height of the corresponding electronic ink input and setting the size of the one or more machine-generated objects to be equivalent in scale for the calculated average height; rendering the one or more machine-generated objects using the determined size for the machine-generated object or objects and an original inter-word spacing of the electronic ink input; receiving input selecting an object from the rendered machine-generated object or objects; and displaying the electronic ink input corresponding to the selected object in place of the selected object. 2. A method according to claim 1 , wherein the size of the one or more machine-generated objects is determined by calculating an average height of at least a portion of the electronic ink input. | 0.86338 |
8,595,030 | 1 | 9 | 1. A system for managing form-generated data related to a patient encounter, wherein the form-generated data is generated by electronic writing system configured to generate location information that identifies the location of a user writing on the form, the form having designated information fields at different locations on the form, the system comprising: a contextualizer configured to translate location information related to a user writing on a form to a contextualized data element, wherein the form has designated information fields at different locations on the form and wherein the contextualized data element comprises contextual information that is associated with the user writing, wherein the contextualizer includes a mapping data set that maps user areas on the form to labels that are associated with the designated information fields and wherein the contextualizer is configured to identify a label from the location information by comparing the location information to the mapping data set, the contextualized data element comprising the label; and wherein the contextualized data element is distributed to an Electronic Medical Record (EMR)/Electronic Health Record (EHR) application, which utilizes the label in the contextualized data element to perform a function that is related to the user writing on the form, via a publish/subscribe protocol in which the EMR/EHR application subscribes to a specific contextualized data element by identifying the label associated with the contextualized data element. | 1. A system for managing form-generated data related to a patient encounter, wherein the form-generated data is generated by electronic writing system configured to generate location information that identifies the location of a user writing on the form, the form having designated information fields at different locations on the form, the system comprising: a contextualizer configured to translate location information related to a user writing on a form to a contextualized data element, wherein the form has designated information fields at different locations on the form and wherein the contextualized data element comprises contextual information that is associated with the user writing, wherein the contextualizer includes a mapping data set that maps user areas on the form to labels that are associated with the designated information fields and wherein the contextualizer is configured to identify a label from the location information by comparing the location information to the mapping data set, the contextualized data element comprising the label; and wherein the contextualized data element is distributed to an Electronic Medical Record (EMR)/Electronic Health Record (EHR) application, which utilizes the label in the contextualized data element to perform a function that is related to the user writing on the form, via a publish/subscribe protocol in which the EMR/EHR application subscribes to a specific contextualized data element by identifying the label associated with the contextualized data element. 9. The system of claim 1 wherein the performed function includes one of patient/insurance billing for services rendered related to the patient encounter, case management related to the patient encounter, and order fulfillment related to the patient encounter. | 0.501923 |
9,519,766 | 14 | 17 | 14. A system of distinguishing between computers and humans through enhanced completely automated public turing test to tell computers and humans apart (e-captcha), comprising: a computer system having a memory that stores computer program instructions and one or more physical processors programmed with the computer program instructions that, when executed by the memory and one or more physical processors, cause the computer system to: provide, via the network, executable instructions configured to generate a user interface that includes an e-captcha challenge, wherein the e-captcha challenge requires-one or more words or phrases to be uttered; receive an audio input via at least one input configured to receive the audio input, the audio input comprising a response to the e-captcha challenge; use an automated speech recognition engine to convert the audio input to text; determine an edit distance between the one or more words or phrases and the text converted from the audio input; determine whether the one or more words or phrases to be uttered were was at least partially correctly uttered based on the edit distance; responsive to a determination that the one or more words or phrases were at least partially correctly uttered, cause information indicating that the response is valid to be provided; and responsive to a determination that the one or more words or phrases were not at least partially correctly uttered, cause information indicating that the response is invalid to be provided. | 14. A system of distinguishing between computers and humans through enhanced completely automated public turing test to tell computers and humans apart (e-captcha), comprising: a computer system having a memory that stores computer program instructions and one or more physical processors programmed with the computer program instructions that, when executed by the memory and one or more physical processors, cause the computer system to: provide, via the network, executable instructions configured to generate a user interface that includes an e-captcha challenge, wherein the e-captcha challenge requires-one or more words or phrases to be uttered; receive an audio input via at least one input configured to receive the audio input, the audio input comprising a response to the e-captcha challenge; use an automated speech recognition engine to convert the audio input to text; determine an edit distance between the one or more words or phrases and the text converted from the audio input; determine whether the one or more words or phrases to be uttered were was at least partially correctly uttered based on the edit distance; responsive to a determination that the one or more words or phrases were at least partially correctly uttered, cause information indicating that the response is valid to be provided; and responsive to a determination that the one or more words or phrases were not at least partially correctly uttered, cause information indicating that the response is invalid to be provided. 17. The system of claim 14 , wherein the computer system is further programmed to: receive a request to provide the e-captcha challenge, wherein the executable instructions are provided responsive to the request, and wherein the request comprises one or more parameters that specify an e-captcha challenge to be provided; and identify the e-captcha challenge based on the one or more parameters. | 0.767373 |
8,332,221 | 1 | 9 | 1. A method comprising acts of: receiving, from a text segmentation and topic assignment system, a first structured text comprising a plurality of text sections, the first structured text further comprising information indicative of a topic assigned to at least one text section of the plurality of text sections; providing the first structured text to a user for review, comprising providing the at least one text section in association with a section heading corresponding to the topic assigned to the at least one text section; receiving input from the user indicating at least one modification to the first structured text; causing the text segmentation and topic assignment system to process the at least one modification to generate a second structured text; receiving the second structured text from the text segmentation and topic assignment system; and providing the second structured text to the user for review. | 1. A method comprising acts of: receiving, from a text segmentation and topic assignment system, a first structured text comprising a plurality of text sections, the first structured text further comprising information indicative of a topic assigned to at least one text section of the plurality of text sections; providing the first structured text to a user for review, comprising providing the at least one text section in association with a section heading corresponding to the topic assigned to the at least one text section; receiving input from the user indicating at least one modification to the first structured text; causing the text segmentation and topic assignment system to process the at least one modification to generate a second structured text; receiving the second structured text from the text segmentation and topic assignment system; and providing the second structured text to the user for review. 9. The method of claim 1 , wherein the information indicative of the topic assigned to the at least one text section comprises the section heading corresponding to the topic assigned to the at least one text section. | 0.811189 |
9,697,490 | 1 | 4 | 1. A system, comprising: a set of one or more interfaces configured to: receive review data associated with a plurality of entities, wherein each of the entities is associated with a particular industry, and wherein the review data comprises review data collected, over a network, from a plurality of disparate, external review websites; wherein the review data is collected by a plurality of instances of different types of helpers that are executed to obtain, over the network, information from the plurality of disparate, external review websites, wherein each type of helper is configured with instructions to fetch review data from a particular type of source, wherein, for a first external review website for which review data is available via an Application Programming Interface (API), an instance of a first helper configured with instructions to obtain review data from the first external review website using the API is executed, and wherein, for a second external review website for which review data is not available via an API, an instance of a second helper configured with instructions to scrape review data from the second external review website is executed; and wherein at least some of the received review data is received from a data store configured to store heterogeneous data records, and wherein the data store includes review data from different external review websites that is stored in heterogeneous record formats; a set of one or more processors configured to: generate, from at least a portion of the received review data collected, over the network, from the plurality of disparate, external review websites, at least one online review benchmark for the particular industry, including by determining at least one of an online review volume benchmark and an online review distribution benchmark, wherein: the online review volume benchmark is determined at least in part by counting a first number of reviews associated with at least some entities in the particular industry on the plurality of disparate, external review websites; and the online review distribution benchmark is determined at least in part by determining, for at least the first and second external review websites included in the plurality of disparate, external review websites, a respective proportion of reviews associated with at least some entities in the particular industry on the respective external review websites; and store the generated at least one online review benchmark for the particular industry; compare, at a time subsequent to generating the at least one online review benchmark for the particular industry, review data associated with a first entity in the particular industry to the stored industry benchmark, including by comparing at least one of an online review volume of the first entity to the online review volume benchmark for the particular industry and an online review distribution of the first entity to the online review distribution benchmark for the particular industry; determine, based at least in part on the comparison of the review data associated with the first entity in the particular industry to the stored industry benchmark, that an adjustment to the online review distribution of the first entity should be performed, wherein the adjustment includes increasing a number of reviews associated with the first entity on one or more external review websites; model an impact that additional reviews on the one or more external review websites would have for the first entity, wherein modeling the impact includes: running a first simulation in which a first volume of additional positive reviews are obtained on the first external review website; determining a first modeled online reputation score based at least in part on the first simulation; running a second simulation in which a second volume of additional positive reviews are obtained on the second external review website, wherein the first volume and the second volume are different; and determining a second modeled online reputation score based at least in part on the second simulation; determine, based at least in part on the first and second modeled online reputation scores, that additional reviews associated with the first external review website should be requested from one or more potential reviewers; in response to determining that additional reviews associated with the first external review website should be requested: identify, in a list of potential reviewers, individuals in the list of potential reviewers that have accounts with the first external review website for which it has been determined that additional