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8,315,868 | 1 | 5 | 1. A vehicle-mounted voice recognition apparatus comprising: a voice input unit for acquiring an inputted voice; a voice recognition unit for performing voice recognition on said acquired inputted voice; a guidance information output unit for providing a guidance on a basis of a result of said voice recognition; and a guidance description determining unit for determining a user's degree of understanding of recognized words to said guidance according to a number of timeout times that a timeout has occurred in a user's operation which is monitored while said voice recognition unit performs the voice recognition, a number of correction times that a correction has been made to the user's operation, or both of the number of timeout times and the number of correction times so as to change said guidance. | 1. A vehicle-mounted voice recognition apparatus comprising: a voice input unit for acquiring an inputted voice; a voice recognition unit for performing voice recognition on said acquired inputted voice; a guidance information output unit for providing a guidance on a basis of a result of said voice recognition; and a guidance description determining unit for determining a user's degree of understanding of recognized words to said guidance according to a number of timeout times that a timeout has occurred in a user's operation which is monitored while said voice recognition unit performs the voice recognition, a number of correction times that a correction has been made to the user's operation, or both of the number of timeout times and the number of correction times so as to change said guidance. 5. The vehicle-mounted voice recognition apparatus according to claim 1 , wherein said apparatus has a user state determining unit for determining the user's state of mind from a signal detected by a sensor, and said recognized word understanding degree determining unit reflects the user's state of mind outputted from said user state determining unit reflect in the determination of the user's degree of understanding of recognized words to the voice guidance. | 0.549708 |
8,423,383 | 71 | 82 | 71. An online system for providing online medical consultation services including quantitative and qualitative medical opinions from a panel of selected medical professionals, the system comprising: (a) creating a database of potential participating medical professionals, along with information about each the potential participating medical professionals, including the qualifications of the potential participating medical professionals; (b) means for a user seeking medical consultation services to access the system; (c) means for user to electronically submit information to the system regarding the desired medical consultation services; (d) means for selecting a panel of medical professionals qualified to provide the desired medical consultation services based upon information in the database; (e) means for electronically providing question(s) based on the user submitted information, and the user submitted information, to the medical professionals in the selected panel such that the medical professionals in the selected panel formulate response(s) to provided question(s) and comments regarding user submitted information; (f) means for electronically forwarding the responses and comments from the medical professionals in the selected panel to the system; (g) means for calculating the degree of consensus of forwarded responses to questions; (h) means for displaying said calculated consensus on a system website accessible to user; and (i) means for displaying said comments forwarded from the medical professionals on a system website accessible to the user. | 71. An online system for providing online medical consultation services including quantitative and qualitative medical opinions from a panel of selected medical professionals, the system comprising: (a) creating a database of potential participating medical professionals, along with information about each the potential participating medical professionals, including the qualifications of the potential participating medical professionals; (b) means for a user seeking medical consultation services to access the system; (c) means for user to electronically submit information to the system regarding the desired medical consultation services; (d) means for selecting a panel of medical professionals qualified to provide the desired medical consultation services based upon information in the database; (e) means for electronically providing question(s) based on the user submitted information, and the user submitted information, to the medical professionals in the selected panel such that the medical professionals in the selected panel formulate response(s) to provided question(s) and comments regarding user submitted information; (f) means for electronically forwarding the responses and comments from the medical professionals in the selected panel to the system; (g) means for calculating the degree of consensus of forwarded responses to questions; (h) means for displaying said calculated consensus on a system website accessible to user; and (i) means for displaying said comments forwarded from the medical professionals on a system website accessible to the user. 82. The system of claim 71 wherein said means for calculating the degree of consensus calculates the degree of consensus in real time, as responses from medical professionals in the selected panel are received by the system. | 0.84926 |
9,257,052 | 1 | 2 | 1. A computer-implemented method of evaluating candidate answers to questions in a target knowledge domain, the method comprising: identifying, by a question answering module, a plurality of evidence items in the target knowledge domain; identifying, by the question answering module, a plurality of answers to questions in the target knowledge domain; determining, by the question answering module, associations between an evidence item and one or more of the answers including forming an association based at least in part on a value associated with an evidence item supporting or contradicting an answer to a question; and assigning a correlation value for an association between a particular evidence item and a particular answer to a particular question, the correlation value indicating a strength with which the particular item supports or contradicts the particular answer to the particular question: identifying, by the question answering module, an evidence item for which a value has not been received, and identifying, by the question answering module, a most valuable evidence item for which a value has not been received based at least in part on the most valuable evidence item having a highest correlation value of the associations between the plurality of evidence items and the answers to the questions; receiving, by the question answering module from a user, a question receiving, by the question answering module, values for the one or more evidence items; and providing, by the question answering module, an answer to the question in dependence upon the associations between each evidence item and one or more of the answers and the values for the one or more evidence items. | 1. A computer-implemented method of evaluating candidate answers to questions in a target knowledge domain, the method comprising: identifying, by a question answering module, a plurality of evidence items in the target knowledge domain; identifying, by the question answering module, a plurality of answers to questions in the target knowledge domain; determining, by the question answering module, associations between an evidence item and one or more of the answers including forming an association based at least in part on a value associated with an evidence item supporting or contradicting an answer to a question; and assigning a correlation value for an association between a particular evidence item and a particular answer to a particular question, the correlation value indicating a strength with which the particular item supports or contradicts the particular answer to the particular question: identifying, by the question answering module, an evidence item for which a value has not been received, and identifying, by the question answering module, a most valuable evidence item for which a value has not been received based at least in part on the most valuable evidence item having a highest correlation value of the associations between the plurality of evidence items and the answers to the questions; receiving, by the question answering module from a user, a question receiving, by the question answering module, values for the one or more evidence items; and providing, by the question answering module, an answer to the question in dependence upon the associations between each evidence item and one or more of the answers and the values for the one or more evidence items. 2. The method of claim 1 further comprising processing, by the question answering module, one or more information sources associated with a target knowledge domain. | 0.845865 |
7,945,598 | 4 | 5 | 4. The computer implemented method of claim 3 , further comprising: searching the metadata in the metadata repository to locate the process information about the multi-step process; determining from the metadata about the multi-step process that the multi-step process comprises a plurality of tasks; and locating, using the pointers in the metadata about the multi-step process, the process information for the plurality of tasks in accordance with the execution order specified by the practice requirements. | 4. The computer implemented method of claim 3 , further comprising: searching the metadata in the metadata repository to locate the process information about the multi-step process; determining from the metadata about the multi-step process that the multi-step process comprises a plurality of tasks; and locating, using the pointers in the metadata about the multi-step process, the process information for the plurality of tasks in accordance with the execution order specified by the practice requirements. 5. The computer implemented method of claim 4 , further comprising: providing, to a user, the process information in the execution order specified by the practice requirements to demonstrate compliance with an audit. | 0.936321 |
9,602,130 | 19 | 21 | 19. The system of claim 18 , wherein the combining of the long binary representations corresponding to respective search characters comprises combining the long binary representations with a bit-wise OR. | 19. The system of claim 18 , wherein the combining of the long binary representations corresponding to respective search characters comprises combining the long binary representations with a bit-wise OR. 21. The system of claim 19 , wherein the processing circuit is further configured to: evaluate a regular expression to generate the set of search characters. | 0.95109 |
8,483,672 | 11 | 13 | 11. Apparatus, comprising: a memory, which is configured to hold a list of target users of a communication network and respective speech phrases that are characteristic of the target users; and a processor, which is configured to maintain the list in the memory, to select a plurality of candidate communication terminals from among multiple communication terminals in the communication network based on a selection criterion, to analyze speech that is communicated via the candidate communication terminals so as to identify one or more of the speech phrases in the speech, and to correlate one of the candidate communication terminals with a target user who is associated in the list with the identified speech phrases. | 11. Apparatus, comprising: a memory, which is configured to hold a list of target users of a communication network and respective speech phrases that are characteristic of the target users; and a processor, which is configured to maintain the list in the memory, to select a plurality of candidate communication terminals from among multiple communication terminals in the communication network based on a selection criterion, to analyze speech that is communicated via the candidate communication terminals so as to identify one or more of the speech phrases in the speech, and to correlate one of the candidate communication terminals with a target user who is associated in the list with the identified speech phrases. 13. The apparatus according to claim 11 , wherein the processor is configured to select the candidate communication terminals using the selection criterion by choosing communication terminals that were not used in the communication network for at least a predefined time period. | 0.501792 |
8,386,232 | 1 | 11 | 1. A computer-executed method comprising the steps of: creating a model by: receiving a data set that includes a plurality of words in a particular language, wherein in the particular language, words are formed by characters; wherein the plurality of words include items for which designated results have not been previously established; wherein an item is either a single character or a segment that comprises a plurality of characters; determining which items are related based on an analysis of the data set; based on the determining which items are related, generating, from items in the data set, clusters of related items; a computer system generating the model based at least on both: the clusters of related items; and training data that includes a plurality of entries, wherein each entry includes an entry item and a designated result for said entry item; wherein the step of generating the model comprises applying features to items in the training data based on the clusters of related items; after generating the model, performing the steps of: receiving a set of input data, wherein the input data includes items that have not been associated with designated results; and applying the model to the input data to determine predicted results for items within the input data. | 1. A computer-executed method comprising the steps of: creating a model by: receiving a data set that includes a plurality of words in a particular language, wherein in the particular language, words are formed by characters; wherein the plurality of words include items for which designated results have not been previously established; wherein an item is either a single character or a segment that comprises a plurality of characters; determining which items are related based on an analysis of the data set; based on the determining which items are related, generating, from items in the data set, clusters of related items; a computer system generating the model based at least on both: the clusters of related items; and training data that includes a plurality of entries, wherein each entry includes an entry item and a designated result for said entry item; wherein the step of generating the model comprises applying features to items in the training data based on the clusters of related items; after generating the model, performing the steps of: receiving a set of input data, wherein the input data includes items that have not been associated with designated results; and applying the model to the input data to determine predicted results for items within the input data. 11. The method of claim 1 , wherein the step of applying the model comprises assigning a feature associated with a particular cluster of the clusters of related items to an item in the input data. | 0.740741 |
8,112,702 | 2 | 3 | 2. The method of claim 1 , further comprising: forming a first synoptic annotation for the first annotated clip of the first video based on the related annotations in the first group. | 2. The method of claim 1 , further comprising: forming a first synoptic annotation for the first annotated clip of the first video based on the related annotations in the first group. 3. The method of claim 2 , wherein the first synoptic annotation contains a summary of content of the related annotations in the first group. | 0.940102 |
8,271,503 | 1 | 5 | 1. A computer program product tangibly embodied in a non-transitory machine-readable storage device that stores instructions for causing data processing apparatus to perform operations for facilitating automated identification of matches between elements of disparate schemas, the operations comprising: calculating a first degree of similarity between elements of a first schema and elements of a second schema using a first matching process; calculating a second degree of similarity between the elements of the first schema and the elements of a second schema using a second matching process; combining the first degree of similarity and the second degree of similarity using a first weighting vector to provide a combined degree of similarity, the first weighting vector comprising a first weighting coefficient corresponding to the first matching process and a second weighting coefficient corresponding to the second matching process; determining a level of ambiguity based on the first combined degree of similarity, the level of ambiguity accounting for at least one of a number of unambiguous matches between elements of the first and second schemas, a number of ambiguous matches between elements of the first and second schemas and a number of impossible matches between elements of the first and second schemas; and adjusting the first weighting coefficient and the second weighting coefficient based on the level of ambiguity to provide a second weighting vector by receiving user feedback relating to a subset of possible matches between the elements of the first schema and the elements of the second schema, the first coefficient and the second coefficient being adjusted based on the user feedback. | 1. A computer program product tangibly embodied in a non-transitory machine-readable storage device that stores instructions for causing data processing apparatus to perform operations for facilitating automated identification of matches between elements of disparate schemas, the operations comprising: calculating a first degree of similarity between elements of a first schema and elements of a second schema using a first matching process; calculating a second degree of similarity between the elements of the first schema and the elements of a second schema using a second matching process; combining the first degree of similarity and the second degree of similarity using a first weighting vector to provide a combined degree of similarity, the first weighting vector comprising a first weighting coefficient corresponding to the first matching process and a second weighting coefficient corresponding to the second matching process; determining a level of ambiguity based on the first combined degree of similarity, the level of ambiguity accounting for at least one of a number of unambiguous matches between elements of the first and second schemas, a number of ambiguous matches between elements of the first and second schemas and a number of impossible matches between elements of the first and second schemas; and adjusting the first weighting coefficient and the second weighting coefficient based on the level of ambiguity to provide a second weighting vector by receiving user feedback relating to a subset of possible matches between the elements of the first schema and the elements of the second schema, the first coefficient and the second coefficient being adjusted based on the user feedback. 5. The computer program product of claim 1 , wherein the first matching process is executed using a first matcher comprising one of a schema-based matcher, a content-based matcher, a type-based matcher and a semantic-based matcher, and the second matching process is executed using a second matcher that is different from the first matcher and that comprises one of a schema-based matcher, a content-based matcher, a type-based matcher and a semantic-based matcher. | 0.620718 |
9,015,028 | 1 | 2 | 1. A method of enabling input into an electronic device that comprises a display and a number of input members, at least some of the input members each having a plurality of characters assigned thereto, the method comprising: detecting, at a location of a cursor, a deletion of a last character of a first default output that has been output on the display; detecting, after detecting the deletion, a selection of an input member having a plurality of characters, including the last character, assigned thereto; subjecting characters of the first default output that have not been deleted and the selected input member to disambiguation, to generate a second default output including the characters of the first default output that have not been deleted and a character other than the last character assigned to the selected input member; and outputting the second default output. | 1. A method of enabling input into an electronic device that comprises a display and a number of input members, at least some of the input members each having a plurality of characters assigned thereto, the method comprising: detecting, at a location of a cursor, a deletion of a last character of a first default output that has been output on the display; detecting, after detecting the deletion, a selection of an input member having a plurality of characters, including the last character, assigned thereto; subjecting characters of the first default output that have not been deleted and the selected input member to disambiguation, to generate a second default output including the characters of the first default output that have not been deleted and a character other than the last character assigned to the selected input member; and outputting the second default output. 2. The method of claim 1 , further comprising employing the characters of the first default output that have not been deleted as context in the subjecting of the characters of the first default output that have not been deleted and the selected input member to disambiguation. | 0.7125 |
