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8,650,517 | 1 | 4 | 1. Within a system comprising a processor and a memory, a method of automatically documenting a circuit design, the method comprising: determining an assignment of a user comment entity of a high level modeling system (HLMS) circuit design to an HLMS block of the HLMS circuit design; wherein the user comment entity specifies explanatory text that is not transformable into hardware, each HLMS block of the HLMS circuit design represents a particular circuit function, and the HLMS circuit design includes signal flows between HLMS blocks; translating each HLMS block and the signal flows of the HLMS circuit design into a hardware description language representation of the particular circuit function of the HLMS block; for the HLMS block assigned the user comment entity, inserting within the hardware description language representation, by the processor, text of the user comment entity that is assigned to the HLMS block as a hardware description language comment; and storing, within the memory, the hardware description language representations of the HLMS blocks. | 1. Within a system comprising a processor and a memory, a method of automatically documenting a circuit design, the method comprising: determining an assignment of a user comment entity of a high level modeling system (HLMS) circuit design to an HLMS block of the HLMS circuit design; wherein the user comment entity specifies explanatory text that is not transformable into hardware, each HLMS block of the HLMS circuit design represents a particular circuit function, and the HLMS circuit design includes signal flows between HLMS blocks; translating each HLMS block and the signal flows of the HLMS circuit design into a hardware description language representation of the particular circuit function of the HLMS block; for the HLMS block assigned the user comment entity, inserting within the hardware description language representation, by the processor, text of the user comment entity that is assigned to the HLMS block as a hardware description language comment; and storing, within the memory, the hardware description language representations of the HLMS blocks. 4. The method of claim 1 , wherein determining an assignment comprises: for the user comment entity, determining a candidate set comprising each HLMS block within a predetermined distance of the user comment entity as determined from a visualization of the HLMS circuit design; removing each HLMS block from the candidate set that does not have a block name matching a portion of text of the user comment entity; and assigning the user comment entity to each HLMS block of the candidate set. | 0.69197 |
9,799,051 | 1 | 4 | 1. A method for uploading media onto a media sharing platform, the method comprising: generating a marketer channel on the media sharing platform; a marketer receiving media from a client, wherein the marketer is a third party to the client, and wherein the client is the source of the media either directly or via an advertisement agency; the marketer authoring a unique keyword, wherein the unique keyword is not associated with any media on the media sharing platform, and wherein the marketer performs an iterative search on a plurality of media sharing platforms with various potential keywords until a unique keyword is identified across all the plurality of media sharing platforms; uploading, using a processor, the media to the media sharing platform; including the unique keyword into at least one of a title and description fields for the uploaded media; the marketer ensuring that the tag field is empty; and incorporating the unique keyword into at least one marketing campaign discrete from the media sharing platform. | 1. A method for uploading media onto a media sharing platform, the method comprising: generating a marketer channel on the media sharing platform; a marketer receiving media from a client, wherein the marketer is a third party to the client, and wherein the client is the source of the media either directly or via an advertisement agency; the marketer authoring a unique keyword, wherein the unique keyword is not associated with any media on the media sharing platform, and wherein the marketer performs an iterative search on a plurality of media sharing platforms with various potential keywords until a unique keyword is identified across all the plurality of media sharing platforms; uploading, using a processor, the media to the media sharing platform; including the unique keyword into at least one of a title and description fields for the uploaded media; the marketer ensuring that the tag field is empty; and incorporating the unique keyword into at least one marketing campaign discrete from the media sharing platform. 4. The method of claim 1 , wherein the media sharing platform is YouTube. | 0.883758 |
8,131,768 | 1 | 8 | 1. A computer implemented method for analyzing data-flow using program expressions or terms, comprising: a. extracting a control flow graph node from a work list; b. representing a symbolic state as a condition-map pair (C, σ) where C denotes a current path condition predicate and σ denotes a map from program variables to symbolic values; c. merging symbolic term values at join nodes to avoid path-explosion by using choose and if-then-else (ite) function operators, where choose is a term of form choose ((C 1 ,t 1 ),(C 2 ,t 2 ),(C 3 ,t 3 )) on expression sort with a non-deterministic choice between the values t 1 (1≦i≦3) given a corresponding condition C 1 ; d. performing simplification of term values using rewriting logic, wherein a set of rules for simplifying choose and ite terms are used along with rules for simplifying Presburger arithmetic expressions; e. determining successors of the graph node to which data must be propagated; f. updating symbolic data for elements of the successors; g. performing anti-unification to generalize similar terms obtained at a loop head; and h. displaying the program analysis for code review. | 1. A computer implemented method for analyzing data-flow using program expressions or terms, comprising: a. extracting a control flow graph node from a work list; b. representing a symbolic state as a condition-map pair (C, σ) where C denotes a current path condition predicate and σ denotes a map from program variables to symbolic values; c. merging symbolic term values at join nodes to avoid path-explosion by using choose and if-then-else (ite) function operators, where choose is a term of form choose ((C 1 ,t 1 ),(C 2 ,t 2 ),(C 3 ,t 3 )) on expression sort with a non-deterministic choice between the values t 1 (1≦i≦3) given a corresponding condition C 1 ; d. performing simplification of term values using rewriting logic, wherein a set of rules for simplifying choose and ite terms are used along with rules for simplifying Presburger arithmetic expressions; e. determining successors of the graph node to which data must be propagated; f. updating symbolic data for elements of the successors; g. performing anti-unification to generalize similar terms obtained at a loop head; and h. displaying the program analysis for code review. 8. The method of claim 1 , comprising simplifying terms at loop nodes using anti-unification. | 0.721557 |
4,697,242 | 26 | 33 | 26. An adaptive computing system for processing input information from an external source into output information delivered to external utilization means comprising, in combination, a message memory for storing a plurality of messages, each of said messages comprising a binary sequence of digits, a classifier memory for storing a plurality of classifiers each having a condition part, an action part and a strength value, said condition part specifying the attributes of a class of messages in said message memory which are to be translated into output messages in accordance with information contained in said action part, means establishing a sequence of major machine cycles, processing means operative during each of said major cycles for generating an output message whenever the condition part of any of said classifiers is satisfied by one or more messages in said message memory, means for replacing the messages present in said message memory at the start of a given major cycle with the output messages generated during said given major cycle, input message handling means connected to said external source for placing input messages into said message memory prior to at least one of said major cycles, output message handling means for selecting output messages having predetermined desired characteristics from said message memory and delivering said output messages to said external utilization means, and means for increasing the strength value of each classifier which generates a message which, during the next major cycle, is delivered to said external utilization means or causes the generation of a further output message by satisfying the condition part of a classifier. | 26. An adaptive computing system for processing input information from an external source into output information delivered to external utilization means comprising, in combination, a message memory for storing a plurality of messages, each of said messages comprising a binary sequence of digits, a classifier memory for storing a plurality of classifiers each having a condition part, an action part and a strength value, said condition part specifying the attributes of a class of messages in said message memory which are to be translated into output messages in accordance with information contained in said action part, means establishing a sequence of major machine cycles, processing means operative during each of said major cycles for generating an output message whenever the condition part of any of said classifiers is satisfied by one or more messages in said message memory, means for replacing the messages present in said message memory at the start of a given major cycle with the output messages generated during said given major cycle, input message handling means connected to said external source for placing input messages into said message memory prior to at least one of said major cycles, output message handling means for selecting output messages having predetermined desired characteristics from said message memory and delivering said output messages to said external utilization means, and means for increasing the strength value of each classifier which generates a message which, during the next major cycle, is delivered to said external utilization means or causes the generation of a further output message by satisfying the condition part of a classifier. 33. An adaptive computing system as set forth in claim 26 including means for replacing classifiers having lower relative strength values with substitute classifiers. | 0.752239 |
8,244,692 | 11 | 13 | 11. A non-transitory computer-readable medium, with instructions stored thereon, which when executed by a computer processor of a computing device cause the computing device to: receive a selection of a compressed file object within a page description language document file present on the computing device, the compressed file object including a compressed file within a compressed archive included in the page description language document file, the compressed file object further including an object definition identifier within a header of the compressed file; read data of the page description language document file following the object definition identifier of the compressed file object until an end-of-object identifier is reached; and opening the compressed file object from the page description language document file. | 11. A non-transitory computer-readable medium, with instructions stored thereon, which when executed by a computer processor of a computing device cause the computing device to: receive a selection of a compressed file object within a page description language document file present on the computing device, the compressed file object including a compressed file within a compressed archive included in the page description language document file, the compressed file object further including an object definition identifier within a header of the compressed file; read data of the page description language document file following the object definition identifier of the compressed file object until an end-of-object identifier is reached; and opening the compressed file object from the page description language document file. 13. The non-transitory computer-readable medium of claim 11 , wherein the instructions to open the compressed file object include further instructions, which when executed by the computer processor of the computing device cause the computing device to: send a file open request to an operating system with reference to the compressed file. | 0.501471 |
8,732,151 | 10 | 14 | 10. One or more computer storage media having a system embodied thereon including computer-executable instructions that, when executed, perform a method for identifying query rewriting replacement terms, the system comprising: an intake component that receives a list of related string pairs, each pair comprising a first string and a second string, wherein the first string of each related string pair is a user search query extracted from user click log data, the user click log data including one or more search query sessions including at least one dwell time associated with one or more search results and a number of links clicked; a statistical machine translation model that, for one or more of the related string pairs: receives the string pair as inputs, identifies one or more pairs of corresponding terms, each pair of corresponding terms including a first term from the first string and a second term from the second string, and calculates a probability of relatedness for each of the one or more pairs of corresponding terms; a characterization component that, upon determining that the probability of relatedness of a pair of corresponding terms calculated by the statistical machine translation model exceeds a threshold, characterizes the second term as a query term replacement for the first term; and a candidate term population component that, for each pair of corresponding terms for which the calculated probability of relatedness exceeds a threshold, incorporates the first term, the second term, and the probability of relatedness for the pair into a query rewriting candidate database. | 10. One or more computer storage media having a system embodied thereon including computer-executable instructions that, when executed, perform a method for identifying query rewriting replacement terms, the system comprising: an intake component that receives a list of related string pairs, each pair comprising a first string and a second string, wherein the first string of each related string pair is a user search query extracted from user click log data, the user click log data including one or more search query sessions including at least one dwell time associated with one or more search results and a number of links clicked; a statistical machine translation model that, for one or more of the related string pairs: receives the string pair as inputs, identifies one or more pairs of corresponding terms, each pair of corresponding terms including a first term from the first string and a second term from the second string, and calculates a probability of relatedness for each of the one or more pairs of corresponding terms; a characterization component that, upon determining that the probability of relatedness of a pair of corresponding terms calculated by the statistical machine translation model exceeds a threshold, characterizes the second term as a query term replacement for the first term; and a candidate term population component that, for each pair of corresponding terms for which the calculated probability of relatedness exceeds a threshold, incorporates the first term, the second term, and the probability of relatedness for the pair into a query rewriting candidate database. 14. The media of claim 10 , wherein the second string of each related string pair is identified by analyzing the user click log data. | 0.797872 |
9,928,244 | 1 | 9 | 1. A method comprising: analyzing an electronic document to generate document identifying data; classifying the electronic document in one or more display categories by applying a classification rule to the document identifying data, wherein the classification of the electronic document represents a prioritization of the electronic document; displaying the classified electronic document in the one of the one or more display categories in which it was classified; receiving a user feedback regarding prioritization of the electronic document; and updating the classification rule based on the feedback from the user, wherein analyzing the electronic document further comprises analyzing the document using semantical analysis of the document comprising, associating one or more concepts with one or more display categories, extracting the one or more concepts from the electronic document, and pattern matching the one or more extracted concepts with the one or more concepts associated with the one or more display categories. | 1. A method comprising: analyzing an electronic document to generate document identifying data; classifying the electronic document in one or more display categories by applying a classification rule to the document identifying data, wherein the classification of the electronic document represents a prioritization of the electronic document; displaying the classified electronic document in the one of the one or more display categories in which it was classified; receiving a user feedback regarding prioritization of the electronic document; and updating the classification rule based on the feedback from the user, wherein analyzing the electronic document further comprises analyzing the document using semantical analysis of the document comprising, associating one or more concepts with one or more display categories, extracting the one or more concepts from the electronic document, and pattern matching the one or more extracted concepts with the one or more concepts associated with the one or more display categories. 9. The method of claim 1 , wherein analyzing the electronic document includes analyzing at least one of (i) an electronic document recipient's address, (ii) metadata attached to the electronic document, (iii) a title of the electronic document, (iv) content attached to the electronic document, and (v) content of the electronic document. | 0.634989 |
6,023,701 | 6 | 7 | 6. A storage medium including machine readable indicia, said storage medium being selectively coupled to a reading device, said reading device being selectively coupled to processing circuitry within a computer system, said reading device being selectively operable to read said machine readable indicia and provide program signals representative thereof, said program signals being effective to cause said computer system to selectively provide a listing of hyperlinks assembled from a designated network page, said program signals being selectively operable to effect an accomplishment of the steps of: presenting a selection screen to the user whereby the user may make a selection to provide a listing of hyperlinks included on at least one designated network page; enabling a user to select a level value representative of a level of linked pages to be perused in providing said listing of hyperlinks; and displaying said listing of hyperlinks to the user. | 6. A storage medium including machine readable indicia, said storage medium being selectively coupled to a reading device, said reading device being selectively coupled to processing circuitry within a computer system, said reading device being selectively operable to read said machine readable indicia and provide program signals representative thereof, said program signals being effective to cause said computer system to selectively provide a listing of hyperlinks assembled from a designated network page, said program signals being selectively operable to effect an accomplishment of the steps of: presenting a selection screen to the user whereby the user may make a selection to provide a listing of hyperlinks included on at least one designated network page; enabling a user to select a level value representative of a level of linked pages to be perused in providing said listing of hyperlinks; and displaying said listing of hyperlinks to the user. 7. The storage medium as set forth in claim 6 wherein said listing of hyperlinks comprises hyperlinks present on said designated network page listed separately from, and exclusive of, the context displayed on said designated network page. | 0.775895 |
8,458,192 | 11 | 12 | 11. A computer-implemented system for determining topical interest comprising: a non-transitory memory; and said system configured to: receive signal information for a user of a document, the information including at least one signal value representing the user's activity with or relationship to the document and a signal weight for each signal value; compute a normalized signal value for each of the at least one signal values; compute a document interest value based on a sum of products of each normalized signal value and the signal weight associated with each signal value for the user; receive topic information for the document, the information including at least one topic and a weight for each topic, where the topic relates to content of the document, and the weight represents a level of confidence that the topic is associated with the document; and generate an update to an interest signature value for the user of a first topic by adding a product of the computed document interest value for the user for the document and a weight of the first topic in the document. | 11. A computer-implemented system for determining topical interest comprising: a non-transitory memory; and said system configured to: receive signal information for a user of a document, the information including at least one signal value representing the user's activity with or relationship to the document and a signal weight for each signal value; compute a normalized signal value for each of the at least one signal values; compute a document interest value based on a sum of products of each normalized signal value and the signal weight associated with each signal value for the user; receive topic information for the document, the information including at least one topic and a weight for each topic, where the topic relates to content of the document, and the weight represents a level of confidence that the topic is associated with the document; and generate an update to an interest signature value for the user of a first topic by adding a product of the computed document interest value for the user for the document and a weight of the first topic in the document. 12. The system of claim 11 , further configured to: generate an update to an interest signature value of a second topic for the user by adding a product of the computed document interest value for the user for the document and a weight of the second topic in the document. | 0.682984 |
8,629,940 | 9 | 16 | 9. A media device, comprising: a program content stream interface configured to receive a program content stream with a program of interest therein, wherein the received program of interest includes at least a video portion, a native language audio portion and a foreign language audio portion, wherein the native language audio portion and the foreign language audio portion each correspond to the video portion; a remote control signal detector configured to detect a remote control communication signal, wherein the detected remote control communication signal is transmitted from a remote control, wherein the remote control communication signal operates the media device, and wherein the remote control communication signal includes a unique identifier identifying the transmitting remote control; and a processor system communicatively coupled to the program content stream interface and the remote control signal detector, and wherein the processor system is configured to: determine a language preference that is at least one of a native language preference and a foreign language preference associated with the transmitting remote control, wherein the determined language preference is based upon the unique identifier in the detected remote control communication signal; communicate the video portion and the native language audio portion to a media presentation device for presentation to a user of the remote control in response to determining that the language preference is for the native language preference; and communicate the video portion and the foreign language audio portion to the media presentation device for presentation to the user of the remote control in response to determining that the language preference is for the foreign language preference. | 9. A media device, comprising: a program content stream interface configured to receive a program content stream with a program of interest therein, wherein the received program of interest includes at least a video portion, a native language audio portion and a foreign language audio portion, wherein the native language audio portion and the foreign language audio portion each correspond to the video portion; a remote control signal detector configured to detect a remote control communication signal, wherein the detected remote control communication signal is transmitted from a remote control, wherein the remote control communication signal operates the media device, and wherein the remote control communication signal includes a unique identifier identifying the transmitting remote control; and a processor system communicatively coupled to the program content stream interface and the remote control signal detector, and wherein the processor system is configured to: determine a language preference that is at least one of a native language preference and a foreign language preference associated with the transmitting remote control, wherein the determined language preference is based upon the unique identifier in the detected remote control communication signal; communicate the video portion and the native language audio portion to a media presentation device for presentation to a user of the remote control in response to determining that the language preference is for the native language preference; and communicate the video portion and the foreign language audio portion to the media presentation device for presentation to the user of the remote control in response to determining that the language preference is for the foreign language preference. 16. The media device of claim 9 , wherein the remote control is a first remote control having a first unique identifier that is used by the processor system to identify the language preference of the first remote control as the native language, wherein the remote control communication signal is a first remote control communication signal transmitted from the first remote control that is received at the remote control signal detector, wherein the remote control signal detector is further configured to receive a second remote control communication signal transmitted from a second remote control, wherein the transmitted second remote control communication signal includes a second unique identifier that is used by the processor system to identify the language preference of the second remote control as the foreign language preference, wherein response to receiving further transmitted remote control communication signals from the first remote control, the video portion and the native language audio portion are communicated, and wherein response to receiving the further transmitted remote control communication signals from the second remote control, the video portion and the foreign language audio portion are communicated. | 0.664037 |
9,711,141 | 38 | 39 | 38. A non-transitory computer-readable storage medium comprising instructions for causing one or more processors to: receive, from a user, a speech input containing a heteronym and one or more additional words; process the speech input using an automatic speech recognition system to determine a phonemic string corresponding to the heteronym as pronounced by the user in the speech input; generate a dialogue response to the speech input, wherein the dialogue response includes the heteronym; and output the dialogue response as a speech output, wherein the heteronym in the dialogue response is pronounced in the speech output according to the phonemic string. | 38. A non-transitory computer-readable storage medium comprising instructions for causing one or more processors to: receive, from a user, a speech input containing a heteronym and one or more additional words; process the speech input using an automatic speech recognition system to determine a phonemic string corresponding to the heteronym as pronounced by the user in the speech input; generate a dialogue response to the speech input, wherein the dialogue response includes the heteronym; and output the dialogue response as a speech output, wherein the heteronym in the dialogue response is pronounced in the speech output according to the phonemic string. 39. The computer-readable storage medium of claim 38 , wherein outputting the dialogue response includes synthesizing the heteronym in the dialogue response using a speech synthesizer, and wherein the dialogue response is synthesized based on the phonemic string. | 0.896701 |
8,718,383 | 17 | 18 | 17. A computer system comprising: a data store comprising a plurality of image index files for generating similarity scores; a search engine configured to receive an image query for a digital image; and an analyzer for calculating an image index file for the digital image, comparing the image index file for the digital image with at least one of the plurality of image index files to generate a similarity score representative of a variable degree of non-matching between the digital image and a digital image corresponding to the at least one image index file of the plurality of image index files, and comparing the similarity score to a similarity threshold score to determine whether to filter the digital image wherein the database further comprises a monosemantic descriptor for each image index file, and the analyzer being further for calculating a monosemantic descriptor for the digital image from the webpage, and comparing the monosemantic descriptor for the digital image from the webpage with the monosemantic descriptor for each image index file in the database before the comparing of the image index file for the digital image from the webpage with at least one image index file in the database; wherein the calculating of the monosemantic descriptor comprises: dividing the digital image from the webpage into a plurality of cells; calculating an image descriptor for each of the plurality of cells; and aligning the image descriptor for each of the plurality of cells to calculate the monosemantic descriptor. | 17. A computer system comprising: a data store comprising a plurality of image index files for generating similarity scores; a search engine configured to receive an image query for a digital image; and an analyzer for calculating an image index file for the digital image, comparing the image index file for the digital image with at least one of the plurality of image index files to generate a similarity score representative of a variable degree of non-matching between the digital image and a digital image corresponding to the at least one image index file of the plurality of image index files, and comparing the similarity score to a similarity threshold score to determine whether to filter the digital image wherein the database further comprises a monosemantic descriptor for each image index file, and the analyzer being further for calculating a monosemantic descriptor for the digital image from the webpage, and comparing the monosemantic descriptor for the digital image from the webpage with the monosemantic descriptor for each image index file in the database before the comparing of the image index file for the digital image from the webpage with at least one image index file in the database; wherein the calculating of the monosemantic descriptor comprises: dividing the digital image from the webpage into a plurality of cells; calculating an image descriptor for each of the plurality of cells; and aligning the image descriptor for each of the plurality of cells to calculate the monosemantic descriptor. 18. The computer system of claim 17 wherein the plurality of image index files are representative of pornographic digital images. | 0.670918 |