reviews should be requested, wherein the identified individuals are identified based at least in part on an evaluation of corresponding email addresses associated with the identified individuals; and facilitate transmission of review requests to the identified individuals, wherein facilitating transmission of the review requests includes facilitating transmission, over the network, of an electronic message to an individual included in the identified individuals, wherein the electronic message includes a link to the first external review website; and at a time subsequent to transmission of the electronic message, perform a follow-up action based at least in part on a determination of whether the individual has performed at least one of opening the electronic message and clicking on the link included in the electronic message; and a memory coupled to the set of one or more processors and configured to provide the set of one or more processors with instructions. | 1. A system, comprising: a set of one or more interfaces configured to: receive review data associated with a plurality of entities, wherein each of the entities is associated with a particular industry, and wherein the review data comprises review data collected, over a network, from a plurality of disparate, external review websites; wherein the review data is collected by a plurality of instances of different types of helpers that are executed to obtain, over the network, information from the plurality of disparate, external review websites, wherein each type of helper is configured with instructions to fetch review data from a particular type of source, wherein, for a first external review website for which review data is available via an Application Programming Interface (API), an instance of a first helper configured with instructions to obtain review data from the first external review website using the API is executed, and wherein, for a second external review website for which review data is not available via an API, an instance of a second helper configured with instructions to scrape review data from the second external review website is executed; and wherein at least some of the received review data is received from a data store configured to store heterogeneous data records, and wherein the data store includes review data from different external review websites that is stored in heterogeneous record formats; a set of one or more processors configured to: generate, from at least a portion of the received review data collected, over the network, from the plurality of disparate, external review websites, at least one online review benchmark for the particular industry, including by determining at least one of an online review volume benchmark and an online review distribution benchmark, wherein: the online review volume benchmark is determined at least in part by counting a first number of reviews associated with at least some entities in the particular industry on the plurality of disparate, external review websites; and the online review distribution benchmark is determined at least in part by determining, for at least the first and second external review websites included in the plurality of disparate, external review websites, a respective proportion of reviews associated with at least some entities in the particular industry on the respective external review websites; and store the generated at least one online review benchmark for the particular industry; compare, at a time subsequent to generating the at least one online review benchmark for the particular industry, review data associated with a first entity in the particular industry to the stored industry benchmark, including by comparing at least one of an online review volume of the first entity to the online review volume benchmark for the particular industry and an online review distribution of the first entity to the online review distribution benchmark for the particular industry; determine, based at least in part on the comparison of the review data associated with the first entity in the particular industry to the stored industry benchmark, that an adjustment to the online review distribution of the first entity should be performed, wherein the adjustment includes increasing a number of reviews associated with the first entity on one or more external review websites; model an impact that additional reviews on the one or more external review websites would have for the first entity, wherein modeling the impact includes: running a first simulation in which a first volume of additional positive reviews are obtained on the first external review website; determining a first modeled online reputation score based at least in part on the first simulation; running a second simulation in which a second volume of additional positive reviews are obtained on the second external review website, wherein the first volume and the second volume are different; and determining a second modeled online reputation score based at least in part on the second simulation; determine, based at least in part on the first and second modeled online reputation scores, that additional reviews associated with the first external review website should be requested from one or more potential reviewers; in response to determining that additional reviews associated with the first external review website should be requested: identify, in a list of potential reviewers, individuals in the list of potential reviewers that have accounts with the first external review website for which it has been determined that additional reviews should be requested, wherein the identified individuals are identified based at least in part on an evaluation of corresponding email addresses associated with the identified individuals; and facilitate transmission of review requests to the identified individuals, wherein facilitating transmission of the review requests includes facilitating transmission, over the network, of an electronic message to an individual included in the identified individuals, wherein the electronic message includes a link to the first external review website; and at a time subsequent to transmission of the electronic message, perform a follow-up action based at least in part on a determination of whether the individual has performed at least one of opening the electronic message and clicking on the link included in the electronic message; and a memory coupled to the set of one or more processors and configured to provide the set of one or more processors with instructions. 4. The system of claim 1 wherein the set of one or more processors is further configured to determine that the first entity does not have an account on a particular external review web site. | 0.798301 |
8,112,667 | 1 | 14 | 1. A method for automatically diagnosing a system problem, comprising: creating a problem description, wherein a problem description index is created from a group consisting of: a line-wise index comprising a document entry in the problem description index for each line in a problem description information of a plurality of previously diagnosed problems, a description-wise index comprising a document entry in the problem description index for the problem description information of each of a plurality of previously diagnosed problems, and a set-wise index comprising a document entry in the problem description index for each set of the problem description information of a plurality of previously diagnosed problems grouped together based on a problem cause; receiving the problem description index and problem description information of a new problem, the problem description index comprising problem description information of previously diagnosed problems, wherein said problem description information of previously diagnosed problems and of said new problem comprises text content describing system events that have occurred; comparing problem description information of the new problem with problem description information in the problem description index, wherein comparing the problem description information of the new problem with problem description information in the problem description index for a line-wise index comprises a line-wise search of the line-wise index, wherein said line-wise search comprises searching each line of text content in the problem description index for each line of text content from the problem description information of the new problem; computing a search score for each document in the problem description index, wherein the search score is a measure of similarity between each document in the problem description index and the problem description information of the new problem; assigning a matching score to each of the previously diagnosed problems based on the search score, wherein the matching score is a measure of similarity between the new problem and each of the previously diagnosed problems; and determining a diagnosis of the new problem, wherein the diagnosis of the new problem is a diagnosis of at least one of the previously diagnosed problems. | 1. A method for automatically diagnosing a system problem, comprising: creating a problem description, wherein a problem description index is created from a group consisting of: a line-wise index comprising a document entry in the problem description index for each line in a problem description information of a plurality of previously diagnosed problems, a description-wise index comprising a document entry in the problem description index for the problem description information of each of a plurality of previously diagnosed problems, and a set-wise index comprising a document entry in the problem description index for each set of the problem description information of a plurality of previously diagnosed problems grouped together based on a problem cause; receiving the problem description index and problem description information of a new problem, the problem description index comprising problem description information of previously diagnosed problems, wherein said problem description information of previously diagnosed problems and of said new problem comprises text content describing system events that have occurred; comparing problem description information of the new problem with problem description information in the problem description index, wherein comparing the problem description information of the new problem with problem description information in the problem description index for a line-wise index comprises a line-wise search of the line-wise index, wherein said line-wise search comprises searching each line of text content in the problem description index for each line of text content from the problem description information of the new problem; computing a search score for each document in the problem description index, wherein the search score is a measure of similarity between each document in the problem description index and the problem description information of the new problem; assigning a matching score to each of the previously diagnosed problems based on the search score, wherein the matching score is a measure of similarity between the new problem and each of the previously diagnosed problems; and determining a diagnosis of the new problem, wherein the diagnosis of the new problem is a diagnosis of at least one of the previously diagnosed problems. 14. The method of claim 1 , wherein comparing the problem description information of the new problem with problem description information in the problem description index for a set-wise index comprises a description-wise search of the set-wise index, wherein said set-wise search comprises searching each set of problem description information in the problem description index for each term of text content from the problem description information of the new problem. | 0.656112 |
8,411,958 | 1 | 5 | 1. A method comprising: receiving a handwritten input character string having an unrecognized character; determining at least one character sub-string preceding the unrecognized character in the input character string; providing, by means of handwriting recognition, one or more candidate characters for the unrecognized character; identifying, amongst stored character strings, one or more character strings comprising an initial character sub-string identical to the determined character sub-string; and selecting, based on the identified one or more character strings, one of the one or more candidate characters that is most likely to be a correct recognition of the unrecognized character, wherein if a complete character string preceding the unrecognized character in the input character string is not identical to any stored character string or an initial sub-string of any stored character string, determining at least one character sub-string comprises: identifying any initial character sub-string of the input character string preceding the unrecognized character, which is identical to one of the stored character strings or to an initial character sub-string of one or more of the stored character strings, and which together with a succeeding character is not identical to any one of the stored character strings or to an initial character sub-string of any one of the stored character strings; and determining a character sub-string consisting of a terminal character sub-string of the input character string preceding the unrecognized character and succeeding any identified initial character sub-string; else determining at least one character sub-string comprises: determining a character sub-string consisting of said complete character string preceding the unrecognized character in the input character string. | 1. A method comprising: receiving a handwritten input character string having an unrecognized character; determining at least one character sub-string preceding the unrecognized character in the input character string; providing, by means of handwriting recognition, one or more candidate characters for the unrecognized character; identifying, amongst stored character strings, one or more character strings comprising an initial character sub-string identical to the determined character sub-string; and selecting, based on the identified one or more character strings, one of the one or more candidate characters that is most likely to be a correct recognition of the unrecognized character, wherein if a complete character string preceding the unrecognized character in the input character string is not identical to any stored character string or an initial sub-string of any stored character string, determining at least one character sub-string comprises: identifying any initial character sub-string of the input character string preceding the unrecognized character, which is identical to one of the stored character strings or to an initial character sub-string of one or more of the stored character strings, and which together with a succeeding character is not identical to any one of the stored character strings or to an initial character sub-string of any one of the stored character strings; and determining a character sub-string consisting of a terminal character sub-string of the input character string preceding the unrecognized character and succeeding any identified initial character sub-string; else determining at least one character sub-string comprises: determining a character sub-string consisting of said complete character string preceding the unrecognized character in the input character string. 5. The method of claim 1 , wherein the determining of a character sub-string comprises: identifying space characters in the input character string; and on basis of a substring directly succeeding a space character and directly preceding the unrecognized character, determining a character sub-string. | 0.893162 |