8,320,959 | 6 | 10 | 6. An electronic device comprising: an input apparatus, the input apparatus including a multiple-axis input device; a memory having a plurality of words and associated frequency values stored therein; and a processor configured to execute instructions to: receive an ambiguous text input; identify a set of words from the plurality of words stored in the memory, the set of words corresponding to the ambiguous text input; determine, from the set of words, a first word as a preferred potential word match for the ambiguous input, based on a comparison of the frequency values associated with words in the set of words, the first word having a first associated frequency value; output at least a subset of the set of words, including the first word and at least one other word selected from the set of words; detect a selection of a second word having a second associated frequency value from the subset of the set of words that is different from the first word, the selection being made by an input from the multi-axis input device, the first associated frequency value being greater than the second associated frequency value; and responsive to the selection, create a revised frequency value. | 6. An electronic device comprising: an input apparatus, the input apparatus including a multiple-axis input device; a memory having a plurality of words and associated frequency values stored therein; and a processor configured to execute instructions to: receive an ambiguous text input; identify a set of words from the plurality of words stored in the memory, the set of words corresponding to the ambiguous text input; determine, from the set of words, a first word as a preferred potential word match for the ambiguous input, based on a comparison of the frequency values associated with words in the set of words, the first word having a first associated frequency value; output at least a subset of the set of words, including the first word and at least one other word selected from the set of words; detect a selection of a second word having a second associated frequency value from the subset of the set of words that is different from the first word, the selection being made by an input from the multi-axis input device, the first associated frequency value being greater than the second associated frequency value; and responsive to the selection, create a revised frequency value. 10. The electronic device of claim 6 , wherein the memory includes a generic word portion and a learning portion, the revised frequency value being stored in the learning portion, and wherein the processor is further configured to: detect a further ambiguous text input; output a third word and a fourth word each corresponding with the further ambiguous text input, the third word having a third associated frequency value, the fourth word having a fourth associated frequency value, and the third associated frequency value being greater than the fourth associated frequency value; determine that the third and fourth associated values are revised frequency values from the learning portion; and responsive to a selection of the fourth word, store as another revised frequency value a frequency value lower than the fourth associated frequency value and being associated with the third word. | 0.517297 |
8,477,095 | 9 | 13 | 9. The system of claim 5 wherein: said human-audible output comprises repeating a previous human-audible output. | 9. The system of claim 5 wherein: said human-audible output comprises repeating a previous human-audible output. 13. The system of claim 9 wherein said pen based computer system is further capable of continuously producing audio output corresponding to a reading of substantially all of the printed words of said first children's book by pressing a button on said pen based computer system. | 0.863007 |
8,965,776 | 2 | 3 | 2. The system of claim 1 , where, when updating the word, the one or more devices are to: obtain a first segment, of the plurality of segments, and generate a first updated segment based on the segment and extrinsic information associated with a first segment of the previous word. | 2. The system of claim 1 , where, when updating the word, the one or more devices are to: obtain a first segment, of the plurality of segments, and generate a first updated segment based on the segment and extrinsic information associated with a first segment of the previous word. 3. The system of claim 2 , where, when identifying the sets of LRPs, the one or more devices are further to: identify reliability values associated with samples within the first updated segment, sort the samples, within the first updated segment, based on the identified reliability values, and select a portion of the sorted samples, associated with the one or more lowest levels of reliability, based on the reliability values. | 0.924392 |
8,744,833 | 1 | 2 | 1. A computer-readable storage medium having computer-executable instructions stored thereon for creating a language model and performing Kana-Kanji conversion, wherein the computer-executable instructions cause a computer that executes the instructions to: receive a Kana character string; divide the Kana character string into substrings and generate Kanji candidates for each substring; obtain a plurality of trigram probabilities of Kanji candidates; determine whether any of the plurality of trigram probabilities is above a first threshold and select a Kanji candidate if a trigram probability is above the first threshold; when it is determined none of the plurality of trigram probabilities is above the first threshold: obtain a plurality of bigram probabilities of Kanji candidates; and determine whether any of the plurality of bigram probabilities is above a second threshold and select a Kanji candidate if a bigram probability is above the second threshold; when it is determined none of the plurality of bigram probabilities is above the second threshold: select a Kanji candidate based on cluster bigram probabilities of the Kanji candidates, wherein at least one cluster in the cluster bigram probabilities includes combining the same Kanji-Kana pairs with different parts-of-speech; and display the selected Kanji candidates based on an order of precedence. | 1. A computer-readable storage medium having computer-executable instructions stored thereon for creating a language model and performing Kana-Kanji conversion, wherein the computer-executable instructions cause a computer that executes the instructions to: receive a Kana character string; divide the Kana character string into substrings and generate Kanji candidates for each substring; obtain a plurality of trigram probabilities of Kanji candidates; determine whether any of the plurality of trigram probabilities is above a first threshold and select a Kanji candidate if a trigram probability is above the first threshold; when it is determined none of the plurality of trigram probabilities is above the first threshold: obtain a plurality of bigram probabilities of Kanji candidates; and determine whether any of the plurality of bigram probabilities is above a second threshold and select a Kanji candidate if a bigram probability is above the second threshold; when it is determined none of the plurality of bigram probabilities is above the second threshold: select a Kanji candidate based on cluster bigram probabilities of the Kanji candidates, wherein at least one cluster in the cluster bigram probabilities includes combining the same Kanji-Kana pairs with different parts-of-speech; and display the selected Kanji candidates based on an order of precedence. 2. The computer-readable storage medium of claim 1 wherein the computer-executable instructions further cause the computer that executes the instructions to store a dictionary of words in the Japanese language. | 0.889006 |
8,849,818 | 29 | 33 | 29. A non-transitory computer readable storage medium storing one or more programs for execution by one or more processors of a respective computer system, the one or more programs comprising instructions for: providing a rating icon for a first document in a group of documents; receiving from a user, through a selection of the rating icon, a user rating of the first document in the group of documents; generating a first user-specific rating for the first document based on the user rating, wherein the first user-specific rating corresponds to the user and is distinct from respective user-specific ratings of other users for the group of documents; receiving a search request sent by the user; identifying a plurality of documents that satisfy the search request, wherein the plurality of documents includes a second document that has not previously been rated by the user and is in the group of documents; and sending a response to the search request, the response including instructions to display a ranked set of links to at least some of the plurality of documents that satisfy the search request, wherein the ranked set of the links includes a link to the second document, wherein the link to the second document is displayed with the first user-specific rating. | 29. A non-transitory computer readable storage medium storing one or more programs for execution by one or more processors of a respective computer system, the one or more programs comprising instructions for: providing a rating icon for a first document in a group of documents; receiving from a user, through a selection of the rating icon, a user rating of the first document in the group of documents; generating a first user-specific rating for the first document based on the user rating, wherein the first user-specific rating corresponds to the user and is distinct from respective user-specific ratings of other users for the group of documents; receiving a search request sent by the user; identifying a plurality of documents that satisfy the search request, wherein the plurality of documents includes a second document that has not previously been rated by the user and is in the group of documents; and sending a response to the search request, the response including instructions to display a ranked set of links to at least some of the plurality of documents that satisfy the search request, wherein the ranked set of the links includes a link to the second document, wherein the link to the second document is displayed with the first user-specific rating. 33. The computer readable storage medium of claim 29 , wherein generating the first user-specific rating for the first document is additionally based on the user's rating of another document in the group of documents. | 0.761538 |
9,978,365 | 1 | 16 | 1. A method comprising: determining weights for attributes by ranking the attributes based on user interactions with a user terminal; storing the weights for the attributes in a memory of the user terminal; processing, on the user terminal after the storing, a voice input in a first analysis, wherein the first analysis includes identifying one of the attributes; identifying, as a result of the first analysis, one domain of a plurality of domains, wherein the identifying the one domain includes retrieving the stored weight for the identified attribute and identifying the one domain based on the identified attribute and the retrieved weight; processing, on the user terminal, the voice input in a second analysis using specialized information of the one domain, wherein each of the plurality of domains comprises different respective specialized information; and outputting as synthesized speech a response resulting from the second analysis. | 1. A method comprising: determining weights for attributes by ranking the attributes based on user interactions with a user terminal; storing the weights for the attributes in a memory of the user terminal; processing, on the user terminal after the storing, a voice input in a first analysis, wherein the first analysis includes identifying one of the attributes; identifying, as a result of the first analysis, one domain of a plurality of domains, wherein the identifying the one domain includes retrieving the stored weight for the identified attribute and identifying the one domain based on the identified attribute and the retrieved weight; processing, on the user terminal, the voice input in a second analysis using specialized information of the one domain, wherein each of the plurality of domains comprises different respective specialized information; and outputting as synthesized speech a response resulting from the second analysis. 16. The method of claim 1 , wherein the retrieved weight of the identified attribute is updated based upon an outcome of the second analysis. | 0.856707 |
8,762,289 | 20 | 23 | 20. The computer-implemented method of claim 3 , further comprising: receiving a search result from the searcher responsive to the query. | 20. The computer-implemented method of claim 3 , further comprising: receiving a search result from the searcher responsive to the query. 23. The computer-implemented method of claim 20 , further comprising comparing the search result to a stored search result designated for the query. | 0.967643 |
9,990,432 | 1 | 4 | 1. A method, comprising the steps of: receiving, by a server computer communicatively coupled to a network and comprising at least one processor executing specific computer-executable instructions within a memory, via a first graphical user interface (GUI) for a multi-lingual domain name search engine displayed on a client computer, a domain name search string; tokenizing, by the server computer, the domain name search string; identifying, by the server computer, a search string token within the domain name search string as a concept seed; executing, by the server computer, a first database command to create a data record storing the search string token in association with a concept id; executing, by the server computer, a crawl of: a domain name search log, or at least one domain name system (DNS) zone file; tokenizing, by the server computer, at least one domain name text string within the domain name search log or the at least one DNS zone file; identifying, by the server computer, within the at least one domain name text string, at least one synonym or translation of the search string token; executing, by the server computer, a second database command to create at least one data record storing: the at least one synonym or translation of the search string token; the concept id; and at least one language associated with the at least one synonym or translation; identifying, by the server computer, based on the search string token in the domain name search string, at least one concept comprising a collection of the at least one data record sharing the concept id; generating, by the server computer, a second GUI including a displayed list recommending at least one available domain name comprising the at least one concept in the at least one language, the displayed list being ordered according to a frequency of use of the at least one concept; and transmitting, by the server computer, the second GUI to the client computer for display. | 1. A method, comprising the steps of: receiving, by a server computer communicatively coupled to a network and comprising at least one processor executing specific computer-executable instructions within a memory, via a first graphical user interface (GUI) for a multi-lingual domain name search engine displayed on a client computer, a domain name search string; tokenizing, by the server computer, the domain name search string; identifying, by the server computer, a search string token within the domain name search string as a concept seed; executing, by the server computer, a first database command to create a data record storing the search string token in association with a concept id; executing, by the server computer, a crawl of: a domain name search log, or at least one domain name system (DNS) zone file; tokenizing, by the server computer, at least one domain name text string within the domain name search log or the at least one DNS zone file; identifying, by the server computer, within the at least one domain name text string, at least one synonym or translation of the search string token; executing, by the server computer, a second database command to create at least one data record storing: the at least one synonym or translation of the search string token; the concept id; and at least one language associated with the at least one synonym or translation; identifying, by the server computer, based on the search string token in the domain name search string, at least one concept comprising a collection of the at least one data record sharing the concept id; generating, by the server computer, a second GUI including a displayed list recommending at least one available domain name comprising the at least one concept in the at least one language, the displayed list being ordered according to a frequency of use of the at least one concept; and transmitting, by the server computer, the second GUI to the client computer for display. 4. The method of claim 1 , wherein recommending the at least one available domain name comprises: determining, by the server computer, a co-occurrence frequency of the at least one concept from: at least one user session; or at least one domain name zone file; and identifying, by the server computer, the at least one available domain name according to a concept dictionary using the co-occurrence frequency of the at least one concept. | 0.501142 |
7,865,821 | 6 | 9 | 6. An electronic document update notifying method for notifying a user of update information of an electronic document, comprising: a document name stylizing step of determining a stylized document name based on a notification object document name designated by a user to identify a notification object document, the stylized document name being a predetermined string pattern representing a plurality of different document names; a notification condition storing step of storing the stylized document name determined by the document name stylizing step; an updated document name generating step, performed by an information processor, of generating an updated document name including a string specifying a location of the electronic document on a network by obtaining the stylized document name stored in the notification condition storing step, obtaining current date and time information when the predetermined string pattern includes a date and time string pattern, and replacing the date and time string pattern of the predetermined string pattern by the obtained current date and time information; a document data obtaining step of obtaining document data after an updating of the document data based on the updated document name; a difference extracting step of extracting difference data of a difference between the document data before the updating obtained at the time of a previous update notification and the document data after the updating; an update information creating step of creating an update information notification based on the difference data. | 6. An electronic document update notifying method for notifying a user of update information of an electronic document, comprising: a document name stylizing step of determining a stylized document name based on a notification object document name designated by a user to identify a notification object document, the stylized document name being a predetermined string pattern representing a plurality of different document names; a notification condition storing step of storing the stylized document name determined by the document name stylizing step; an updated document name generating step, performed by an information processor, of generating an updated document name including a string specifying a location of the electronic document on a network by obtaining the stylized document name stored in the notification condition storing step, obtaining current date and time information when the predetermined string pattern includes a date and time string pattern, and replacing the date and time string pattern of the predetermined string pattern by the obtained current date and time information; a document data obtaining step of obtaining document data after an updating of the document data based on the updated document name; a difference extracting step of extracting difference data of a difference between the document data before the updating obtained at the time of a previous update notification and the document data after the updating; an update information creating step of creating an update information notification based on the difference data. 9. The electronic document update notifying method according to claim 6 , wherein the stylized document name is specified by a user's input. | 0.753521 |
7,735,062 | 17 | 18 | 17. The article of manufacture of claim 12 , wherein the instructions to present the elements, facilitate the selection of the elements, and present the indication of the merging action to be taken comprise instructions to: present the elements of the first version in a first visual list; present the elements of the second version in a second visual list; annotate the elements of the first and second lists with specific visual marks that denote the element status, as one of new, changed, or conflict; annotate the elements of the first and second lists with specific visual marks that denote non-existing elements, including marks in the first list for elements that are deleted in the second list and marks in the second list for elements that are deleted in the first list; select all the elements of the first list automatically; and upon user selection of elements or non-existing elements in the second list, update the first list with specific visual marks that denote the merge action to be taken, as one of merge, remove, or replace. | 17. The article of manufacture of claim 12 , wherein the instructions to present the elements, facilitate the selection of the elements, and present the indication of the merging action to be taken comprise instructions to: present the elements of the first version in a first visual list; present the elements of the second version in a second visual list; annotate the elements of the first and second lists with specific visual marks that denote the element status, as one of new, changed, or conflict; annotate the elements of the first and second lists with specific visual marks that denote non-existing elements, including marks in the first list for elements that are deleted in the second list and marks in the second list for elements that are deleted in the first list; select all the elements of the first list automatically; and upon user selection of elements or non-existing elements in the second list, update the first list with specific visual marks that denote the merge action to be taken, as one of merge, remove, or replace. 18. The article of manufacture of claim 17 , wherein the instructions to take the indicated actions, thereby merging the first version with the second version, comprise instructions to: determine which new elements of the second list are selected, merge the corresponding element of the second version into the first design model version; determine which deleted elements of the second list are selected, remove the corresponding element from the first version; determine which changed or conflict elements of the second list are selected; and replace the corresponding element of the first version with the corresponding element of the second version. | 0.825295 |