8,548,973 | 1 | 3 | 1. A method comprising: extracting metadata attributes and associated attribute values from web search results, the web search results returned in response to a search request submitted by a user from a computer of the user to a web search engine, the search request comprising search criteria input by the user to the search engine, the search request input by the user using a user interface to the computer, the web search results comprising entries organized into a results list, each entry comprising data extracted from a data object searched by the web search engine and meeting the search criteria, the metadata attributes and associated attribute values extracted from the data objects corresponding to the entries of the results list, each metadata attribute comprising one or more associated attribute values, each metadata attribute comprising a category, each associated attribute value comprising value that corresponding to an associated metadata attribute, wherein the search request of the user does not include, in the search request, the metadata attribute and associated attribute values returned by the web search engine; prioritizing one or more of the extracted metadata attributes and the attribute values, wherein one or more of: the metadata attributes are prioritized based on a number of times attribute values of each metadata attribute occurs in the results list, wherein the metadata attributes are organized so a metadata attribute with a largest number of attribute value occurrences is displayed first in the display of metadata attributes to the user; and the metadata attributes are prioritized based on a number of occurrences for each metadata attribute and wherein presenting the metadata attributes to the user further comprises presenting a subset of metadata attributes to the user, the subset comprising metadata attributes occurring most often in the results list; presenting the prioritized extracted metadata attributes to a user for selection by the user, the prioritized extracted metadata attributes presented to the user on a portion of an electronic display displaying the web search results; receiving input from the user indicating a selected metadata attribute of the metadata attributes; presenting attribute values associated with the selected metadata attribute to the user for selection by the user, the extracted attribute values of the selected metadata attribute are presented to the user on a portion of an electronic display displaying the web search results; receiving input from the user indicating a selected attribute value of the attribute values associated with the selected metadata attribute; filtering the web search results based on the selected attribute value, wherein each entry in the filtered web search results comprises the selected attribute value; and displaying a filtered results list to the user, the filtered results list comprising the filtered web search results. | 1. A method comprising: extracting metadata attributes and associated attribute values from web search results, the web search results returned in response to a search request submitted by a user from a computer of the user to a web search engine, the search request comprising search criteria input by the user to the search engine, the search request input by the user using a user interface to the computer, the web search results comprising entries organized into a results list, each entry comprising data extracted from a data object searched by the web search engine and meeting the search criteria, the metadata attributes and associated attribute values extracted from the data objects corresponding to the entries of the results list, each metadata attribute comprising one or more associated attribute values, each metadata attribute comprising a category, each associated attribute value comprising value that corresponding to an associated metadata attribute, wherein the search request of the user does not include, in the search request, the metadata attribute and associated attribute values returned by the web search engine; prioritizing one or more of the extracted metadata attributes and the attribute values, wherein one or more of: the metadata attributes are prioritized based on a number of times attribute values of each metadata attribute occurs in the results list, wherein the metadata attributes are organized so a metadata attribute with a largest number of attribute value occurrences is displayed first in the display of metadata attributes to the user; and the metadata attributes are prioritized based on a number of occurrences for each metadata attribute and wherein presenting the metadata attributes to the user further comprises presenting a subset of metadata attributes to the user, the subset comprising metadata attributes occurring most often in the results list; presenting the prioritized extracted metadata attributes to a user for selection by the user, the prioritized extracted metadata attributes presented to the user on a portion of an electronic display displaying the web search results; receiving input from the user indicating a selected metadata attribute of the metadata attributes; presenting attribute values associated with the selected metadata attribute to the user for selection by the user, the extracted attribute values of the selected metadata attribute are presented to the user on a portion of an electronic display displaying the web search results; receiving input from the user indicating a selected attribute value of the attribute values associated with the selected metadata attribute; filtering the web search results based on the selected attribute value, wherein each entry in the filtered web search results comprises the selected attribute value; and displaying a filtered results list to the user, the filtered results list comprising the filtered web search results. 3. The method of claim 1 , further comprising: displaying a display object of an expandable tree structure in response to receiving the web search results; and receiving a selection of the display object of the expandable tree structure from the user, wherein presenting the extracted metadata attributes to the user is in response to receiving the selection of the display object from the user. | 0.878909 |
7,761,461 | 36 | 37 | 36. A computer-readable medium containing a program which, when executed by a processor, performs a process of querying physical data logically represented by a data abstraction model, wherein the physical data being queried is contained in data structures generated from a data source having a different schema from the data structures containing the physical data being queried, the process comprising: receiving an abstract query comprising logical fields and corresponding values, wherein each of the logical fields is defined in the data abstraction model and wherein one or more of the logical fields are result fields to be returned by execution of the abstract query; and transforming, by operation of a processor, the abstract query into an executable query capable of being executed against the physical data; wherein the transforming is done using the data abstraction model and wherein the data abstraction model defines a specific path for traversing the data structures containing the physical data to reach the one or more result fields. | 36. A computer-readable medium containing a program which, when executed by a processor, performs a process of querying physical data logically represented by a data abstraction model, wherein the physical data being queried is contained in data structures generated from a data source having a different schema from the data structures containing the physical data being queried, the process comprising: receiving an abstract query comprising logical fields and corresponding values, wherein each of the logical fields is defined in the data abstraction model and wherein one or more of the logical fields are result fields to be returned by execution of the abstract query; and transforming, by operation of a processor, the abstract query into an executable query capable of being executed against the physical data; wherein the transforming is done using the data abstraction model and wherein the data abstraction model defines a specific path for traversing the data structures containing the physical data to reach the one or more result fields. 37. The computer-readable medium of claim 36 , wherein the specific path is derived from relationships in the data source. | 0.896959 |
8,832,088 | 1 | 5 | 1. A computer-implemented method comprising: receiving a search result obtained in response to a query, wherein the search result identifies a resource and has an associated score S; computing a recent impression probability for the query for a recent time period and an overall impression probability for the query for an overall time period, wherein each impression probability corresponds to a ratio of (i) a count of search result impressions selected by users to (ii) a count of all search result impressions presented to users in the respective time periods, wherein the search result impressions were impressions provided in response to the query by a search engine during the respective time period, and wherein the overall time period is a time period that began before the recent time period and is longer than the recent time period; computing a QtoA ratio of the recent impression probability to the overall impression probability; determining that users prefer newer resources over older resources for the query based on the QtoA ratio; determining that the resource is a new resource; and associating a new score S′ with the resource in place of S, wherein the new score S′ signifies a better result than the score S signifies, based on determining, that users prefer newer resources over older resources for the query and that the resource is a new resource. | 1. A computer-implemented method comprising: receiving a search result obtained in response to a query, wherein the search result identifies a resource and has an associated score S; computing a recent impression probability for the query for a recent time period and an overall impression probability for the query for an overall time period, wherein each impression probability corresponds to a ratio of (i) a count of search result impressions selected by users to (ii) a count of all search result impressions presented to users in the respective time periods, wherein the search result impressions were impressions provided in response to the query by a search engine during the respective time period, and wherein the overall time period is a time period that began before the recent time period and is longer than the recent time period; computing a QtoA ratio of the recent impression probability to the overall impression probability; determining that users prefer newer resources over older resources for the query based on the QtoA ratio; determining that the resource is a new resource; and associating a new score S′ with the resource in place of S, wherein the new score S′ signifies a better result than the score S signifies, based on determining, that users prefer newer resources over older resources for the query and that the resource is a new resource. 5. The method of claim 1 , wherein S′ has a boosted value that is higher relative to boosted scores of other resources that are older than the resource. | 0.880315 |
8,341,610 | 5 | 6 | 5. A method as in claim 4 , further comprising using the flowchart in a problem determination engine. | 5. A method as in claim 4 , further comprising using the flowchart in a problem determination engine. 6. A method as in claim 5 , further comprising: gathering information from operation of the problem determination engine; and providing the gathered information for use in interactively repeating the authoring, generating and testing steps. | 0.91157 |
8,760,498 | 12 | 15 | 12. A television receiver device that processes disparity data for closed captions, comprising: circuitry configured to receive closed caption data including closed caption text within a first Standard service block associated with a first Standard caption service having a service number in the range of 1-6; receive closed caption disparity data within a second Standard service block associated with a second Standard caption service having a service number equal to n, where n is between 1 and 6; parse the disparity data from the second Standard caption service having the service number n, the disparity data including a linkage field which associates said disparity data with the first Standard caption service; and process the caption text and the disparity data to produce an output suitable for defining a display of the caption text. | 12. A television receiver device that processes disparity data for closed captions, comprising: circuitry configured to receive closed caption data including closed caption text within a first Standard service block associated with a first Standard caption service having a service number in the range of 1-6; receive closed caption disparity data within a second Standard service block associated with a second Standard caption service having a service number equal to n, where n is between 1 and 6; parse the disparity data from the second Standard caption service having the service number n, the disparity data including a linkage field which associates said disparity data with the first Standard caption service; and process the caption text and the disparity data to produce an output suitable for defining a display of the caption text. 15. The device according to claim 12 , further comprising a three dimensional television display configured to display the closed caption text as a stereoscopic image produced by the compositor. | 0.63806 |
9,342,489 | 7 | 8 | 7. The method of claim 4 wherein the phrase is identified as a critical phrase if the probability determined for the phrase is above a predetermined percentage of the probabilities determined for all phrases identified. | 7. The method of claim 4 wherein the phrase is identified as a critical phrase if the probability determined for the phrase is above a predetermined percentage of the probabilities determined for all phrases identified. 8. The method of claim 7 wherein the predetermined percentage is 85%. | 0.97406 |
8,046,353 | 18 | 20 | 18. The search database system of claim 11 , further comprising: a data receiving module coupled with the hierarchical database engine, the data receiving module to receive data that is to be merged into the hierarchical database and forward the received data to the hierarchical database engine to allow the received data to be merged into the hierarchical database. | 18. The search database system of claim 11 , further comprising: a data receiving module coupled with the hierarchical database engine, the data receiving module to receive data that is to be merged into the hierarchical database and forward the received data to the hierarchical database engine to allow the received data to be merged into the hierarchical database. 20. The search database system of claim 18 , wherein the data receiving module is to merge data upon receipt of a single merge query that has syntax configured to insert the received data into the hierarchical database and update the data in the hierarchical database with the received data, wherein the received data is updated upon determining that the received data changes values of the data stored in the hierarchical database. | 0.928854 |
8,903,824 | 1 | 11 | 1. A method for processing a random-walk based vertex-proximity query on a graph, wherein the method comprises: dividing a graph of vertices into multiple clusters that are each estimated to contain a random walk for a duration of time, wherein said dividing comprises dividing the graph of vertices into multiple clusters based on a conductance value computed for each of the multiple clusters, and wherein the conductance value for each of the multiple clusters is defined as ϕ ( S ) = ∂ ( S ) min { VOL ( S ) , VOL ( S _ ) } ∈ [ 0 , 1 ] , wherein VOL ( S ) = ∑ v ∈ S d v wherein d v denotes the degree of vertex v and ∂(s) denotes the set of edges with exactly one end-point in cluster S; computing meta-information for each of the multiple clusters, wherein the meta-information is related to the random walk inside each of the multiple clusters from the graph; dynamically updating the multiple clusters and corresponding meta-information upon modification of the graph; identifying a subset of one or more clusters from the multiple updated clusters that are relevant to a query vertex; and processing a nearest-neighbor query on the subset of one or more identified clusters by: pre-computing an intra-cluster random walk for each of the multiple clusters prior to the nearest-neighbor query; identifying the pre-computed intra-cluster random walk for each cluster in the subset of one or more identified clusters subsequent to the nearest-neighbor query; and computing a stationary distribution of an inter-cluster random walk across the subset of one or more identified clusters starting from the query vertex subsequent to the nearest-neighbor query, wherein said computing the stationary distribution of the inter-cluster random walk comprises combining the computed intra-cluster random walk for each cluster in the subset of one or more identified clusters; wherein at least one of the steps is carried out by a computer device. | 1. A method for processing a random-walk based vertex-proximity query on a graph, wherein the method comprises: dividing a graph of vertices into multiple clusters that are each estimated to contain a random walk for a duration of time, wherein said dividing comprises dividing the graph of vertices into multiple clusters based on a conductance value computed for each of the multiple clusters, and wherein the conductance value for each of the multiple clusters is defined as ϕ ( S ) = ∂ ( S ) min { VOL ( S ) , VOL ( S _ ) } ∈ [ 0 , 1 ] , wherein VOL ( S ) = ∑ v ∈ S d v wherein d v denotes the degree of vertex v and ∂(s) denotes the set of edges with exactly one end-point in cluster S; computing meta-information for each of the multiple clusters, wherein the meta-information is related to the random walk inside each of the multiple clusters from the graph; dynamically updating the multiple clusters and corresponding meta-information upon modification of the graph; identifying a subset of one or more clusters from the multiple updated clusters that are relevant to a query vertex; and processing a nearest-neighbor query on the subset of one or more identified clusters by: pre-computing an intra-cluster random walk for each of the multiple clusters prior to the nearest-neighbor query; identifying the pre-computed intra-cluster random walk for each cluster in the subset of one or more identified clusters subsequent to the nearest-neighbor query; and computing a stationary distribution of an inter-cluster random walk across the subset of one or more identified clusters starting from the query vertex subsequent to the nearest-neighbor query, wherein said computing the stationary distribution of the inter-cluster random walk comprises combining the computed intra-cluster random walk for each cluster in the subset of one or more identified clusters; wherein at least one of the steps is carried out by a computer device. 11. The method of claim 1 , wherein dynamically updating the corresponding meta-information upon modification of the graph comprises providing at least one low-rank update to one or more matrices comprising the corresponding meta-information. | 0.65625 |
8,069,028 | 14 | 15 | 14. The handheld electronic device of claim 13 , wherein the output of the plurality of language objects and the indicator is a visual output. | 14. The handheld electronic device of claim 13 , wherein the output of the plurality of language objects and the indicator is a visual output. 15. The handheld electronic device of claim 14 , wherein the output of the plurality of language objects and the indicator is disposed in a window on the display. | 0.95547 |
9,800,705 | 13 | 14 | 13. The non-transitory computer storage medium of claim 12 , wherein the online application is a specific social media application that is currently active on the first device. | 13. The non-transitory computer storage medium of claim 12 , wherein the online application is a specific social media application that is currently active on the first device. 14. The non-transitory computer storage medium of claim 13 , wherein the instructions to cause the data processing apparatus to assign the received activity status to the current activity status comprise instructions to cause the data processing apparatus to assign the activity status to indicate the first device is active on the specific social media application. | 0.840453 |
8,442,822 | 8 | 11 | 8. A non-transitory machine-readable medium comprising a plurality of instructions which when executed result in a system cause a machine to perform one or more operations comprising: determining a fuzzy rule to discriminate a speech segment from a non-speech segment, wherein an antecedent of the fuzzy rule includes an input variable indicating a characteristic of media data and an input variable membership, and wherein a consequent of the fuzzy rule includes an output variable indicating a likelihood of the media data being speech and an output variable membership; extracting an instance of the input variable from a segment; training an input variable membership function associated with the input variable membership and an output variable membership function associated with the output variable membership; operating the instance of the input variable, the input variable membership function, the output variable, and the output variable membership function, to determine whether the segment is the speech segment or the non-speech segment; fuzzifying the input variable based upon the instance of the input variable and the input variable membership function, to provide a fuzzified input indicating a first degree that the input variable belongs to the input variable membership; reshaping the output variable membership function based upon the fuzzified input, to provide an output set indicating a group of a second degree that the output variable belongs to the output variable membership; defuzzifying the output set to provide a defuzzified output; labeling whether the segment is the speech segment or the non-speech segment based upon the defuzzied output; finding a centroid of the output set to provide the defuzzified output, if the fuzzy rule comprises one rule; multiplying each of a plurality of weights with the output set obtained through each of the plurality of rules, to provide each of a plurality of weighted output sets, if the fuzzy rule comprises a plurality of rules; and aggregating the plurality of weighted output sets to provide an output union; and finding a centroid of the output union to provide the defuzzied output. | 8. A non-transitory machine-readable medium comprising a plurality of instructions which when executed result in a system cause a machine to perform one or more operations comprising: determining a fuzzy rule to discriminate a speech segment from a non-speech segment, wherein an antecedent of the fuzzy rule includes an input variable indicating a characteristic of media data and an input variable membership, and wherein a consequent of the fuzzy rule includes an output variable indicating a likelihood of the media data being speech and an output variable membership; extracting an instance of the input variable from a segment; training an input variable membership function associated with the input variable membership and an output variable membership function associated with the output variable membership; operating the instance of the input variable, the input variable membership function, the output variable, and the output variable membership function, to determine whether the segment is the speech segment or the non-speech segment; fuzzifying the input variable based upon the instance of the input variable and the input variable membership function, to provide a fuzzified input indicating a first degree that the input variable belongs to the input variable membership; reshaping the output variable membership function based upon the fuzzified input, to provide an output set indicating a group of a second degree that the output variable belongs to the output variable membership; defuzzifying the output set to provide a defuzzified output; labeling whether the segment is the speech segment or the non-speech segment based upon the defuzzied output; finding a centroid of the output set to provide the defuzzified output, if the fuzzy rule comprises one rule; multiplying each of a plurality of weights with the output set obtained through each of the plurality of rules, to provide each of a plurality of weighted output sets, if the fuzzy rule comprises a plurality of rules; and aggregating the plurality of weighted output sets to provide an output union; and finding a centroid of the output union to provide the defuzzied output. 11. The machine readable medium of claim 8 , wherein the input variable comprises at least one variable selected from a group of percentage of low-energy frames (LEFP), high zero-crossing rate ratio (HZGRR), variance of spectral centroid (SGV), variance of spectral flux (SFV), variance of spectral roll-off point (SRPV) and 4 Hz modulation energy (4 Hz). | 0.501404 |
9,524,305 | 4 | 5 | 4. The method of claim 3 , wherein the received query is modified upon determining that the value predefined to represent NULL values would be returned by applying the extended comparison operator to the column data, the method further comprising: executing the modified query. | 4. The method of claim 3 , wherein the received query is modified upon determining that the value predefined to represent NULL values would be returned by applying the extended comparison operator to the column data, the method further comprising: executing the modified query. 5. The method of claim 4 , wherein the value predefined to represent NULL values is the largest value capable of being stored in the column data, wherein searching the set of column values comprises identifying column values matching at least one of: (i) the value predefined to represent NULL values, (ii) a value between a specified value in the first predicate and the value predefined to represent NULL values, and (iii) the specified value in the first predicate, wherein the extended comparison operator comprises each individual extended operator selected from the group comprising: (i) an extended greater than operator, (ii) an extended equals operator, and (iii) an extended greater than or equals operator. | 0.719484 |
8,543,384 | 1 | 2 | 1. A word recognition system for recognizing an input signal entered by a user via a shorthand-on-keyboard interface, the system comprising: a core lexicon comprising commonly used words, wherein the commonly used words were selected for the core lexicon based on an associated frequency of use value for each word being above a pre-determined threshold, and wherein the frequency of use values are not based on candidate selections by the user; an extended lexicon comprising words not contained in the core lexicon; a recognition module for recognizing words associated with the input signal; a selector module for outputting an output word associated with the input signal from the core lexicon; and a module for admitting, from the extended lexicon to the core lexicon, a candidate word associated with the input signal, upon a first selection of the candidate word by the user, to create an augmented core lexicon. | 1. A word recognition system for recognizing an input signal entered by a user via a shorthand-on-keyboard interface, the system comprising: a core lexicon comprising commonly used words, wherein the commonly used words were selected for the core lexicon based on an associated frequency of use value for each word being above a pre-determined threshold, and wherein the frequency of use values are not based on candidate selections by the user; an extended lexicon comprising words not contained in the core lexicon; a recognition module for recognizing words associated with the input signal; a selector module for outputting an output word associated with the input signal from the core lexicon; and a module for admitting, from the extended lexicon to the core lexicon, a candidate word associated with the input signal, upon a first selection of the candidate word by the user, to create an augmented core lexicon. 2. The system of claim 1 , further comprising a user selection interface presenting candidate words associated with the input signal from at least one of the core lexicon and the extended lexicon, for selection by the user. | 0.849324 |
9,897,983 | 7 | 8 | 7. The method as claimed in claim 6 , wherein shortest hyperpaths are determined by taking account of two preferences. | 7. The method as claimed in claim 6 , wherein shortest hyperpaths are determined by taking account of two preferences. 8. The method as claimed in claim 7 , wherein the shortest hyperpaths are determined by taking account of two preferences by a label correction algorithm. | 0.964236 |