8,793,575 | 11 | 15 | 11. One or more non-transitory computer-readable storage media storing computer-readable instructions that, when executed, instruct one or more processors to perform operations comprising: identifying progress of a user through a collection of electronic books, the progress of the user being based at least in part on a number of pages that have been consumed, the collection of electronic books comprising a plurality of electronic books; causing display of a progress bar indicating the progress of the user through the collection of electronic books, the progress bar indicating the progress of the user by visualizing an amount of consumed text with respect to an amount of unconsumed text; identifying a number of sessions of consumption of an electronic book of the collection of electronic books; causing display of information indicating the number of sessions of consumption of the electronic book; identifying an estimated amount of time to complete the electronic book of the collection of electronic books; and causing display of the estimated amount of time to complete the electronic book. | 11. One or more non-transitory computer-readable storage media storing computer-readable instructions that, when executed, instruct one or more processors to perform operations comprising: identifying progress of a user through a collection of electronic books, the progress of the user being based at least in part on a number of pages that have been consumed, the collection of electronic books comprising a plurality of electronic books; causing display of a progress bar indicating the progress of the user through the collection of electronic books, the progress bar indicating the progress of the user by visualizing an amount of consumed text with respect to an amount of unconsumed text; identifying a number of sessions of consumption of an electronic book of the collection of electronic books; causing display of information indicating the number of sessions of consumption of the electronic book; identifying an estimated amount of time to complete the electronic book of the collection of electronic books; and causing display of the estimated amount of time to complete the electronic book. 15. The one or more non-transitory computer-readable storage media of claim 11 , wherein the operations further comprise: causing display of a cumulative reading rate of the user for a section of the electronic book. | 0.717277 |
9,286,886 | 46 | 47 | 46. The at least one computer-readable storage medium of claim 41 , wherein the comparing comprises selecting the corresponding text fragment based at least in part on a similarity measure between one or more linguistic features of the at least a portion of the input text and the corresponding text fragment. | 46. The at least one computer-readable storage medium of claim 41 , wherein the comparing comprises selecting the corresponding text fragment based at least in part on a similarity measure between one or more linguistic features of the at least a portion of the input text and the corresponding text fragment. 47. The at least one computer-readable storage medium of claim 46 , wherein the similarity measure is determined based at least in part on a ratio of words that appear in both the at least a portion of the input text and the corresponding text fragment. | 0.945238 |
9,460,085 | 2 | 3 | 2. The method of claim 1 wherein the analyzing further comprises: processing the question-answer pair by one or more components of the target QA system. | 2. The method of claim 1 wherein the analyzing further comprises: processing the question-answer pair by one or more components of the target QA system. 3. The method of claim 2 wherein at least one of the components is user selectable to perform domain-specific question-answer set analysis and give recommendations customized for the user that is an expert in the domain. | 0.951499 |
10,043,500 | 1 | 2 | 1. A method of making audio music selection and creating a mixtape, comprising: importing one or more audio digital data files from an audio music repository; sorting and filtering the audio digital data files based on one or more selection criteria; creating the mixtape from the audio digital data files sorting and filtering results, the results comprising one or more sorted and filtered audio digital data files; wherein the sorting and filtering of the audio digital data files comprise: spectral analyzing each of the audio digital data files to extract one or more low level acoustic feature parameters of the audio digital data file; from the low level acoustic feature parameter values, determining one or more high level acoustic feature parameters of the analyzed audio digital data file; determining a first similarity score of each of the analyzed audio digital data files by comparing the acoustic feature parameter values of the analyzed audio digital data file against desired acoustic feature parameter values determined from the selection criteria; and sorting the analyzed audio digital data files according to their first similarity scores; and filtering out the analyzed audio digital data files with first similarity scores lower than a filter threshold; and compiling a similarity matrix comprising a second similarity score of each of the analyzed audio digital data file comprising: determining the second similarity score of each of the analyzed audio digital data files by comparing the acoustic feature parameter values of the analyzed audio digital data file against the acoustic feature parameter values of another one of the analyzed audio digital data files; including the second similarity score in the similarity matrix with reference to the two analyzed audio digital data files compared; excluding second similarity scores that are identical from the similarity matrix; and excluding second similarity scores that are below a similarity threshold from the similarity matrix; wherein the the similarity matrix is used to identify candidate audio digital data files with similar acoustic feature to those audio digital data files in the mixtape. | 1. A method of making audio music selection and creating a mixtape, comprising: importing one or more audio digital data files from an audio music repository; sorting and filtering the audio digital data files based on one or more selection criteria; creating the mixtape from the audio digital data files sorting and filtering results, the results comprising one or more sorted and filtered audio digital data files; wherein the sorting and filtering of the audio digital data files comprise: spectral analyzing each of the audio digital data files to extract one or more low level acoustic feature parameters of the audio digital data file; from the low level acoustic feature parameter values, determining one or more high level acoustic feature parameters of the analyzed audio digital data file; determining a first similarity score of each of the analyzed audio digital data files by comparing the acoustic feature parameter values of the analyzed audio digital data file against desired acoustic feature parameter values determined from the selection criteria; and sorting the analyzed audio digital data files according to their first similarity scores; and filtering out the analyzed audio digital data files with first similarity scores lower than a filter threshold; and compiling a similarity matrix comprising a second similarity score of each of the analyzed audio digital data file comprising: determining the second similarity score of each of the analyzed audio digital data files by comparing the acoustic feature parameter values of the analyzed audio digital data file against the acoustic feature parameter values of another one of the analyzed audio digital data files; including the second similarity score in the similarity matrix with reference to the two analyzed audio digital data files compared; excluding second similarity scores that are identical from the similarity matrix; and excluding second similarity scores that are below a similarity threshold from the similarity matrix; wherein the the similarity matrix is used to identify candidate audio digital data files with similar acoustic feature to those audio digital data files in the mixtape. 2. The method of claim 1 , wherein the first similarity score the analyzed audio digital data file is defined by: s = 1 n ∑ i = 1 n [ c i ( x i - y i ) 2 ] ; wherein: s is the first similarity score of the analyzed audio digital data file; n is total number of acoustic feature parameters compared; c i is an importance coefficient for the acoustic feature parameter i; x i is the analyzed audio digital data file's acoustic feature parameter i's value; and y i is the desired acoustic feature parameter i's value. | 0.50096 |
9,898,529 | 1 | 2 | 1. A method for augmenting a semantic model from unstructured data, the method comprising: determining, by a computer processor, a root of a first element selected from a domain of a semantic model, wherein the domain includes a plurality of elements that lack relationship information between the plurality of elements; generating, by the computer processor, a search token, based at least in part on morphological rules applied to the root of the first element and a preposition added to the root of the first element, wherein a selection of the preposition that is added to the root of the first element depends upon whether the root is determined as a noun or a verb, as root occurs in the first element of the domain of the semantic model; performing, by the computer processor, a search of one or more unstructured data sources, based on the search token that is generated; determining, by the computer processor, whether results of the search include at least one phrase that contains an approximate match to the search token; in response to determining the results of the search include at least one phrase that contains an approximate match to the search token, generating a triple from the at least one phrase, and adding the triple to the semantic model; and adding, by the computer processor, a predicate of the triple to a second element of the domain of the semantic model forming a second triple, wherein the predicate of the triple expresses a relationship between the first element of the domain of the semantic model and the second element of the domain of the semantic model. | 1. A method for augmenting a semantic model from unstructured data, the method comprising: determining, by a computer processor, a root of a first element selected from a domain of a semantic model, wherein the domain includes a plurality of elements that lack relationship information between the plurality of elements; generating, by the computer processor, a search token, based at least in part on morphological rules applied to the root of the first element and a preposition added to the root of the first element, wherein a selection of the preposition that is added to the root of the first element depends upon whether the root is determined as a noun or a verb, as root occurs in the first element of the domain of the semantic model; performing, by the computer processor, a search of one or more unstructured data sources, based on the search token that is generated; determining, by the computer processor, whether results of the search include at least one phrase that contains an approximate match to the search token; in response to determining the results of the search include at least one phrase that contains an approximate match to the search token, generating a triple from the at least one phrase, and adding the triple to the semantic model; and adding, by the computer processor, a predicate of the triple to a second element of the domain of the semantic model forming a second triple, wherein the predicate of the triple expresses a relationship between the first element of the domain of the semantic model and the second element of the domain of the semantic model. 2. The method of claim 1 , further comprising: determining, by the computer processor, a second relationship between at least one of: a third element of the at least one phrase and a fourth element of the at least one phrase, and a corresponding element of the semantic model, based on the determining of at least one phrase that contains an approximate match to the search token; and adding to the domain of the semantic model, by the computer processor, at least one of: the third element of the at least one phrase and the fourth element of the at least one phrase, and the second relationship. | 0.726898 |