8,255,224 | 1 | 5 | 1. A computer-implemented method comprising: receiving, at a computer system, context information that is identified based on a map that was presented on a first computing device in response to a non-verbal user action that was received as input by the first computing device, wherein the non-verbal user action comprises a user providing a request to obtain the map on the first computing device or the user inputting a search query into an interface provided by the first computing device; identifying, by the computer system and using the context information, a geographic location that is independent of the geographic location of the first computing device; identifying, by the computer system, a grammar that is associated with the identified geographic location and that includes a vocabulary with terms that are relevant to the identified geographic location and outputting a grammar indicator for use in selecting the identified grammar for voice recognition processing of vocal input from the user, to cause a grammar used to process vocal. | 1. A computer-implemented method comprising: receiving, at a computer system, context information that is identified based on a map that was presented on a first computing device in response to a non-verbal user action that was received as input by the first computing device, wherein the non-verbal user action comprises a user providing a request to obtain the map on the first computing device or the user inputting a search query into an interface provided by the first computing device; identifying, by the computer system and using the context information, a geographic location that is independent of the geographic location of the first computing device; identifying, by the computer system, a grammar that is associated with the identified geographic location and that includes a vocabulary with terms that are relevant to the identified geographic location and outputting a grammar indicator for use in selecting the identified grammar for voice recognition processing of vocal input from the user, to cause a grammar used to process vocal. 5. The method of claim 1 , wherein the non-verbal user action comprises the user inputting the search query into the interface provided by the first computing device, and wherein the search query includes text that is associated with the geographic location. | 0.647541 |
9,697,016 | 1 | 15 | 1. A system, comprising: one or more processing units; and a plurality of components, each of which is executed by at least one of the one or more processing units, the plurality of components comprising: a reference component configured to access a set of metadata correlated with user interface controls for configuring user-customizable options or preference settings of a computer application being executed; an indexing component configured to distinguish respective subsets of the set of metadata that are associated with respective ones of the user interface controls; and a searching component configured to receive a set of search data input by a user of the executing computer application, compare the set of search data with the subsets of the set of metadata, and identify a matching subset of metadata that satisfies a condition pertaining to the search data defined by a function, the search data input being received from the user via an interface of the computer application being executed. | 1. A system, comprising: one or more processing units; and a plurality of components, each of which is executed by at least one of the one or more processing units, the plurality of components comprising: a reference component configured to access a set of metadata correlated with user interface controls for configuring user-customizable options or preference settings of a computer application being executed; an indexing component configured to distinguish respective subsets of the set of metadata that are associated with respective ones of the user interface controls; and a searching component configured to receive a set of search data input by a user of the executing computer application, compare the set of search data with the subsets of the set of metadata, and identify a matching subset of metadata that satisfies a condition pertaining to the search data defined by a function, the search data input being received from the user via an interface of the computer application being executed. 15. The system of claim 1 , wherein the plurality of components further comprises: a user history component configured to track user-specific user interface control activity relative to user use of the computer application and record user-specific user interface control activity at least as a function of time and of computer application; and a machine learning component configured to analyze recorded user-specific user interface control activity and to weight the user interface controls in response to the analysis and modify the condition or the function of time and computer application via the weights. | 0.500818 |
8,719,318 | 8 | 13 | 8. A computing system for responding to natural language input, comprising: one or more data stores having a knowledge base stored therein; and one or more computing devices configured to: receive a natural language question comprising a sequence of words; identify a first translation template of a plurality of translation templates using at least a first word of the sequence of words, the first translation template including a sequence of known and unknown strings, a first query and a second query, a first known string of the known and unknown strings corresponding to the first word; identify a first object in the knowledge base using a second word of the sequence of words and the first query of the first translation template; and generate one or more results using the knowledge base, the first object, and the second query of the first translation template. | 8. A computing system for responding to natural language input, comprising: one or more data stores having a knowledge base stored therein; and one or more computing devices configured to: receive a natural language question comprising a sequence of words; identify a first translation template of a plurality of translation templates using at least a first word of the sequence of words, the first translation template including a sequence of known and unknown strings, a first query and a second query, a first known string of the known and unknown strings corresponding to the first word; identify a first object in the knowledge base using a second word of the sequence of words and the first query of the first translation template; and generate one or more results using the knowledge base, the first object, and the second query of the first translation template. 13. The computing system of claim 8 , wherein the one or more computing devices are further configured to generate an explanation representing how the one or more results were generated, the explanation including a natural language translation of the second query and one or more processing steps of the second query with reference to the knowledge base. | 0.754848 |
8,196,095 | 6 | 10 | 6. The method of claim 1 , wherein each page of the created mobile website comprises one or more of: a name provided to identify the page; a title to be used as a title tag in a markup generated when the page is created; a size limit; a capability to display an image and to make the image linkable; and a capability to display descriptive text and to make the text linkable. | 6. The method of claim 1 , wherein each page of the created mobile website comprises one or more of: a name provided to identify the page; a title to be used as a title tag in a markup generated when the page is created; a size limit; a capability to display an image and to make the image linkable; and a capability to display descriptive text and to make the text linkable. 10. The method of claim 6 , wherein each web page of the mobile website is laid out such that a link leading back to a home page of the mobile website is presented at the top and bottom thereof and in between which is located the image and the descriptive text. | 0.932418 |
9,424,835 | 8 | 13 | 8. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: accessing a unit database of acoustic units and, for each acoustic unit, linguistic data corresponding to the acoustic unit; for each acoustic unit: generating an acoustic fingerprint; determining a probability of the linguistic data corresponding to the acoustic unit occurring in a text corpus; and storing data that associates the acoustic unit with (i) the acoustic fingerprint of the acoustic unit and (ii) the probability of the linguistic data corresponding to the acoustic unit occurring in the text corpus; providing at least a portion of the stored data to a finite state transducer training engine that is configured to train one or more finite state transducers that are used in generating speech from text; determining that the unit database of acoustic units has been updated to include one or more new acoustic units that do not have an associated probability of the linguistic data corresponding to the acoustic unit occurring in the text corpus; for each new acoustic unit in the updated unit database: generating an acoustic fingerprint for the new acoustic unit; identifying an acoustic unit that (i) has an acoustic fingerprint that is indicated as similar to the fingerprint of the new acoustic unit, and (ii) has a stored associated probability; and storing data that associates the new acoustic unit with (i) the acoustic fingerprint of the new acoustic unit and (ii) the probability associated with the acoustic unit that has the acoustic fingerprint that is indicated as similar to the fingerprint of the new acoustic unit; and providing at least a portion of the new stored data to the finite state transducer training engine. | 8. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: accessing a unit database of acoustic units and, for each acoustic unit, linguistic data corresponding to the acoustic unit; for each acoustic unit: generating an acoustic fingerprint; determining a probability of the linguistic data corresponding to the acoustic unit occurring in a text corpus; and storing data that associates the acoustic unit with (i) the acoustic fingerprint of the acoustic unit and (ii) the probability of the linguistic data corresponding to the acoustic unit occurring in the text corpus; providing at least a portion of the stored data to a finite state transducer training engine that is configured to train one or more finite state transducers that are used in generating speech from text; determining that the unit database of acoustic units has been updated to include one or more new acoustic units that do not have an associated probability of the linguistic data corresponding to the acoustic unit occurring in the text corpus; for each new acoustic unit in the updated unit database: generating an acoustic fingerprint for the new acoustic unit; identifying an acoustic unit that (i) has an acoustic fingerprint that is indicated as similar to the fingerprint of the new acoustic unit, and (ii) has a stored associated probability; and storing data that associates the new acoustic unit with (i) the acoustic fingerprint of the new acoustic unit and (ii) the probability associated with the acoustic unit that has the acoustic fingerprint that is indicated as similar to the fingerprint of the new acoustic unit; and providing at least a portion of the new stored data to the finite state transducer training engine. 13. The system of claim 8 , wherein identifying an acoustic unit that has an acoustic fingerprint that is indicated as similar to the fingerprint of the new acoustic unit comprises: calculating a respective similarity measure between the acoustic fingerprints of the new acoustic units and the acoustic fingerprints of each other acoustic unit in the unit database; determining a nearest acoustic fingerprint to the acoustic fingerprint of the new acoustic units according to the similarity measure; and identifying the acoustic unit associated with the nearest acoustic fingerprint. | 0.500856 |
5,537,630 | 4 | 8 | 4. The method of claim 3, wherein said step of graphically displaying each parameter further comprises displaying each icon for said plurality of parameters in a tree structure, wherein each icon in said tree structure has a graphic connection to said icon representing said object. | 4. The method of claim 3, wherein said step of graphically displaying each parameter further comprises displaying each icon for said plurality of parameters in a tree structure, wherein each icon in said tree structure has a graphic connection to said icon representing said object. 8. The method of claim 4, wherein said step of displaying a list of selections for a parameter comprises displaying a constant as a selection for said parameter within said list of selections, wherein selection of said constant by a user allows said user to enter a number for said parameter. | 0.831019 |
9,405,379 | 9 | 10 | 9. A method comprising: receiving, by a computing device, information that indicates a touch contact on a touch device; determining, by the computing device, a classification of the touch contact based at least in part on contextual information, the contextual information comprising hand position information that indicates a distance from a hand of a user to the touch device, the classification indicating that the touch contact is intentional or unintentional; and determining, by the computing device, whether the classification of the touch contact is accurate, the determining being based, at least in part, on a history of the touch contact. | 9. A method comprising: receiving, by a computing device, information that indicates a touch contact on a touch device; determining, by the computing device, a classification of the touch contact based at least in part on contextual information, the contextual information comprising hand position information that indicates a distance from a hand of a user to the touch device, the classification indicating that the touch contact is intentional or unintentional; and determining, by the computing device, whether the classification of the touch contact is accurate, the determining being based, at least in part, on a history of the touch contact. 10. The method of claim 9 , wherein the receiving comprises receiving information from a touch device that includes at least one of a touch pad or a touch screen. | 0.890541 |
9,576,196 | 4 | 11 | 4. A computing device comprising: at least one processor; a memory including instructions operable to be executed by the at least one processor to perform a set of actions to configure the at least one processor to: identify a first region of an image; identify a second region of the image; extract first contextual features from the first region, the first contextual features relating to a context of the first region; process the extracted first contextual features using a first classifier to determine that the extracted first contextual features are consistent with image data comprising a glyph; determine that the first region contains a glyph; determine that the second region does not contain a glyph; and stop further processing of the second region in response to determining the second region does not contain a glyph; identify candidate locations of the first region, the candidate locations comprising a first candidate location having a first local pixel pattern; extract second contextual features from the first local pixel pattern; process, using a second classifier, the first local pixel pattern to determine a first feature descriptor, wherein the first feature descriptor is based on spatial relationships between the first local pixel pattern within the first region; process, using the second classifier, the first local pixel pattern to determine a second feature descriptor, wherein the second feature descriptor relates to content of the first candidate location; and determine that the first candidate location contains a glyph using the first feature descriptor and the second feature descriptor. | 4. A computing device comprising: at least one processor; a memory including instructions operable to be executed by the at least one processor to perform a set of actions to configure the at least one processor to: identify a first region of an image; identify a second region of the image; extract first contextual features from the first region, the first contextual features relating to a context of the first region; process the extracted first contextual features using a first classifier to determine that the extracted first contextual features are consistent with image data comprising a glyph; determine that the first region contains a glyph; determine that the second region does not contain a glyph; and stop further processing of the second region in response to determining the second region does not contain a glyph; identify candidate locations of the first region, the candidate locations comprising a first candidate location having a first local pixel pattern; extract second contextual features from the first local pixel pattern; process, using a second classifier, the first local pixel pattern to determine a first feature descriptor, wherein the first feature descriptor is based on spatial relationships between the first local pixel pattern within the first region; process, using the second classifier, the first local pixel pattern to determine a second feature descriptor, wherein the second feature descriptor relates to content of the first candidate location; and determine that the first candidate location contains a glyph using the first feature descriptor and the second feature descriptor. 11. The computing device of claim 4 , wherein the first region of the image is the entire image. | 0.918919 |
9,569,538 | 1 | 2 | 1. A method comprising: ingesting, using natural language processing (NLP), content of a work of authorship; ingesting, using NLP, content displayed on an Internet forum; identifying, based on the ingested content of the work of authorship and the ingested content displayed on the Internet forum, a relationship between the work of authorship and the displayed content; causing, based on the identified relationship, information associated with the work of authorship to be displayed on the Internet forum such that the information is visually-associated with the displayed content, wherein the displayed content includes a user question posted to the Internet forum by a user, wherein the information includes an answer to the user question, and wherein the identifying the relationship comprises: identifying the user question as a question; and generating, in response to the identifying the user question as a question and based on a search of the ingested content of the work of authorship, the answer to the user question. | 1. A method comprising: ingesting, using natural language processing (NLP), content of a work of authorship; ingesting, using NLP, content displayed on an Internet forum; identifying, based on the ingested content of the work of authorship and the ingested content displayed on the Internet forum, a relationship between the work of authorship and the displayed content; causing, based on the identified relationship, information associated with the work of authorship to be displayed on the Internet forum such that the information is visually-associated with the displayed content, wherein the displayed content includes a user question posted to the Internet forum by a user, wherein the information includes an answer to the user question, and wherein the identifying the relationship comprises: identifying the user question as a question; and generating, in response to the identifying the user question as a question and based on a search of the ingested content of the work of authorship, the answer to the user question. 2. The method of claim 1 , wherein the information is an excerpt from the work of authorship. | 0.933476 |
4,028,538 | 5 | 6 | 5. An electronic calculator as in claim 4 wherein: said keyboard input means includes a first control key operable with one or more other keys for designating a particular line of one or more alphameric statements stored in said memory means, and a second control key for initiating nondestructive recall of a designated line of one or more alphameric statements from said memory means to said buffer storage means; and said processing means is responsive to actuation of said second control key, following actuation of said first control key and one or more other keys designating a particular line of one or more alphameric statements stored in said memory means, for nondestructively transferring that line of one or more alphameric statements from said memory means to said buffer storage means to enable the user to observe a display thereof by said alphameric output display means. | 5. An electronic calculator as in claim 4 wherein: said keyboard input means includes a first control key operable with one or more other keys for designating a particular line of one or more alphameric statements stored in said memory means, and a second control key for initiating nondestructive recall of a designated line of one or more alphameric statements from said memory means to said buffer storage means; and said processing means is responsive to actuation of said second control key, following actuation of said first control key and one or more other keys designating a particular line of one or more alphameric statements stored in said memory means, for nondestructively transferring that line of one or more alphameric statements from said memory means to said buffer storage means to enable the user to observe a display thereof by said alphameric output display means. 6. An electronic calculator as in claim 5 wherein said processing means is responsive to successive actuations of said second control key for transferring successive lines of one or more alphameric statements each from said memory means to said buffer storage means to enable the user to observe a visual display thereof by said alphameric output display means. | 0.906863 |
7,519,529 | 35 | 36 | 35. The method of claim 31 , the reformulation process further comprising at least one of: reformulating a search query to a search engine; adding terms and controls that identify the appropriate topic or genre of an article; or automatically adding terms in a search engine that are valuable for discovering a structure of an article. | 35. The method of claim 31 , the reformulation process further comprising at least one of: reformulating a search query to a search engine; adding terms and controls that identify the appropriate topic or genre of an article; or automatically adding terms in a search engine that are valuable for discovering a structure of an article. 36. The method of claim 35 , further comprising at least one of: analyzing at least one of topic terms, an abstract, or a reference to determine a genre of articles that have a standard structure of an abstract and a standard list of academic references at the end of the article; or determining more about topics that appear in children-oriented articles. | 0.876817 |
9,942,426 | 9 | 13 | 9. A method of receiving edits to an electronic document, comprising: presenting an electronic document to a user via a display device of a multifunctional peripheral device; receiving edits to the electronic document; and storing the edits with the electronic document as an electronically separate image surface that is viewable as a thumbnail on the display device as a preview, and the electronically separate image surface is different from an image surface of the electronic document, wherein the electronic document remains unchanged when the image surface with edits is placed over the image surface of the electronic document for printing. | 9. A method of receiving edits to an electronic document, comprising: presenting an electronic document to a user via a display device of a multifunctional peripheral device; receiving edits to the electronic document; and storing the edits with the electronic document as an electronically separate image surface that is viewable as a thumbnail on the display device as a preview, and the electronically separate image surface is different from an image surface of the electronic document, wherein the electronic document remains unchanged when the image surface with edits is placed over the image surface of the electronic document for printing. 13. The method of claim 9 , comprising: securely storing a signature in a data storage; after storing the signature, receiving verification information to authenticate the user; and after authenticating the user, inserting the signature into the electronic document. | 0.829706 |