8,832,015 | 1 | 5 | 1. A computer-implemented method for identifying data files that have a common characteristic, the method comprising: receiving a plurality of data files, the plurality of data files including one or more data files having the common characteristic; generating, using one or more processors, a list that includes key terms from the plurality of data files; using the list to generate a rule set, the rule set being generated using the one or more processors by: generating a potential rule by selecting one or more key terms from the list that satisfy a term evaluation metric; evaluating the potential rule using a rule evaluation metric configured to determine a relevancy of the potential rule to the one or more data files having the common characteristic, the rule evaluation metric being further configured to determine an applicability of the potential rule to data not included in the plurality of data files; adding the potential rule to the rule set if the rule evaluation metric is satisfied; based upon the potential rule being added to the rule set, removing data files covered by the potential rule from the plurality of data files; and repeating the potential rule generation and evaluation until a stopping criterion is met; and after the stopping criterion has been met, identifying with the rule set, other data files that have the common characteristic using the one or more processors. | 1. A computer-implemented method for identifying data files that have a common characteristic, the method comprising: receiving a plurality of data files, the plurality of data files including one or more data files having the common characteristic; generating, using one or more processors, a list that includes key terms from the plurality of data files; using the list to generate a rule set, the rule set being generated using the one or more processors by: generating a potential rule by selecting one or more key terms from the list that satisfy a term evaluation metric; evaluating the potential rule using a rule evaluation metric configured to determine a relevancy of the potential rule to the one or more data files having the common characteristic, the rule evaluation metric being further configured to determine an applicability of the potential rule to data not included in the plurality of data files; adding the potential rule to the rule set if the rule evaluation metric is satisfied; based upon the potential rule being added to the rule set, removing data files covered by the potential rule from the plurality of data files; and repeating the potential rule generation and evaluation until a stopping criterion is met; and after the stopping criterion has been met, identifying with the rule set, other data files that have the common characteristic using the one or more processors. 5. The method of claim 1 , wherein the common characteristic is a category or a classification. | 0.914875 |
5,473,367 | 21 | 22 | 21. The method of claim 20 wherein the indicators are switches. | 21. The method of claim 20 wherein the indicators are switches. 22. The method of claim 21 wherein a user of chair person video terminal is an instructor and users of the video terminals are students. | 0.973186 |
9,111,151 | 1 | 16 | 1. A NoC processor comprising: multiple cores, each of the cores is assigned with an addressing string with L based-D words, and the addressing string does not have two neighboring identical words, wherein L present of an addressing string length is an integer larger than 1, D present of a word selection is an integer larger than 2; and a Kautz NoC, wherein each of the cores is unidirectionally link to other (D−1) cores through the Kautz NoC, and in the two connected cores, the last (L−1) words associated with the addressing string of one core are same as the first (L−1) words associated with the addressing string of the other core. | 1. A NoC processor comprising: multiple cores, each of the cores is assigned with an addressing string with L based-D words, and the addressing string does not have two neighboring identical words, wherein L present of an addressing string length is an integer larger than 1, D present of a word selection is an integer larger than 2; and a Kautz NoC, wherein each of the cores is unidirectionally link to other (D−1) cores through the Kautz NoC, and in the two connected cores, the last (L−1) words associated with the addressing string of one core are same as the first (L−1) words associated with the addressing string of the other core. 16. The NoC processor according to claim 1 , wherein a packet routed between the cores comprises a control flag, a target address, an instruction, and data, and the control flag is present of a priority of the packet. | 0.880638 |
8,280,823 | 65 | 66 | 65. The method of claim 60 , wherein the required term of experience is rounded up to a unit of time. | 65. The method of claim 60 , wherein the required term of experience is rounded up to a unit of time. 66. The method of claim 65 , wherein the unit of time is a number of seconds, minutes, hours, days, weeks, months, years, or decades. | 0.969773 |
8,633,838 | 21 | 22 | 21. A computer-implemented method of compressing data, comprising carrying out steps of the computer-implemented method by a computer system with at least a processor and a memory, the computer-implemented steps of the method including: a) receiving a current instance of data in memory comprising in an input buffer; b) receiving at least one dedupe exclude range defining a portion of the input buffer to be excluded from dedupe compression, wherein the dedupe exclude range logically partitions the input buffer into at least one dedupe view range, wherein dedupe compression processing is a substitution of a dedupe item for occurrences of a recurring range of data, wherein the dedupe item identifies the location of a prior occurrence of the recurring range within at least one of a representation of the current instance of data and a representation of a prior instance of data in the input buffer; and c) performing by the processor the following steps for each selected dedupe view range eligible for dedupe compression: i) performing a repeat pattern recognition (RPR) compression on the selected dedupe view range to store a corresponding RPR processed range at a next available position in a reference log; and ii) performing a dedupe compression on each RPR processed range to store a corresponding dedupe processed range at a next available position in a temporary buffer; and iii) making the reference log available for communication to a target computer. | 21. A computer-implemented method of compressing data, comprising carrying out steps of the computer-implemented method by a computer system with at least a processor and a memory, the computer-implemented steps of the method including: a) receiving a current instance of data in memory comprising in an input buffer; b) receiving at least one dedupe exclude range defining a portion of the input buffer to be excluded from dedupe compression, wherein the dedupe exclude range logically partitions the input buffer into at least one dedupe view range, wherein dedupe compression processing is a substitution of a dedupe item for occurrences of a recurring range of data, wherein the dedupe item identifies the location of a prior occurrence of the recurring range within at least one of a representation of the current instance of data and a representation of a prior instance of data in the input buffer; and c) performing by the processor the following steps for each selected dedupe view range eligible for dedupe compression: i) performing a repeat pattern recognition (RPR) compression on the selected dedupe view range to store a corresponding RPR processed range at a next available position in a reference log; and ii) performing a dedupe compression on each RPR processed range to store a corresponding dedupe processed range at a next available position in a temporary buffer; and iii) making the reference log available for communication to a target computer. 22. The computer-implemented method of claim 21 further comprising: b) (i) storing the selected dedupe view range as a NODEDUPE item at a next available position of a temporary buffer for each selected dedupe view range ineligible for dedupe compression. | 0.774423 |
7,752,079 | 1 | 22 | 1. A system, comprising: an electronic commerce computer system comprising a plurality of computing devices, the computer system including: a first user interface, generated on a first computing device of the computer system, through which buyers place orders over a network with a merchant and view the status of such orders, the first interface including: a first portion for displaying at least a subset of the negotiated terms between a buyer and the merchant for an accepted order, and a second portion including: a message field for a buyer to generate and submit to the merchant messages that are linked to particular orders, and a display of at least a subset of a record of at least one message previously exchanged between the buyer and the merchant linked to the accepted order, wherein the at least one message is separate from the negotiated terms between the buyer and the merchant for an accepted order; and a second user interface, generated on a second computing device of the computer system, through which the merchant views accepted orders, order-specific messages from buyers, and status information of accepted orders, the second interface including a message field for the merchant to generate and submit messages to the buyers that are linked to specific accepted orders. | 1. A system, comprising: an electronic commerce computer system comprising a plurality of computing devices, the computer system including: a first user interface, generated on a first computing device of the computer system, through which buyers place orders over a network with a merchant and view the status of such orders, the first interface including: a first portion for displaying at least a subset of the negotiated terms between a buyer and the merchant for an accepted order, and a second portion including: a message field for a buyer to generate and submit to the merchant messages that are linked to particular orders, and a display of at least a subset of a record of at least one message previously exchanged between the buyer and the merchant linked to the accepted order, wherein the at least one message is separate from the negotiated terms between the buyer and the merchant for an accepted order; and a second user interface, generated on a second computing device of the computer system, through which the merchant views accepted orders, order-specific messages from buyers, and status information of accepted orders, the second interface including a message field for the merchant to generate and submit messages to the buyers that are linked to specific accepted orders. 22. The system of claim 1 , wherein the second user interface provides functionality for the merchant to modify the order in response to a message from a buyer. | 0.915254 |
7,676,516 | 14 | 16 | 14. The method of claim 13 , wherein the step of using said database network router further includes: (iii) receiving a log-in and connection request from said second client; (iv) reporting said log-in to said database server; and (v) using said database connection for said second client. | 14. The method of claim 13 , wherein the step of using said database network router further includes: (iii) receiving a log-in and connection request from said second client; (iv) reporting said log-in to said database server; and (v) using said database connection for said second client. 16. The method of claim 14 , wherein the step of using said database network router includes: (i) sending at least one query to one of the database servers; and (ii) monitoring a processing of said at least one query by said one database server, said monitoring including receiving information, regarding said at least one query, selected from the group consisting of a CPU time of said database server, a response time of said database server, a read/write load of said database server and an execution load of said database server. | 0.709695 |
7,809,561 | 4 | 5 | 4. The method for verification of speaker authentication according to claim 2 , wherein said parameter is a parameter dependent on said speaker template. | 4. The method for verification of speaker authentication according to claim 2 , wherein said parameter is a parameter dependent on said speaker template. 5. The method for verification of speaker authentication according to claim 4 , wherein said parameter dependent on said speaker template is said discriminating threshold. | 0.926038 |
8,761,499 | 7 | 8 | 7. The system of claim 1 , wherein the at least one processor is further configured to: determine harmfulness of the input video segments; analyze occurrence information of the harmful input video segments based on the harmfulness determination results of the input video segments; and finally determine harmfulness of the input video based on the analyzed occurrence information of the harmful input video segments and the generated global harmfulness determination policy. | 7. The system of claim 1 , wherein the at least one processor is further configured to: determine harmfulness of the input video segments; analyze occurrence information of the harmful input video segments based on the harmfulness determination results of the input video segments; and finally determine harmfulness of the input video based on the analyzed occurrence information of the harmful input video segments and the generated global harmfulness determination policy. 8. The system of claim 7 , wherein the at least one processor is further configured to determine the harmfulness of the input video segments by using a hash-based discrimination method. | 0.954701 |
9,442,899 | 1 | 10 | 1. An image forming apparatus comprising: a scanner that obtains an image file by document scanning; a character recognition processor that obtains a text string from each line of text by performing character recognition on the image file, wherein each text string respectively corresponds to one line of text in the image file; a text string splitter that splits each text string into a plurality of short text strings in accordance with a predetermined rule, wherein at least one of the plurality of short text strings which form one text string corresponding to one line of text in the image file comprises a plurality of characters; a font size determining portion that determines a uniform font size for each text string such that the plurality of short text strings, which form the text string and which include the at least one short text string comprising the plurality of characters, have the same uniform font size; a position determining portion that determines an x-axis position for each of the short text strings to be embedded in the image file, based on x-coordinates of characters at a forefront of the respective short text strings, the short text strings including the at least one short string text string comprising the plurality of characters, wherein an x-axis of each short text string is aligned along forward and backward reading directions; and an embedding portion that embeds text data of the short text strings in the image file at the respective determined x-axis positions in the determined uniform font size for the entire text string. | 1. An image forming apparatus comprising: a scanner that obtains an image file by document scanning; a character recognition processor that obtains a text string from each line of text by performing character recognition on the image file, wherein each text string respectively corresponds to one line of text in the image file; a text string splitter that splits each text string into a plurality of short text strings in accordance with a predetermined rule, wherein at least one of the plurality of short text strings which form one text string corresponding to one line of text in the image file comprises a plurality of characters; a font size determining portion that determines a uniform font size for each text string such that the plurality of short text strings, which form the text string and which include the at least one short text string comprising the plurality of characters, have the same uniform font size; a position determining portion that determines an x-axis position for each of the short text strings to be embedded in the image file, based on x-coordinates of characters at a forefront of the respective short text strings, the short text strings including the at least one short string text string comprising the plurality of characters, wherein an x-axis of each short text string is aligned along forward and backward reading directions; and an embedding portion that embeds text data of the short text strings in the image file at the respective determined x-axis positions in the determined uniform font size for the entire text string. 10. The image forming apparatus as recited in claim 1 , wherein the font size determining portion defines the font size for the text string as a common font size for the short text strings. | 0.88143 |
8,370,119 | 17 | 18 | 17. The system of claim 13 , wherein the program module for modeling the website design patterns based on the occurrences of layout elements and URL tokens, comprises a sub-module for modeling the website design patterns using a coupled Special Word with Background (SWB) modeling technique. | 17. The system of claim 13 , wherein the program module for modeling the website design patterns based on the occurrences of layout elements and URL tokens, comprises a sub-module for modeling the website design patterns using a coupled Special Word with Background (SWB) modeling technique. 18. The system of claim 17 , wherein the sub-module for modeling the website design patterns using the coupled SWB modeling technique, comprises a sub-module for identifying global functional patterns, page-specific patterns and common background patterns of the website. | 0.875 |
6,029,131 | 7 | 11 | 7. A synthetic speech system comprising: means for storing speech to be synthesized in a computer memory; means for a processor to read said speech from said computer memory and for said processor to detect natural timing boundaries in words to be spoken by said synthetic speech system, to produce natural timing intervals; means for identifying phonemes in said natural timing intervals; means for assigning first time durations for each of said phonemes; means for changing a selected first time duration of a selected phoneme to achieve a desired time duration for a selected natural timing interval containing said selected phoneme; means for setting a plurality of said natural timing intervals to substantially the same second time duration, a particular phoneme having a computed time duration in response to number of phonemes within said selected natural timing interval and said second time duration; and means for applying said synthesized speech to an electromechanical acoustic coupler to make audible speech; wherein respective time durations of at least certain respective phonemes are based upon respective selectable parameters indicative of respective degrees to which said respective time durations may be adjusted without undesirably degrading speech produced by said system. | 7. A synthetic speech system comprising: means for storing speech to be synthesized in a computer memory; means for a processor to read said speech from said computer memory and for said processor to detect natural timing boundaries in words to be spoken by said synthetic speech system, to produce natural timing intervals; means for identifying phonemes in said natural timing intervals; means for assigning first time durations for each of said phonemes; means for changing a selected first time duration of a selected phoneme to achieve a desired time duration for a selected natural timing interval containing said selected phoneme; means for setting a plurality of said natural timing intervals to substantially the same second time duration, a particular phoneme having a computed time duration in response to number of phonemes within said selected natural timing interval and said second time duration; and means for applying said synthesized speech to an electromechanical acoustic coupler to make audible speech; wherein respective time durations of at least certain respective phonemes are based upon respective selectable parameters indicative of respective degrees to which said respective time durations may be adjusted without undesirably degrading speech produced by said system. 11. The system as in claim 7 wherein said computer memory is a computer disk. | 0.784916 |
9,997,160 | 5 | 6 | 5. The method of claim 1 , wherein the speech recognition component comprises a stored launch command for launching the application via an audible user launch command. | 5. The method of claim 1 , wherein the speech recognition component comprises a stored launch command for launching the application via an audible user launch command. 6. The method of claim 5 , further comprising: loading the stored launch command; receiving the audible user launch command to launch the application; determining whether the audible user launch command relates to the application; and launching the application, based on the audible user launch command. | 0.924401 |
8,046,222 | 8 | 14 | 8. A system comprising: one or more computers, and one or more storage devices storing instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising: receiving a probability of an n-gram identifying a word; determining a number of atomic units in the n-gram; identifying a scaling weight depending on the number of atomic units in the n-gram; and applying the scaling weight to the probability of the n-gram identifying a word to determine a scaled probability of the n-gram identifying a word. | 8. A system comprising: one or more computers, and one or more storage devices storing instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising: receiving a probability of an n-gram identifying a word; determining a number of atomic units in the n-gram; identifying a scaling weight depending on the number of atomic units in the n-gram; and applying the scaling weight to the probability of the n-gram identifying a word to determine a scaled probability of the n-gram identifying a word. 14. The system of claim 8 , the operations further comprising: determining scaled probabilities of lesser order n-grams identifying words, the lesser order n-grams being derived from the n-gram; and removing the n-gram from a dictionary when a scaled probability of a combination of lesser order n-grams identifying a word differs from the scaled probability of the n-gram identifying a word by a specified threshold amount. | 0.680241 |
8,965,751 | 1 | 6 | 1. A method executed at least in part by a computing device for translating content feed data, the method comprising: a processor of a social networking server receiving a formatted content having an activity event from a content feed provider; the processor receiving a language specific request through an input device; the processor determining a preferred language associated with a client device; the processor translating portions of the formatted content to a language specific string by matching the activity event to an activity template from an activity template lookup table, wherein alternative meanings of the translated portions are encompassed by including additional language independent tag value pairs within a multi value template; and the processor transmitting the translated language specific string to the client device. | 1. A method executed at least in part by a computing device for translating content feed data, the method comprising: a processor of a social networking server receiving a formatted content having an activity event from a content feed provider; the processor receiving a language specific request through an input device; the processor determining a preferred language associated with a client device; the processor translating portions of the formatted content to a language specific string by matching the activity event to an activity template from an activity template lookup table, wherein alternative meanings of the translated portions are encompassed by including additional language independent tag value pairs within a multi value template; and the processor transmitting the translated language specific string to the client device. 6. The method of claim 1 , wherein the preferred language is manually determined based on a user selection. | 0.9538 |
9,384,188 | 1 | 6 | 1. A computer-implemented method comprising: executing speech recognition on a captured input and generating lexicalized hypotheses from the captured input; normalizing the lexicalized hypotheses to generate normalized hypotheses for the lexicalized hypotheses, wherein a normalized hypothesis comprising one or more tokens; generating a confusion network comprising token representations of normalized hypotheses, wherein each arc of the confusion network represents a token of a normalized hypothesis; and transforming the generated confusion network into a multi-arc confusion network, wherein the transforming comprising realigning at least one token of the confusion network to span multiples arcs of the confusion network. | 1. A computer-implemented method comprising: executing speech recognition on a captured input and generating lexicalized hypotheses from the captured input; normalizing the lexicalized hypotheses to generate normalized hypotheses for the lexicalized hypotheses, wherein a normalized hypothesis comprising one or more tokens; generating a confusion network comprising token representations of normalized hypotheses, wherein each arc of the confusion network represents a token of a normalized hypothesis; and transforming the generated confusion network into a multi-arc confusion network, wherein the transforming comprising realigning at least one token of the confusion network to span multiples arcs of the confusion network. 6. The computer-implemented method according to claim 1 , wherein: the generating of lexicalized hypotheses further comprises generating an acoustic score for each of the lexicalized hypotheses, the generating of the normalized hypotheses further comprises rescoring the acoustic score for each of the lexicalized hypotheses to generate a score for each of the normalized hypotheses by applying a token language model to acoustic scores of the lexicalized hypotheses. | 0.58079 |
9,176,668 | 1 | 12 | 1. A method for inputting text, comprising: at a computer system having one or more processors, a touch screen, and memory storing programs for execution by the one or more processors, displaying a virtual keyboard on the touch screen; detecting user touches on the virtual keyboard; in response to the user touches, generating text input to the computer system and displaying the text input as it is generated; detecting a swipe gesture on the touch screen, the swipe gesture including an initial touchdown point and a direction; determining the direction of the swipe gesture; determining a context based upon the text input, wherein the context includes one of the text input representing a partial word, a complete word, and a sentence; determining a type of the swipe gesture, wherein the type includes one of a single finger swipe gesture, a two finger swipe gesture, and a three finger swipe gesture; and performing a predetermined editing function in relation to the text input based on a respective combination of the determined direction, context and type of the swipe gesture, wherein each of a plurality of different respective combinations is associated with a different editing function and wherein the predetermined editing function is performed upon determination of the combination without further user input. | 1. A method for inputting text, comprising: at a computer system having one or more processors, a touch screen, and memory storing programs for execution by the one or more processors, displaying a virtual keyboard on the touch screen; detecting user touches on the virtual keyboard; in response to the user touches, generating text input to the computer system and displaying the text input as it is generated; detecting a swipe gesture on the touch screen, the swipe gesture including an initial touchdown point and a direction; determining the direction of the swipe gesture; determining a context based upon the text input, wherein the context includes one of the text input representing a partial word, a complete word, and a sentence; determining a type of the swipe gesture, wherein the type includes one of a single finger swipe gesture, a two finger swipe gesture, and a three finger swipe gesture; and performing a predetermined editing function in relation to the text input based on a respective combination of the determined direction, context and type of the swipe gesture, wherein each of a plurality of different respective combinations is associated with a different editing function and wherein the predetermined editing function is performed upon determination of the combination without further user input. 12. The method of claim 1 , further comprising: in response to a sixth distinct combination of the determined direction and type of the swipe gesture, ceasing display of the virtual keyboard and replacing the virtual keyboard with a second keyboard layout that is invisible. | 0.732422 |
7,970,616 | 18 | 19 | 18. The machine-implemented method of claim 14 , wherein: the user interface includes a link to a speed reading user interface, the machine-implemented method further comprising: providing information for presenting the speed reading interface as a result of receiving information with respect to a selection of the link to the speed reading interface, the presented speed reading interface including a text input portal; and providing information for displaying, at a same given location of a display screen and at a given rate, one word or symbol at a time from the text input portal. | 18. The machine-implemented method of claim 14 , wherein: the user interface includes a link to a speed reading user interface, the machine-implemented method further comprising: providing information for presenting the speed reading interface as a result of receiving information with respect to a selection of the link to the speed reading interface, the presented speed reading interface including a text input portal; and providing information for displaying, at a same given location of a display screen and at a given rate, one word or symbol at a time from the text input portal. 19. The machine-implemented method of claim 18 , wherein: the text input portal includes text from the presented textual output to the user, and the displaying of one word or symbol at a time further comprises displaying respective ones of the at least one placeholder symbol corresponding to the replaced at least particular ones of the articles or the adjectives. | 0.813776 |