7,904,290 | 2 | 3 | 2. A method according to claim 1 , wherein the step of exporting said elements includes the steps of: creating a table in said document; and placing in said table said exported elements. | 2. A method according to claim 1 , wherein the step of exporting said elements includes the steps of: creating a table in said document; and placing in said table said exported elements. 3. A method according to claim 2 , wherein the placing step includes the step of placing in said table additional data associated with said exported elements. | 0.969116 |
7,555,430 | 9 | 11 | 9. A method of recognizing spoken input received from a source of the spoken input wherein the method comprises steps of: a. receiving the spoken input from the source of the spoken input; b. performing a first pass speech recognition technique on the spoken input; c. forming first pass results; and d. selectively performing a second pass speech recognition technique on the spoken input according to the first pass results; wherein the first pass results identify an alignment of speech and silence associated with the spoken input, and further a plurality of speech recognition techniques are performed on a selected portion of the spoken input is selected based upon the alignment associated with the spoken input, wherein the second pass speech recognition technique does not process at least some portions of the spoken input which are aligned with silence. | 9. A method of recognizing spoken input received from a source of the spoken input wherein the method comprises steps of: a. receiving the spoken input from the source of the spoken input; b. performing a first pass speech recognition technique on the spoken input; c. forming first pass results; and d. selectively performing a second pass speech recognition technique on the spoken input according to the first pass results; wherein the first pass results identify an alignment of speech and silence associated with the spoken input, and further a plurality of speech recognition techniques are performed on a selected portion of the spoken input is selected based upon the alignment associated with the spoken input, wherein the second pass speech recognition technique does not process at least some portions of the spoken input which are aligned with silence. 11. The method according to claim 9 , wherein each of the plurality of speech recognition techniques forms a score corresponding to the selected portion of the spoken input. | 0.769947 |
8,751,486 | 1 | 2 | 1. A computer-implemented method for executing structured queries on unstructured data, the method comprising: receiving a structured query in a structured query language; determining a schema that defines a structure for unstructured data, wherein the schema identifies a plurality of fields in the unstructured data; conducting a pilot query on the unstructured data, wherein the pilot query is conducted using the schema, and wherein conducting the pilot query identifies one or more fields in the unstructured data; converting the structured query in the structured query language into an unstructured query in an unstructured query language, wherein the conversion is done using the one or more fields identified by conducting the pilot query, and wherein the unstructured query is used to access the unstructured data; conducting a search on the unstructured data using the unstructured query; caching the one or more fields identified by conducting the pilot query; conducting a new pilot query on the unstructured data, wherein conducting the new pilot query identifies one or more new fields in the unstructured data; and merging the one or more cached fields with the one or more new fields. | 1. A computer-implemented method for executing structured queries on unstructured data, the method comprising: receiving a structured query in a structured query language; determining a schema that defines a structure for unstructured data, wherein the schema identifies a plurality of fields in the unstructured data; conducting a pilot query on the unstructured data, wherein the pilot query is conducted using the schema, and wherein conducting the pilot query identifies one or more fields in the unstructured data; converting the structured query in the structured query language into an unstructured query in an unstructured query language, wherein the conversion is done using the one or more fields identified by conducting the pilot query, and wherein the unstructured query is used to access the unstructured data; conducting a search on the unstructured data using the unstructured query; caching the one or more fields identified by conducting the pilot query; conducting a new pilot query on the unstructured data, wherein conducting the new pilot query identifies one or more new fields in the unstructured data; and merging the one or more cached fields with the one or more new fields. 2. The computer-implemented method of claim 1 , wherein the pilot query is conducted on a subset of the unstructured data. | 0.879447 |
8,406,382 | 12 | 17 | 12. A non-transitory computer readable storage medium that stores a computer program that is executable by a computer to: receive a call from a party; prompt the party for information; capture verbal communication spoken by the party; create a voice model associated with the party, the voice model being created by processing the captured verbal communication spoken by the party; store the created voice model; verify the identity of the party; and update a previously stored voice model of the party, the previously stored voice model of the party having been registered during a previous call received from the party, wherein the creation of the voice model is imperceptible to the party. | 12. A non-transitory computer readable storage medium that stores a computer program that is executable by a computer to: receive a call from a party; prompt the party for information; capture verbal communication spoken by the party; create a voice model associated with the party, the voice model being created by processing the captured verbal communication spoken by the party; store the created voice model; verify the identity of the party; and update a previously stored voice model of the party, the previously stored voice model of the party having been registered during a previous call received from the party, wherein the creation of the voice model is imperceptible to the party. 17. The non-transitory computer readable medium according to claim 12 , wherein the previously stored voice model of the party was created during a registration in which the party was not notified that the previously stored voice model was being created for voice registration. | 0.649367 |
7,565,012 | 7 | 8 | 7. The method of claim 1 , wherein in the photographing of the document, the document is photographed using an image data input device, and the moving picture data is generated from the photographed document. | 7. The method of claim 1 , wherein in the photographing of the document, the document is photographed using an image data input device, and the moving picture data is generated from the photographed document. 8. The method of claim 7 , wherein said image input device is one of a camera, a camcorder, a personal digital assistant, and a personal computer camera. | 0.938057 |
10,115,374 | 14 | 15 | 14. A non-transitory computer readable storage medium having computer readable program code embodied therewith, the computer readable program code comprising instructions for: receiving a textual data set comprising a plurality of character codes, each respective character code comprising a code value indicating at least one respective text character to be visually rendered; and determining, based on the textual data set, a drawing instruction set comprising, for each respective code value within the plurality of character codes, a respective at least one drawing instruction in a rendering language to draw at least part of a glyph of the respective code value, wherein each respective at least one drawing instruction excludes an indication of a correspondence with the respective character code, wherein each respective at least one drawing instruction for a specified text character excludes instructions to repeat drawing instructions specified for other instances of the specified text character. | 14. A non-transitory computer readable storage medium having computer readable program code embodied therewith, the computer readable program code comprising instructions for: receiving a textual data set comprising a plurality of character codes, each respective character code comprising a code value indicating at least one respective text character to be visually rendered; and determining, based on the textual data set, a drawing instruction set comprising, for each respective code value within the plurality of character codes, a respective at least one drawing instruction in a rendering language to draw at least part of a glyph of the respective code value, wherein each respective at least one drawing instruction excludes an indication of a correspondence with the respective character code, wherein each respective at least one drawing instruction for a specified text character excludes instructions to repeat drawing instructions specified for other instances of the specified text character. 15. The non-transitory computer readable storage medium of claim 14 , wherein a reference glyph indicated by a respective character code within the plurality of character codes specifies at least one drawing instruction that defines a plurality of respective drawing segments, where each respective drawing segment is defined by a respective path, where a respective drawing instruction for at least one respective drawing segment in the plurality of respective drawing segments specifies one respective path, and wherein the determining the drawing instruction set comprises determining respective drawing instructions to draw a plurality of sub paths, where each sub-path comprises a partial portion of the one respective path. | 0.664982 |
7,617,195 | 2 | 6 | 2. The method according to claim 1 , wherein the second percentage at (c) is computed by giving weight only to those keywords and their set of neighboring keywords in the first list that match in the second list and a threshold percentage of the keywords in their set of neighboring keywords. | 2. The method according to claim 1 , wherein the second percentage at (c) is computed by giving weight only to those keywords and their set of neighboring keywords in the first list that match in the second list and a threshold percentage of the keywords in their set of neighboring keywords. 6. The method according to claim 2 , wherein the first list of rated keywords includes one or more keywords translated from a second language different from a first language that is identified as being a primary language of the first document. | 0.954323 |
7,984,004 | 1 | 6 | 1. A system that facilitates outputting indications of usefulness of query suggestions, the system comprising: a processor; and a memory that comprises a plurality of components that are executable by the processor, the plurality of components comprising: a query suggestion generator component that receives a query and generates a query suggestion based at least in part upon the received query; a model component that outputs an indication of usefulness with respect to the query suggestion, wherein the model component is a logistic regression model that has been trained to model user interaction with query suggestions output by a search engine, wherein the logistic regression model has been trained based at least in part upon queries provided to the search engine by users, search results returned to the users subsequent to the search engine performing searches using the queries, search results selected by the users subsequent to the search results being returned to the users, query suggestions provided to the users responsive to the queries being provided to the search engine by the users, query suggestions selected by the users upon the users being provided with the query suggestions, and search results selected by the users subsequent to the users selecting the query suggestions; and a grouping component that groups query suggestions into a plurality of different groups, wherein the model component individually assigns an indication of usefulness to each of the groups of query suggestions, wherein the groups are assembled based at least in part upon an amount of overlap between search results that correspond to query suggestions. | 1. A system that facilitates outputting indications of usefulness of query suggestions, the system comprising: a processor; and a memory that comprises a plurality of components that are executable by the processor, the plurality of components comprising: a query suggestion generator component that receives a query and generates a query suggestion based at least in part upon the received query; a model component that outputs an indication of usefulness with respect to the query suggestion, wherein the model component is a logistic regression model that has been trained to model user interaction with query suggestions output by a search engine, wherein the logistic regression model has been trained based at least in part upon queries provided to the search engine by users, search results returned to the users subsequent to the search engine performing searches using the queries, search results selected by the users subsequent to the search results being returned to the users, query suggestions provided to the users responsive to the queries being provided to the search engine by the users, query suggestions selected by the users upon the users being provided with the query suggestions, and search results selected by the users subsequent to the users selecting the query suggestions; and a grouping component that groups query suggestions into a plurality of different groups, wherein the model component individually assigns an indication of usefulness to each of the groups of query suggestions, wherein the groups are assembled based at least in part upon an amount of overlap between search results that correspond to query suggestions. 6. The system of claim 1 , wherein the plurality of components further comprises: an interface component that receives user input with respect to the received query and/or the query suggestion that is presented to an issuer of the query; and a data collector component that collects data pertaining to the user input, wherein the collected data is used to refine the model component. | 0.694577 |