8,091,023 | 19 | 20 | 19. The handheld electronic device of claim 16 wherein the operations further comprise: generating as the character set for a character of the canonicalized form of the word a character set comprising both diacritical and non-diacritical forms of the character. | 19. The handheld electronic device of claim 16 wherein the operations further comprise: generating as the character set for a character of the canonicalized form of the word a character set comprising both diacritical and non-diacritical forms of the character. 20. The handheld electronic device of claim 19 wherein the character of the canonicalized form of the word and an alternate character are both assigned to a predetermined input member, and wherein the operations further comprise: generating as the character set for the character of the canonicalized form of the word a character set further comprising both diacritical and non-diacritical forms of the alternate character. | 0.903732 |
9,542,928 | 6 | 10 | 6. A system for generating a natural language output, the system comprising: a computing device associated with a natural language engine having one or more processors and one or more computer-storage media; and a data store coupled with the natural language engine, wherein the natural language engine: identifies an answer to a query; maps the answer to at least one set of triples from a knowledge base; identifies a sentence structure associated with the at least one set of triples and associated with a plurality of constraints, the sentence structure comprising one or more variables that are to be substituted with the at least one set of triples when forming a sentence, wherein one variable of the one or more variables is associated with at least two constraints of the plurality of constraints such that each of the at least two constraints limits the type of value that may be substituted for the one variable; identifies that the plurality of constraints associated with the sentence structure are satisfied such that only valid sentences are output; and communicates an output answer to the query in the form of the sentence. | 6. A system for generating a natural language output, the system comprising: a computing device associated with a natural language engine having one or more processors and one or more computer-storage media; and a data store coupled with the natural language engine, wherein the natural language engine: identifies an answer to a query; maps the answer to at least one set of triples from a knowledge base; identifies a sentence structure associated with the at least one set of triples and associated with a plurality of constraints, the sentence structure comprising one or more variables that are to be substituted with the at least one set of triples when forming a sentence, wherein one variable of the one or more variables is associated with at least two constraints of the plurality of constraints such that each of the at least two constraints limits the type of value that may be substituted for the one variable; identifies that the plurality of constraints associated with the sentence structure are satisfied such that only valid sentences are output; and communicates an output answer to the query in the form of the sentence. 10. The system of claim 6 , wherein the natural language engine communicates the output answer when the plurality of constraints associated with the sentence structure is satisfied. | 0.792431 |
9,690,894 | 1 | 7 | 1. One or more tangible computer-readable media at least collectively storing a non-transitory code executable by one or more processors, the code being configured to, when executed by the one or more processors, cause operations to be performed including: accessing an algorithmic description representation of a circuit design, the algorithmic description representation being specified in a first language and including at least one programming language construct associated with a first safety data type; and compiling the algorithmic description representation of the circuit design, the compiling including: identifying the at least one programming language construct, accessing a first safety data type definition associated with the first safety data type, and generating a second representation of the circuit design based on the algorithmic description representation and the first safety data type definition, the second representation being provided in a second language and including at least one safety feature for a portion of the circuit design associated with the at least one programming language construct. | 1. One or more tangible computer-readable media at least collectively storing a non-transitory code executable by one or more processors, the code being configured to, when executed by the one or more processors, cause operations to be performed including: accessing an algorithmic description representation of a circuit design, the algorithmic description representation being specified in a first language and including at least one programming language construct associated with a first safety data type; and compiling the algorithmic description representation of the circuit design, the compiling including: identifying the at least one programming language construct, accessing a first safety data type definition associated with the first safety data type, and generating a second representation of the circuit design based on the algorithmic description representation and the first safety data type definition, the second representation being provided in a second language and including at least one safety feature for a portion of the circuit design associated with the at least one programming language construct. 7. The media of claim 1 , wherein the at least one programming language construct includes at least one variable defined using the first safety data type. | 0.773529 |
9,014,982 | 5 | 6 | 5. The method of claim 4 , wherein the data analysis question concerns interdependency or lack thereof of two or more attributes computed from the one or more geophysical data elements. | 5. The method of claim 4 , wherein the data analysis question concerns interdependency or lack thereof of two or more attributes computed from the one or more geophysical data elements. 6. The method of claim 5 , wherein interdependency refers to one or more of three types of interdependency relationships: (i) information shared among the attributes, called mutual information; (ii) information in either one of the attributes; and (iii) information contained in one attribute but excluding that shared with any other attribute. | 0.728707 |
9,268,512 | 9 | 10 | 9. The method according to claim 8 , further comprising an output step of outputting the image generated in the processing step. | 9. The method according to claim 8 , further comprising an output step of outputting the image generated in the processing step. 10. The method according to claim 9 , wherein the output step comprises a print control step of causing a printing apparatus to print the image generated in the processing step. | 0.948962 |
8,885,184 | 18 | 19 | 18. The system of claim 17 wherein: the job ticket is defined according to Job Definition Format (JDF), and the print controller is further operable to analyze JDF instructions of the job ticket to process the print job. | 18. The system of claim 17 wherein: the job ticket is defined according to Job Definition Format (JDF), and the print controller is further operable to analyze JDF instructions of the job ticket to process the print job. 19. The system of claim 18 wherein: the communication includes a tag identifying the language of the job ticket as JDF. | 0.921192 |
8,694,372 | 18 | 19 | 18. The system of claim 12 , wherein the processing module is configured to compute the projected effectiveness based on historical data that relates past changes of marketing attributes of marketable items to corresponding resultant changes to historical marketing-related performance parameters for those marketable items. | 18. The system of claim 12 , wherein the processing module is configured to compute the projected effectiveness based on historical data that relates past changes of marketing attributes of marketable items to corresponding resultant changes to historical marketing-related performance parameters for those marketable items. 19. The system of claim 18 , wherein the processing module includes a machine-learning system. | 0.985894 |
8,528,018 | 1 | 3 | 1. A computer implemented method, comprising: evaluating a video file in a network environment; determining an identity of at least one speaker associated with the video file in order to generate an identity attribute; performing speech to text operations associated with the video file in order to generate at least one text attribute; generating a visual worthiness rating based, at least, on the identity attribute and the text attribute associated with the video file, wherein the visual worthiness rating is reflective of a visual significance of image content in the video file; rendering the visual worthiness rating to the end user prior to the video file being played; and wherein an additional attribute is generated and used in formulating the visual worthiness rating, the attribute being an author attribute reflective of authorship of the video file. | 1. A computer implemented method, comprising: evaluating a video file in a network environment; determining an identity of at least one speaker associated with the video file in order to generate an identity attribute; performing speech to text operations associated with the video file in order to generate at least one text attribute; generating a visual worthiness rating based, at least, on the identity attribute and the text attribute associated with the video file, wherein the visual worthiness rating is reflective of a visual significance of image content in the video file; rendering the visual worthiness rating to the end user prior to the video file being played; and wherein an additional attribute is generated and used in formulating the visual worthiness rating, the attribute being an author attribute reflective of authorship of the video file. 3. The method of claim 1 , wherein separate visual worthiness ratings are developed for particular sections of the video file. | 0.852459 |
8,631,457 | 6 | 7 | 6. The method of claim 1 further comprising applying a policy to the textual data to determine a violation of the policy based on the textual data. | 6. The method of claim 1 further comprising applying a policy to the textual data to determine a violation of the policy based on the textual data. 7. The method of claim 6 further comprising blocking a software application from network resources based on the violation. | 0.937243 |
9,953,279 | 1 | 10 | 1. A computer implemented method for improving a business intelligence ecosystem, the method comprising: receiving, from a user device, a selection of a business intelligence artifact; determining one or more execution profiles for the selected business intelligence artifact; determining an initial examination score for the selected business intelligence artifact; identifying, via one or more improvement modules on a server, one or more candidate improvements to the business intelligence ecosystem based on a configurable set of rules; applying, via the one or more improvement modules on a server, one or more of the identified candidate improvements to modify the business intelligence ecosystem in which one or more of the candidate improvements was identified; executing the selected business intelligence artifact in the modified business intelligence ecosystem, wherein the selected business intelligence artifact is executed at least partially based on one or more of the execution profiles; determining examination data for the business intelligence artifact executed in the modified business intelligence ecosystem; reverting, via the one or more improvement modules, modifications to the modified business intelligence ecosystem by reverting the applied one or more candidate improvements; and identifying, via the one or more improvement modules, one or more qualified selected improvements based on a comparison of the examination data and the initial examination score, wherein at least one of the qualified selected improvements comprises at least one of the identified candidate improvements. | 1. A computer implemented method for improving a business intelligence ecosystem, the method comprising: receiving, from a user device, a selection of a business intelligence artifact; determining one or more execution profiles for the selected business intelligence artifact; determining an initial examination score for the selected business intelligence artifact; identifying, via one or more improvement modules on a server, one or more candidate improvements to the business intelligence ecosystem based on a configurable set of rules; applying, via the one or more improvement modules on a server, one or more of the identified candidate improvements to modify the business intelligence ecosystem in which one or more of the candidate improvements was identified; executing the selected business intelligence artifact in the modified business intelligence ecosystem, wherein the selected business intelligence artifact is executed at least partially based on one or more of the execution profiles; determining examination data for the business intelligence artifact executed in the modified business intelligence ecosystem; reverting, via the one or more improvement modules, modifications to the modified business intelligence ecosystem by reverting the applied one or more candidate improvements; and identifying, via the one or more improvement modules, one or more qualified selected improvements based on a comparison of the examination data and the initial examination score, wherein at least one of the qualified selected improvements comprises at least one of the identified candidate improvements. 10. The method of claim 1 further comprising: generating a set of instructions associated with applying at least one of the qualified selected improvements to the business intelligence ecosystem; and receiving a confirmation that at least one of the qualified selected improvements to the business intelligence ecosystem was applied at least partially based on the generated instructions. | 0.837793 |
8,086,038 | 18 | 19 | 18. The system of claim 17 , wherein the feature determination unit comprises a size determination module having an input and an output for determining the junction size, the input of the size determination module coupled to the output of the location determination module to receive the junction location, the output of the size determination module coupled to the input of the descriptor creation unit. | 18. The system of claim 17 , wherein the feature determination unit comprises a size determination module having an input and an output for determining the junction size, the input of the size determination module coupled to the output of the location determination module to receive the junction location, the output of the size determination module coupled to the input of the descriptor creation unit. 19. The system of claim 18 , wherein the feature determination unit comprises an orientation determination module having an input and an output for determining a junction orientation, the input of the orientation determination module coupled to the output of the size determination module to receive the junction size, the output of the orientation determination module coupled to the input of the descriptor creation unit. | 0.879556 |
9,563,424 | 13 | 14 | 13. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving in a processor in the system, the processor having a first execution mode and a different second execution mode, a program that has an initial machine language instruction for execution by the processor in the first execution mode, wherein the initial machine language instruction, when executed by the processor in the first execution mode, performs a first operation; determining, by an instruction selector of a plugin configured to execute the program on the processor, that a portion of the initial machine language instruction, when the portion is interpreted by the processor as an instruction in the second execution mode, causes the processor to perform a second operation that is different from the first operation and that satisfies one or more risk criteria; in response, generating, by the instruction selector of the plugin, one or more alternative machine language instructions to replace the initial machine language instruction for execution by the processor in the first execution mode, wherein the one or more alternative machine language instructions, when interpreted by the processor as instructions in the first execution mode, cause the processor to perform a third operation that is similar to the first operation, and wherein the one or more alternative machine language instructions, when interpreted by the processor as one or more instructions in the different second execution mode, cause the processor to perform a fourth operation that is different from the second operation and that does not satisfy the one or more risk criteria of the second operation being performed by the processor when the portion of the initial machine language instruction is interpreted as an instruction in the second execution mode; and replacing, by the instruction selector of the plugin, the initial machine language instruction with the one or more alternative machine language instructions in the program. | 13. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving in a processor in the system, the processor having a first execution mode and a different second execution mode, a program that has an initial machine language instruction for execution by the processor in the first execution mode, wherein the initial machine language instruction, when executed by the processor in the first execution mode, performs a first operation; determining, by an instruction selector of a plugin configured to execute the program on the processor, that a portion of the initial machine language instruction, when the portion is interpreted by the processor as an instruction in the second execution mode, causes the processor to perform a second operation that is different from the first operation and that satisfies one or more risk criteria; in response, generating, by the instruction selector of the plugin, one or more alternative machine language instructions to replace the initial machine language instruction for execution by the processor in the first execution mode, wherein the one or more alternative machine language instructions, when interpreted by the processor as instructions in the first execution mode, cause the processor to perform a third operation that is similar to the first operation, and wherein the one or more alternative machine language instructions, when interpreted by the processor as one or more instructions in the different second execution mode, cause the processor to perform a fourth operation that is different from the second operation and that does not satisfy the one or more risk criteria of the second operation being performed by the processor when the portion of the initial machine language instruction is interpreted as an instruction in the second execution mode; and replacing, by the instruction selector of the plugin, the initial machine language instruction with the one or more alternative machine language instructions in the program. 14. The system of claim 13 , wherein the operations further comprise: receiving an intermediate language instruction, wherein generating the one or more alternative machine language instructions comprises selecting the one or more alternative machine language instructions to correspond to the intermediate language instruction. | 0.688213 |
9,639,898 | 11 | 14 | 11. A computer-implemented method, comprising: extracting, by at least a processor of a computer, tax related data from a tax transaction database that stores respective records describing respective tax return forms filed by respective taxpayers, where each record includes values in tax return fields describing a taxpayer who filed the tax return form, further where the tax return fields include tax return lines; transforming and storing, by at least the processor, the extracted tax related data in a tax data warehouse that includes fact tables and dimension tables by populating a fact table with i) values in the tax return form lines and ii) a form type identifier that identifies a type of the tax return form, where populating the fact table with values according to tax return form lines improves a granularity of analysis that can be performed using the tax data warehouse as compared to the tax transaction database; where the tax data warehouse includes a dimension table that has i) a row for each tax form type and ii) columns storing contextual information about tax return form lines; generating and storing, by at least the processor, a plurality of key performance indicator (KPI) queries where each KPI query includes a different combination of tax metrics from the tax transaction database and one or more query elements associated with a criteria from the tax metrics; generating, by at least the processor, a plurality of materialized views where each materialized view corresponds to one of the plurality of KPI queries and stores aggregated key performance indicator (KPI) values for the corresponding KPI query; storing, by at least the processor, the plurality of materialized views in the tax data warehouse; generating and displaying, by at least the processor, an interface that displays one or more of the plurality of KPI queries for selection; and in response to a first KPI query being selected from the interface, causing the first KPI query to be executed on at least a first materialized view from the plurality of materialized views that corresponds to the first KPI query to return results corresponding to the first KPI query. | 11. A computer-implemented method, comprising: extracting, by at least a processor of a computer, tax related data from a tax transaction database that stores respective records describing respective tax return forms filed by respective taxpayers, where each record includes values in tax return fields describing a taxpayer who filed the tax return form, further where the tax return fields include tax return lines; transforming and storing, by at least the processor, the extracted tax related data in a tax data warehouse that includes fact tables and dimension tables by populating a fact table with i) values in the tax return form lines and ii) a form type identifier that identifies a type of the tax return form, where populating the fact table with values according to tax return form lines improves a granularity of analysis that can be performed using the tax data warehouse as compared to the tax transaction database; where the tax data warehouse includes a dimension table that has i) a row for each tax form type and ii) columns storing contextual information about tax return form lines; generating and storing, by at least the processor, a plurality of key performance indicator (KPI) queries where each KPI query includes a different combination of tax metrics from the tax transaction database and one or more query elements associated with a criteria from the tax metrics; generating, by at least the processor, a plurality of materialized views where each materialized view corresponds to one of the plurality of KPI queries and stores aggregated key performance indicator (KPI) values for the corresponding KPI query; storing, by at least the processor, the plurality of materialized views in the tax data warehouse; generating and displaying, by at least the processor, an interface that displays one or more of the plurality of KPI queries for selection; and in response to a first KPI query being selected from the interface, causing the first KPI query to be executed on at least a first materialized view from the plurality of materialized views that corresponds to the first KPI query to return results corresponding to the first KPI query. 14. The computer-implemented method of claim 11 , wherein the transforming further comprising: configuring the fact tables and dimension tables in at least one star schema. | 0.857616 |