9,542,477 | 6 | 7 | 6. A system comprising: a first computer comprising a processor executing a master topic model (MTM) computer module, the first computer configured to generate a first term vector identifying a first topic in a plurality of documents in a document corpus; a second computer comprising a processor executing a periodic new model (PNM) computer module, the second computer configured to generate a second term vector identifying a second topic in the plurality of documents in the document corpus; and a third computer comprising a processor executing a change detection computer module, the third computer configured to: (a) link each of the first and second topics across the plurality of documents in the document corpus by matching the first and second topics across the plurality of documents in the document corpus, a link indicating a tag associated with metadata that the first and second topics are each identified in at least one document in the document corpus, (b) assign a relatedness score weight to each of the linked first and second topics based on co-occurrence of each of the linked first and second topics across the plurality of documents in the document corpus, (c) determine whether the first and second linked topics are related across the plurality of documents in the document corpus based at least in part on the relatedness score weight; (d) execute a master topic computer model based on a multi-component extension of latent Dirichlet allocation having a first set of model parameters; and the second computer's processor further executing a periodic new topic module to detect a new topic, the new topic module configuring the second computer to perform a new topic model based on the multi-component extension of latent Dirichlet allocation having a second set of model parameters different from the first set of model parameters. | 6. A system comprising: a first computer comprising a processor executing a master topic model (MTM) computer module, the first computer configured to generate a first term vector identifying a first topic in a plurality of documents in a document corpus; a second computer comprising a processor executing a periodic new model (PNM) computer module, the second computer configured to generate a second term vector identifying a second topic in the plurality of documents in the document corpus; and a third computer comprising a processor executing a change detection computer module, the third computer configured to: (a) link each of the first and second topics across the plurality of documents in the document corpus by matching the first and second topics across the plurality of documents in the document corpus, a link indicating a tag associated with metadata that the first and second topics are each identified in at least one document in the document corpus, (b) assign a relatedness score weight to each of the linked first and second topics based on co-occurrence of each of the linked first and second topics across the plurality of documents in the document corpus, (c) determine whether the first and second linked topics are related across the plurality of documents in the document corpus based at least in part on the relatedness score weight; (d) execute a master topic computer model based on a multi-component extension of latent Dirichlet allocation having a first set of model parameters; and the second computer's processor further executing a periodic new topic module to detect a new topic, the new topic module configuring the second computer to perform a new topic model based on the multi-component extension of latent Dirichlet allocation having a second set of model parameters different from the first set of model parameters. 7. The system of claim 6 , wherein the third computer is further configured to determine one or more differences between the first and second topics to identify at least one new topic and add the at least one new topic as a model parameter to the first computer. | 0.649733 |
5,570,455 | 1 | 2 | 1. In a speech compression system including an analog-to-digital converter, an electronic calculation unit, and an electronic memory device, a method for encoding an analog speech signal comprising the steps of: (a) extracting, using the analog-to-digital converter and the electronic calculation unit, a first sequence of digital samples from said analog speech signal; (b) summing, using the electronic calculation unit, each digital sample in the first sequence with the previous digital samples to obtain a cumulative sum sequence of sample point sums; (c) determining, using the electronic calculation unit, the logarithm of each sample point sum in said sum sequence to obtain a sequence of logarithms; (d) summing, using the electronic calculation unit, each logarithm with the previous logarithms in said sequence of logarithms to obtain a cumulative sequence of summed logarithms; (e) obtaining, using the electronic calculation unit, a scaled average of the summed logarithms from step (d); and (f) storing the scaled average obtained in said (e) step in the electronic memory device; and wherein said scaled average represents said analog speech signal in compressed form. | 1. In a speech compression system including an analog-to-digital converter, an electronic calculation unit, and an electronic memory device, a method for encoding an analog speech signal comprising the steps of: (a) extracting, using the analog-to-digital converter and the electronic calculation unit, a first sequence of digital samples from said analog speech signal; (b) summing, using the electronic calculation unit, each digital sample in the first sequence with the previous digital samples to obtain a cumulative sum sequence of sample point sums; (c) determining, using the electronic calculation unit, the logarithm of each sample point sum in said sum sequence to obtain a sequence of logarithms; (d) summing, using the electronic calculation unit, each logarithm with the previous logarithms in said sequence of logarithms to obtain a cumulative sequence of summed logarithms; (e) obtaining, using the electronic calculation unit, a scaled average of the summed logarithms from step (d); and (f) storing the scaled average obtained in said (e) step in the electronic memory device; and wherein said scaled average represents said analog speech signal in compressed form. 2. The method of claim 1 further comprising the steps of: (g) storing scaled averages representing standardized patterns; (h) performing steps (a), (b), (c) and (d), (e) and (f) on an input analog speech signal to be recognized in order to yield an input scaled average; and (i) comparing the scaled non-linear average obtained in step (h) to the non-linear averages stored in said (e) step to identify a match. | 0.523202 |
9,740,881 | 7 | 13 | 7. A computer system, comprising: one or more processors; and memory including instructions that, when executed by the one or more processors, cause the computer system to at least: analyze a query response, corresponding to a query that has been submitted by a user, to obtain an advertisement targeted at the user, the advertisement being part of the query response; provide the query and the query response to a noise engine configured to generate noise information for the computer system based at least in part on the advertisement, the query comprising information that identifies at least one of the user or the query; receive the noise information from the noise engine, the noise information generated by the noise engine based at least in part on the query and the advertisement, and the noise information comprising a noise schedule with respective query execution times; cause one or more additional queries to be generated based at least in part on the noise information; and cause the one or more additional queries to be transmitted to the search engine according to the respective query execution times of the noise schedule. | 7. A computer system, comprising: one or more processors; and memory including instructions that, when executed by the one or more processors, cause the computer system to at least: analyze a query response, corresponding to a query that has been submitted by a user, to obtain an advertisement targeted at the user, the advertisement being part of the query response; provide the query and the query response to a noise engine configured to generate noise information for the computer system based at least in part on the advertisement, the query comprising information that identifies at least one of the user or the query; receive the noise information from the noise engine, the noise information generated by the noise engine based at least in part on the query and the advertisement, and the noise information comprising a noise schedule with respective query execution times; cause one or more additional queries to be generated based at least in part on the noise information; and cause the one or more additional queries to be transmitted to the search engine according to the respective query execution times of the noise schedule. 13. The computer system of claim 7 , further comprising instructions to: determine the respective query execution times for executing the one or more additional queries; and execute the one or more additional queries in accordance with the respective query execution times. | 0.844 |
9,697,200 | 11 | 15 | 11. A computer-readable storage medium storing computer-executable instructions for updating a domain model in a Conversational Understanding (CU) service, comprising: displaying tools used to create an application that interacts with the CU service; receiving examples of natural language sentences used to interact with the application; receiving one or more additional sentences associated with the natural language sentences, wherein the one or more additional sentences are generated by automatically rephrasing the natural language sentences; detecting a domain, an intent action, and an intent object; displaying the detected domain, the intent action and the intent object; receiving input that labels the examples; and automatically updating models for the CU service based on the received input. | 11. A computer-readable storage medium storing computer-executable instructions for updating a domain model in a Conversational Understanding (CU) service, comprising: displaying tools used to create an application that interacts with the CU service; receiving examples of natural language sentences used to interact with the application; receiving one or more additional sentences associated with the natural language sentences, wherein the one or more additional sentences are generated by automatically rephrasing the natural language sentences; detecting a domain, an intent action, and an intent object; displaying the detected domain, the intent action and the intent object; receiving input that labels the examples; and automatically updating models for the CU service based on the received input. 15. The computer-readable storage medium of claim 11 , further comprising performing slot tagging on the natural language sentences. | 0.807018 |
9,031,493 | 10 | 11 | 10. A computer-implemented method of correlating an electronic book with a narration, the electronic book including text, the method comprising: determining, using one or more processors, a match between a first portion of the narration and a first segment of the text; storing, using one or more processors, instructions for presenting with emphasis the first segment simultaneously with the first portion; determining, using one or more processors, that a second portion of the narration that immediately follows the first portion does not match a second segment of the text that immediately follows the first segment; applying, using one or more processors and responsive to determining that the second portion does not match the second segment, a correlation algorithm to determine an association between the second portion and a component of the electronic book, applying the correlation algorithm comprising: responsive to determining the second portion is a repeat of the first portion, identifying the first segment of the text as the component of the book; responsive to determining the second segment of text is spatially proximate to an illustration, identifying the illustration as the component of the book; responsive to determining the first segment of the text is immediately followed by a chapter break, identifying the chapter break as the component of the book; and responsive to determining the second portion corresponds to a third segment of the text that follows the second segment of the text, identifying the third segment of the text as the component of the book; and storing, using one or more processors, instructions to present with emphasis the component simultaneously with the second portion. | 10. A computer-implemented method of correlating an electronic book with a narration, the electronic book including text, the method comprising: determining, using one or more processors, a match between a first portion of the narration and a first segment of the text; storing, using one or more processors, instructions for presenting with emphasis the first segment simultaneously with the first portion; determining, using one or more processors, that a second portion of the narration that immediately follows the first portion does not match a second segment of the text that immediately follows the first segment; applying, using one or more processors and responsive to determining that the second portion does not match the second segment, a correlation algorithm to determine an association between the second portion and a component of the electronic book, applying the correlation algorithm comprising: responsive to determining the second portion is a repeat of the first portion, identifying the first segment of the text as the component of the book; responsive to determining the second segment of text is spatially proximate to an illustration, identifying the illustration as the component of the book; responsive to determining the first segment of the text is immediately followed by a chapter break, identifying the chapter break as the component of the book; and responsive to determining the second portion corresponds to a third segment of the text that follows the second segment of the text, identifying the third segment of the text as the component of the book; and storing, using one or more processors, instructions to present with emphasis the component simultaneously with the second portion. 11. The method of claim 10 , further comprising: determining a second association between the second portion and a second component of the electronic book responsive to user input; and overwriting the stored instructions with new instructions to present with emphasis the second component simultaneously with the second portion. | 0.781041 |
7,801,759 | 14 | 17 | 14. The system of claim 1 wherein the scoring component receives a characterization of the plurality of attributes and calculates the score based on the characterization, and is configured to communicate the score to the database which stores the score with the information about the concept. | 14. The system of claim 1 wherein the scoring component receives a characterization of the plurality of attributes and calculates the score based on the characterization, and is configured to communicate the score to the database which stores the score with the information about the concept. 17. The system of claim 14 wherein the information about the enterprise development concept further includes the priority assigned to the enterprise development concept and wherein the system further comprises a graphical user interface displays information about concepts in a priority order based on the priority of the concepts and in a score order based on the score of the concepts. | 0.944635 |
9,047,868 | 7 | 12 | 7. A non-transitory computer-readable medium comprising a module configured to execute in one or more processors of a computing device, the module being further configured to: receive text data, wherein the text data originated from a user computing device; construct an n-gram based on the text data; generate, for the n-gram, a hash value, wherein a number of bits of the hash value is based on a privacy setting; determine language model information associated with the hash value; store the hash value and the language model information in an electronic data store; and transmit the hash value and the language model information to a server device. | 7. A non-transitory computer-readable medium comprising a module configured to execute in one or more processors of a computing device, the module being further configured to: receive text data, wherein the text data originated from a user computing device; construct an n-gram based on the text data; generate, for the n-gram, a hash value, wherein a number of bits of the hash value is based on a privacy setting; determine language model information associated with the hash value; store the hash value and the language model information in an electronic data store; and transmit the hash value and the language model information to a server device. 12. The non-transitory computer-readable medium of claim 7 , wherein the network interface module is further configured to receive the text data directly from the user computing device. | 0.88842 |
5,546,575 | 62 | 63 | 62. A database access method, comprising: providing a user access request to a computer system comprising a mass storage memory, a random access memory, a database image stored in the mass storage memory, and a record information table and a translation table stored in the random access memory, the database image comprising a plurality of partitions, the partitions comprising a plurality of subpartitions, the subpartitions comprising a plurality of compacted records, the compacted records comprising a plurality of fields, the fields comprising a plurality of compacted data values, the record information table equating the partitions and subpartitions to a plurality of record type designations, such that a particular one of the partitions comprises a subset of the compacted records all having an identical length and a particular one of the subpartitions comprises a subset of the compacted records all having a particular one of the record type designations specifying a plurality of pack methods dependent on the fields; identifying from the user access request a requested one of the fields and a requested one of the compacted data values; searching the database image for a requested one of the compacted records, such that the contents of the requested field corresponds to the requested compacted data value; and reading the requested compacted record from the database image. | 62. A database access method, comprising: providing a user access request to a computer system comprising a mass storage memory, a random access memory, a database image stored in the mass storage memory, and a record information table and a translation table stored in the random access memory, the database image comprising a plurality of partitions, the partitions comprising a plurality of subpartitions, the subpartitions comprising a plurality of compacted records, the compacted records comprising a plurality of fields, the fields comprising a plurality of compacted data values, the record information table equating the partitions and subpartitions to a plurality of record type designations, such that a particular one of the partitions comprises a subset of the compacted records all having an identical length and a particular one of the subpartitions comprises a subset of the compacted records all having a particular one of the record type designations specifying a plurality of pack methods dependent on the fields; identifying from the user access request a requested one of the fields and a requested one of the compacted data values; searching the database image for a requested one of the compacted records, such that the contents of the requested field corresponds to the requested compacted data value; and reading the requested compacted record from the database image. 63. A database access method according to claim 62, further comprising: preparing a report which specifies the frequency of occurrence of the data values within the field, based on the translation table; and outputting the report to the user. | 0.914849 |
8,990,088 | 1 | 6 | 1. A method to be executed at least in part in a computing device for providing a speech processing technology authoring tool and framework, the method comprising: enabling a user to specify at least two terminals through a user interface of the authoring tool executed by the computing device; enabling the user to create a mapping between the at least two terminals through the user interface of the authoring tool; enabling the user to specify a rule based on the mapping by combining the at least two terminals into list rules; displaying the user specified at least two terminals and the rule on the user interface to provide visual feedback to the user including listing element names, element types, and referencing relationships, displayed items comprising a display form and a spoken form of the user specified at least two terminals and the rule, wherein the specified terminals are reordered independently on the display form and the spoken form side of the displayed items using subscript indicators to allow the user to see a relationship between parts of the rule on either side of the displayed items; enabling the user to specify at least one test scenario for validating the rule; performing the specified test scenario to determine a performance of the rule in a speech processing environment, wherein the specified test scenario is executed within the user interface of the authoring tool to provide immediate feedback to the user; presenting test results including a summary and detailed test results, such that the user is enabled to modify the rule through the user interface; and specifying an ordering of expected test results for validating a weighting of the rule. | 1. A method to be executed at least in part in a computing device for providing a speech processing technology authoring tool and framework, the method comprising: enabling a user to specify at least two terminals through a user interface of the authoring tool executed by the computing device; enabling the user to create a mapping between the at least two terminals through the user interface of the authoring tool; enabling the user to specify a rule based on the mapping by combining the at least two terminals into list rules; displaying the user specified at least two terminals and the rule on the user interface to provide visual feedback to the user including listing element names, element types, and referencing relationships, displayed items comprising a display form and a spoken form of the user specified at least two terminals and the rule, wherein the specified terminals are reordered independently on the display form and the spoken form side of the displayed items using subscript indicators to allow the user to see a relationship between parts of the rule on either side of the displayed items; enabling the user to specify at least one test scenario for validating the rule; performing the specified test scenario to determine a performance of the rule in a speech processing environment, wherein the specified test scenario is executed within the user interface of the authoring tool to provide immediate feedback to the user; presenting test results including a summary and detailed test results, such that the user is enabled to modify the rule through the user interface; and specifying an ordering of expected test results for validating a weighting of the rule. 6. The method of claim 1 , the authoring tool abstracts underlying logic to a graphical user interface (GUI) for enabling specification of terminals, list rules, sequence rules, and grammar rules. | 0.866485 |
8,639,496 | 8 | 13 | 8. A computer-implemented method, comprising: accessing text by a processing system, the text comprising a plurality of words; tagging, by the processing system, each of the plurality of words with one of a plurality of parts of speech (POS) tags; creating, by the processing system, a plurality of tokens, each token comprising one of the plurality of words and its associated POS tag; clustering, by the processing system, one or more of the created tokens into a chunk of tokens, the one or more tokens clustered into the chunk of tokens based on the POS tags of the one or more tokens; and forming, by the processing system, a phrase based on the chunk of tokens, the phrase comprising the words of the one or more tokens clustered into the chunk of tokens. | 8. A computer-implemented method, comprising: accessing text by a processing system, the text comprising a plurality of words; tagging, by the processing system, each of the plurality of words with one of a plurality of parts of speech (POS) tags; creating, by the processing system, a plurality of tokens, each token comprising one of the plurality of words and its associated POS tag; clustering, by the processing system, one or more of the created tokens into a chunk of tokens, the one or more tokens clustered into the chunk of tokens based on the POS tags of the one or more tokens; and forming, by the processing system, a phrase based on the chunk of tokens, the phrase comprising the words of the one or more tokens clustered into the chunk of tokens. 13. The computer-implemented method of claim 8 , further comprising providing a count of the number of the words in the text, the count based on the plurality of created tokens. | 0.788783 |
8,447,753 | 9 | 10 | 9. The computer program product of claim 8 , wherein computer executable program code for receiving a trusted output from a trusted multidimensional expression engine further comprises: computer executable program code for receiving an input stream of complex multidimensional expression queries to form a received input stream of complex multidimensional expression queries; and computer executable program code for processing the received input stream of complex multidimensional expression queries through the trusted multidimensional expression engine. | 9. The computer program product of claim 8 , wherein computer executable program code for receiving a trusted output from a trusted multidimensional expression engine further comprises: computer executable program code for receiving an input stream of complex multidimensional expression queries to form a received input stream of complex multidimensional expression queries; and computer executable program code for processing the received input stream of complex multidimensional expression queries through the trusted multidimensional expression engine. 10. The computer program product of claim 9 , wherein computer executable program code for processing the received input stream of complex multidimensional expression queries through the trusted multidimensional expression engine further comprises: computer executable program code for processing the received input stream of complex multidimensional expression queries through a progressive set of test cases; computer executable program code for determining whether a query of the received input stream fails a semantic test case; computer executable program code responsive to a determination that the query fails the semantic test case, for identifying a failing test case and associated query; and computer executable program code responsive to a determination that the failing test case requires correction to the failing test case, updating the failing test case. | 0.651442 |
9,269,348 | 1 | 2 | 1. A method comprising: receiving a stress pattern for both a language and an accent in the language; detecting, according to the stress pattern, incorrect stress patterns in selected acoustic units representing speech to be synthesized, wherein the selected acoustic units were selected by a separate unit-selection speech synthesizer; performing an analysis of the incorrect stress patterns, wherein the analysis comprises a word level analysis, a phrase level analysis, and a sentence level analysis on the incorrect stress patterns, wherein the word level analysis, the phrase level analysis, and the sentence level analysis are performed in series; and modifying, via a processor and prior to waveform synthesis, the incorrect stress patterns in the selected acoustic units according to the analysis, to yield corrected stress patterns, wherein the corrected stress patterns conform to the stress pattern for the language. | 1. A method comprising: receiving a stress pattern for both a language and an accent in the language; detecting, according to the stress pattern, incorrect stress patterns in selected acoustic units representing speech to be synthesized, wherein the selected acoustic units were selected by a separate unit-selection speech synthesizer; performing an analysis of the incorrect stress patterns, wherein the analysis comprises a word level analysis, a phrase level analysis, and a sentence level analysis on the incorrect stress patterns, wherein the word level analysis, the phrase level analysis, and the sentence level analysis are performed in series; and modifying, via a processor and prior to waveform synthesis, the incorrect stress patterns in the selected acoustic units according to the analysis, to yield corrected stress patterns, wherein the corrected stress patterns conform to the stress pattern for the language. 2. The method of claim 1 , wherein the word level analysis, the phrase level analysis, and the sentence level analysis are performed in parallel. | 0.824879 |
9,773,056 | 1 | 12 | 1. A computer program product having a non-transitory computer-readable medium having computer program logic recorded thereon for locating at least one object in a text input, the computer program product comprising: means for parsing the text input into parsed terms, wherein the text input is a request to search for information in a repository, wherein the means for parsing the text input further comprises means for determining a fractional length, the fractional length indicating a number of term units from a first term unit to a next functional term unit; means for analyzing the parsed terms to locate an object term and a plurality of non-object terms, wherein the object term is a noun and each non-object term is one of a verb, adverb, adjective, pronoun, preposition, conjunction, article, and interjection, wherein at least one object term is placed into an object string and at least one non-object term is placed into one of a plurality of non-object buckets; and means for providing the object string and the plurality of non-object buckets to a search engine, wherein the search engine compares the object string and the non-object buckets with information in the repository. | 1. A computer program product having a non-transitory computer-readable medium having computer program logic recorded thereon for locating at least one object in a text input, the computer program product comprising: means for parsing the text input into parsed terms, wherein the text input is a request to search for information in a repository, wherein the means for parsing the text input further comprises means for determining a fractional length, the fractional length indicating a number of term units from a first term unit to a next functional term unit; means for analyzing the parsed terms to locate an object term and a plurality of non-object terms, wherein the object term is a noun and each non-object term is one of a verb, adverb, adjective, pronoun, preposition, conjunction, article, and interjection, wherein at least one object term is placed into an object string and at least one non-object term is placed into one of a plurality of non-object buckets; and means for providing the object string and the plurality of non-object buckets to a search engine, wherein the search engine compares the object string and the non-object buckets with information in the repository. 12. The computer program product of claim 1 , wherein the search engine compares the object string and the plurality of non-object buckets with information in the repository according to one of an exact match, a simple keyword match, or a grammatical locator. | 0.703661 |
9,275,395 | 2 | 3 | 2. The method of claim 1 , wherein recommending the identified non-social media webpage for engagement in the social media includes recommending material from the identified non-social media webpage for engagement in the social media. | 2. The method of claim 1 , wherein recommending the identified non-social media webpage for engagement in the social media includes recommending material from the identified non-social media webpage for engagement in the social media. 3. The method of claim 2 , wherein the material includes the selected keyword. | 0.962963 |