5,542,024 | 35 | 36 | 35. An information processing system as claimed in claim 34, wherein said second means includes a Graphical User Interface having a simplified screen display, said simplified screen display having a first display portion for displaying said alphanumeric statements and a second display portion for displaying said plurality of option actions as a plurality of simplified decision boxes to be selected by the user. | 35. An information processing system as claimed in claim 34, wherein said second means includes a Graphical User Interface having a simplified screen display, said simplified screen display having a first display portion for displaying said alphanumeric statements and a second display portion for displaying said plurality of option actions as a plurality of simplified decision boxes to be selected by the user. 36. An information processing system as claimed in claim 35, which further includes a list file with pointers to selected option actions in one or more of said knowledge records. | 0.940746 |
10,067,957 | 8 | 12 | 8. A non-transitory computer-readable medium having stored therein computer executable code that causes one or more processors to execute the steps of: receiving schema data, which describes the format and properties of an instance object to be transmitted or received; dynamically generating parser classes for deserialization based on the schema, wherein generating the parser classes comprises: iterating over each property defined in the schema and in any other schema referenced in the schema, generating a new parser class for each non-scalar, non-array property defined by the schema; for each property, reading associative values representing attributes of the property; generating a class variable based on the read associated values; and associating each generated class variable with one or more accessor and mutator methods to update and retrieve the class variables; and storing the generated parser classes and methods in memory in order for a host programming language to transmit or receive instance data to or from an application programming interface. | 8. A non-transitory computer-readable medium having stored therein computer executable code that causes one or more processors to execute the steps of: receiving schema data, which describes the format and properties of an instance object to be transmitted or received; dynamically generating parser classes for deserialization based on the schema, wherein generating the parser classes comprises: iterating over each property defined in the schema and in any other schema referenced in the schema, generating a new parser class for each non-scalar, non-array property defined by the schema; for each property, reading associative values representing attributes of the property; generating a class variable based on the read associated values; and associating each generated class variable with one or more accessor and mutator methods to update and retrieve the class variables; and storing the generated parser classes and methods in memory in order for a host programming language to transmit or receive instance data to or from an application programming interface. 12. The non-transitory computer-readable medium of claim 8 , further comprising: a property with a type attribute of union, which defines more than one type; wherein the computer executable code further causes one or more processors to execute the steps of: matching the property to defined types in an order that the defined types are listed in the schema; and responsive to a match, using a matching defined type to create a class property. | 0.635914 |
8,341,224 | 16 | 20 | 16. A computer-readable storage medium, excluding a signal, having computer-executable instructions for resolving text change conflicts, comprising: receiving at a client an update notification from a server that includes an update to a current version of text that is co-edited by clients, wherein the server selects a text change from text changes received from one or more of the clients that is used to update the current version of the text; determining at the client, when the client has a non-selected text change to update the current version of the text that was sent to the server but not selected by the server to update the current version of the text; initiating a conflict resolution procedure on the client to create an updated text change to reflect the updated current version of the text and the non-selected text change; and submitting the updated text change to the server to update the current version of the text. | 16. A computer-readable storage medium, excluding a signal, having computer-executable instructions for resolving text change conflicts, comprising: receiving at a client an update notification from a server that includes an update to a current version of text that is co-edited by clients, wherein the server selects a text change from text changes received from one or more of the clients that is used to update the current version of the text; determining at the client, when the client has a non-selected text change to update the current version of the text that was sent to the server but not selected by the server to update the current version of the text; initiating a conflict resolution procedure on the client to create an updated text change to reflect the updated current version of the text and the non-selected text change; and submitting the updated text change to the server to update the current version of the text. 20. The computer-readable storage medium of claim 16 , further comprising the server selecting a received text change to update the current version of the text based on a first person wins policy that selects a first text change received. | 0.738462 |
9,396,738 | 6 | 7 | 6. The method of claim 1 , wherein said packet loss information includes an estimate of a number of packets lost in the communication of said speech signal via said communications network; and wherein said packet loss information includes packet loss location information. | 6. The method of claim 1 , wherein said packet loss information includes an estimate of a number of packets lost in the communication of said speech signal via said communications network; and wherein said packet loss information includes packet loss location information. 7. The method of claim 6 , further comprising: receiving said packets communicated via the communications network; processing said packets to recover said speech signal; and generating the estimate of the number of packets lost in the communication of said speech signal from the recovered speech signal. | 0.929986 |
7,983,901 | 1 | 3 | 1. A method, performed using one or more processors, of generating annotated training data for training a natural language understanding system, comprising: generating, with the natural language understanding system running on one or more of the processors, a proposed annotation for each of a plurality of units of unannotated training data received via one or more input components; calculating, with one or more of the processors, a confidence measure for each of a plurality of different portions of the proposed annotations for a given unit of the training data; displaying on an output component a proposed annotation for a given unit of the training data, with portions of the proposed annotation for which the confidence measure is below a threshold being displayed in a visually contrasting way to other portions of the proposed annotation; receiving user selection of a selected portion of the proposed annotation displayed; displaying on the output component at least some proposed alternative annotations, for the selected portion of the proposed annotation, in an order based on the confidence measures of the proposed alternative annotations, with one or more input components providing one or more user-actuable inputs configured for verification of the proposed alternative annotations and one or more user-actuable inputs configured for deletion of the proposed alternative annotations; when an input for verification is received, responding, with one or more of the processors, to the input for verification of one of the proposed alternative annotations by storing the verified annotation for the portion of the given unit of the training data; and when an input for deletion is received, responding, with one or more of the processors, to the input for deletion of one of the proposed alternative annotations by presenting on an output component at least some of the remaining proposed alternative annotations in an order based on the confidence measures of the remaining proposed alternative annotations. | 1. A method, performed using one or more processors, of generating annotated training data for training a natural language understanding system, comprising: generating, with the natural language understanding system running on one or more of the processors, a proposed annotation for each of a plurality of units of unannotated training data received via one or more input components; calculating, with one or more of the processors, a confidence measure for each of a plurality of different portions of the proposed annotations for a given unit of the training data; displaying on an output component a proposed annotation for a given unit of the training data, with portions of the proposed annotation for which the confidence measure is below a threshold being displayed in a visually contrasting way to other portions of the proposed annotation; receiving user selection of a selected portion of the proposed annotation displayed; displaying on the output component at least some proposed alternative annotations, for the selected portion of the proposed annotation, in an order based on the confidence measures of the proposed alternative annotations, with one or more input components providing one or more user-actuable inputs configured for verification of the proposed alternative annotations and one or more user-actuable inputs configured for deletion of the proposed alternative annotations; when an input for verification is received, responding, with one or more of the processors, to the input for verification of one of the proposed alternative annotations by storing the verified annotation for the portion of the given unit of the training data; and when an input for deletion is received, responding, with one or more of the processors, to the input for deletion of one of the proposed alternative annotations by presenting on an output component at least some of the remaining proposed alternative annotations in an order based on the confidence measures of the remaining proposed alternative annotations. 3. The method of claim 1 in which displaying proposed alternative annotations comprises: displaying the proposed alternative annotations in order based on descending value of the corresponding confidence measures. | 0.502336 |
9,063,753 | 20 | 23 | 20. A non-transitory computer-readable medium storing one or more programs comprising instructions, which when executed by a device, cause the device to: store a business object at a business object infrastructure repository, the business object having a plurality of nodes, including at least one exit node associated with a code snippet written in a programming language; responsive to a request received from a client device, execute the business object at a processing framework until the exit node is reached; load, from the repository, the code snippet that is written in the programming language and associated with the exit node; select a virtual machine interpreter, from a plurality of virtual machine interpreters, for the code snippet that is associated with the exit node of the business object based on the programming language of the code snippet that is associated with the exit node of the business object; prepare, by a scripting framework, data associated with the exit node; call the code snippet via the selected virtual machine interpreter; update, by the scripting framework, any changed data resulting from execution of the code snippet; and return to the business object via the exit node and resume execution of the business object; wherein the exit node that is included in the business object and associated with the code snippet written in the programming language is a first exit node that is included in the business object and associated with a first code snippet written in a first programming language; wherein the load, from the repository, the code snippet that is written in the programming language and associated with the exit node comprises: load, from the repository, the first code snippet that is written in the first programming language and associated with the first exit node; wherein the select a virtual machine interpreter, from a plurality of virtual machine interpreters, for the code snippet based on the programming language of the code snippet comprises: select a first virtual machine interpreter for the first code snippet based on the first programming language of the first code snippet; and wherein the return to the business object via the exit node and resume execution of the business object comprises: return to the business object via the first exit node and resume execution of the business object until a second exit node of the business object is reached, the second exit node of the business object being associated with a second code snippet written in a second programming language that is different than the first programming language in which the first code snippet that is associated with the first exit node of the business object is written. | 20. A non-transitory computer-readable medium storing one or more programs comprising instructions, which when executed by a device, cause the device to: store a business object at a business object infrastructure repository, the business object having a