9,195,714 | 15 | 19 | 15. A computer system, comprising: a memory that, during operation, stores instructions; and a processor that, during operation, retrieves instructions from the memory and executes at least some of the instructions to cause the computer system to: initiate, based on receipt of a source document, a routine for identification of one or more candidate duplicate documents of the source document from a document corpus, said identification comprising: receive the source document, wherein a document type of the source document is the same as a document type of at least some of a plurality of reference documents in the document corpus; determine a plurality of queries from content of the source document; execute the plurality of queries on the document corpus, wherein the plurality of queries includes: a first query configured to return a first list of reference documents that identifies at least some of the plurality of reference documents of the document corpus, wherein reference documents in the first list are associated with scores representing at least in part relevance with respect to the source document; a second query different from the first query by at least one search term from the first query and configured to return a second list of reference documents that also identifies at least some of the plurality of reference documents of the document corpus, wherein reference documents in the second list are associated with scores representing at least in part relevance with respect to the source document; based, at least in part, on scores for the reference documents for the first and second list, select one or more of the reference documents having a respective score that meets a score threshold as potential duplicates of the same received source document that initiated the routine; and store an identification of the one or more potential duplicate documents. | 15. A computer system, comprising: a memory that, during operation, stores instructions; and a processor that, during operation, retrieves instructions from the memory and executes at least some of the instructions to cause the computer system to: initiate, based on receipt of a source document, a routine for identification of one or more candidate duplicate documents of the source document from a document corpus, said identification comprising: receive the source document, wherein a document type of the source document is the same as a document type of at least some of a plurality of reference documents in the document corpus; determine a plurality of queries from content of the source document; execute the plurality of queries on the document corpus, wherein the plurality of queries includes: a first query configured to return a first list of reference documents that identifies at least some of the plurality of reference documents of the document corpus, wherein reference documents in the first list are associated with scores representing at least in part relevance with respect to the source document; a second query different from the first query by at least one search term from the first query and configured to return a second list of reference documents that also identifies at least some of the plurality of reference documents of the document corpus, wherein reference documents in the second list are associated with scores representing at least in part relevance with respect to the source document; based, at least in part, on scores for the reference documents for the first and second list, select one or more of the reference documents having a respective score that meets a score threshold as potential duplicates of the same received source document that initiated the routine; and store an identification of the one or more potential duplicate documents. 19. The system of claim 15 , wherein at least some of the instructions further cause the computer system to: prior to said determination that one or more reference documents are potential duplicates of the received source document, determine that a reference document is identified in the first and second lists of documents, the first and second lists of documents having a respective different score for the reference document; and assign the highest of the different scores to the reference document. | 0.500992 |
7,801,838 | 12 | 13 | 12. A multimedia recognition system, comprising: a plurality of indexers configured to: receive multimedia data, and analyze the multimedia data based on training data to generate a plurality of documents; and a memory system configured to: store the documents from the indexers, receive user augmentation relating to one of the documents, and provide the user augmentation to one or more of the indexers for retraining based on the user augmentation, wherein when receiving user augmentation relating to one of the documents, the memory system is configured to: receive an attachment for the one of the documents from a user, and store the attachment. | 12. A multimedia recognition system, comprising: a plurality of indexers configured to: receive multimedia data, and analyze the multimedia data based on training data to generate a plurality of documents; and a memory system configured to: store the documents from the indexers, receive user augmentation relating to one of the documents, and provide the user augmentation to one or more of the indexers for retraining based on the user augmentation, wherein when receiving user augmentation relating to one of the documents, the memory system is configured to: receive an attachment for the one of the documents from a user, and store the attachment. 13. The system of claim 12 , wherein when providing the user augmentation to one or more of the indexers, the memory system is configured to send the attachment to the one or more of the indexers for retraining based on the attachment. | 0.698718 |
9,031,845 | 8 | 10 | 8. The mobile system of claim 1 , wherein the command or query includes a search query that is to be executed on-board the vehicle by searching one or more data sources that are on-board the vehicle using at least one recognized word or phrase of the natural language utterance, and wherein the search query relates to an aspect of the vehicle. | 8. The mobile system of claim 1 , wherein the command or query includes a search query that is to be executed on-board the vehicle by searching one or more data sources that are on-board the vehicle using at least one recognized word or phrase of the natural language utterance, and wherein the search query relates to an aspect of the vehicle. 10. The mobile system of claim 8 , wherein the instructions cause the one or more physical processors to: provide a natural language speech response to the natural language utterance based on one or more results from the search of the one or more data sources. | 0.939759 |
9,183,286 | 5 | 15 | 5. The method of claim 1 , including: analyzing the predefined subject matter in a patent database, the method being for use with a set of target patents stored on a computer storage device, each of said target patents corresponding to the predefined subject matter; creating a feature space based on terms found in said set of target patents stored on the computer storage device; creating the partition taxonomy based on a clustered configuration of said feature space; creating a contingency table by comparing said edited partition taxonomy and said classification taxonomy to provide entries in said contingency table; and identifying relationships in said contingency table which help determine the presence of an area in a corporate portfolio in which no intellectual property exists within the classification taxonomy. | 5. The method of claim 1 , including: analyzing the predefined subject matter in a patent database, the method being for use with a set of target patents stored on a computer storage device, each of said target patents corresponding to the predefined subject matter; creating a feature space based on terms found in said set of target patents stored on the computer storage device; creating the partition taxonomy based on a clustered configuration of said feature space; creating a contingency table by comparing said edited partition taxonomy and said classification taxonomy to provide entries in said contingency table; and identifying relationships in said contingency table which help determine the presence of an area in a corporate portfolio in which no intellectual property exists within the classification taxonomy. 15. The method of claim 5 further comprising performing a time dimension analysis on at least one of said entries in said contingency table. | 0.973333 |
8,316,032 | 23 | 24 | 23. A non-transitory computer readable medium encoded with a computer program comprising instructions that when executed, operate to cause a computer to perform operations comprising: identifying image content for a plurality of distinct book content items, the image content defining images that appear in the plurality of distinct book content items; identifying descriptor points from the image content, the descriptor points defining localized features of the image content of the distinct book content items, the localized features being characteristics of a portion of the image content; representing the distinct book content items as a weighted graph in computer memory, where each of the distinct book content items is represented as a distinct node in the weighted graph and where an edge exists in the weighted graph between each pair of distinct nodes that represent distinct book content items that both include a matching descriptor point, matching descriptor points being identified based on similarities between pairs of descriptor points, and each edge in the weighted graph being weighted based on a relative importance of the distinct node from which the edge originates; for each distinct node: identifying matching image content in the image content of other distinct book content items that are represented by other distinct nodes, each matching image content being image content that has descriptor points that match the corresponding descriptor points of image content in the distinct book content item corresponding to the distinct node; generating edges in the weighted graph connecting the distinct node to other distinct nodes corresponding to the other distinct book content items, each edge representing one or more matches of image content in the distinct book content item to matching image content in another distinct book content item; and determining a rank score for each distinct book content item based on the edges between the distinct nodes representing the distinct book content items, the rank score being a score indicative of the importance of each distinct book content item relative to other distinct book content items. | 23. A non-transitory computer readable medium encoded with a computer program comprising instructions that when executed, operate to cause a computer to perform operations comprising: identifying image content for a plurality of distinct book content items, the image content defining images that appear in the plurality of distinct book content items; identifying descriptor points from the image content, the descriptor points defining localized features of the image content of the distinct book content items, the localized features being characteristics of a portion of the image content; representing the distinct book content items as a weighted graph in computer memory, where each of the distinct book content items is represented as a distinct node in the weighted graph and where an edge exists in the weighted graph between each pair of distinct nodes that represent distinct book content items that both include a matching descriptor point, matching descriptor points being identified based on similarities between pairs of descriptor points, and each edge in the weighted graph being weighted based on a relative importance of the distinct node from which the edge originates; for each distinct node: identifying matching image content in the image content of other distinct book content items that are represented by other distinct nodes, each matching image content being image content that has descriptor points that match the corresponding descriptor points of image content in the distinct book content item corresponding to the distinct node; generating edges in the weighted graph connecting the distinct node to other distinct nodes corresponding to the other distinct book content items, each edge representing one or more matches of image content in the distinct book content item to matching image content in another distinct book content item; and determining a rank score for each distinct book content item based on the edges between the distinct nodes representing the distinct book content items, the rank score being a score indicative of the importance of each distinct book content item relative to other distinct book content items. 24. The computer readable media of claim 23 , wherein the operations further comprise determining edge weights for each edge based on a number of matching descriptor points between distinct book content items. | 0.763039 |
8,812,531 | 14 | 15 | 14. One or more computer-readable non-transitory storage media embodying software that is operable when executed by one or more computing devices to: derive a concept matrix from a plurality of sample documents using singular value decomposition of a term-document matrix, the concept matrix identifying a latent pattern of word usage in the plurality of sample documents around a concept; derive concept terms by extracting significant terms from search text and inferring relevant terms therefrom using the concept matrix; and generate a first query for a first index of a first search engine and a second query for a second index of a second search engine, the first and second queries comprising at least one of the derived concept terms. | 14. One or more computer-readable non-transitory storage media embodying software that is operable when executed by one or more computing devices to: derive a concept matrix from a plurality of sample documents using singular value decomposition of a term-document matrix, the concept matrix identifying a latent pattern of word usage in the plurality of sample documents around a concept; derive concept terms by extracting significant terms from search text and inferring relevant terms therefrom using the concept matrix; and generate a first query for a first index of a first search engine and a second query for a second index of a second search engine, the first and second queries comprising at least one of the derived concept terms. 15. The media of claim 14 , the software further operable when executed by the one or more computing devices to remove noise terms that are unrelated to said significant terms. | 0.539267 |
9,219,716 | 11 | 12 | 11. A method of tokenization, comprising: accessing a string of characters; producing, by a processor, an intermediate string of characters by replacing a first portion of the string of characters with a first token mapped to a value of the first portion of the string of characters by a first token table; and producing a tokenized string of characters by replacing a second portion of the intermediate string of characters with a second token mapped to a value of the second portion of the intermediate string of characters by a second token table, wherein the second portion of the intermediate string of characters includes at least one character replaced by the first token. | 11. A method of tokenization, comprising: accessing a string of characters; producing, by a processor, an intermediate string of characters by replacing a first portion of the string of characters with a first token mapped to a value of the first portion of the string of characters by a first token table; and producing a tokenized string of characters by replacing a second portion of the intermediate string of characters with a second token mapped to a value of the second portion of the intermediate string of characters by a second token table, wherein the second portion of the intermediate string of characters includes at least one character replaced by the first token. 12. The method of claim 11 , further comprising one or more of: modifying the first portion of the string of characters before replacing the first portion, and modifying the second portion of the string of characters before replacing the second portion. | 0.770417 |
7,558,791 | 10 | 11 | 10. A computer program product stored in a memory device with software for translating and mapping a directory entry to be formatted in a destination directory schema, wherein said directory entry can be formatted in a source directory schema, wherein said translating is directed by an ontology of said destination directory schema comprising a plurality of classes expressed in a statement format of a resource description framework and a plurality of mapping functions, said computer program product comprising (a) computer-implemented instructions, when executed by a processor, for converting said directory entry between a directory-specific data format and said statement format that incorporates instructions for creating a triple in said statement format for each attribute of said directory entry in said directory-specific data format defined by said source directory schema; (b) computer-implemented instructions, when executed by a processor, for translating said directory entry in said statement format from a class of an ontology of said source directory schema to said class of said ontology of said destination directory schema, wherein said instructions comprise instructions for locating said class of said ontology of said destination directory schema in a database, instructions for translating each triple of said directory entry, and instructions for locating, for each triple of said directory entry, a mapping function corresponding to a set of said class of said ontology of said source directory schema, said class of said ontology in said destination directory schema and a predicate of said triple in said database; (c) computer-implemented instructions, when executed by a processor, for mapping said directory entry by providing each triple of said directory entry in said statement format expressed as a temporary individual entry and said class of said ontology of said destination directory schema to said mapping function in an execution environment which produces a replacement individual entry that replaces said triple of said directory entry in said statement format with a replacement triple in said destination directory schema; (d) computer-implemented instructions, when executed by a processor, for converting said directory entry between said statement format and said directory-specific data format. | 10. A computer program product stored in a memory device with software for translating and mapping a directory entry to be formatted in a destination directory schema, wherein said directory entry can be formatted in a source directory schema, wherein said translating is directed by an ontology of said destination directory schema comprising a plurality of classes expressed in a statement format of a resource description framework and a plurality of mapping functions, said computer program product comprising (a) computer-implemented instructions, when executed by a processor, for converting said directory entry between a directory-specific data format and said statement format that incorporates instructions for creating a triple in said statement format for each attribute of said directory entry in said directory-specific data format defined by said source directory schema; (b) computer-implemented instructions, when executed by a processor, for translating said directory entry in said statement format from a class of an ontology of said source directory schema to said class of said ontology of said destination directory schema, wherein said instructions comprise instructions for locating said class of said ontology of said destination directory schema in a database, instructions for translating each triple of said directory entry, and instructions for locating, for each triple of said directory entry, a mapping function corresponding to a set of said class of said ontology of said source directory schema, said class of said ontology in said destination directory schema and a predicate of said triple in said database; (c) computer-implemented instructions, when executed by a processor, for mapping said directory entry by providing each triple of said directory entry in said statement format expressed as a temporary individual entry and said class of said ontology of said destination directory schema to said mapping function in an execution environment which produces a replacement individual entry that replaces said triple of said directory entry in said statement format with a replacement triple in said destination directory schema; (d) computer-implemented instructions, when executed by a processor, for converting said directory entry between said statement format and said directory-specific data format. 11. The computer program product of claim 10 , wherein said execution environment further comprises a virtual machine, wherein said virtual machine is configured to execute a bytecodes encoding said mapping function, and said bytecodes are stored in said state database indexed by said class of said ontology of said destination directory schema. | 0.51676 |