8,892,495 | 4 | 5 | 4. The programmable environmental controller according to claim 1 , wherein the local area network communicates information with a second programmable environmental controller, comprising a second local area network interface port configured to communicate digital data through the local area network; at least one second climate sensor configured to sense second environmental climate conditions; at least one second movement sensor configured to detect a second movement of an individual in a vicinity of the at least one second movement sensor; and at least one second automated processor configured to receive the sensed second environmental conditions and the detected second movement, to jointly classify a temporal pattern of the sensed second environmental climate conditions and the detected second movement, and to communicate at least one second signal in dependence on the jointly classified pattern. | 4. The programmable environmental controller according to claim 1 , wherein the local area network communicates information with a second programmable environmental controller, comprising a second local area network interface port configured to communicate digital data through the local area network; at least one second climate sensor configured to sense second environmental climate conditions; at least one second movement sensor configured to detect a second movement of an individual in a vicinity of the at least one second movement sensor; and at least one second automated processor configured to receive the sensed second environmental conditions and the detected second movement, to jointly classify a temporal pattern of the sensed second environmental climate conditions and the detected second movement, and to communicate at least one second signal in dependence on the jointly classified pattern. 5. The programmable environmental controller according to claim 4 , wherein the programmable environmental controller and the second programmable environmental controller communicate with each other through the local area network and together interact to at least control a single environmental control system. | 0.805031 |
8,572,488 | 12 | 13 | 12. In a computer-based system, a method of conforming a transcript comprising words and phrases of dialog to a dialog audio track of a time-based media program, the method comprising: receiving an augmented version of the transcript and, for each word or phrase of a plurality of the words and phrases within the transcript, timing information comprising a temporal location within the time-based media program where the word or phrase is spoken in the dialog audio track, and a confidence level indicating a quality of a match between each of the words or phrases from the transcript and words or phrases recognized in their corresponding identified temporal location within the time-based media; receiving the time-based media program; and providing an interactive graphical interface for a user, the graphical interface including a transcript display portion for displaying text from a portion of the transcript and a media display portion, simultaneously displayed with the transcript display portion, for displaying a portion of the time-based media program spanning the identified temporal location corresponding to the displayed text from the portion of the transcript according to the timing information; displaying text from the portion of the transcript in the transcript display portion, wherein the text from the transcript is displayed with a visual attribute corresponding to the confidence levels for matches of the text from the portion of the transcript according to the timing information; in response to a request from the user, playing the portion of the time-based media in the media display portion while the corresponding text and visual attribute corresponding to the confidence levels are displayed in the transcript display portion; and enabling the user to make corrections to the displayed text from the transcript in the transcript display portion so as to have the text match the words or phrases spoken in the dialog audio track while the text from the portion of the transcript and the corresponding visual attribute corresponding to the corresponding confidence level are displayed and the corresponding portion of the time-based media is played. | 12. In a computer-based system, a method of conforming a transcript comprising words and phrases of dialog to a dialog audio track of a time-based media program, the method comprising: receiving an augmented version of the transcript and, for each word or phrase of a plurality of the words and phrases within the transcript, timing information comprising a temporal location within the time-based media program where the word or phrase is spoken in the dialog audio track, and a confidence level indicating a quality of a match between each of the words or phrases from the transcript and words or phrases recognized in their corresponding identified temporal location within the time-based media; receiving the time-based media program; and providing an interactive graphical interface for a user, the graphical interface including a transcript display portion for displaying text from a portion of the transcript and a media display portion, simultaneously displayed with the transcript display portion, for displaying a portion of the time-based media program spanning the identified temporal location corresponding to the displayed text from the portion of the transcript according to the timing information; displaying text from the portion of the transcript in the transcript display portion, wherein the text from the transcript is displayed with a visual attribute corresponding to the confidence levels for matches of the text from the portion of the transcript according to the timing information; in response to a request from the user, playing the portion of the time-based media in the media display portion while the corresponding text and visual attribute corresponding to the confidence levels are displayed in the transcript display portion; and enabling the user to make corrections to the displayed text from the transcript in the transcript display portion so as to have the text match the words or phrases spoken in the dialog audio track while the text from the portion of the transcript and the corresponding visual attribute corresponding to the corresponding confidence level are displayed and the corresponding portion of the time-based media is played. 13. The method of claim 12 , wherein the time-based media includes a video component. | 0.946338 |
10,095,788 | 9 | 11 | 9. The one or more computer storage media of claim 8 , wherein identifying deeplinks for the web page based on the deeplink hierarchy comprises: comparing the search query with one or more keywords associated with each node in the deeplink hierarchy to identify a relevant node in the deeplink hierarchy; and identifying the deeplinks from the relevant node in the deeplink hierarchy. | 9. The one or more computer storage media of claim 8 , wherein identifying deeplinks for the web page based on the deeplink hierarchy comprises: comparing the search query with one or more keywords associated with each node in the deeplink hierarchy to identify a relevant node in the deeplink hierarchy; and identifying the deeplinks from the relevant node in the deeplink hierarchy. 11. The one or more computer storage media of claim 9 , wherein the search result is a paid search result and the deeplinks correspond with monetizable web pages. | 0.954366 |
8,924,923 | 10 | 16 | 10. A method of generating a multi-level test case in an apparatus of generating a multi-level test case from a unified modeling language (UML) sequence diagram (SD) based on a multiple condition control flow graph (MCCFG), the method comprising the steps of: model-converting, by a model converting unit, the unified modeling language (UML) sequence diagram (SD) according to a unified modeling language (UML) sequence diagram (SD) metamodel and a multiple condition control flow graph (MCCFG) metamodel to generate the multiple condition control flow graph (MCCFG); and converting, by a coverage criteria unit, the multiple condition control flow graph (MCCFG) into a plurality of test cases. | 10. A method of generating a multi-level test case in an apparatus of generating a multi-level test case from a unified modeling language (UML) sequence diagram (SD) based on a multiple condition control flow graph (MCCFG), the method comprising the steps of: model-converting, by a model converting unit, the unified modeling language (UML) sequence diagram (SD) according to a unified modeling language (UML) sequence diagram (SD) metamodel and a multiple condition control flow graph (MCCFG) metamodel to generate the multiple condition control flow graph (MCCFG); and converting, by a coverage criteria unit, the multiple condition control flow graph (MCCFG) into a plurality of test cases. 16. The method of claim 10 , wherein in the converting, the multiple condition control flow graph (MCCFG) is changed into a tree structure and the tree structure is converted into the plurality of test cases according to a selection command by a user. | 0.824721 |
9,934,224 | 10 | 14 | 10. The method of claim 1 , wherein the subset of the document corresponds to a cursor location on the document. | 10. The method of claim 1 , wherein the subset of the document corresponds to a cursor location on the document. 14. The method of claim 10 , wherein the subset of the document corresponding to the cursor location on the document includes a first predefined number of words before the cursor position and a second predefined number of words after the cursor position. | 0.955765 |
8,478,759 | 1 | 2 | 1. An information presentation apparatus, comprising: a program information database accumulating program information; a viewing history database accumulating a program viewing history of a user; a dictionary recording correspondence between a keyword and a category; an interesting scene extraction unit extracting a scene of a program that interests the user using the viewing history database and the program information database; an interesting scene registration unit that associates the interesting scene extracted by the interesting scene extraction unit and the keyword in the program information accompanying the interesting scene, and registers the keyword associating the interesting scene in an interesting scene registration information database according to the dictionary on a category-by-category basis; and an interesting scene presentation unit searching the interesting scene registration information database and retrieving and presenting the interesting scene matching with the input category, responsive to an input of information including the category from a mobile terminal, wherein the interesting scene presentation unit receives an input of information related to a plurality of categories and probabilities for the respective categories, and calculates a score of the interesting scene registered by the interesting scene registration unit. | 1. An information presentation apparatus, comprising: a program information database accumulating program information; a viewing history database accumulating a program viewing history of a user; a dictionary recording correspondence between a keyword and a category; an interesting scene extraction unit extracting a scene of a program that interests the user using the viewing history database and the program information database; an interesting scene registration unit that associates the interesting scene extracted by the interesting scene extraction unit and the keyword in the program information accompanying the interesting scene, and registers the keyword associating the interesting scene in an interesting scene registration information database according to the dictionary on a category-by-category basis; and an interesting scene presentation unit searching the interesting scene registration information database and retrieving and presenting the interesting scene matching with the input category, responsive to an input of information including the category from a mobile terminal, wherein the interesting scene presentation unit receives an input of information related to a plurality of categories and probabilities for the respective categories, and calculates a score of the interesting scene registered by the interesting scene registration unit. 2. The information presentation apparatus according to claim 1 , further comprising an information display style determination unit converting an information display style, wherein the information display style determination unit converts the display style of the interesting scene retrieved by the interesting scene presentation unit according to the type of the mobile terminal and transmits the converted display style to the mobile terminal. | 0.746871 |
8,661,338 | 17 | 23 | 17. A non-transitory computer readable medium having stored thereon instructions for dynamic document layout which when executed by a processor, causes the processor to perform steps comprising: comparing one or more elements of at least a portion of an original document against the same types of elements in at least a portion each of a plurality of stored documents, wherein the portion of the original document is the portion that requires adjustment or re-layout; determining a particular stored document, amongst the plurality of stored documents, with the portion which is closest to the portion of the original document based on the comparing; identifying a designated output device and designated output device characteristics; applying one or more mutators to the portion of the original document which were applied to mutate the portion of the identified particular stored document, to form a mutated portion in the original document, having obtained one or more mutators from a list of stored mutators which correspond to the identified stored document, wherein the applying further comprises determining which of the one or more stored mutators to apply based on one or more characteristics of the designated output device and the identified particular stored document that matches the portion of the original document; the applying further comprises determining an order for the applying of the determined one or more mutators to the portion of the original document, according to a stored priority order. | 17. A non-transitory computer readable medium having stored thereon instructions for dynamic document layout which when executed by a processor, causes the processor to perform steps comprising: comparing one or more elements of at least a portion of an original document against the same types of elements in at least a portion each of a plurality of stored documents, wherein the portion of the original document is the portion that requires adjustment or re-layout; determining a particular stored document, amongst the plurality of stored documents, with the portion which is closest to the portion of the original document based on the comparing; identifying a designated output device and designated output device characteristics; applying one or more mutators to the portion of the original document which were applied to mutate the portion of the identified particular stored document, to form a mutated portion in the original document, having obtained one or more mutators from a list of stored mutators which correspond to the identified stored document, wherein the applying further comprises determining which of the one or more stored mutators to apply based on one or more characteristics of the designated output device and the identified particular stored document that matches the portion of the original document; the applying further comprises determining an order for the applying of the determined one or more mutators to the portion of the original document, according to a stored priority order. 23. The medium as set forth in claim 17 further comprising identifying an output device on which the outputting of the original document with the applied mutators will occur wherein one of the elements in the comparing is the type of output device used in the outputting. | 0.815143 |
6,108,619 | 9 | 12 | 9. A method in a computer system for assigning semantic characterization to a content stream, comprising the steps of: a) accessing the content stream; b) extracting tokens from the content stream, each token comprising metadata about a part of the content stream; c) associating one or more profiles with the extracted tokens; and d) representing at least a portion of the content stream in a semantic space corresponding to one or more profiles. | 9. A method in a computer system for assigning semantic characterization to a content stream, comprising the steps of: a) accessing the content stream; b) extracting tokens from the content stream, each token comprising metadata about a part of the content stream; c) associating one or more profiles with the extracted tokens; and d) representing at least a portion of the content stream in a semantic space corresponding to one or more profiles. 12. A method as recited in claim 9, wherein the step of representing comprises instantiating a profile. | 0.792339 |
9,659,218 | 1 | 10 | 1. A method comprising: applying, by a processing device, a machine-learned model to audio-visual content features of first content item segments of a first content item, the machine-learned model trained based on user interaction signals and audio-visual content features of a training set of training segments of training content items, wherein the user interaction signals comprise scrubbing in the training segments; calculating, based on applying the machine-learned model, a salience score for each of the first segments of the first content item; and selecting, based on the calculated salience scores, one of the first content item segments as a starting point for playback of the first content item. | 1. A method comprising: applying, by a processing device, a machine-learned model to audio-visual content features of first content item segments of a first content item, the machine-learned model trained based on user interaction signals and audio-visual content features of a training set of training segments of training content items, wherein the user interaction signals comprise scrubbing in the training segments; calculating, based on applying the machine-learned model, a salience score for each of the first segments of the first content item; and selecting, based on the calculated salience scores, one of the first content item segments as a starting point for playback of the first content item. 10. The method of claim 1 , wherein additional user interaction signals corresponding to the playback of the first content item are used as feedback to training the machine-learned model. | 0.901786 |
8,073,893 | 66 | 67 | 66. The apparatus of claim 65 , wherein said computing platform is further adapted to determine whether said first quantity is greater than said second quantity based, at least in part, on said first and second binary strings. | 66. The apparatus of claim 65 , wherein said computing platform is further adapted to determine whether said first quantity is greater than said second quantity based, at least in part, on said first and second binary strings. 67. The apparatus of claim 66 , wherein said computing platform is further adapted to determine whether said first quantity is equal to said second quantity based, at least in part, on said first and second binary strings. | 0.920373 |
8,041,662 | 17 | 20 | 17. A method of classifying a domain name, comprising: identifying a set of character-based n-grams corresponding to a domain name string, the domain name string being associated with a domain name; determining a domain name vector point in a multidimensional space, wherein each dimension in the multidimensional space corresponds to each character-based n-gram in the set of character-based n-grams, the multidimensional space including a plurality of domain name classifications that each include a corresponding one or more vector points; comparing the domain name vector point in the multidimensional space with a first plurality of vector points located in the multidimensional space to determine first distances between the domain name vector point and the first plurality of vector points in the multidimensional space, wherein each of the first plurality of vector points corresponds to each of a first plurality of domain names classified in a first domain name classification; comparing the domain name vector point in the multidimensional space with a second plurality of vector points located in the multidimensional space to determine second distances between the domain name vector point and the second plurality of vector points in the multidimensional space, wherein each of the second plurality of vector points corresponds to each of a second plurality of domain names classified in a second domain name classification; determining whether the domain name is correlated to the first plurality of domain names based on the first distances between the domain name vector point and the first plurality of vector points in the multidimensional space; determining whether the domain name is correlated to the second plurality of domain names based on the second distances between the domain name vector point and the second plurality of vector points in the multidimensional space; and classifying the domain name in the first domain name classification if it is determined that the first plurality of vector points and the domain name vector point are correlated. | 17. A method of classifying a domain name, comprising: identifying a set of character-based n-grams corresponding to a domain name string, the domain name string being associated with a domain name; determining a domain name vector point in a multidimensional space, wherein each dimension in the multidimensional space corresponds to each character-based n-gram in the set of character-based n-grams, the multidimensional space including a plurality of domain name classifications that each include a corresponding one or more vector points; comparing the domain name vector point in the multidimensional space with a first plurality of vector points located in the multidimensional space to determine first distances between the domain name vector point and the first plurality of vector points in the multidimensional space, wherein each of the first plurality of vector points corresponds to each of a first plurality of domain names classified in a first domain name classification; comparing the domain name vector point in the multidimensional space with a second plurality of vector points located in the multidimensional space to determine second distances between the domain name vector point and the second plurality of vector points in the multidimensional space, wherein each of the second plurality of vector points corresponds to each of a second plurality of domain names classified in a second domain name classification; determining whether the domain name is correlated to the first plurality of domain names based on the first distances between the domain name vector point and the first plurality of vector points in the multidimensional space; determining whether the domain name is correlated to the second plurality of domain names based on the second distances between the domain name vector point and the second plurality of vector points in the multidimensional space; and classifying the domain name in the first domain name classification if it is determined that the first plurality of vector points and the domain name vector point are correlated. 20. The method of claim 17 , wherein the domain name is correlated to the second plurality of domain names based on distances between the domain name vector point and a subset of nearest vector points in the second plurality of vector points. | 0.502058 |
10,025,780 | 22 | 23 | 22. The media of claim 19 , wherein the software is further operable when executed to: access a social graph of an online social network, the 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 the two nodes, the nodes comprising: a first node that corresponds to the first user who submitted the input associated with the first translation; a plurality of second nodes that each correspond to a second user or a concept; and calculate a credibility-score of the first user based on social-graph information associated with the first node. | 22. The media of claim 19 , wherein the software is further operable when executed to: access a social graph of an online social network, the 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 the two nodes, the nodes comprising: a first node that corresponds to the first user who submitted the input associated with the first translation; a plurality of second nodes that each correspond to a second user or a concept; and calculate a credibility-score of the first user based on social-graph information associated with the first node. 23. The media of claim 22 , wherein the credibility-score of the first user is further based on social-graph information associated with one or more second nodes of the plurality of second nodes. | 0.940074 |
9,043,209 | 21 | 22 | 21. A non-transitory computer-readable medium storing a language model creation program comprising instructions for causing an information processing device to realize: a language model creating unit configured to execute a language model creation process of: acquiring a first content-specific language model which represents an appearance probability that a specific word appears in a first content, the first content comprising a first word sequence, a second content-specific language model which represents an appearance probability that the specific word appears in a second content, the second content comprising a second word sequence, a first probability parameter representing a probability that a content represented by a target word sequence is the first content, and a second probability parameter representing a probability that the content represented by the target word sequence is the second content, the target word sequence being at least a part of a speech recognition hypothesis generated in a speech recognition process; and creating a language model based on the first probability parameter, the second probability parameter, the first content-specific language model and the second content-specific language model, the created language model representing a combined appearance probability which is a probability that the specific word appears within at least a portion of the target word sequence. | 21. A non-transitory computer-readable medium storing a language model creation program comprising instructions for causing an information processing device to realize: a language model creating unit configured to execute a language model creation process of: acquiring a first content-specific language model which represents an appearance probability that a specific word appears in a first content, the first content comprising a first word sequence, a second content-specific language model which represents an appearance probability that the specific word appears in a second content, the second content comprising a second word sequence, a first probability parameter representing a probability that a content represented by a target word sequence is the first content, and a second probability parameter representing a probability that the content represented by the target word sequence is the second content, the target word sequence being at least a part of a speech recognition hypothesis generated in a speech recognition process; and creating a language model based on the first probability parameter, the second probability parameter, the first content-specific language model and the second content-specific language model, the created language model representing a combined appearance probability which is a probability that the specific word appears within at least a portion of the target word sequence. 22. The non-transitory computer-readable medium storing the language model creation program according to claim 21 , wherein: the language model creating unit is configured to create the language model such that the combined appearance probability increases as a sum of (i) a product of a first coefficient and the probability represented by the first content-specific language model, and (ii) a product of a second coefficient and the probability represented by the second content-specific language model, becomes larger, wherein the first coefficient increases in value as the first probability parameter becomes larger, the second coefficient increases in value as the second probability parameter becomes larger. | 0.529605 |
7,603,272 | 3 | 9 | 3. The system of claim 2 , wherein the module configured to control the processor to align the transitions T further, for all transitions T in the topological ordered lattice, further performs the following: A. using time slots of transitions T, finding the most overlapping location on the pivot baseline; B. if there is no transition at that location that precedes T in the lattice, performing the following: i. if a transition with the same label already occurs at that location, adding posterior probability of T to the posterior probability of that transition; ii. if a transition with the same label does not already occur at that location, inserting a new transition to that location with the label and posterior probability of T; C. if there is a transition at that location that precedes T in the lattice, performing the following: i. inserting a new state S n to the pivot alignment; ii. assigning that state a time information; iii. changing the destination state of all transitions originating from state S s to S n ; and iv. inserting a transition between states S n and S e and assigning it the label and posterior of T, wherein the lattice is transformed into a block diagonal matrix. | 3. The system of claim 2 , wherein the module configured to control the processor to align the transitions T further, for all transitions T in the topological ordered lattice, further performs the following: A. using time slots of transitions T, finding the most overlapping location on the pivot baseline; B. if there is no transition at that location that precedes T in the lattice, performing the following: i. if a transition with the same label already occurs at that location, adding posterior probability of T to the posterior probability of that transition; ii. if a transition with the same label does not already occur at that location, inserting a new transition to that location with the label and posterior probability of T; C. if there is a transition at that location that precedes T in the lattice, performing the following: i. inserting a new state S n to the pivot alignment; ii. assigning that state a time information; iii. changing the destination state of all transitions originating from state S s to S n ; and iv. inserting a transition between states S n and S e and assigning it the label and posterior of T, wherein the lattice is transformed into a block diagonal matrix. 9. The system of claim 3 , further comprising a module configured to control the processor to apply the probabilities of the block diagonal matrix confidence scores to spoken language understanding. | 0.56 |