plurality of nodes, including at least one exit node associated with a code snippet written in a programming language; responsive to a request received from a client device, execute the business object at a processing framework until the exit node is reached; load, from the repository, the code snippet that is written in the programming language and associated with the exit node; select a virtual machine interpreter, from a plurality of virtual machine interpreters, for the code snippet that is associated with the exit node of the business object based on the programming language of the code snippet that is associated with the exit node of the business object; prepare, by a scripting framework, data associated with the exit node; call the code snippet via the selected virtual machine interpreter; update, by the scripting framework, any changed data resulting from execution of the code snippet; and return to the business object via the exit node and resume execution of the business object; wherein the exit node that is included in the business object and associated with the code snippet written in the programming language is a first exit node that is included in the business object and associated with a first code snippet written in a first programming language; wherein the load, from the repository, the code snippet that is written in the programming language and associated with the exit node comprises: load, from the repository, the first code snippet that is written in the first programming language and associated with the first exit node; wherein the select a virtual machine interpreter, from a plurality of virtual machine interpreters, for the code snippet based on the programming language of the code snippet comprises: select a first virtual machine interpreter for the first code snippet based on the first programming language of the first code snippet; and wherein the return to the business object via the exit node and resume execution of the business object comprises: return to the business object via the first exit node and resume execution of the business object until a second exit node of the business object is reached, the second exit node of the business object being associated with a second code snippet written in a second programming language that is different than the first programming language in which the first code snippet that is associated with the first exit node of the business object is written. 23. The medium of claim 20 , wherein the programming language is associated with at least one of Advanced Business Application Programming (ABAP), an ABAP Scripting Language (ABSL), JavaScript, or a Ruby-based language. | 0.879405 |
7,519,579 | 7 | 11 | 7. A computer-readable storage medium storing computer-executable instructions for updating a summary page of a text file, comprising: performing a query to identify data in the text file stored in accordance with a software application of a client device, wherein the data is identified by metadata stored with the text file, wherein the software application is at least one member of a group comprising: a word processing application and a notes application; in response to performing the query, generating, on the software application of the client device, a dynamic container for the query, wherein the dynamic container facilitates a synchronous relationship between the text file and the summary page; obtaining a result of the query on the software application of the client device, wherein the result includes information within the text file, wherein the result is determined by the query; storing the result in the dynamic container; displaying the query result on the summary page, wherein modification to the query of the text file results in a new query result being displayed on the summary page, wherein the summary page includes a plurality of links, wherein each link indicates a portion of the text file that is displayed on the summary page, wherein selection of one of the links causes navigation to the portion of the text file that is displayed on the summary page and is indicate by the selected link; and upon receiving an input that modifies the data of the text file in relation to one of the categories identified in the summary page, automatically updating the dynamic container with the modification to cause the dynamic container to automatically update the summary page with the modification. | 7. A computer-readable storage medium storing computer-executable instructions for updating a summary page of a text file, comprising: performing a query to identify data in the text file stored in accordance with a software application of a client device, wherein the data is identified by metadata stored with the text file, wherein the software application is at least one member of a group comprising: a word processing application and a notes application; in response to performing the query, generating, on the software application of the client device, a dynamic container for the query, wherein the dynamic container facilitates a synchronous relationship between the text file and the summary page; obtaining a result of the query on the software application of the client device, wherein the result includes information within the text file, wherein the result is determined by the query; storing the result in the dynamic container; displaying the query result on the summary page, wherein modification to the query of the text file results in a new query result being displayed on the summary page, wherein the summary page includes a plurality of links, wherein each link indicates a portion of the text file that is displayed on the summary page, wherein selection of one of the links causes navigation to the portion of the text file that is displayed on the summary page and is indicate by the selected link; and upon receiving an input that modifies the data of the text file in relation to one of the categories identified in the summary page, automatically updating the dynamic container with the modification to cause the dynamic container to automatically update the summary page with the modification. 11. The computer-readable storage medium of claim 7 , wherein the metadata is embedded in the text file. | 0.875598 |
9,230,035 | 2 | 3 | 2. The computer-implemented method of claim 1 , wherein the classifying the first text content and the reply text content of the user comprises at least one of: extracting, by the processor device, emotional symbols from the text content of the first user and the each said reply text content of the other users, wherein the emotional symbols belong to a predefined symbol set; and extracting, by the processor device, emotional words from the text content of the first user and from each reply text content of the other users. | 2. The computer-implemented method of claim 1 , wherein the classifying the first text content and the reply text content of the user comprises at least one of: extracting, by the processor device, emotional symbols from the text content of the first user and the each said reply text content of the other users, wherein the emotional symbols belong to a predefined symbol set; and extracting, by the processor device, emotional words from the text content of the first user and from each reply text content of the other users. 3. The computer-implemented method of claim 2 , wherein the classifying comprises: classifying, by the processor device, the emotional symbols and/or emotional words extracted from the first text content into corresponding emotion types from the predetermined set of emotion types as a first emotion type, and classifying the emotional symbols and/or emotional words extracted from each of the reply text content of the other users into corresponding second emotion types from the predetermined set of emotion types, wherein correspondence relations between the emotional symbols and/or emotional words and the emotion types are obtained by searching a database in which correspondence relations between the emotional symbols and/or emotional words and the emotion types are stored. | 0.844347 |
9,569,504 | 18 | 19 | 18. A non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform operations comprising: identifying, by the one or more computers, a set of documents based on a search query from a user device; identifying, by the one or more computers, a particular document in the set of documents; determining, by the one or more computers, a number of first links that are included in the set of documents and that (i) have anchor text that includes a term of the search query, and (ii) refer to the particular document; determining, by the one or more computers, whether the number of the first links that are included in the documents in the set of documents and that (i) have anchor text that includes a term of the search query, and (ii) refer to the particular document exceeds a predetermined threshold; in response to determining, by the one or more computers, that the number of the first links that are included in the documents in the set of documents and that (i) have anchor text that includes a term of the search query, and (ii) refer to the particular document exceeds the predetermined threshold, using a first value to modify a quality score that is associated with the particular document; and storing, by the one or more computers, the modified quality score for use in ranking the particular document in response to subsequently received search queries. | 18. A non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform operations comprising: identifying, by the one or more computers, a set of documents based on a search query from a user device; identifying, by the one or more computers, a particular document in the set of documents; determining, by the one or more computers, a number of first links that are included in the set of documents and that (i) have anchor text that includes a term of the search query, and (ii) refer to the particular document; determining, by the one or more computers, whether the number of the first links that are included in the documents in the set of documents and that (i) have anchor text that includes a term of the search query, and (ii) refer to the particular document exceeds a predetermined threshold; in response to determining, by the one or more computers, that the number of the first links that are included in the documents in the set of documents and that (i) have anchor text that includes a term of the search query, and (ii) refer to the particular document exceeds the predetermined threshold, using a first value to modify a quality score that is associated with the particular document; and storing, by the one or more computers, the modified quality score for use in ranking the particular document in response to subsequently received search queries. 19. The non-transitory computer-readable medium of claim 18 , the operations further comprising: in response to determining, by the one or more computers, that the number of the first links that are included in the documents in the set of documents and that (i) have anchor text that includes a term of the search query, and (ii) refer to the particular document does not exceed the predetermined threshold, using a second value to modify a quality score that is associated with the particular document, wherein the second value is different than the first value; and storing the modified quality score for use in ranking the particular document in response to subsequently received search queries. | 0.507757 |
8,392,175 | 1 | 5 | 1. A method of extracting phrases from a corpus of documents, the method comprising: generating, by a processor, a set of candidate phrases from the documents in the corpus, wherein each candidate phrase corresponds to a group of two or more words that occur consecutively in at least one of the documents in the corpus, each candidate phrase having an occurrence count associated therewith, wherein generating the set of candidate phrases includes: extracting a group of consecutive words from one of the documents in the corpus; determining whether the group of consecutive words appears as one of the candidate phrases in a first list of candidate phrases; in the event that the group of consecutive words appears as one of the candidate phrases in the first list, incrementing an occurrence count associated with the one of the candidate phrases; and in the event that the group of consecutive words does not appear as one of the candidate phrases in the first list: determining whether the group of consecutive words appears as one of the candidate phrases in a second list of candidate phrases, wherein each candidate phrase in the second list is associated with an occurrence count equal to 1; in the event that the group of consecutive words appears as one of the candidate phrases in the second list, promoting the one of the candidate phrases to the first list; and in the event that the group of consecutive words does not appear as one of the candidate phrases in the second list, adding the group of consecutive words as a new candidate phrase to the second list; computing, by the processor, a statistical metric for each candidate phrase based at least in part on the occurrence count, wherein the statistical metric indicates a likelihood of the words within the candidate phrase occurring consecutively by chance; and selecting, by the processor, a plurality of the candidate phrases as meaningful phrases, the selection being based on the statistical metric. | 1. A method of extracting phrases from a corpus of documents, the method comprising: generating, by a processor, a set of candidate phrases from the documents in the corpus, wherein each candidate phrase corresponds to a group of two or more words that occur consecutively in at least one of the documents in the corpus, each candidate phrase having an occurrence count associated therewith, wherein generating the set of candidate phrases includes: extracting a group of consecutive words from one of the documents in the corpus; determining whether the group of consecutive words appears as one of the candidate phrases in a first list of candidate phrases; in the event that the group of consecutive words appears as one of the candidate phrases in the first list, incrementing an occurrence count associated with the one of the candidate phrases; and in the event that the group of consecutive words does not appear as one of the candidate phrases in the first list: determining whether the group of consecutive words appears as one of the candidate phrases in a second list of candidate phrases, wherein each candidate phrase in the second list is associated with an occurrence count equal to 1; in the event that the group of consecutive words appears as one of the candidate phrases in the second list, promoting the one of the candidate phrases to the first list; and in the event that the group of consecutive words does not appear as one of the candidate phrases in the second list, adding the group of consecutive words as a new candidate phrase to the second list; computing, by the processor, a statistical metric for each candidate phrase based at least in part on the occurrence count, wherein the statistical metric indicates a likelihood of the words within the candidate phrase occurring consecutively by chance; and selecting, by the processor, a plurality of the candidate phrases as meaningful phrases, the selection being based on the statistical metric. 5. The method of claim 1 further comprising: constructing, by the processor, a document vector for each of a plurality of documents from the corpus, the document vector including a first plurality of components corresponding to words and a second plurality of components corresponding to at least some of the meaningful phrases; and forming, by the processor, a plurality of clusters of documents based at least in part on the document vectors. | 0.794635 |