8,370,337 | 10 | 12 | 10. A method performed by one or more server devices for ranking search results, the method comprising: referencing data from one or more user queries, wherein the data comprises one or more of general search engine results and vertical search engine results; generating a training set, wherein the training set comprises one or more search results extracted from the data; associating one or more click-based judgments with each of the one or more search results in the training set, wherein associating one or more click-based judgments with each of the one or more search results in the training set comprises: (1) determining that a plurality user queries are tail queries, wherein a tail query satisfies a threshold for the data referenced from one or more user queries; (2) aggregating the search results of plurality of tail queries into one or more classes of tail queries, such that the training set comprises at least one class of tail queries having a plurality of tail queries; and (3) associating one or more click-based judgments with each of the one or more classes of tail queries in the training set; determining one or more identifiable features from the training set based on the associated one or more click-based judgments; and based on determining one or more identifiable features from the training set, generating a rule set for ranking search results. | 10. A method performed by one or more server devices for ranking search results, the method comprising: referencing data from one or more user queries, wherein the data comprises one or more of general search engine results and vertical search engine results; generating a training set, wherein the training set comprises one or more search results extracted from the data; associating one or more click-based judgments with each of the one or more search results in the training set, wherein associating one or more click-based judgments with each of the one or more search results in the training set comprises: (1) determining that a plurality user queries are tail queries, wherein a tail query satisfies a threshold for the data referenced from one or more user queries; (2) aggregating the search results of plurality of tail queries into one or more classes of tail queries, such that the training set comprises at least one class of tail queries having a plurality of tail queries; and (3) associating one or more click-based judgments with each of the one or more classes of tail queries in the training set; determining one or more identifiable features from the training set based on the associated one or more click-based judgments; and based on determining one or more identifiable features from the training set, generating a rule set for ranking search results. 12. The method of claim 10 , wherein associating one or more click-based judgments with each of the one or more search results in the training set comprises: presenting one or more human judges with the training set; and generating a corresponding set of click-based judgments based on feedback from one or more human judges, wherein the click-based judgments correspond to user preferences regarding one or more of the plurality of search results in the training set. | 0.693316 |
8,229,728 | 1 | 14 | 1. A method for generating one or more translation models for a statistical machine translation system, comprising the steps of: selecting a plurality of parallel sentences having one or more phrases using at least one of word occurrence frequency information and a minimum BLEU score criterion, each of said parallel sentences having a source language sentence and a target language sentence; manually aligning, via an alignment tool, words and phrases between said source language sentences and said target language sentences of said parallel sentences; extracting alignment patterns from said manually aligned sentences; estimating word alignments from said alignment patterns; estimating one or more word alignment models to generate one or more final word alignments; extracting source-target phrases using the final word alignments; and estimating the one or more translation models from the extracted source-target phrases. | 1. A method for generating one or more translation models for a statistical machine translation system, comprising the steps of: selecting a plurality of parallel sentences having one or more phrases using at least one of word occurrence frequency information and a minimum BLEU score criterion, each of said parallel sentences having a source language sentence and a target language sentence; manually aligning, via an alignment tool, words and phrases between said source language sentences and said target language sentences of said parallel sentences; extracting alignment patterns from said manually aligned sentences; estimating word alignments from said alignment patterns; estimating one or more word alignment models to generate one or more final word alignments; extracting source-target phrases using the final word alignments; and estimating the one or more translation models from the extracted source-target phrases. 14. The method of claim 1 , wherein in the estimating word alignments step, the word alignments are automatically generated from maximum BLEU score criterion. | 0.8736 |
7,984,006 | 8 | 12 | 8. A non-transitory computer readable storage medium storing instructions which when executed by a computer cause the computer to perform a method for inferring activities associated with a user, the method comprising: receiving a cluster of locations that indicates a location trace of the user during a period of time and corresponding context; smoothing the location trace, which involves statistically removing a first subset of locations from the location cluster, and interpolating a second subset of locations into the location cluster; deriving a set of venues based on the location cluster from a venue database, wherein a visit to a respective venue is identified as a hypothetical visit responsive to the venue being located within a location range which includes the received location cluster and being associated with the corresponding context; deriving a set of activity types associated with the venues from a venue-to-activity mapping; associating attributes of the venues to the activity types based on the corresponding context; identifying a subset of the activity types of which the associated attributes are similar to a query context; assigning a weight to each identified activity type based on similarity between its attributes and the query context; and producing a probability distribution of various activity types based on the subset of the activity types and their weights, wherein the probability distribution predicts likelihoods that the user engages in each identified activity type associated with the query context and the location. | 8. A non-transitory computer readable storage medium storing instructions which when executed by a computer cause the computer to perform a method for inferring activities associated with a user, the method comprising: receiving a cluster of locations that indicates a location trace of the user during a period of time and corresponding context; smoothing the location trace, which involves statistically removing a first subset of locations from the location cluster, and interpolating a second subset of locations into the location cluster; deriving a set of venues based on the location cluster from a venue database, wherein a visit to a respective venue is identified as a hypothetical visit responsive to the venue being located within a location range which includes the received location cluster and being associated with the corresponding context; deriving a set of activity types associated with the venues from a venue-to-activity mapping; associating attributes of the venues to the activity types based on the corresponding context; identifying a subset of the activity types of which the associated attributes are similar to a query context; assigning a weight to each identified activity type based on similarity between its attributes and the query context; and producing a probability distribution of various activity types based on the subset of the activity types and their weights, wherein the probability distribution predicts likelihoods that the user engages in each identified activity type associated with the query context and the location. 12. The computer readable storage medium of claim 8 , wherein deriving the set of venues comprises: identifying a location based on one or more locations indicated by the location trace within a time period; and identifying a number of venues in the vicinity of the identified location based on the venue database. | 0.653422 |
8,332,883 | 28 | 30 | 28. The medium of claim 27 , wherein the instructions further cause a machine to present the media instance and one or more of the brain wave data, the first energy, the second energy, the third energy, the fourth energy, the first difference, the second difference, the first output signal, the second output signal or the third output signal via a rendering. | 28. The medium of claim 27 , wherein the instructions further cause a machine to present the media instance and one or more of the brain wave data, the first energy, the second energy, the third energy, the fourth energy, the first difference, the second difference, the first output signal, the second output signal or the third output signal via a rendering. 30. The medium of claim 28 , wherein the instructions further cause a machine to enable a user to remotely manipulate one or more of the media instance, the brain wave data, the first energy, the second energy, the third energy, the fourth energy, the first difference, the second difference, the first output signal, the second output signal, the third output signal or the rendering. | 0.896058 |
8,490,003 | 8 | 14 | 8. A system for proximity based text exchange comprising: one or more processors; at least one memory storing program instructions executable by the one or more processors; a message handler, comprising at least a portion of the program instructions, able to receive a text exchange associated with a distance value, wherein the text exchange is conveyed by an text exchange application, wherein the text exchange application is linked to a group session comprising of a plurality of participants, wherein the text exchange is a real-time text based communication between the plurality of participants utilizing a plurality of computing devices; and a proximity engine, comprising at least a portion of the program instructions, configured to identify a distance value associated with the text exchange and determine at least one recipient of the received text exchange based on the distance value associated with the proximity exchange and the proximity value associated with the at least one recipient; and an engine, comprising at least a portion of the program instructions, configured to communicate the proximity exchange to a computing device utilized by the determined at least one recipient when the proximity value of the at east one recipient is equivalent to the distance value of the proximity exchange. | 8. A system for proximity based text exchange comprising: one or more processors; at least one memory storing program instructions executable by the one or more processors; a message handler, comprising at least a portion of the program instructions, able to receive a text exchange associated with a distance value, wherein the text exchange is conveyed by an text exchange application, wherein the text exchange application is linked to a group session comprising of a plurality of participants, wherein the text exchange is a real-time text based communication between the plurality of participants utilizing a plurality of computing devices; and a proximity engine, comprising at least a portion of the program instructions, configured to identify a distance value associated with the text exchange and determine at least one recipient of the received text exchange based on the distance value associated with the proximity exchange and the proximity value associated with the at least one recipient; and an engine, comprising at least a portion of the program instructions, configured to communicate the proximity exchange to a computing device utilized by the determined at least one recipient when the proximity value of the at east one recipient is equivalent to the distance value of the proximity exchange. 14. The system of claim 8 , wherein the proximity value is associated with a contact of the text exchange application. | 0.891144 |
9,348,880 | 1 | 5 | 1. A method comprising: obtaining a first data object as a result of executing a first search query against a first data source of a plurality of heterogeneous data sources; obtaining a second data object as a result of executing a second search query against a second data source, that is not the first data source, of the plurality of heterogeneous data sources; determining, based on one or more resolution rules, whether the first data object and the second data object represent similar objects or identical objects; in response to determining that the first data object and the second data object represent similar objects or identical objects: generating an intermediate data object based on grouping the first data object with the second data object; generating a unique identifier for the intermediate data object based on hashing one or more data object properties that uniquely identify the intermediate data object; determining whether a repository data object that shares the unique identifier is stored in a repository that has a particular data model; in response to determining that the repository data object is not stored in the repository, generating a stub data object that is referenced by the unique identifier and that is stored in the repository; resolving the intermediate data object with the stub data object; deduplicating data associated with the intermediate data object and the stub data object; storing the deduplicated data in the repository that has the particular data model; wherein the method is performed by one or more computing devices. | 1. A method comprising: obtaining a first data object as a result of executing a first search query against a first data source of a plurality of heterogeneous data sources; obtaining a second data object as a result of executing a second search query against a second data source, that is not the first data source, of the plurality of heterogeneous data sources; determining, based on one or more resolution rules, whether the first data object and the second data object represent similar objects or identical objects; in response to determining that the first data object and the second data object represent similar objects or identical objects: generating an intermediate data object based on grouping the first data object with the second data object; generating a unique identifier for the intermediate data object based on hashing one or more data object properties that uniquely identify the intermediate data object; determining whether a repository data object that shares the unique identifier is stored in a repository that has a particular data model; in response to determining that the repository data object is not stored in the repository, generating a stub data object that is referenced by the unique identifier and that is stored in the repository; resolving the intermediate data object with the stub data object; deduplicating data associated with the intermediate data object and the stub data object; storing the deduplicated data in the repository that has the particular data model; wherein the method is performed by one or more computing devices. 5. The method of claim 1 , wherein a change to data in one of the plurality of heterogeneous data sources and a change to data in the repository are synchronized based on vector clocks, repository rankings, or data source rankings. | 0.634494 |
8,571,859 | 1 | 2 | 1. A method comprising: selecting, by a computing device, a first gender-specific speaker adaptation technique based on characteristics of a first set of feature vectors, wherein the first set of feature vectors correspond to a first unit of input speech, and wherein the first set of feature vectors are configured for use in automatic speech recognition (ASR) of the first unit of input speech, wherein the first gender-specific speaker adaptation technique is associated with a particular gender; modifying a second set of feature vectors based on the first gender-specific speaker adaptation technique, wherein the second set of feature vectors correspond to a second unit of input speech, and wherein the modified second set of feature vectors are configured for use in ASR of the second unit of input speech; based on characteristics of the second set of feature vectors and the first gender-specific speaker adaptation technique being associated with a particular gender, selecting a first speaker-dependent speaker adaptation technique that is associated with a particular speaker of the particular gender; and modifying a third set of feature vectors based on the first speaker-dependent speaker adaptation technique, wherein the third set of feature vectors correspond to a third unit of input speech, and wherein the modified third set of feature vectors are configured for use in ASR of the third unit of input speech. | 1. A method comprising: selecting, by a computing device, a first gender-specific speaker adaptation technique based on characteristics of a first set of feature vectors, wherein the first set of feature vectors correspond to a first unit of input speech, and wherein the first set of feature vectors are configured for use in automatic speech recognition (ASR) of the first unit of input speech, wherein the first gender-specific speaker adaptation technique is associated with a particular gender; modifying a second set of feature vectors based on the first gender-specific speaker adaptation technique, wherein the second set of feature vectors correspond to a second unit of input speech, and wherein the modified second set of feature vectors are configured for use in ASR of the second unit of input speech; based on characteristics of the second set of feature vectors and the first gender-specific speaker adaptation technique being associated with a particular gender, selecting a first speaker-dependent speaker adaptation technique that is associated with a particular speaker of the particular gender; and modifying a third set of feature vectors based on the first speaker-dependent speaker adaptation technique, wherein the third set of feature vectors correspond to a third unit of input speech, and wherein the modified third set of feature vectors are configured for use in ASR of the third unit of input speech. 2. The method of claim 1 further comprising: determining that (i) the third unit of input speech was originated proximate to a particular location, and (ii) the particular speaker is also associated with an environment-specific, speaker-dependent speaker adaptation technique, wherein the environment-specific, speaker-dependent speaker adaptation technique is associated with the particular location; selecting the environment-specific, speaker-dependent speaker adaptation technique; and modifying a fourth set of feature vectors based on the environment-specific, speaker-dependent speaker adaptation technique, wherein the fourth set of feature vectors correspond to a fourth unit of input speech, and wherein the modified fourth set of feature vectors are configured for use in ASR of the fourth unit of input speech. | 0.629396 |
9,669,260 | 4 | 6 | 4. A method for tracking positions of parts of an object, comprising controlling a processor to: obtain a training depth image; extract a foreground depth image of an object comprising parts from the training depth image, the foreground depth image of the object comprising some of the pixels of the training depth image and corresponding depth information, extract patterns by storing differences between the depth information of each pixel of the foreground depth image of the object and the depth information of pixels surrounding the each pixel of the foreground depth image of the object as a vector, each of the surrounding pixels being in either the foreground depth image of the object or a portion of the training depth image outside the foreground depth image of the object, and each of the surrounding pixels being located at a predetermined distance in a radial direction from the each pixel, said predetermined distance being greater than 1 pixel; generate a database including information about a part of the object corresponding to each pixel from the foreground depth image of the object; extract features for each pixel of the foreground depth image of the object by calculating a histogram based on a frequency of the extracted patterns; generate a feature set for each of the parts of the of the object based on the extracted features; train a classifier to distinguish a position of each part of the object in the foreground depth image of the object by using the generated feature set for each part of the foreground depth image of the object; extract features of each pixel of an input image object of an input depth image in a state in which the classifier is trained; and track three-dimensional position information of each part of the input image object in the input depth image using the trained classifier and the extracted features of each pixel of the input image object. | 4. A method for tracking positions of parts of an object, comprising controlling a processor to: obtain a training depth image; extract a foreground depth image of an object comprising parts from the training depth image, the foreground depth image of the object comprising some of the pixels of the training depth image and corresponding depth information, extract patterns by storing differences between the depth information of each pixel of the foreground depth image of the object and the depth information of pixels surrounding the each pixel of the foreground depth image of the object as a vector, each of the surrounding pixels being in either the foreground depth image of the object or a portion of the training depth image outside the foreground depth image of the object, and each of the surrounding pixels being located at a predetermined distance in a radial direction from the each pixel, said predetermined distance being greater than 1 pixel; generate a database including information about a part of the object corresponding to each pixel from the foreground depth image of the object; extract features for each pixel of the foreground depth image of the object by calculating a histogram based on a frequency of the extracted patterns; generate a feature set for each of the parts of the of the object based on the extracted features; train a classifier to distinguish a position of each part of the object in the foreground depth image of the object by using the generated feature set for each part of the foreground depth image of the object; extract features of each pixel of an input image object of an input depth image in a state in which the classifier is trained; and track three-dimensional position information of each part of the input image object in the input depth image using the trained classifier and the extracted features of each pixel of the input image object. 6. The method for tracking a position according to claim 4 , wherein the training of the classifier is performed using a randomized forests algorithm. | 0.91082 |