7,996,246 | 3 | 4 | 3. The system of claim 1 , wherein the display generation unit is further operable to display each quality score graphically in relation to its resident care area. | 3. The system of claim 1 , wherein the display generation unit is further operable to display each quality score graphically in relation to its resident care area. 4. The system of claim 3 , wherein the display generation unit is operable to display each quality score graphically in relation to its resident care area by: displaying a first data range in relation to a graphical scale, the first data range representing a range of scores in the resident care area indicative of a substantial certainty of citation in the resident care area; displaying a second data range in relation to the graphical scale, the second data range representing a range of scores in the resident care area indicative of an uncertain likelihood of citation in the resident care area; displaying a third data range in relation to the graphical scale, the third data range representing a range of scores in the resident care area indicative of a substantial certainty of no citation in the resident care area; and displaying an indicator indicating a location of the quality score in relation to the graphical scale. | 0.720588 |
7,707,635 | 1 | 8 | 1. A method of scanning computer network traffic for viruses, the method comprising: providing a scripting language for creating script patterns that identify network viruses, the scripting language allowing for the use of conditional flow control in a script pattern to identify a particular network virus; creating a first script pattern using the scripting language, the first script pattern comprising a first set of instructions for identifying a first particular network virus; creating a second script pattern using the scripting language, the second script pattern comprising a second set instructions for identifying a second particular network virus, the first and second set of instructions including at least one instruction for conditional branching to skip a next instruction; generating a data stream from network data packets received over a computer network; and scanning a portion of the data stream for existence of the first particular network virus by executing the first script pattern and for existence of the second particular network virus by executing the second script pattern. | 1. A method of scanning computer network traffic for viruses, the method comprising: providing a scripting language for creating script patterns that identify network viruses, the scripting language allowing for the use of conditional flow control in a script pattern to identify a particular network virus; creating a first script pattern using the scripting language, the first script pattern comprising a first set of instructions for identifying a first particular network virus; creating a second script pattern using the scripting language, the second script pattern comprising a second set instructions for identifying a second particular network virus, the first and second set of instructions including at least one instruction for conditional branching to skip a next instruction; generating a data stream from network data packets received over a computer network; and scanning a portion of the data stream for existence of the first particular network virus by executing the first script pattern and for existence of the second particular network virus by executing the second script pattern. 8. The method of claim 1 wherein the first script pattern and the second script pattern are created to detect viruses in network data packets conforming to a particular communication protocol. | 0.636364 |
9,928,466 | 6 | 12 | 6. The computing device of claim 5 , wherein the operations further comprise: obtaining, from the trained predictive model, a predicted segmentation for a new search query, wherein the new search query includes a plurality of terms, and wherein the predicted segmentation identifies at least one phrase that comprises at least one term in the plurality of terms. | 6. The computing device of claim 5 , wherein the operations further comprise: obtaining, from the trained predictive model, a predicted segmentation for a new search query, wherein the new search query includes a plurality of terms, and wherein the predicted segmentation identifies at least one phrase that comprises at least one term in the plurality of terms. 12. The computing device of claim 6 , wherein the collection of phrases includes at least one set of terms that appear in search queries with a threshold frequency. | 0.946753 |
5,497,320 | 32 | 35 | 32. A two-level document processing method preparing a downstream document on the basis of an upstream document, and an upstream document on the basis of a downstream document, said method comprising the steps of: comparing the upstream document with an upstream deletion document including information which is particular to the upstream document and is unnecessary for the downstream document; selecting, from the upstream document and based on results of the step of comparing, a deletion candidate to be deleted from the upstream document until no more deletion candidate is found; searching said upstream deletion document for corresponding information corresponding to the deletion candidate; displaying, if the corresponding information is present in the upstream deletion document, the deletion candidate and the corresponding information to have approval of deletion; deleting the deletion candidate from the upstream document if the deletion of the deletion candidate is approved; storing the deletion candidate and the corresponding information into a deletion file after the deletion of the deletion candidate; storing the deletion candidate and the corresponding information into a non-deletion file without deleting the deletion candidate from the upstream document if the deletion of the deletion candidate is not approved; storing the deletion candidate into an upstream special candidate file without deleting the deletion candidate from the upstream document, if the corresponding information corresponding to the deletion candidate is absent in the upstream deletion document; comparing the upstream document with a downstream addition document including information which is particular to the downstream document and is unnecessary for the upstream document; selecting, from the downstream addition document and based on the results of the step of comparing, an addition candidate to be added to the upstream document until no more addition candidate is found; searching said downstream addition document for corresponding information corresponding to the addition candidate; displaying, if the corresponding information is present in the downstream addition document, the addition candidate and the corresponding information to have approval of addition; adding the addition candidate to the upstream document if the addition of the addition candidate is approved; storing the addition candidate and the corresponding information into an addition file; storing the addition candidate and the corresponding information into a non-addition file without adding the addition candidate to the upstream document, if the addition of the addition candidate is not approved; storing the addition candidate into a downstream special candidate file without adding the addition candidate to the upstream document, if the corresponding information corresponding to the addition candidate is absent in the downstream addition document; outputting the input upstream document and the output downstream document contrastively in order to clarify the addition and deletion portions; comparing the downstream document with a downstream deletion document including information which is particular to the downstream document and is unnecessary for the upstream document; selecting, from the downstream deletion document and based on results of the step of comparing, a deletion candidate to be deleted from the downstream document until no more deletion candidate is found; searching the downstream deletion document for corresponding information corresponding to the deletion candidate; displaying, if the corresponding information is present in the downstream deletion document, the deletion candidate and the corresponding information to have approval of deletion; deleting the deletion candidate from the downstream document if the deletion of the deletion candidate is approved; storing the deletion candidate and the corresponding information into a deletion file; storing the deletion candidate and the corresponding information into a non-deletion file without deleting the deletion candidate from the downstream document, if the deletion of the deletion candidate is not approved; storing the deletion candidate into a downstream special candidate file without deleting the deletion candidate from the downstream document, if the corresponding information corresponding to the deletion candidate is absent in the downstream deletion document; comparing the downstream document with an upstream addition document including information which is particular to the upstream document and is unnecessary for the downstream document; selecting, from the upstream addition document and based on results of the step of comparing, an addition candidate to be added to the downstream document until no more addition candidate is found; searching the upstream addition document for corresponding information corresponding to the addition candidate; displaying, if the corresponding information is present in the upstream addition document, the addition candidate and the corresponding information to have approval of addition; adding the addition candidate to the downstream document if the addition of the addition candidate is approved; storing the addition candidate and the corresponding information into an addition file; storing the addition candidate and the corresponding information into a non-addition file without adding the addition candidate to the downstream document if the addition of the addition candidate is not approved; storing the addition candidate into an upstream special candidate file without adding the addition candidate to the downstream document, if the corresponding information corresponding to the addition candidate is absent in the upstream addition document; outputting the input downstream document and the output upstream document contrastively in order to clarify the addition and deletion portions; selecting shortage candidate from an upstream document under shortage decision rules used as a reference to decide whether some kinds of information are lacking or not; searching an upstream addition/deletion document for corresponding information corresponding to the shortage candidate; repeating the steps of selecting and searching until no more shortage candidate is found in said upstream document; storing the shortage candidate and the corresponding information into an excessive deletion file under a decision that an excessive deletion has been made if the corresponding information corresponding to the shortage candidate is present in the upstream addition/deletion document; adding the corresponding information to the upstream document, and deleting the corresponding information from the upstream addition/deletion document, searching a repository for the corresponding information corresponding to the shortage candidate if the corresponding information is not present in the upstream addition/deletion document, said repository being resources of information; adding the corresponding information to the upstream document, and storing the shortage candidate and the corresponding information to an addition file, if the corresponding information is present in the repository, and if the addition of the corresponding information is decided to be reasonable; storing the shortage candidate and the corresponding information into a non-addition file without adding the corresponding information to the upstream document, if the corresponding information is present in the repository, and the addition of the corresponding information is decided to be unreasonable; storing the shortage candidate into an upstream shortage candidate file without adding the corresponding information to the upstream document, if the corresponding information is absent in the repository; selecting an excessive candidate from an upstream document under excess decision rules used as a reference to decide whether some kinds of information are excessive or not, after no more shortage candidate is found in the upstream document under shortage decision rules; searching an upstream addition/deletion document for corresponding information corresponding to the excessive candidate, said upstream addition/deletion document storing information which is present in the upstream document and is absent in the downstream document; repeating the steps of selecting and searching until no more excessive candidate is found in said upstream document; storing the excessive candidate and the corresponding information into an excessive addition file under a decision that an excessive addition has been made, if the corresponding information corresponding to the excessive candidate is present in the upstream addition/deletion document; deleting the excessive candidate from the upstream document, and deleting the corresponding information from the upstream addition/deletion document after storing the excessive addition; deleting the excessive candidate from the upstream document and storing the excessive candidate into a deletion file, if the corresponding information corresponding to the excessive candidate is absent in the upstream addition/deletion document, and if the deletion of the excessive candidate is decided to be reasonable; storing the excessive candidate into a non-deletion file without deleting the excessive candidate from the upstream document, if the corresponding information corresponding to the excessive document is absent in the upstream addition/deletion document, and the deletion of the excessive candidate is decided to be unreasonable; outputting the upstream document and the upstream addition/deletion document contrastively in order to clarity the addition and deletion portions before and after refinement; selecting shortage candidate from an downstream document under shortage decision rules used as a reference to decide whether some kinds of information are lacking or not; searching an downstream addition/deletion document for corresponding information corresponding to the shortage candidate; repeating the steps of selecting and searching until no more shortage candidate is found in said downstream document; storing the shortage candidate and the corresponding information into an excessive deletion file under a decision that an excessive deletion has been made if the corresponding information corresponding to the shortage candidate is present in the downstream addition/deletion document; adding the corresponding information to the downstream document, and deleting the corresponding information from the downstream addition/deletion document, searching a repository for the corresponding information corresponding to the shortage candidate if the corresponding information is not present in the downstream addition/deletion document, said repository being resources of information; adding the corresponding information to the downstream document, and storing the shortage candidate and the corresponding information to an addition file, if the corresponding information is present in the repository, and if the addition of the corresponding information is decided to be reasonable; storing the shortage candidate and the corresponding information into a non-addition file without adding the corresponding information to the downstream document, if the corresponding information is present in the repository, and the addition of the corresponding information is decided to be unreasonable; storing the shortage candidate into an downstream shortage candidate file without adding the corresponding information to the downstream document, if the corresponding information is absent in the repository; selecting an excessive candidate from an downstream document under excess decision rules used as a reference to decide whether some kinds of information are excessive or not, after no more shortage candidate is found in the downstream document under shortage decision rules; searching an downstream addition/deletion document for corresponding information corresponding to the excessive candidate, said downstream addition/deletion document storing information which is present in the downstream document and is absent in the downstream document; repeating the steps of selecting and searching until no more excessive candidate is found in said downstream document; storing the excessive candidate and the corresponding information into an excessive addition file under a decision that an excessive addition has been made, if the corresponding information corresponding to the excessive candidate is present in the downstream addition/deletion document; deleting the excessive candidate from the downstream document, and deleting the corresponding information from the downstream addition/deletion document after storing the excessive addition; deleting the excessive candidate from the downstream document and storing the excessive candidate into a deletion file, if the corresponding information corresponding to the excessive candidate is absent in the downstream addition/deletion document, and if the deletion of the excessive candidate is decided to be reasonable; storing the excessive candidate into a non-deletion file without deleting the excessive candidate from the downstream document, if the corresponding information corresponding to the excessive document is absent in the downstream addition/deletion document, and the deletion of the excessive candidate is decided to be unreasonable; outputting the downstream document and the downstream addition/deletion document contrastively in order to clarity the addition and deletion portions before and after refinement; deciding whether a query about a product relates to upstream information or downstream information; searching an upstream document, upstream addition/deletion document, upstream repository, downstream document, downstream addition/deletion document, and downstream repository, for an answering candidate, in accordance with results of the step of deciding until non-searched portion remains; presenting the answering candidate if it is found; storing an unsatisfied answering candidate, or a fact that no answering candidate is found, into a non-answering file; deciding information value of information associated with the upstream under upstream file elimination decision rules, said information associated with the upstream being stored in the deletion file, non-deletion file, addition file, non-addition file, special candidate file, document shortage file, excessive deletion file, excessive addition file, and non-answering file; extracting valuable information as a processing candidate; comparing the processing candidate with technical information as past results, until the last processing candidate; and storing the processing candidate into the repository in accordance with results of comparison. | 32. A two-level document processing method preparing a downstream document on the basis of an upstream document, and an upstream document on the basis of a downstream document, said method comprising the steps of: comparing the upstream document with an upstream deletion document including information which is particular to the upstream document and is unnecessary for the downstream document; selecting, from the upstream document and based on results of the step of comparing, a deletion candidate to be deleted from the upstream document until no more deletion candidate is found; searching said upstream deletion document for corresponding information corresponding to the deletion candidate; displaying, if the corresponding information is present in the upstream deletion document, the deletion candidate and the corresponding information to have approval of deletion; deleting the deletion candidate from the upstream document if the deletion of the deletion candidate is approved; storing the deletion candidate and the corresponding information into a deletion file after the deletion of the deletion candidate; storing the deletion candidate and the corresponding information into a non-deletion file without deleting the deletion candidate from the upstream document if the deletion of the deletion candidate is not approved; storing the deletion candidate into an upstream special candidate file without deleting the deletion candidate from the upstream document, if the corresponding information corresponding to the deletion candidate is absent in the upstream deletion document; comparing the upstream document with a downstream addition document including information which is particular to the downstream document and is unnecessary for the upstream document; selecting, from the downstream addition document and based on the results of the step of comparing, an addition candidate to be added to the upstream document until no more addition candidate is found; searching said downstream addition document for corresponding information corresponding to the addition candidate; displaying, if the corresponding information is present in the downstream addition document, the addition candidate and the corresponding information to have approval of addition; adding the addition candidate to the upstream document if the addition of the addition candidate is approved; storing the addition candidate and the corresponding information into an addition file; storing the addition candidate and the corresponding information into a non-addition file without adding the addition candidate to the upstream document, if the addition of the addition candidate is not approved; storing the addition candidate into a downstream special candidate file without adding the addition candidate to the upstream document, if the corresponding information corresponding to the addition candidate is absent in the downstream addition document; outputting the input upstream document and the output downstream document contrastively in order to clarify the addition and deletion portions; comparing the downstream document with a downstream deletion document including information which is particular to the downstream document and is unnecessary for the upstream document; selecting, from the downstream deletion document and based on results of the step of comparing, a deletion candidate to be deleted from the downstream document until no more deletion candidate is found; searching the downstream deletion document for corresponding information corresponding to the deletion candidate; displaying, if the corresponding information is present in the downstream deletion document, the deletion candidate and the corresponding information to have approval of deletion; deleting the deletion candidate from the downstream document if the deletion of the deletion candidate is approved; storing the deletion candidate and the corresponding information into a deletion file; storing the deletion candidate and the corresponding information into a non-deletion file without deleting the deletion candidate from the downstream document, if the deletion of the deletion candidate is not approved; storing the deletion candidate into a downstream special candidate file without deleting the deletion candidate from the downstream document, if the corresponding information corresponding to the deletion candidate is absent in the downstream deletion document; comparing the downstream document with an upstream addition document including information which is particular to the upstream document and is unnecessary for the downstream document; selecting, from the upstream addition document and based on results of the step of comparing, an addition candidate to be added to the downstream document until no more addition candidate is found; searching the upstream addition document for corresponding information corresponding to the addition candidate; displaying, if the corresponding information is present in the upstream addition document, the addition candidate and the corresponding information to have approval of addition; adding the addition candidate to the downstream document if the addition of the addition candidate is approved; storing the addition candidate and the corresponding information into an addition file; storing the addition candidate and the corresponding information into a non-addition file without adding the addition candidate to the downstream document if the addition of the addition candidate is not approved; storing the addition candidate into an upstream special candidate file without adding the addition candidate to the downstream document, if the corresponding information corresponding to the addition candidate is absent in the upstream addition document; outputting the input downstream document and the output upstream document contrastively in order to clarify the addition and deletion portions; selecting shortage candidate from an upstream document under shortage decision rules used as a reference to decide whether some kinds of information are lacking or not; searching an upstream addition/deletion document for corresponding information corresponding to the shortage candidate; repeating the steps of selecting and searching until no more shortage candidate is found in said upstream document; storing the shortage candidate and the corresponding information into an excessive deletion file under a decision that an excessive deletion has been made if the corresponding information corresponding to the shortage candidate is present in the upstream addition/deletion document; adding the corresponding information to the upstream document, and deleting the corresponding information from the upstream addition/deletion document, searching a repository for the corresponding information corresponding to the shortage candidate if the corresponding information is not present in the upstream addition/deletion document, said repository being resources of information; adding the corresponding information to the upstream document, and storing the shortage candidate and the corresponding information to an addition file, if the corresponding information is present in the repository, and if the addition of the corresponding information is decided to be reasonable; storing the shortage candidate and the corresponding information into a non-addition file without adding the corresponding information to the upstream document, if the corresponding information is present in the repository, and the addition of the corresponding information is decided to be unreasonable; storing the shortage candidate into an upstream shortage candidate file without adding the corresponding information to the upstream document, if the corresponding information is absent in the repository; selecting an excessive candidate from an upstream document under excess decision rules used as a reference to decide whether some kinds of information are excessive or not, after no more shortage candidate is found in the upstream document under shortage decision rules; searching an upstream addition/deletion document for corresponding information corresponding to the excessive candidate, said upstream addition/deletion document storing information which is present in the upstream document and is absent in the downstream document; repeating the steps of selecting and searching until no more excessive candidate is found in said upstream document; storing the excessive candidate and the corresponding information into an excessive addition file under a decision that an excessive addition has been made, if the corresponding information corresponding to the excessive candidate is present in the upstream addition/deletion document; deleting the excessive candidate from the upstream document, and deleting the corresponding information from the upstream addition/deletion document after storing the excessive addition; deleting the excessive candidate from the upstream document and storing the excessive candidate into a deletion file, if the corresponding information corresponding to the excessive candidate is absent in the upstream addition/deletion document, and if the deletion of the excessive candidate is decided to be reasonable; storing the excessive candidate into a non-deletion file without deleting the excessive candidate from the upstream document, if the corresponding information corresponding to the excessive document is absent in the upstream addition/deletion document, and the deletion of the excessive candidate is decided to be unreasonable; outputting the upstream document and the upstream addition/deletion document contrastively in order to clarity the addition and deletion portions before and after refinement; selecting shortage candidate from an downstream document under shortage decision rules used as a reference to decide whether some kinds of information are lacking or not; searching an downstream addition/deletion document for corresponding information corresponding to the shortage candidate; repeating the steps of selecting and searching until no more shortage candidate is found in said downstream document; storing the shortage candidate and the corresponding information into an excessive deletion file under a decision that an excessive deletion has been made if the corresponding information corresponding to the shortage candidate is present in the downstream addition/deletion document; adding the corresponding information to the downstream document, and deleting the corresponding information from the downstream addition/deletion document, searching a repository for the corresponding information corresponding to the shortage candidate if the corresponding information is not present in the downstream addition/deletion document, said repository being resources of information; adding the corresponding information to the downstream document, and storing the shortage candidate and the corresponding information to an addition file, if the corresponding information is present in the repository, and if the addition of the corresponding information is decided to be reasonable; storing the shortage candidate and the corresponding information into a non-addition file without adding the corresponding information to the downstream document, if the corresponding information is present in the repository, and the addition of the corresponding information is decided to be unreasonable; storing the shortage candidate into an downstream shortage candidate file without adding the corresponding information to the downstream document, if the corresponding information is absent in the repository; selecting an excessive candidate from an downstream document under excess decision rules used as a reference to decide whether some kinds of information are excessive or not, after no more shortage candidate is found in the downstream document under shortage decision rules; searching an downstream addition/deletion document for corresponding information corresponding to the excessive candidate, said downstream addition/deletion document storing information which is present in the downstream document and is absent in the downstream document; repeating the steps of selecting and searching until no more excessive candidate is found in said downstream document; storing the excessive candidate and the corresponding information into an excessive addition file under a decision that an excessive addition has been made, if the corresponding information corresponding to the excessive candidate is present in the downstream addition/deletion document; deleting the excessive candidate from the downstream document, and deleting the corresponding information from the downstream addition/deletion document after storing the excessive addition; deleting the excessive candidate from the downstream document and storing the excessive candidate into a deletion file, if the corresponding information corresponding to the excessive candidate is absent in the downstream addition/deletion document, and if the deletion of the excessive candidate is decided to be reasonable; storing the excessive candidate into a non-deletion file without deleting the excessive candidate from the downstream document, if the corresponding information corresponding to the excessive document is absent in the downstream addition/deletion document, and the deletion of the excessive candidate is decided to be unreasonable; outputting the downstream document and the downstream addition/deletion document contrastively in order to clarity the addition and deletion portions before and after refinement; deciding whether a query about a product relates to upstream information or downstream information; searching an upstream document, upstream addition/deletion document, upstream repository, downstream document, downstream addition/deletion document, and downstream repository, for an answering candidate, in accordance with results of the step of deciding until non-searched portion remains; presenting the answering candidate if it is found; storing an unsatisfied answering candidate, or a fact that no answering candidate is found, into a non-answering file; deciding information value of information associated with the upstream under upstream file elimination decision rules, said information associated with the upstream being stored in the deletion file, non-deletion file, addition file, non-addition file, special candidate file, document shortage file, excessive deletion file, excessive addition file, and non-answering file; extracting valuable information as a processing candidate; comparing the processing candidate with technical information as past results, until the last processing candidate; and storing the processing candidate into the repository in accordance with results of comparison. 35. The two-level document processing method as claimed in claim 32, further comprising the steps of: comparing the processing candidate with an upstream document of another product as technical information; and storing the processing candidate into an existence repository as another expression of existing information, if the processing candidate has corresponding information in the upstream document of another product. | 0.697857 |