9,847,964 | 16 | 19 | 16. An electronic device comprising: a network interface; a processor; and memory, comprising instructions executable by the processor to configure the processor to: connect the electronic device, using the network interface, to a commissioning device via a first communication path of multiple communication paths, the commissioning device being configured to manage pairing of the electronic device to an account managed by a remote service; receive, via the first communication path using the network interface, service configuration details from the commissioning device, the commissioning device having previously retrieved the service configuration details from the remote service, the service configuration details configured to enable the electronic device to connect to the remote service using any of the multiple communication paths, and the service configuration details comprising an account identifier, a pairing token that is uniquely associated with the account identifier, and a list of certificates; connect the electronic device to the remote service via a second communication path of the multiple communication paths using the stored service configuration details and the network interface; validate the connection between the electronic device and the remote service using the account identifier, the pairing token that is uniquely associated with the account identifier, and the list of certificates; and obtain, via the network interface, additional service configuration details from the remote service via the second communication path. | 16. An electronic device comprising: a network interface; a processor; and memory, comprising instructions executable by the processor to configure the processor to: connect the electronic device, using the network interface, to a commissioning device via a first communication path of multiple communication paths, the commissioning device being configured to manage pairing of the electronic device to an account managed by a remote service; receive, via the first communication path using the network interface, service configuration details from the commissioning device, the commissioning device having previously retrieved the service configuration details from the remote service, the service configuration details configured to enable the electronic device to connect to the remote service using any of the multiple communication paths, and the service configuration details comprising an account identifier, a pairing token that is uniquely associated with the account identifier, and a list of certificates; connect the electronic device to the remote service via a second communication path of the multiple communication paths using the stored service configuration details and the network interface; validate the connection between the electronic device and the remote service using the account identifier, the pairing token that is uniquely associated with the account identifier, and the list of certificates; and obtain, via the network interface, additional service configuration details from the remote service via the second communication path. 19. The electronic device of claim 16 , wherein the instructions are executable to configure the processor to: receive, via a connection with the remote service or via the commissioning device, an update configuration details request; based on the received update configuration details request: change a service endpoint within the remote service through which the electronic device contacts the remote service; and update a list of certificates for authenticating communications between the electronic device and other devices or the remote service. | 0.500907 |
9,798,812 | 15 | 17 | 15. A system comprising: one or more processors; and a memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to: receive a plurality of data items imported into a social-networking system by a first user of the social-networking system, the plurality of data items being related to an entity; access one or more data stores storing a social graph of the social-networking system, the social graph comprising a plurality of nodes and a plurality of edges between nodes, the nodes comprising user nodes corresponding to users of the social-networking system and concept nodes corresponding to concepts; identify one or more nodes of the social graph that likely match the entity; determine, by the one or more processors, a confidence score for each of the one or more of the identified nodes, the confidence score indicating a relative likelihood that the identified node matches the entity, wherein the confidence score is based in part on an interaction between the entity and a test message sent to the entity; and update at least one of the identified nodes with at least one of the data items. | 15. A system comprising: one or more processors; and a memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to: receive a plurality of data items imported into a social-networking system by a first user of the social-networking system, the plurality of data items being related to an entity; access one or more data stores storing a social graph of the social-networking system, the social graph comprising a plurality of nodes and a plurality of edges between nodes, the nodes comprising user nodes corresponding to users of the social-networking system and concept nodes corresponding to concepts; identify one or more nodes of the social graph that likely match the entity; determine, by the one or more processors, a confidence score for each of the one or more of the identified nodes, the confidence score indicating a relative likelihood that the identified node matches the entity, wherein the confidence score is based in part on an interaction between the entity and a test message sent to the entity; and update at least one of the identified nodes with at least one of the data items. 17. The system of claim 15 , wherein the confidence score for each of the one or more of the identified nodes is determined based on whether the identified node and the user have communicated with each other within a predetermined time period. | 0.705097 |
9,898,654 | 15 | 17 | 15. A system for assisting a user performing a procedure, the method comprising: a camera for capturing images of user activity while the user is performing the procedure; a computer processing system for converting the images of user activity into a text representation of user activity, comparing the text representation of user activity to procedure documentation, searching through additional information pertaining to a given step with which the user is experiencing difficulty to identify one or more relevant sub-portions; and a display or speaker for at least one of visually and audibly indicating a corrective action to the user responsive to a mismatch result from said comparing step, wherein only the one or more relevant sub-portions are audibly or visually provided to the user while other portions of the additional information are skipped from being audibly or visually provided to the user. | 15. A system for assisting a user performing a procedure, the method comprising: a camera for capturing images of user activity while the user is performing the procedure; a computer processing system for converting the images of user activity into a text representation of user activity, comparing the text representation of user activity to procedure documentation, searching through additional information pertaining to a given step with which the user is experiencing difficulty to identify one or more relevant sub-portions; and a display or speaker for at least one of visually and audibly indicating a corrective action to the user responsive to a mismatch result from said comparing step, wherein only the one or more relevant sub-portions are audibly or visually provided to the user while other portions of the additional information are skipped from being audibly or visually provided to the user. 17. The system of claim 15 , wherein the computer processing system is implemented as a server using a cloud computing configuration. | 0.867265 |
9,076,164 | 2 | 3 | 2. The method of claim 1 wherein said receiving results of statistical ranking of terms derived from said document further comprises providing a user interface display summarizing the results of the statistical ranking of terms derived from said document. | 2. The method of claim 1 wherein said receiving results of statistical ranking of terms derived from said document further comprises providing a user interface display summarizing the results of the statistical ranking of terms derived from said document. 3. The method of claim 2 wherein said receiving results of statistical ranking of terms derived from said document further comprises displaying said terms with at least one of the group consisting of a count indicating the total occurrences of a specific term of said term within said document, a name indicating a specific term or terms, a positional standard deviation for said term within said document, and a ranking. | 0.805991 |
7,849,393 | 1 | 4 | 1. An electronic book, comprising: a screen for displaying an electronic book content; at least one first component with at least one underlying link, wherein, upon selection of the at least one first component, the at least one first component links to at least an Internet web site for providing a plurality of streaming video, audio and text data when connected to the electronic book, wherein location information for each of the plurality of streaming video, audio and text data is provided in at least one hidden links table and the at least one hidden links table is provided in conjunction with downloading the content of the electronic book from a remote provider, and each of the at least one hidden links table is associated with the at least one first component with the at least one underlying link, and wherein the location information provides access to the plurality of streaming video, audio and text data, and wherein the at least one first component is a part of a content of the electronic book; and a control function wherein the control function allows selection of one or more of the plurality of streaming video, audio and text data while displaying the content of the electronic book, and wherein the selected data is displayed on the screen of the electronic book, wherein the hidden links table is updatable from a most current links table using information transmitted via the Internet web site from the remote provider, wherein the most current links table is downloaded to the electronic book either periodically by the remote provider, or when a new electronic book content is downloaded to the electronic book by the remote provider, and wherein the control function includes an on-screen show links button, upon selection of which a link menu is displayed on the screen of the electronic book along with the content of the electronic book, wherein the link menu shows all of the first components with the underlying links contained in the content of the electronic book displayed on the screen of the electronic book, and shows linked materials including a number of links, link numbers and descriptions of the linked materials that each of the first components with the underlying links is able to be linked to, such that, by choosing one of the links, a user is able to link to one of the linked materials. | 1. An electronic book, comprising: a screen for displaying an electronic book content; at least one first component with at least one underlying link, wherein, upon selection of the at least one first component, the at least one first component links to at least an Internet web site for providing a plurality of streaming video, audio and text data when connected to the electronic book, wherein location information for each of the plurality of streaming video, audio and text data is provided in at least one hidden links table and the at least one hidden links table is provided in conjunction with downloading the content of the electronic book from a remote provider, and each of the at least one hidden links table is associated with the at least one first component with the at least one underlying link, and wherein the location information provides access to the plurality of streaming video, audio and text data, and wherein the at least one first component is a part of a content of the electronic book; and a control function wherein the control function allows selection of one or more of the plurality of streaming video, audio and text data while displaying the content of the electronic book, and wherein the selected data is displayed on the screen of the electronic book, wherein the hidden links table is updatable from a most current links table using information transmitted via the Internet web site from the remote provider, wherein the most current links table is downloaded to the electronic book either periodically by the remote provider, or when a new electronic book content is downloaded to the electronic book by the remote provider, and wherein the control function includes an on-screen show links button, upon selection of which a link menu is displayed on the screen of the electronic book along with the content of the electronic book, wherein the link menu shows all of the first components with the underlying links contained in the content of the electronic book displayed on the screen of the electronic book, and shows linked materials including a number of links, link numbers and descriptions of the linked materials that each of the first components with the underlying links is able to be linked to, such that, by choosing one of the links, a user is able to link to one of the linked materials. 4. The electronic book of claim 1 , wherein the electronic book is adapted to be displayed on a personal computer. | 0.849604 |