10,109,278 | 4 | 8 | 4. A computer-implemented method for aligning content, the computer-implemented method comprising: as implemented by one or more computing devices configured with specific computer-executable instructions, obtaining a textual transcription of an item of content comprising audio content; identifying a portion of the textual transcription that includes text also included in a portion of a companion item of textual content, wherein the textual content includes body text and matter other than body text; determining a level of correlation between words in the portion of the companion item of textual content and words in the portion of the textual transcription; determining that the level of correlation satisfies a threshold value; in response to determining that the level of correlation satisfies a threshold value, identifying the portion of the companion item of textual content as including body text; identifying a second portion of the companion item of textual content that does not satisfy the threshold value with respect to any portion of the textual transcription; determining that the second portion of the companion item of textual content that does not satisfy the threshold value is front matter based at least in part on a determination that the second portion of the companion item of textual content that does not satisfy the threshold value appears within the companion item of textual content prior to an earliest portion of the companion item of textual content for which a corresponding portion of the audio content is identified; and generating content synchronization information that indicates (a) portions of the audio content that correspond to body text of the companion item of textual content and (b) further indicates that the matter other than body text in the textual content does not correspond to any portion of the audio content, wherein the matter other than body text includes the second portion of the companion item of textual content determined to be front matter, wherein the content synchronization information indicates that the body text included in the portion of the companion item of textual content should be presented in synchronization with a portion of the audio content that corresponds to the body text included in the portion of the textual transcription. | 4. A computer-implemented method for aligning content, the computer-implemented method comprising: as implemented by one or more computing devices configured with specific computer-executable instructions, obtaining a textual transcription of an item of content comprising audio content; identifying a portion of the textual transcription that includes text also included in a portion of a companion item of textual content, wherein the textual content includes body text and matter other than body text; determining a level of correlation between words in the portion of the companion item of textual content and words in the portion of the textual transcription; determining that the level of correlation satisfies a threshold value; in response to determining that the level of correlation satisfies a threshold value, identifying the portion of the companion item of textual content as including body text; identifying a second portion of the companion item of textual content that does not satisfy the threshold value with respect to any portion of the textual transcription; determining that the second portion of the companion item of textual content that does not satisfy the threshold value is front matter based at least in part on a determination that the second portion of the companion item of textual content that does not satisfy the threshold value appears within the companion item of textual content prior to an earliest portion of the companion item of textual content for which a corresponding portion of the audio content is identified; and generating content synchronization information that indicates (a) portions of the audio content that correspond to body text of the companion item of textual content and (b) further indicates that the matter other than body text in the textual content does not correspond to any portion of the audio content, wherein the matter other than body text includes the second portion of the companion item of textual content determined to be front matter, wherein the content synchronization information indicates that the body text included in the portion of the companion item of textual content should be presented in synchronization with a portion of the audio content that corresponds to the body text included in the portion of the textual transcription. 8. The computer-implemented method of claim 4 , wherein the companion item of textual content is an electronic book. | 0.930705 |
8,224,832 | 23 | 24 | 23. The system of claim 16 , wherein the at least one processor is configured to provide an output document constructed from an updatable reference document and including material representing one or more of: a complete reconstruction of the monitored document with all changes thereto after the given time; additions to the monitored document after the given time; deletions to the monitored document after the given time; and modifications of the monitored document after the given time. | 23. The system of claim 16 , wherein the at least one processor is configured to provide an output document constructed from an updatable reference document and including material representing one or more of: a complete reconstruction of the monitored document with all changes thereto after the given time; additions to the monitored document after the given time; deletions to the monitored document after the given time; and modifications of the monitored document after the given time. 24. The system of claim 23 , wherein the at least one processor is configured to cause display of all or part of the output document on one or more electronic display devices. | 0.927746 |
9,501,696 | 14 | 16 | 14. The computing system of claim 13 , further comprising: a third processor; the first memory storing instructions, when executed by the first processor, further configure the system to notify a second plugin service associated to the one or more zones in the plurality of zones; and, a third memory storing instructions that, when executed by the third processor, configure the system to: extract, by the second plugin service, a second metadata element associated to the one or more zones in the plurality of zones; assign the second metadata element to the one or more objects associated to the document type; store the one or more objects associated to the document type in the metadata storage; and, invoke the one or more triggers assigned to the one or more zones in the plurality of zones. | 14. The computing system of claim 13 , further comprising: a third processor; the first memory storing instructions, when executed by the first processor, further configure the system to notify a second plugin service associated to the one or more zones in the plurality of zones; and, a third memory storing instructions that, when executed by the third processor, configure the system to: extract, by the second plugin service, a second metadata element associated to the one or more zones in the plurality of zones; assign the second metadata element to the one or more objects associated to the document type; store the one or more objects associated to the document type in the metadata storage; and, invoke the one or more triggers assigned to the one or more zones in the plurality of zones. 16. The computing system of claim 14 , wherein the document associated with the document type is a file in a binary format and wherein extracting the first metadata element or the second metadata element associated to each zone in the plurality of zones comprises metadata extraction from the binary format. | 0.862578 |
8,195,468 | 27 | 29 | 27. The method of claim 26 , wherein the identified domain agent updates the context stack and the semantic knowledge-based model in response to processing the request. | 27. The method of claim 26 , wherein the identified domain agent updates the context stack and the semantic knowledge-based model in response to processing the request. 29. The method of claim 27 , wherein the speech recognition engine creates a speech-based transcription of the follow-up natural language utterance using the updated semantic knowledge-based model. | 0.950253 |
8,626,768 | 1 | 6 | 1. One or more computer storage media storing computer-useable instructions that, when used by one or more computing devices, cause the one or more computing devices to perform a method comprising: receiving an initial set of search queries including one or more input search queries, the one or more input search queries having been manually determined to be relevant to a given subject area; generating an expanded set of search queries by analyzing search engine session data to identify a plurality of additional search queries related to the one or more input search queries, the expanded set of search queries including the one or more input search queries and the plurality of additional search queries; employing the expanded set of search queries to identify a plurality of URLs relevant to the given subject area, each URL in the plurality of URLs associated with a website, wherein employing the expanded set of search queries to identify the plurality of URLs relevant to the given subject area comprises analyzing search engine session data and user web browsing data based on the expanded set of search queries to identify the plurality of URLs; determining for each URL in the plurality of URLs a section of the website that is relevant to the given subject area; periodically crawling, based on the section identified for each URL, documents associated with the plurality of URLs, to provide a plurality of content items from the URLs; employing a classifier to identify relevant content items from the plurality of content items, the relevant content items being determined by the classifier to be relevant to the given subject area; clustering the relevant content items into a plurality of clusters, each cluster including a group of content items associated with a particular event or topic within the given subject area, wherein the particular event or topic is associated with a main content item identified based on the number of hyperlinks in the relevant content items to the main content item; ranking the plurality of clusters against one another, wherein ranking comprises: (1) retrieving content from social network sites; (2) counting within the content from social network sites the number of hyperlinks to URLs corresponding with the relevant content items within the plurality of clusters; and (3) ranking a first cluster higher than a second cluster when the first cluster has more hyperlinks than the second cluster; and generating a user interface allowing a user to view and interact with the plurality of clusters, wherein the user interface provides, for each cluster, the main content item and a plurality of related content items, wherein the plurality of related content items comprises the content from social network sites having hyperlinks to URLs corresponding with the relevant content items. | 1. One or more computer storage media storing computer-useable instructions that, when used by one or more computing devices, cause the one or more computing devices to perform a method comprising: receiving an initial set of search queries including one or more input search queries, the one or more input search queries having been manually determined to be relevant to a given subject area; generating an expanded set of search queries by analyzing search engine session data to identify a plurality of additional search queries related to the one or more input search queries, the expanded set of search queries including the one or more input search queries and the plurality of additional search queries; employing the expanded set of search queries to identify a plurality of URLs relevant to the given subject area, each URL in the plurality of URLs associated with a website, wherein employing the expanded set of search queries to identify the plurality of URLs relevant to the given subject area comprises analyzing search engine session data and user web browsing data based on the expanded set of search queries to identify the plurality of URLs; determining for each URL in the plurality of URLs a section of the website that is relevant to the given subject area; periodically crawling, based on the section identified for each URL, documents associated with the plurality of URLs, to provide a plurality of content items from the URLs; employing a classifier to identify relevant content items from the plurality of content items, the relevant content items being determined by the classifier to be relevant to the given subject area; clustering the relevant content items into a plurality of clusters, each cluster including a group of content items associated with a particular event or topic within the given subject area, wherein the particular event or topic is associated with a main content item identified based on the number of hyperlinks in the relevant content items to the main content item; ranking the plurality of clusters against one another, wherein ranking comprises: (1) retrieving content from social network sites; (2) counting within the content from social network sites the number of hyperlinks to URLs corresponding with the relevant content items within the plurality of clusters; and (3) ranking a first cluster higher than a second cluster when the first cluster has more hyperlinks than the second cluster; and generating a user interface allowing a user to view and interact with the plurality of clusters, wherein the user interface provides, for each cluster, the main content item and a plurality of related content items, wherein the plurality of related content items comprises the content from social network sites having hyperlinks to URLs corresponding with the relevant content items. 6. The one or more computer storage media of claim 1 , wherein clustering the relevant content items into the plurality of clusters is further based at least in part on ad hoc clustering in which text of the relevant content items is analyzed to identify relationships among the relevant content items. | 0.606771 |
8,001,465 | 1 | 20 | 1. A computer executable method for displaying elements of an information array within a predetermined two dimensional display space, wherein the predetermined two dimensional display space is divided into cells formed at intersections of columns and rows, the elements of the information array have corresponding cells for display, and at least two of said elements include text, said method comprising the steps of: (a) determining display space requirement (DSR) for displaying the elements; (b) moderating the DSR value of at least one element to determine its moderated display space requirement (ModDSR) value, wherein said moderating step comprises: (i) selecting an element whose DSR value is larger than the DSR value of at least one element in the column or row to which said element corresponds; and (ii) reducing the DSR value of the selected element such that the amount of reduction depends on the difference between the DSR value of said element and a value representative of the DSR values of the elements corresponding to the column or row to which said element corresponds; (c) allocating column widths and row heights, based on the ModDSR values or on values obtained by using the ModDSR values, such that the total width of all the columns and the total height of all the rows do not exceed the width and height, respectively, of the predetermined two dimensional display space; and (d) displaying the elements in the space allocated to the corresponding cells. | 1. A computer executable method for displaying elements of an information array within a predetermined two dimensional display space, wherein the predetermined two dimensional display space is divided into cells formed at intersections of columns and rows, the elements of the information array have corresponding cells for display, and at least two of said elements include text, said method comprising the steps of: (a) determining display space requirement (DSR) for displaying the elements; (b) moderating the DSR value of at least one element to determine its moderated display space requirement (ModDSR) value, wherein said moderating step comprises: (i) selecting an element whose DSR value is larger than the DSR value of at least one element in the column or row to which said element corresponds; and (ii) reducing the DSR value of the selected element such that the amount of reduction depends on the difference between the DSR value of said element and a value representative of the DSR values of the elements corresponding to the column or row to which said element corresponds; (c) allocating column widths and row heights, based on the ModDSR values or on values obtained by using the ModDSR values, such that the total width of all the columns and the total height of all the rows do not exceed the width and height, respectively, of the predetermined two dimensional display space; and (d) displaying the elements in the space allocated to the corresponding cells. 20. A compacted display format generated by employing the method in claim 1 . | 0.946003 |
9,524,288 | 9 | 10 | 9. An Fault Tree (FT) diagram generation aid method, comprising: an import procedure for obtaining a connection relationship of ruled lines and character strings from first data which is data of an FT diagram expressing a tree structure by the ruled lines and the character strings on a sheet of a spreadsheet program, acquiring an event included in the FT diagram and a connection relationship between events from an obtained connection relationship of the ruled lines and the character strings, and generating second data describing the tree structure of the FT diagram in a markup language based on the event included in the FT diagram and the connection relationship between events; and an editing procedure for editing the second data to generate third data describing the tree structure of the edited FT diagram in the markup language, wherein the import procedure generates the second data by: repeatedly executing first processing of setting a specific cell surrounded by a ruled line as a cell to be analyzed, discovering a lower event of the cell to be analyzed by following a ruled line extending on a right side of the cell to be analyzed and by searching a cell surrounded by a ruled line beyond the same, arranging an element of the lower event between a start tag and an end tag of the cell to be analyzed in the second data, and setting a cell of the lower event as a new cell to be analyzed until no additional new lower event is discovered after a cell of a top event is set as a first cell to be analyzed; repeatedly executing second processing of, when no additional new lower event is discovered, discovering a same-rank event of the cell to be analyzed by following a ruled line branching downward from a ruled line extending to a left side from the cell to be analyzed and by searching a cell surrounded by a ruled line beyond the same, arranging an element of the same-rank event in parallel with the element of the cell to be analyzed in the second data, setting the cell of the same-rank event as a new cell to be analyzed, and repeating the first processing until no additional new lower event is discovered, until no additional new same-rank event is discovered; and repeatedly executing third processing of, when no additional new same-rank event is discovered, setting a higher event of the same-rank event discovered immediately before as a new cell to be analyzed, and repeating the second processing until no additional new same-rank event is discovered, until the cell to be analyzed becomes the cell of the top event. | 9. An Fault Tree (FT) diagram generation aid method, comprising: an import procedure for obtaining a connection relationship of ruled lines and character strings from first data which is data of an FT diagram expressing a tree structure by the ruled lines and the character strings on a sheet of a spreadsheet program, acquiring an event included in the FT diagram and a connection relationship between events from an obtained connection relationship of the ruled lines and the character strings, and generating second data describing the tree structure of the FT diagram in a markup language based on the event included in the FT diagram and the connection relationship between events; and an editing procedure for editing the second data to generate third data describing the tree structure of the edited FT diagram in the markup language, wherein the import procedure generates the second data by: repeatedly executing first processing of setting a specific cell surrounded by a ruled line as a cell to be analyzed, discovering a lower event of the cell to be analyzed by following a ruled line extending on a right side of the cell to be analyzed and by searching a cell surrounded by a ruled line beyond the same, arranging an element of the lower event between a start tag and an end tag of the cell to be analyzed in the second data, and setting a cell of the lower event as a new cell to be analyzed until no additional new lower event is discovered after a cell of a top event is set as a first cell to be analyzed; repeatedly executing second processing of, when no additional new lower event is discovered, discovering a same-rank event of the cell to be analyzed by following a ruled line branching downward from a ruled line extending to a left side from the cell to be analyzed and by searching a cell surrounded by a ruled line beyond the same, arranging an element of the same-rank event in parallel with the element of the cell to be analyzed in the second data, setting the cell of the same-rank event as a new cell to be analyzed, and repeating the first processing until no additional new lower event is discovered, until no additional new same-rank event is discovered; and repeatedly executing third processing of, when no additional new same-rank event is discovered, setting a higher event of the same-rank event discovered immediately before as a new cell to be analyzed, and repeating the second processing until no additional new same-rank event is discovered, until the cell to be analyzed becomes the cell of the top event. 10. The FT diagram generation aid method according to claim 9 , further comprising: an export procedure for expressing the tree structure of the FT diagram by the ruled lines and the character strings on the sheet of the spreadsheet program based on the third data, wherein fourth data is generated. | 0.803289 |