9,767,801 | 8 | 12 | 8. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers to perform operations comprising: receiving, by a dialog cancellation detector of a system that includes the dialog cancellation detector, a cancellation score database, and a dialog engine, a request that is input at a user device in response to a prompt; identifying, by the dialog cancellation detector, an expected input type that is associated with the prompt; identifying, by the dialog cancellation detector and using the cancellation score database, a cancellation score that is predefined for a potential cancellation term included in the request that is input at the user device in response to the prompt, based on the identified expected input type that is associated with the prompt, wherein the cancellation score that is predefined for the potential cancellation term is different for different expected input types that are associated with different prompts; determining, by the dialog cancellation detector, that the identified cancellation score satisfies a first threshold score; in response to determining that the identified cancellation score satisfies the first threshold score, identifying, by the dialog cancellation detector, the potential cancellation term included in the request that is input at the user device in response to the prompt as a cancellation command; and in response to identifying the potential cancellation term included in the request that is input at the user device as a cancellation command, outputting, by the dialog cancellation detector and to the dialog engine, an indication to the user device that the potential cancellation term included in the request that is input at the user device is a cancellation command rather than a refinement of a previous input. | 8. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers to perform operations comprising: receiving, by a dialog cancellation detector of a system that includes the dialog cancellation detector, a cancellation score database, and a dialog engine, a request that is input at a user device in response to a prompt; identifying, by the dialog cancellation detector, an expected input type that is associated with the prompt; identifying, by the dialog cancellation detector and using the cancellation score database, a cancellation score that is predefined for a potential cancellation term included in the request that is input at the user device in response to the prompt, based on the identified expected input type that is associated with the prompt, wherein the cancellation score that is predefined for the potential cancellation term is different for different expected input types that are associated with different prompts; determining, by the dialog cancellation detector, that the identified cancellation score satisfies a first threshold score; in response to determining that the identified cancellation score satisfies the first threshold score, identifying, by the dialog cancellation detector, the potential cancellation term included in the request that is input at the user device in response to the prompt as a cancellation command; and in response to identifying the potential cancellation term included in the request that is input at the user device as a cancellation command, outputting, by the dialog cancellation detector and to the dialog engine, an indication to the user device that the potential cancellation term included in the request that is input at the user device is a cancellation command rather than a refinement of a previous input. 12. The system of claim 8 , wherein the expected input type comprises email dictation or a Short Messaging Service (SMS) dictation input type. | 0.733083 |
7,849,026 | 13 | 19 | 13. A method for creating a digital life form, comprising: defining a digital life form; providing access for the digital life form to an environment; defining a plurality of potential actions for the digital life form; providing at least one object in the environment; providing the object with at least one characteristic; providing the digital life form with the ability to form percepts based on the characteristics of objects; providing the digital life form with the ability to select from said plurality of potential actions based, at least in part, on the percepts; and providing consequences to the digital life form for such actions; wherein the digital life form selects from said plurality of potential actions in order to avoid certain of the consequences. | 13. A method for creating a digital life form, comprising: defining a digital life form; providing access for the digital life form to an environment; defining a plurality of potential actions for the digital life form; providing at least one object in the environment; providing the object with at least one characteristic; providing the digital life form with the ability to form percepts based on the characteristics of objects; providing the digital life form with the ability to select from said plurality of potential actions based, at least in part, on the percepts; and providing consequences to the digital life form for such actions; wherein the digital life form selects from said plurality of potential actions in order to avoid certain of the consequences. 19. The method of claim 13 , and further including: providing a strategy for selecting from said plurality of actions. | 0.713592 |
8,773,733 | 1 | 13 | 1. A digital image capture device, comprising: an image sensor for capturing a digital image; an optical system for forming an image of a scene onto the image sensor; a data processing system; a storage memory; and a program memory communicatively connected to the data processing system and storing instructions configured to cause the data processing system to implement a method for extracting textual information from a document containing text characters, wherein the method includes: capturing a plurality of digital images of the document using the image sensor, wherein all of the digital images include substantially the same image content; automatically analyzing each of the captured digital images using an optical character recognition process to determine extracted textual data for each captured digital image; and merging the extracted textual data for the captured digital images to determine the textual information for the document, wherein differences between the extracted textual data for corresponding portions of the captured digital images are analyzed to determine the textual information for corresponding portions of the document; and storing the textual information in the storage memory. | 1. A digital image capture device, comprising: an image sensor for capturing a digital image; an optical system for forming an image of a scene onto the image sensor; a data processing system; a storage memory; and a program memory communicatively connected to the data processing system and storing instructions configured to cause the data processing system to implement a method for extracting textual information from a document containing text characters, wherein the method includes: capturing a plurality of digital images of the document using the image sensor, wherein all of the digital images include substantially the same image content; automatically analyzing each of the captured digital images using an optical character recognition process to determine extracted textual data for each captured digital image; and merging the extracted textual data for the captured digital images to determine the textual information for the document, wherein differences between the extracted textual data for corresponding portions of the captured digital images are analyzed to determine the textual information for corresponding portions of the document; and storing the textual information in the storage memory. 13. The digital image capture device of claim 1 , further including using an image alignment process to align the captured digital images before they are analyzed using the optical character recognition process. | 0.866625 |
10,127,277 | 1 | 3 | 1. A computer program product for processing a structured query language (SQL) statement, the SQL statement comprising at least an OUTER JOIN operation, the computer program product comprising: one or more computer readable storage media and program instructions stored on the one or more computer readable storage media, the program instructions comprising program instructions to: determine whether a first query and a second query are equivalent, the first and second queries being respectively the left side and the right side operands of the OUTER JOIN operation; determine whether an output of the right side of the OUTER JOIN operation contains an output of the left side of the OUTER JOIN operation; determine whether partitioning columns of a GROUP BY operation are the same as partitioning columns of the OUTER JOIN operation; determine whether columns quantified by the right side of the OUTER JOIN operation include one or both of multiple distinct aggregation operations or multiple aggregation operations; determine whether there are no filter predicates or having clause in the GROUP BY operation; determine whether a SELECT output of the SQL statement does not refer to database columns from the left side of the OUTER JOIN operation which are not also partitioning columns of the OUTER JOIN operation; and responsive to determining that: (i) output of the first side of the OUTER JOIN operation contains the output of the left side of the OUTER JOIN operation, (ii) the partitioning columns of the GROUP BY operation are the same as the partitioning columns of the OUTER JOIN operation, (iii) the columns quantified by the right side of the OUTER JOIN operation include one or both of multiple distinct aggregation operations or multiple aggregation operations, (iv) there are no filter predicates or having clause in the GROUP BY operation, (v) the first query and the second query are equivalent, and (vi) the SELECT output of the SQL statement does not refer to database columns from the left side of the OUTER JOIN operation which are not also partitioning columns of the OUTER JOIN operation, transform the SQL statement into an optimized query SQL statement by removing the OUTER JOIN operation. | 1. A computer program product for processing a structured query language (SQL) statement, the SQL statement comprising at least an OUTER JOIN operation, the computer program product comprising: one or more computer readable storage media and program instructions stored on the one or more computer readable storage media, the program instructions comprising program instructions to: determine whether a first query and a second query are equivalent, the first and second queries being respectively the left side and the right side operands of the OUTER JOIN operation; determine whether an output of the right side of the OUTER JOIN operation contains an output of the left side of the OUTER JOIN operation; determine whether partitioning columns of a GROUP BY operation are the same as partitioning columns of the OUTER JOIN operation; determine whether columns quantified by the right side of the OUTER JOIN operation include one or both of multiple distinct aggregation operations or multiple aggregation operations; determine whether there are no filter predicates or having clause in the GROUP BY operation; determine whether a SELECT output of the SQL statement does not refer to database columns from the left side of the OUTER JOIN operation which are not also partitioning columns of the OUTER JOIN operation; and responsive to determining that: (i) output of the first side of the OUTER JOIN operation contains the output of the left side of the OUTER JOIN operation, (ii) the partitioning columns of the GROUP BY operation are the same as the partitioning columns of the OUTER JOIN operation, (iii) the columns quantified by the right side of the OUTER JOIN operation include one or both of multiple distinct aggregation operations or multiple aggregation operations, (iv) there are no filter predicates or having clause in the GROUP BY operation, (v) the first query and the second query are equivalent, and (vi) the SELECT output of the SQL statement does not refer to database columns from the left side of the OUTER JOIN operation which are not also partitioning columns of the OUTER JOIN operation, transform the SQL statement into an optimized query SQL statement by removing the OUTER JOIN operation. 3. The computer program product of claim 1 , further comprising program instructions to: determine whether the partitioning columns of the GROUP BY operation quantified by the right side of the OUTER JOIN operation are the same as the partitioning columns of the GROUP BY operation quantified by the left side of the OUTER JOIN operation; and wherein said OUTER JOIN operation is removed from the query only in response to determining that the partitioning columns of the GROUP BY operation quantified by the right side of the OUTER JOIN operation are the same as the partitioning columns of the GROUP BY operation quantified by the left side of the OUTER JOIN operation. | 0.767981 |
10,037,563 | 1 | 4 | 1. A system for determining order details based on phrase matching, the system comprising: a non-transitory memory; and one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising: receiving, from a user device via a network, a first phrase and a user device identifier that identifies a user account at the system; accessing, based on the user device identifier, the user account from a plurality of user accounts, the accessing for obtaining information comprising a plurality of phrases associated with the user account; determining whether the first phrase matches a second phrase of the plurality of phrases that are stored at the system for the plurality of user accounts, the second phrase identifying a first order from a first merchant and a second order from a second merchant; in response to a determination that the first phrase matches the second phrase, determining, based on the first phrase, first details of the first order and second details of the second order; communicating a first request, based on the first details, via the network to the first merchant for a first list of items; communicating a second request, based on the second details, via the network to the second merchant for a second list of items; and processing a first payment for the first request and a second payment for the second request. | 1. A system for determining order details based on phrase matching, the system comprising: a non-transitory memory; and one or more hardware processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising: receiving, from a user device via a network, a first phrase and a user device identifier that identifies a user account at the system; accessing, based on the user device identifier, the user account from a plurality of user accounts, the accessing for obtaining information comprising a plurality of phrases associated with the user account; determining whether the first phrase matches a second phrase of the plurality of phrases that are stored at the system for the plurality of user accounts, the second phrase identifying a first order from a first merchant and a second order from a second merchant; in response to a determination that the first phrase matches the second phrase, determining, based on the first phrase, first details of the first order and second details of the second order; communicating a first request, based on the first details, via the network to the first merchant for a first list of items; communicating a second request, based on the second details, via the network to the second merchant for a second list of items; and processing a first payment for the first request and a second payment for the second request. 4. The system of claim 1 , wherein the first phrase is received from the user device via a text message. | 0.514019 |
7,827,035 | 1 | 22 | 1. A speech-input enabled computing method for processing spoken commands, comprising: supporting user interaction with a plurality of concurrently active software constructs, comprising at least one application which executes under an operating system, through a graphic user interface, the focus being granted to a software construct based on at least a user speech input; storing a representation of a set of command grammars corresponding to commands for at least a portion of the plurality of software constructs, at least a portion of which stored representation represent commands currently available for processing; processing the user speech input with a speech analyzer based on at least a portion of said stored representation, to determine if a user speech input corresponds to a represented command grammar available for processing, and if so processing a corresponding command by the operating system or the respective application to which it relates; and modifying the graphic user interface in dependence on said processing. | 1. A speech-input enabled computing method for processing spoken commands, comprising: supporting user interaction with a plurality of concurrently active software constructs, comprising at least one application which executes under an operating system, through a graphic user interface, the focus being granted to a software construct based on at least a user speech input; storing a representation of a set of command grammars corresponding to commands for at least a portion of the plurality of software constructs, at least a portion of which stored representation represent commands currently available for processing; processing the user speech input with a speech analyzer based on at least a portion of said stored representation, to determine if a user speech input corresponds to a represented command grammar available for processing, and if so processing a corresponding command by the operating system or the respective application to which it relates; and modifying the graphic user interface in dependence on said processing. 22. A computer readable medium for controlling a programmable computer to perform the steps of claim 1 . | 0.970353 |
9,401,138 | 1 | 8 | 1. A segment information generation device comprising: a waveform cutout unit implemented at least by hardware including a processor that cuts out a speech waveform from natural speech at a time period not depending on a pitch frequency of the natural speech, continuously; a feature parameter extraction unit implemented at least by hardware including a processor that extracts a feature parameter of a speech waveform from the speech waveform cut out by the waveform cutout unit; a time domain waveform generation unit implemented at least by hardware including a processor that generates a time domain waveform based on the feature parameter; a spectrum shape change degree estimation unit implemented at least by hardware including a processor that estimates a degree of change in spectrum shape indicating a degree of change in spectrum shape of natural speech; and a period control unit implemented at least by hardware including a processor that determines a time period to cut out a speech waveform from the natural speech based on the degree of change in spectrum shape. | 1. A segment information generation device comprising: a waveform cutout unit implemented at least by hardware including a processor that cuts out a speech waveform from natural speech at a time period not depending on a pitch frequency of the natural speech, continuously; a feature parameter extraction unit implemented at least by hardware including a processor that extracts a feature parameter of a speech waveform from the speech waveform cut out by the waveform cutout unit; a time domain waveform generation unit implemented at least by hardware including a processor that generates a time domain waveform based on the feature parameter; a spectrum shape change degree estimation unit implemented at least by hardware including a processor that estimates a degree of change in spectrum shape indicating a degree of change in spectrum shape of natural speech; and a period control unit implemented at least by hardware including a processor that determines a time period to cut out a speech waveform from the natural speech based on the degree of change in spectrum shape. 8. The segment information generation device according to claim 1 , further comprising: a natural speech storage unit comprising a storage device that stores information indicating a natural speech waveform of the natural speech. | 0.605172 |
7,734,628 | 1 | 9 | 1. A method for authoring rule sets, the method comprising: using a graphical interface to create at least one rule for inclusion in a rule set containing a plurality of rules, the graphical interface capable of illustrating logical syntax and precedence rules among operators and operands in the rule; displaying a single graph comprising a plurality of curves, each curve representing a priority function associated with one of the rules; and manually manipulating one or more of the curves within the single graph to change the associated priority function. | 1. A method for authoring rule sets, the method comprising: using a graphical interface to create at least one rule for inclusion in a rule set containing a plurality of rules, the graphical interface capable of illustrating logical syntax and precedence rules among operators and operands in the rule; displaying a single graph comprising a plurality of curves, each curve representing a priority function associated with one of the rules; and manually manipulating one or more of the curves within the single graph to change the associated priority function. 9. The method of claim 1 , further comprising associating a priority with each rule in the rule set. | 0.87013 |
9,979,748 | 1 | 7 | 1. A computer-implemented method, comprising: at a domain name system (DNS) computing system that is in network communication with one or more clients that submit DNS requests and in network communication with one or more servers associated with respective domains: performing an analysis of one or more substrings associated with a target domain name relative to a database of malicious substrings; when the analysis indicates a correspondence with one or more malicious substrings, automatically retrieving content associated with the target domain name and generating one or more vectors based on the content; comparing the one or more vectors with a corpus of vectors associated with malicious content, wherein comparing includes determining a similarity score for the target domain name relative to a first document in the corpus and the similarity score is based on a distance between the one or more vectors for the target domain name and one or more vectors in the corpus for the first document; and automatically generating a domain classification based on comparing the one or more vectors with the corpus of vectors, wherein automatically generating includes automatically generating domain name information for the target domain name indicating an association with malware if the similarity score is above a threshold. | 1. A computer-implemented method, comprising: at a domain name system (DNS) computing system that is in network communication with one or more clients that submit DNS requests and in network communication with one or more servers associated with respective domains: performing an analysis of one or more substrings associated with a target domain name relative to a database of malicious substrings; when the analysis indicates a correspondence with one or more malicious substrings, automatically retrieving content associated with the target domain name and generating one or more vectors based on the content; comparing the one or more vectors with a corpus of vectors associated with malicious content, wherein comparing includes determining a similarity score for the target domain name relative to a first document in the corpus and the similarity score is based on a distance between the one or more vectors for the target domain name and one or more vectors in the corpus for the first document; and automatically generating a domain classification based on comparing the one or more vectors with the corpus of vectors, wherein automatically generating includes automatically generating domain name information for the target domain name indicating an association with malware if the similarity score is above a threshold. 7. The computer-implemented method of claim 1 , wherein the target domain name is a first target domain name, the method further comprising: determining an autonomous system number associated with a second target domain name; determining an entity identifier associated with the second target domain name; and determining if the autonomous system number is associated with the identify identifier. | 0.540509 |