8,983,900 | 1 | 6 | 1. A system for combining online transactional processing and online analytical processing in an in-memory database, comprising: the in-memory database that is configured to execute a method comprising: retrieving two or more tables from an online transaction processing system; identifying related tables among the two or more tables; determining, by a hardware processor, relationships between the related tables, wherein determining the relationships comprises analyzing metadata of the related tables; determining a measure based on the relationships; and outputting the measure. | 1. A system for combining online transactional processing and online analytical processing in an in-memory database, comprising: the in-memory database that is configured to execute a method comprising: retrieving two or more tables from an online transaction processing system; identifying related tables among the two or more tables; determining, by a hardware processor, relationships between the related tables, wherein determining the relationships comprises analyzing metadata of the related tables; determining a measure based on the relationships; and outputting the measure. 6. The system of claim 1 , wherein the outputting of the measure comprises: associating the measure with a subset of columns and/or rows from the related tables and outputting the measure with the subset of columns and/or rows. | 0.718362 |
7,477,697 | 13 | 15 | 13. The method according to claim 12 , wherein the physical transmission constraint is determined by assessing if the relationship B C >>N/T is fulfilled, wherein B C is the coherence bandwidth, N is the number of data symbols per data word and T is the duration of one of the data symbols. | 13. The method according to claim 12 , wherein the physical transmission constraint is determined by assessing if the relationship B C >>N/T is fulfilled, wherein B C is the coherence bandwidth, N is the number of data symbols per data word and T is the duration of one of the data symbols. 15. The method according to claim 13 , wherein, when the physical transmission constraint B C >>N/T is at least approximately fulfilled, the data words of data blocks relating to the preamble are multiplexed in space-time and the data words of data blocks relating to the user data sequence are multiplexed in space-frequency. | 0.849353 |
8,355,903 | 40 | 41 | 40. The apparatus as recited in claim 32 , wherein the processor is further configured to (1) identify a plurality of the angle data structures for the narrative story based at least in part on a plurality of the angle data structures that are associated with the applicability conditions deemed applicable to the received domain related data and information, (2) prioritize the plurality of identified angle data structures, and (3) automatically render a narrative story that is descriptive of the at least one member as influenced by the thematic natures of the identified angle data structures and in accordance with the prioritization of those identified angle data structures. | 40. The apparatus as recited in claim 32 , wherein the processor is further configured to (1) identify a plurality of the angle data structures for the narrative story based at least in part on a plurality of the angle data structures that are associated with the applicability conditions deemed applicable to the received domain related data and information, (2) prioritize the plurality of identified angle data structures, and (3) automatically render a narrative story that is descriptive of the at least one member as influenced by the thematic natures of the identified angle data structures and in accordance with the prioritization of those identified angle data structures. 41. The apparatus as recited in claim 40 , wherein each of the angle data structures are further associated with an importance value, and wherein the processor is further configured to prioritize the plurality of identified angle data structures based at least in part on their associated importance values. | 0.878943 |
9,448,974 | 3 | 4 | 3. The server of claim 1 , wherein the document types include a word processing type, a spreadsheet type, and a slideshow type. | 3. The server of claim 1 , wherein the document types include a word processing type, a spreadsheet type, and a slideshow type. 4. The server of claim 3 , wherein the word processing type is associated with Word™ XML documents, wherein the spreadsheet type is associated with Excel™ XML documents, and wherein the slideshow type is associated with PowerPoint™ XML documents. | 0.869427 |
10,152,473 | 4 | 5 | 4. The English input method of claim 1 , wherein looking up a target English word having a length between the original length and the target length includes: assigning the original length of an input English character string to a pre-configured intermediate variable; determining whether the intermediate variable is greater than the target length; when the intermediate variable is not greater than the target length, retrieving English words having a length equal to the intermediate variable from a pre-configured English word database to obtain the target English words; and incrementing the intermediate variable by one and proceeding to the step of determining whether the value stored in the intermediate variable is greater than the target length. | 4. The English input method of claim 1 , wherein looking up a target English word having a length between the original length and the target length includes: assigning the original length of an input English character string to a pre-configured intermediate variable; determining whether the intermediate variable is greater than the target length; when the intermediate variable is not greater than the target length, retrieving English words having a length equal to the intermediate variable from a pre-configured English word database to obtain the target English words; and incrementing the intermediate variable by one and proceeding to the step of determining whether the value stored in the intermediate variable is greater than the target length. 5. The English input method of claim 4 , wherein: the pre-configured English word database includes one or more segments; English words in each segment have a same length; and the English words in each segment are sorted according to a frequency of use. | 0.922106 |
8,214,215 | 13 | 16 | 13. A method comprising the following computer-executable acts: receiving a distorted speech utterance, the distorted speech utterance comprising additive distortion and convolutive distortion; utilizing a first model to jointly output estimates of the additive distortion and the convolutive distortion, the first model being a nonlinear phase-sensitive model that jointly models additive distortion and convolutive distortion in speech utterances; updating parameters of a Hidden Markov Model based at least in part upon the estimates of the additive distortion and the convolutive distortion in the speech utterance; utilizing the Hidden Markov Model with updated parameters to recognize at least one word in the received speech utterance; and performing a computing task responsive to the at least one word in the received speech utterance being recognized. | 13. A method comprising the following computer-executable acts: receiving a distorted speech utterance, the distorted speech utterance comprising additive distortion and convolutive distortion; utilizing a first model to jointly output estimates of the additive distortion and the convolutive distortion, the first model being a nonlinear phase-sensitive model that jointly models additive distortion and convolutive distortion in speech utterances; updating parameters of a Hidden Markov Model based at least in part upon the estimates of the additive distortion and the convolutive distortion in the speech utterance; utilizing the Hidden Markov Model with updated parameters to recognize at least one word in the received speech utterance; and performing a computing task responsive to the at least one word in the received speech utterance being recognized. 16. The method of claim 13 , further comprising linearizing output of a first model using a vector Taylor series first order approximation. | 0.930221 |
8,762,289 | 3 | 13 | 3. A computer-implemented method for training a human searcher for use with a computer, comprising: consulting a searcher database to monitor a busy status of a searcher; determining whether the searcher is a pro searcher when the searcher status indicates that the searcher is idle; determining whether an apprentice searcher is logged-in; receiving a training request from a master searcher; alerting said apprentice searcher, a pro searcher, or a master searcher to which training is to be provided with a message to participate in query training said alerting emulating processing of a user request; obtaining a training query from a query database at a searcher ranking and matching keywords to which said apprentice searcher, said pro searcher, or said master searcher is assigned; receiving from said apprentice searcher, said pro searcher, or said master searcher, an acceptance of the training query as the user request; determining whether the training query has been previously provided to said apprentice searcher, said pro searcher, or said master searcher; transmitting the training query to emulate a request from a user to said apprentice searcher, said pro searcher, or said master searcher when the searcher is selected to participate in query training when determining that the training query has not been previously provided to said apprentice searcher, said pro searcher, or said master searcher; comparing search results produced by said apprentice searcher, said pro searcher, or said master searcher to previously provided search results; assigning a grade to said apprentice searcher, said pro searcher, or said master searcher; sending feedback to said apprentice searcher, said pro searcher, or said master searcher regarding performance when determining that a user provides feedback for the user request; marking the training query as completed for said apprentice searcher, said pro searcher, or said master searcher; determining whether ranking of said apprentice searcher, said pro searcher, or said master searcher requires adjustment; and adjusting rank of said apprentice searcher, said pro searcher, or said master searcher based on said determining. | 3. A computer-implemented method for training a human searcher for use with a computer, comprising: consulting a searcher database to monitor a busy status of a searcher; determining whether the searcher is a pro searcher when the searcher status indicates that the searcher is idle; determining whether an apprentice searcher is logged-in; receiving a training request from a master searcher; alerting said apprentice searcher, a pro searcher, or a master searcher to which training is to be provided with a message to participate in query training said alerting emulating processing of a user request; obtaining a training query from a query database at a searcher ranking and matching keywords to which said apprentice searcher, said pro searcher, or said master searcher is assigned; receiving from said apprentice searcher, said pro searcher, or said master searcher, an acceptance of the training query as the user request; determining whether the training query has been previously provided to said apprentice searcher, said pro searcher, or said master searcher; transmitting the training query to emulate a request from a user to said apprentice searcher, said pro searcher, or said master searcher when the searcher is selected to participate in query training when determining that the training query has not been previously provided to said apprentice searcher, said pro searcher, or said master searcher; comparing search results produced by said apprentice searcher, said pro searcher, or said master searcher to previously provided search results; assigning a grade to said apprentice searcher, said pro searcher, or said master searcher; sending feedback to said apprentice searcher, said pro searcher, or said master searcher regarding performance when determining that a user provides feedback for the user request; marking the training query as completed for said apprentice searcher, said pro searcher, or said master searcher; determining whether ranking of said apprentice searcher, said pro searcher, or said master searcher requires adjustment; and adjusting rank of said apprentice searcher, said pro searcher, or said master searcher based on said determining. 13. The computer-implemented method of claim 3 , further comprising, after said alerting, presenting said searcher with a notification to initiate a search for a response to the query. | 0.613445 |
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