9,785,633 | 1 | 2 | 1. A foreign language service assisting apparatus for assisting conversations held for services between a first user using a first language and a second user using a second language, comprising a processor programmed to: acquire first information, via a first acquisition unit controlled by a controller, on a first article serving as an initial candidate for a certain article, the first information including a first part of a sentence inputted by a microphone having a speech recognition function in the first language, and the first article being associated with at least one article attribute of the certain article; acquire second information, via a second acquisition unit controlled by the controller, on at least one second article associated with the first article as a subsequent candidate for the certain article, and to acquire one or more subsequent speech candidates expected to be subsequently spoken by the second user, based on the first information and the at least one article attribute of the first article, each of the second information and the one or more subsequent speech candidates including a second part of the sentence in the first language; translate, via a translation unit controlled by the controller, into the second language, parts included in the first information, the second information, and the one or more subsequent speech candidates; present, via a presentation unit in communication with the controller, at least the translated parts; and accept, via an accepting unit in communication with the controller, a selection associated with the first article, a selection associated with the second article, or a selection associated with the one or more subsequent speech candidates, wherein the information on the article attribute is a particular attribute category, wherein the second acquisition unit acquires the second information from an article information storing unit that stores a plurality of article information entries including attribute information corresponding to preset plural kinds of attribute categories, based on attribute information corresponding to an attribute category associated with an article name of the first article, and particular attribute information corresponding to the particular attribute category, and wherein the second acquisition unit acquires, as the second information, at least one of part or all of first article information entries and part or all of second article information entries from the article information entries stored in the article information storing unit, wherein the first article information entries includes same attribute information as the attribute information corresponding to the attribute category associated with the article name of the first article, and also includes attribute information different from the particular attribute information corresponding to the particular attribute category indicating a color different from a particular color corresponding to the attribute category associated with color, wherein the second article information entries includes same attribute information as the attribute information corresponding to the attribute category associated with the article name of the first article, also includes same attribute information as the particular attribute information corresponding to the particular attribute category, and attribute information indicating a same color as the particular color, and excluding third article information entries associated with the first article, and wherein the particular attribute category is an attribute category associated with color, and the particular attribute information is information indicating the particular color. | 1. A foreign language service assisting apparatus for assisting conversations held for services between a first user using a first language and a second user using a second language, comprising a processor programmed to: acquire first information, via a first acquisition unit controlled by a controller, on a first article serving as an initial candidate for a certain article, the first information including a first part of a sentence inputted by a microphone having a speech recognition function in the first language, and the first article being associated with at least one article attribute of the certain article; acquire second information, via a second acquisition unit controlled by the controller, on at least one second article associated with the first article as a subsequent candidate for the certain article, and to acquire one or more subsequent speech candidates expected to be subsequently spoken by the second user, based on the first information and the at least one article attribute of the first article, each of the second information and the one or more subsequent speech candidates including a second part of the sentence in the first language; translate, via a translation unit controlled by the controller, into the second language, parts included in the first information, the second information, and the one or more subsequent speech candidates; present, via a presentation unit in communication with the controller, at least the translated parts; and accept, via an accepting unit in communication with the controller, a selection associated with the first article, a selection associated with the second article, or a selection associated with the one or more subsequent speech candidates, wherein the information on the article attribute is a particular attribute category, wherein the second acquisition unit acquires the second information from an article information storing unit that stores a plurality of article information entries including attribute information corresponding to preset plural kinds of attribute categories, based on attribute information corresponding to an attribute category associated with an article name of the first article, and particular attribute information corresponding to the particular attribute category, and wherein the second acquisition unit acquires, as the second information, at least one of part or all of first article information entries and part or all of second article information entries from the article information entries stored in the article information storing unit, wherein the first article information entries includes same attribute information as the attribute information corresponding to the attribute category associated with the article name of the first article, and also includes attribute information different from the particular attribute information corresponding to the particular attribute category indicating a color different from a particular color corresponding to the attribute category associated with color, wherein the second article information entries includes same attribute information as the attribute information corresponding to the attribute category associated with the article name of the first article, also includes same attribute information as the particular attribute information corresponding to the particular attribute category, and attribute information indicating a same color as the particular color, and excluding third article information entries associated with the first article, and wherein the particular attribute category is an attribute category associated with color, and the particular attribute information is information indicating the particular color. 2. The apparatus according to claim 1 , further comprising an original language input unit configured to input an original language as the first language, wherein the second acquisition unit acquires, from an article information storing unit storing information associated with a plurality of articles, article information associated with at least one article that satisfies at least one condition set based on original data expressed in the first language and input by the original language input unit, presents a list of the article information, and sets, as the first article, an article selected from the presented list by the first or second user. | 0.569921 |
9,430,449 | 10 | 19 | 10. An authoring environment for managing an editable preview of a webpage, the authoring environment comprising: a memory for storing executable instructions; and a processor for executing the instructions to: receive a first request to generate a first editable preview of the webpage, the first editable preview being a rendering of the webpage that allows a first content editor to edit content in context and to view edits to the webpage in a visual format that conforms to how the webpage will actually be rendered by a first particular browsing application; receive a second request to generate a second editable preview of the webpage, the second editable preview being a rendering of the webpage that allows a second content editor to edit content in context and to view edits to the webpage in a visual format that conforms to how the webpage will actually be rendered by a second particular browsing application; obtain assets of the webpage from at least one content repository; generate the first and second editable previews of the webpage using the obtained assets, each of the first and second editable previews rendered differently depending upon the operating system and browser application that is utilized to render the webpage, the webpage comprising assets arranged according to a layout, wherein the combination of the operating system and browser is different for the respective first and second editable previews; apply a first and second emulation template to the webpage to generate a first and second view, respectively, of the webpage as it would be rendered by a respective particular computing system that utilizes a respective particular browser application on a respective operating system; provide a first generated editable preview of the webpage for the first particular computing system and the first particular browser application to a first content editor for editing; determine a portion of the obtained assets of the first editable preview where editing of the portion of the assets requires a localization action to be completed; lock the determined portion of the assets of the first editable preview of the webpage; and receive a selection of a non-locked asset for editing by the first content editor, the authoring environment being separate from a preview server that publishes the webpage. | 10. An authoring environment for managing an editable preview of a webpage, the authoring environment comprising: a memory for storing executable instructions; and a processor for executing the instructions to: receive a first request to generate a first editable preview of the webpage, the first editable preview being a rendering of the webpage that allows a first content editor to edit content in context and to view edits to the webpage in a visual format that conforms to how the webpage will actually be rendered by a first particular browsing application; receive a second request to generate a second editable preview of the webpage, the second editable preview being a rendering of the webpage that allows a second content editor to edit content in context and to view edits to the webpage in a visual format that conforms to how the webpage will actually be rendered by a second particular browsing application; obtain assets of the webpage from at least one content repository; generate the first and second editable previews of the webpage using the obtained assets, each of the first and second editable previews rendered differently depending upon the operating system and browser application that is utilized to render the webpage, the webpage comprising assets arranged according to a layout, wherein the combination of the operating system and browser is different for the respective first and second editable previews; apply a first and second emulation template to the webpage to generate a first and second view, respectively, of the webpage as it would be rendered by a respective particular computing system that utilizes a respective particular browser application on a respective operating system; provide a first generated editable preview of the webpage for the first particular computing system and the first particular browser application to a first content editor for editing; determine a portion of the obtained assets of the first editable preview where editing of the portion of the assets requires a localization action to be completed; lock the determined portion of the assets of the first editable preview of the webpage; and receive a selection of a non-locked asset for editing by the first content editor, the authoring environment being separate from a preview server that publishes the webpage. 19. The authoring environment according to claim 10 , wherein the authoring environment is communicatively coupled with a content manager that determines how the first content editor can modify the assets of the editable preview based upon access rights of the first content editor. | 0.798859 |
8,924,853 | 1 | 6 | 1. An apparatus for a device having media playback functionality, said apparatus comprising: a detector, comprising a processor, configured to detect selection of a portion of media played back by the media playback functionality, the portion of media comprising media occurring within a time window that is based on a selected time proximity to a temporal indication associated with the selection; a converted-form media provider configured to provide a converted-form media representation of the selected media portion; and a display element configured to display the converted-form media representation provided by said converted-form media provider. | 1. An apparatus for a device having media playback functionality, said apparatus comprising: a detector, comprising a processor, configured to detect selection of a portion of media played back by the media playback functionality, the portion of media comprising media occurring within a time window that is based on a selected time proximity to a temporal indication associated with the selection; a converted-form media provider configured to provide a converted-form media representation of the selected media portion; and a display element configured to display the converted-form media representation provided by said converted-form media provider. 6. The apparatus of claim 1 wherein said converted-form media provider further comprises a language translator configured to translate at least a portion of a representation of the portion of media, provided in a first language, into a second language. | 0.507813 |
9,288,175 | 1 | 2 | 1. A method for extending a conversation across applications, comprising: providing a conversation repository for storing a plurality of conversations for a plurality of applications; identifying a conversation in the conversation repository having a context that is relevant to a first application object of a first application of the plurality of applications in response to a conversation call from a second application of the plurality of applications; and causing a user interface to display the identified conversation. | 1. A method for extending a conversation across applications, comprising: providing a conversation repository for storing a plurality of conversations for a plurality of applications; identifying a conversation in the conversation repository having a context that is relevant to a first application object of a first application of the plurality of applications in response to a conversation call from a second application of the plurality of applications; and causing a user interface to display the identified conversation. 2. The method of claim 1 , wherein the identified conversation includes a post relevant to the first application object a facet of the first application object and causing comprises causing the user interface to display the conversation including the post and the facet. | 0.809322 |
9,594,829 | 7 | 8 | 7. A system for identifying potential contexts for an unstructured data source, the system comprising: one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage devices, and program instructions stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, the program instructions configured to cause the system to perform a method comprising: identifying potential clues from the unstructured data source, the potential clues each associated with one or more contexts; determining a first set of potential contexts for the unstructured data source based on the potential clues; calculating an associated confidence value for each potential context in the first set of the potential contexts based on the potential clues; returning a second set of potential contexts from the first set of potential contexts, the second set of potential contexts comprising at least a first context with a highest confidence value; identifying a first concept which is not consistent with the first context; and modifying the first concept to be consistent with the first context, wherein modifying the first concept comprises at least one alteration of the first concept from the group consisting of spelling, grammar, and format. | 7. A system for identifying potential contexts for an unstructured data source, the system comprising: one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage devices, and program instructions stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, the program instructions configured to cause the system to perform a method comprising: identifying potential clues from the unstructured data source, the potential clues each associated with one or more contexts; determining a first set of potential contexts for the unstructured data source based on the potential clues; calculating an associated confidence value for each potential context in the first set of the potential contexts based on the potential clues; returning a second set of potential contexts from the first set of potential contexts, the second set of potential contexts comprising at least a first context with a highest confidence value; identifying a first concept which is not consistent with the first context; and modifying the first concept to be consistent with the first context, wherein modifying the first concept comprises at least one alteration of the first concept from the group consisting of spelling, grammar, and format. 8. The system of claim 7 , wherein the method further comprises: inputting the second set of potential contexts into a data mining technique. | 0.798571 |
7,925,657 | 27 | 30 | 27. The system of claim 25 , wherein the system further comprises: a data communication network coupled to the server device; and a data processing device programmed to transmit the search query over the data communication network to the server device. | 27. The system of claim 25 , wherein the system further comprises: a data communication network coupled to the server device; and a data processing device programmed to transmit the search query over the data communication network to the server device. 30. The system of claim 27 , wherein estimating the breadth of the search query comprises estimating the breadth based on a total number of documents in a result set that is responsive to the search query. | 0.865132 |
8,046,226 | 1 | 4 | 1. A method for a user to create a report through voice output, comprising: (a) receiving by a computer information from user input via a user input device; (b) processing by the computer the information, wherein said processing step utilizes a heuristic algorithm; (c) performing by the computer a task resultant from said processing step, wherein said performing step further comprises a step of making a heuristic selection of one or more macros from a macro library; (d) preparing by the computer a response based on said performing step; (e) communicating the response as voice output verbalized through use of a voice output device; and (f) repeating said steps (a)-(e) until the report is completed. | 1. A method for a user to create a report through voice output, comprising: (a) receiving by a computer information from user input via a user input device; (b) processing by the computer the information, wherein said processing step utilizes a heuristic algorithm; (c) performing by the computer a task resultant from said processing step, wherein said performing step further comprises a step of making a heuristic selection of one or more macros from a macro library; (d) preparing by the computer a response based on said performing step; (e) communicating the response as voice output verbalized through use of a voice output device; and (f) repeating said steps (a)-(e) until the report is completed. 4. The method of claim 1 , wherein said performing step further comprises a step of selecting a statement of the report. | 0.672131 |
9,111,010 | 7 | 9 | 7. A social bookmarking data processing system configured for displaying search results for weighted, multi-term content searches, the system comprising: a hardware processor and memory; a social bookmarking system coupled to a data store of social bookmarks and coupled to a plurality of content sources over a computer communications network; a multi-term search engine module executing by the hardware processor and memory in a computing platform and coupled to the social bookmarking system, the module comprising program code enabled to perform a content search for both content and content meta-data according to specified weighted search terms, to retrieve search results for the content search, to compute a relevance for each of the weighted search terms, and to present both the search results and also a relevance indicator for each computed relevance for each of the weighted search terms found in connection with each of the search results in a user interface to the search engine. | 7. A social bookmarking data processing system configured for displaying search results for weighted, multi-term content searches, the system comprising: a hardware processor and memory; a social bookmarking system coupled to a data store of social bookmarks and coupled to a plurality of content sources over a computer communications network; a multi-term search engine module executing by the hardware processor and memory in a computing platform and coupled to the social bookmarking system, the module comprising program code enabled to perform a content search for both content and content meta-data according to specified weighted search terms, to retrieve search results for the content search, to compute a relevance for each of the weighted search terms, and to present both the search results and also a relevance indicator for each computed relevance for each of the weighted search terms found in connection with each of the search results in a user interface to the search engine. 9. The system of claim 7 , wherein the program code of the module is further enabled to sort the search results in the user interface according to search results matching user interests of a user requesting the content search. | 0.797127 |
9,852,233 | 10 | 15 | 10. A system comprising: a machine having a memory and at least one processor; and a memory storing instructions that, when executed by the at least one processor, causes the machine to perform operations comprising: accessing a plurality of social activity signals associated with a user and indicative of actions that are performed by the user and viewable by other users; receiving, from the user, user-entered text in a search field for a search engine; determining predicted queries based on the user-entered text and the plurality of social activity signal, each one of the predicted queries comprising predicted text and at least a portion of the user-entered text, the predicted text being absent from the user-entered text, the determining comprising: determining potential predicted queries based on the user-entered text; and assigning a corresponding predicted query value to each one of the potential predicted queries based on a determination for each potential predicted query of whether the potential predicted query corresponds to one of the social activity signals indicative of an action performed by the user that is viewable by other users, the assigning including: for each potential predicted query determined to correspond to one of the plurality of social activity signals indicative of an action performed by the user that is viewable by other users, determining a corresponding social activity type of the one of the plurality of social activity signals indicative of an action performed by the user that is viewable by other users; and for each potential predicted query determined to correspond to the one of the plurality of social activity signals, calculating the corresponding predicted query value based on a corresponding weight of the corresponding social activity type; and causing the predicted queries to be displayed, to the user, in an autocomplete user interface element of the search field. | 10. A system comprising: a machine having a memory and at least one processor; and a memory storing instructions that, when executed by the at least one processor, causes the machine to perform operations comprising: accessing a plurality of social activity signals associated with a user and indicative of actions that are performed by the user and viewable by other users; receiving, from the user, user-entered text in a search field for a search engine; determining predicted queries based on the user-entered text and the plurality of social activity signal, each one of the predicted queries comprising predicted text and at least a portion of the user-entered text, the predicted text being absent from the user-entered text, the determining comprising: determining potential predicted queries based on the user-entered text; and assigning a corresponding predicted query value to each one of the potential predicted queries based on a determination for each potential predicted query of whether the potential predicted query corresponds to one of the social activity signals indicative of an action performed by the user that is viewable by other users, the assigning including: for each potential predicted query determined to correspond to one of the plurality of social activity signals indicative of an action performed by the user that is viewable by other users, determining a corresponding social activity type of the one of the plurality of social activity signals indicative of an action performed by the user that is viewable by other users; and for each potential predicted query determined to correspond to the one of the plurality of social activity signals, calculating the corresponding predicted query value based on a corresponding weight of the corresponding social activity type; and causing the predicted queries to be displayed, to the user, in an autocomplete user interface element of the search field. 15. The system of claim 10 , wherein the plurality of social activity signals comprise at least one of a like of content, a share of content, a follow of content, a comment on content, a status update, and a calendar event. | 0.577652 |
9,558,462 | 1 | 2 | 1. A method for identifying conditional actions in a business process comprising: determining a plurality of pairs of text fragments that respectively include text fragments that are similar according to a pre-defined similarity standard; for each pair of at least a subset of the pairs, determining at least one difference between the text fragments of the corresponding pair; merging, by at least one hardware processor, at least two particular pairs of the subset of the pairs; and outputting the merged particular pairs to indicate the conditional actions in the business process; wherein, using the merged particular pairs, conditions and actions of the business process are visually associated to enable a user to construct or modify a business model or design or modify an automated business process. | 1. A method for identifying conditional actions in a business process comprising: determining a plurality of pairs of text fragments that respectively include text fragments that are similar according to a pre-defined similarity standard; for each pair of at least a subset of the pairs, determining at least one difference between the text fragments of the corresponding pair; merging, by at least one hardware processor, at least two particular pairs of the subset of the pairs; and outputting the merged particular pairs to indicate the conditional actions in the business process; wherein, using the merged particular pairs, conditions and actions of the business process are visually associated to enable a user to construct or modify a business model or design or modify an automated business process. 2. The method of claim 1 , wherein the merging comprises merging the at least two particular pairs in response to determining that the particular pairs have at least one text fragment in common. | 0.737838 |
7,493,335 | 1 | 6 | 1. A computer implemented object process graph relational database interface system, comprising: a computing device interfaced to a storage medium and a display medium; a relational database table definer for creating a database schema that is stored in a relational database management system, said database schema corresponding to an object process graph in an object process graph system for creating and executing application programs, thereby providing a mapping between said object process graph in said object process graph system and said database schema in said relational database management system, wherein said object process graph system comprises: said object process graph for defining an application, said object process graph including process and control structures for control over an order and a timing of data validation, transformation and display, said object process graph being dynamic, directed and cyclical, and said object process graph including at least one data node, at least one process node, and at least one application state node; and an interpreter for interpreting said object process graph to execute said application, said object process graph capable of being changed while being interpreted by said interpreter. | 1. A computer implemented object process graph relational database interface system, comprising: a computing device interfaced to a storage medium and a display medium; a relational database table definer for creating a database schema that is stored in a relational database management system, said database schema corresponding to an object process graph in an object process graph system for creating and executing application programs, thereby providing a mapping between said object process graph in said object process graph system and said database schema in said relational database management system, wherein said object process graph system comprises: said object process graph for defining an application, said object process graph including process and control structures for control over an order and a timing of data validation, transformation and display, said object process graph being dynamic, directed and cyclical, and said object process graph including at least one data node, at least one process node, and at least one application state node; and an interpreter for interpreting said object process graph to execute said application, said object process graph capable of being changed while being interpreted by said interpreter. 6. The system of claim 1 , further comprising: a graph data object definer for creating a set of data node descriptions for importing into the object process graph system. | 0.909524 |
9,262,384 | 10 | 11 | 10. The apparatus of claim 1 , wherein the system is configured such that the at least some of the one or more computer-readable semantic tags are searchable. | 10. The apparatus of claim 1 , wherein the system is configured such that the at least some of the one or more computer-readable semantic tags are searchable. 11. The apparatus of claim 10 , wherein the system is configured such that the at least one reference is stored with the at least one data structure. | 0.989118 |
9,143,579 | 9 | 10 | 9. The method of claim 1 , wherein the determining of the relevance between the user profile and each of the other user profiles comprises determining the relevance between the user profile and each of the other user profiles based at least in part on a weighted combination of an interaction factor, a reliance factor, and a commonality factor specified by the predefined relevance heuristic. | 9. The method of claim 1 , wherein the determining of the relevance between the user profile and each of the other user profiles comprises determining the relevance between the user profile and each of the other user profiles based at least in part on a weighted combination of an interaction factor, a reliance factor, and a commonality factor specified by the predefined relevance heuristic. 10. The method of claim 9 , wherein: the interaction factor includes a measure of interaction between the user associated with the user profile and each other user associated with each of the other user profiles; the reliance factor includes a measure of reliance between the user associated with the user profile and each other user associated with each of the other user profiles; and the commonality factor includes a measure of shared commonalities between the user profile and each of the other user profiles. | 0.814841 |
7,613,997 | 5 | 8 | 5. A computing system in accordance with claim 1 , wherein the act of determining that at least one group identifier is associated with the hierarchically-structured document comprises the following: an act of reading a pre-processor directive that indicates that the at least one group identifier is associated with the hierarchically-structured document. | 5. A computing system in accordance with claim 1 , wherein the act of determining that at least one group identifier is associated with the hierarchically-structured document comprises the following: an act of reading a pre-processor directive that indicates that the at least one group identifier is associated with the hierarchically-structured document. 8. A computing system in accordance with claim 5 , wherein the act of accessing a hierarchically-structured document comprises the following: an act of reading the hierarchically-structured document from persistent memory. | 0.91974 |
8,782,081 | 4 | 5 | 4. The computer implemented method of claim 1 , wherein the query template is associated with a metadata mapping associating the plurality of variables to a data model of the database. | 4. The computer implemented method of claim 1 , wherein the query template is associated with a metadata mapping associating the plurality of variables to a data model of the database. 5. The computer implemented method of claim 4 , wherein the metadata mapping includes associating one or more of the plurality of variables with data constraints of the data model, the method further comprising: validating at least one of the received values based on the data constraints associated with the respective variable of the received value. | 0.926814 |
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