9,372,844 | 1 | 11 | 1. A computer implemented method for displaying on a display surface an automatically generated graphical display using a symbolic annotation language, the method comprising: analyzing a business process flow genus to form an alphabet of a compact symbolic language representing a plurality of semantics from a number of symbols, wherein the analyzing comprises determining a representative set of process flow description attributes; capturing a business process flow using one or more symbols of the number of symbols, the business process flow being a species of the business process flow genus, wherein capturing the business process flow using the one or more symbols of the number of symbols comprises mapping the representative set of process flow description attributes into the compact symbolic language while observing a constraint and analyzing characteristics of the business process flow, the business process flow being a species of the business process flow genus; testing the captured business process flow using a schematic of the business process flow; mapping the one or more symbols of the number of symbols to a plurality of constructs of a markup language, one or more constructs of the plurality of constructs to be rendered in a graphical display, wherein mapping the one or more symbols of the number of symbols to the plurality of constructs of the markup language comprises mapping the one or more symbols of the compact symbolic language to a plurality of instances of schematic symbols, mapping the plurality of instances of schematic symbols to the plurality of constructs of the markup language, and mapping the plurality of constructs of the markup language to a plurality of computer-automated processes; and automatically generating and displaying, on a display surface, the business process flow in a graphical user interface using the symbolic annotation language wherein displaying the business process flow is based on the plurality of computer-automated processes. | 1. A computer implemented method for displaying on a display surface an automatically generated graphical display using a symbolic annotation language, the method comprising: analyzing a business process flow genus to form an alphabet of a compact symbolic language representing a plurality of semantics from a number of symbols, wherein the analyzing comprises determining a representative set of process flow description attributes; capturing a business process flow using one or more symbols of the number of symbols, the business process flow being a species of the business process flow genus, wherein capturing the business process flow using the one or more symbols of the number of symbols comprises mapping the representative set of process flow description attributes into the compact symbolic language while observing a constraint and analyzing characteristics of the business process flow, the business process flow being a species of the business process flow genus; testing the captured business process flow using a schematic of the business process flow; mapping the one or more symbols of the number of symbols to a plurality of constructs of a markup language, one or more constructs of the plurality of constructs to be rendered in a graphical display, wherein mapping the one or more symbols of the number of symbols to the plurality of constructs of the markup language comprises mapping the one or more symbols of the compact symbolic language to a plurality of instances of schematic symbols, mapping the plurality of instances of schematic symbols to the plurality of constructs of the markup language, and mapping the plurality of constructs of the markup language to a plurality of computer-automated processes; and automatically generating and displaying, on a display surface, the business process flow in a graphical user interface using the symbolic annotation language wherein displaying the business process flow is based on the plurality of computer-automated processes. 11. The method of claim 1 , wherein the one or more symbols of the number of symbols are mapped to the plurality of constructs of the markup language such that the plurality of constructs of the markup language describes the captured business process flow. | 0.669251 |
8,600,747 | 5 | 8 | 5. The system of claim 4 , wherein an previously encoded assumption by the previously encoded assumption dialog motivator triggers a transfer of the user to a main menu of an interactive voice response system when the user is not classified as wanting a concrete call type. | 5. The system of claim 4 , wherein an previously encoded assumption by the previously encoded assumption dialog motivator triggers a transfer of the user to a main menu of an interactive voice response system when the user is not classified as wanting a concrete call type. 8. The system of claim 5 , wherein before the spoken dialog service transfers the user to the main menu, the spoken dialog service requests the user's telephone number. | 0.933597 |
8,521,532 | 1 | 4 | 1. A speech-conversion processing apparatus, comprising: a character-string structure analyzer operable to analyze a character-string structure within address data selected for speech conversion in accordance with speech-conversion rule data to identify specific elements of an address, where the specific elements of the address of the character-string structure comprises a street name; a general purpose dictionary in which text data of common words are stored in association with corresponding pronunciation symbols; an individually-created general dictionary in which data associated with pronunciation symbols not stored in the general purpose dictionary is stored; a pronunciation-symbol dictionary in which speech-conversion pronunciation symbols are specifically associated with character strings of a specific element of the address of the character-string structure, wherein the pronunciation-symbol dictionary comprises speech-conversion symbols specifically associated with character strings of street names; a data reader operable to search the pronunciation-symbol dictionary, the individually-created general dictionary, and the general purpose dictionary, according to a predetermined scheme, for a character string of the specific element of the address, the character string being obtained by dividing the address data into the specific elements of the address based on a result of the analysis performed by the character-string structure analyzer, and to read data associated with the speech-conversion pronunciation symbols; a speech data creator operable to create speech data for all the elements of address character strings in accordance with the data associated with the speech-conversion pronunciation symbols; and a speech generation section operable to generate speech from the speech data created by the speech data creator; wherein in the predetermined scheme the pronunciation-symbol dictionary is searched first. | 1. A speech-conversion processing apparatus, comprising: a character-string structure analyzer operable to analyze a character-string structure within address data selected for speech conversion in accordance with speech-conversion rule data to identify specific elements of an address, where the specific elements of the address of the character-string structure comprises a street name; a general purpose dictionary in which text data of common words are stored in association with corresponding pronunciation symbols; an individually-created general dictionary in which data associated with pronunciation symbols not stored in the general purpose dictionary is stored; a pronunciation-symbol dictionary in which speech-conversion pronunciation symbols are specifically associated with character strings of a specific element of the address of the character-string structure, wherein the pronunciation-symbol dictionary comprises speech-conversion symbols specifically associated with character strings of street names; a data reader operable to search the pronunciation-symbol dictionary, the individually-created general dictionary, and the general purpose dictionary, according to a predetermined scheme, for a character string of the specific element of the address, the character string being obtained by dividing the address data into the specific elements of the address based on a result of the analysis performed by the character-string structure analyzer, and to read data associated with the speech-conversion pronunciation symbols; a speech data creator operable to create speech data for all the elements of address character strings in accordance with the data associated with the speech-conversion pronunciation symbols; and a speech generation section operable to generate speech from the speech data created by the speech data creator; wherein in the predetermined scheme the pronunciation-symbol dictionary is searched first. 4. The speech-conversion processing apparatus according to claim 1 , wherein the data associated with the speech-conversion pronunciation symbols comprises a reference list of speech-conversion pronunciation symbols. | 0.83125 |
4,841,387 | 1 | 13 | 1. An arrangement for recording information relating to an event on a recording medium, and for indexing handwritten notations concerning the event to the recorded information, comprising: a recording device for recording information onto a recording medium; means for receiving handwritten notations on a writing surface; means for sensing relative positions of the recorded information on the recording medium, and for sensing positions of corresponding handwritten notations on the writing surface; means for correlating the respective positions of the recorded information to the positions of the handwritten notations; and playback means for locating and reproducing portions of the recorded information in response to identification of corresponding portions of the handwritten notations. | 1. An arrangement for recording information relating to an event on a recording medium, and for indexing handwritten notations concerning the event to the recorded information, comprising: a recording device for recording information onto a recording medium; means for receiving handwritten notations on a writing surface; means for sensing relative positions of the recorded information on the recording medium, and for sensing positions of corresponding handwritten notations on the writing surface; means for correlating the respective positions of the recorded information to the positions of the handwritten notations; and playback means for locating and reproducing portions of the recorded information in response to identification of corresponding portions of the handwritten notations. 13. An arrangement according to claim 1, wherein said recording device is a video recording device and wherein said recording medium is a video recording medium. | 0.88757 |
7,750,911 | 2 | 4 | 2. The system of claim 1 wherein the curve model generation software is responsive to repetitive strokes made with the pen stylus when the first 2D curve is sketched to generate the first 2D vector curve when a settle command is received after the first 2D curve is sketched and before the second 2D curve is sketched, and is responsive to repetitive strokes made with the pen stylus when the second 2D curve is sketched to generate the second 2D vector curve when the settle command is received after the second 2D curve is sketched. | 2. The system of claim 1 wherein the curve model generation software is responsive to repetitive strokes made with the pen stylus when the first 2D curve is sketched to generate the first 2D vector curve when a settle command is received after the first 2D curve is sketched and before the second 2D curve is sketched, and is responsive to repetitive strokes made with the pen stylus when the second 2D curve is sketched to generate the second 2D vector curve when the settle command is received after the second 2D curve is sketched. 4. The system of claim 2 wherein the curve model generation software is responsive to a check gesture as the settle command. | 0.962919 |
7,627,475 | 15 | 19 | 15. A method of recognizing emotional states in a voice of a telephone caller, the method comprising: providing a first plurality of voice samples; obtaining a second plurality of voice samples of a telephone caller, from a telephone call; identifying each sample of said pluralities of samples as belonging to a predominant emotional state; dividing each sample into at least one of frames, subframes, and segments; extracting at least one acoustic feature for each sample of the pluralities of samples; calculating statistics of the speech samples from the at least one feature; classifying an emotional state in the first plurality of samples with at least one neural network; training the at least one neural network to recognize an emotional state from the statistics by comparing the results of identifying and classifying for the first plurality of samples; classifying an emotion in the second plurality of voice samples obtained from a telephone call with the at least one trained neural network; storing the voice samples and the emotional states in a storage medium, in a manner to allow later retrieval of the stored voice samples and emotional states; outputting in a human-recognizable format an indication of the emotional state of the telephone caller; and routing the call containing said voice samples to at least one predetermined location according to the at least one classified emotional state. | 15. A method of recognizing emotional states in a voice of a telephone caller, the method comprising: providing a first plurality of voice samples; obtaining a second plurality of voice samples of a telephone caller, from a telephone call; identifying each sample of said pluralities of samples as belonging to a predominant emotional state; dividing each sample into at least one of frames, subframes, and segments; extracting at least one acoustic feature for each sample of the pluralities of samples; calculating statistics of the speech samples from the at least one feature; classifying an emotional state in the first plurality of samples with at least one neural network; training the at least one neural network to recognize an emotional state from the statistics by comparing the results of identifying and classifying for the first plurality of samples; classifying an emotion in the second plurality of voice samples obtained from a telephone call with the at least one trained neural network; storing the voice samples and the emotional states in a storage medium, in a manner to allow later retrieval of the stored voice samples and emotional states; outputting in a human-recognizable format an indication of the emotional state of the telephone caller; and routing the call containing said voice samples to at least one predetermined location according to the at least one classified emotional state. 19. The method of claim 15 , further comprising annotating and organizing the voice samples and emotional states based on the emotional content. | 0.743772 |
8,368,970 | 8 | 12 | 8. An image reader, comprising: a reading unit configured to receive light reflected from a document; and an automatic document feeder configured to supply the document to the reading unit, the automatic document feeder having defined therein a document transfer channel along which the document travels within the automatic document feeder, the document transfer channel including an automatic document feeder (ADF) window through which the reading unit receives the light reflected from the document and a reference bar for positioning the document on the ADF window, wherein at least one of the reference bar and the ADF window comprises a surface having a surface tension that is less than or equal to about 40 dyne-per-centimeter, and wherein the at least one of the reference bar and the ADF window includes a base material and a surface material disposed on a surface of the base material, the surface material having a surface tension that is less than or equal to about 40 dyne-per-centimeter. | 8. An image reader, comprising: a reading unit configured to receive light reflected from a document; and an automatic document feeder configured to supply the document to the reading unit, the automatic document feeder having defined therein a document transfer channel along which the document travels within the automatic document feeder, the document transfer channel including an automatic document feeder (ADF) window through which the reading unit receives the light reflected from the document and a reference bar for positioning the document on the ADF window, wherein at least one of the reference bar and the ADF window comprises a surface having a surface tension that is less than or equal to about 40 dyne-per-centimeter, and wherein the at least one of the reference bar and the ADF window includes a base material and a surface material disposed on a surface of the base material, the surface material having a surface tension that is less than or equal to about 40 dyne-per-centimeter. 12. The image reader according to claim 8 , wherein the surface material includes a film adhered to the surface of the base material. | 0.814763 |
9,251,279 | 37 | 44 | 37. A computer system comprising: a database having a plurality of records in respective categories of information, each record having one or more facets to the respective category of information; means for receiving user input of a first search term formed of a first parameter indicative of at least one category of information of the database; a search engine to implement a search of the database for records of the at least one category of information; means for displaying, in response to the user input of the first search term, simultaneously displaying both in a same screen view: (a) a set of search results, including records from the database of the at least one category of information, and (b) a listing of any one or combination of facets and facet values of the records in the set of search results, the listing serving as suggested additional parameters for further refining the first search term upon user selection of the any one or combination of facets and facet values, wherein at least one of the any one or combination of facets and facet values is defined by a community of users and corresponds to content generated by the community of users; means for enabling user input of a second search term formed of a second parameter; and a refinement engine for refining the search based on the listing and additional user input or the second search term. | 37. A computer system comprising: a database having a plurality of records in respective categories of information, each record having one or more facets to the respective category of information; means for receiving user input of a first search term formed of a first parameter indicative of at least one category of information of the database; a search engine to implement a search of the database for records of the at least one category of information; means for displaying, in response to the user input of the first search term, simultaneously displaying both in a same screen view: (a) a set of search results, including records from the database of the at least one category of information, and (b) a listing of any one or combination of facets and facet values of the records in the set of search results, the listing serving as suggested additional parameters for further refining the first search term upon user selection of the any one or combination of facets and facet values, wherein at least one of the any one or combination of facets and facet values is defined by a community of users and corresponds to content generated by the community of users; means for enabling user input of a second search term formed of a second parameter; and a refinement engine for refining the search based on the listing and additional user input or the second search term. 44. A system as claimed in claim 37 , wherein the listing of any one or combination of facets and facet values is ordered based on relevance to the data set. | 0.90808 |
10,165,307 | 16 | 18 | 16. A system comprising: at least one processor; memory, operatively connected to the at least one processor and storing instructions that, when executed by the at least one processor, cause the at least one processor to perform a method, the method comprising: accessing information related to an event; identifying a named entity and an associated entity attribute from the information related to the event, the associated entity attribute including an entity type and an entity popularity parameter, wherein the entity type includes at least one of an organization, person, or location, and the entity popularity parameter defines at least a popularity of the named entity in the information related to the event; accessing trending information based on the named entity and the associated entity attribute; collecting, by using the trending information, training data for identification of one or more entities in a video related to the event, wherein the training data is image data of at least one entity likely associated with the event; training a model using the training data, to learn features of the one or more entities, while presenting the video; performing recognition processing of the one or more entities in the video to identify the one or more entities while the video is being presented, wherein the recognition processing is performed using the trained model; performing a search to retrieve content relevant to the one or more entities identified by the recognition processing; and presenting, in real-time during presenting of the video, the content relevant to the one or more entities identified. | 16. A system comprising: at least one processor; memory, operatively connected to the at least one processor and storing instructions that, when executed by the at least one processor, cause the at least one processor to perform a method, the method comprising: accessing information related to an event; identifying a named entity and an associated entity attribute from the information related to the event, the associated entity attribute including an entity type and an entity popularity parameter, wherein the entity type includes at least one of an organization, person, or location, and the entity popularity parameter defines at least a popularity of the named entity in the information related to the event; accessing trending information based on the named entity and the associated entity attribute; collecting, by using the trending information, training data for identification of one or more entities in a video related to the event, wherein the training data is image data of at least one entity likely associated with the event; training a model using the training data, to learn features of the one or more entities, while presenting the video; performing recognition processing of the one or more entities in the video to identify the one or more entities while the video is being presented, wherein the recognition processing is performed using the trained model; performing a search to retrieve content relevant to the one or more entities identified by the recognition processing; and presenting, in real-time during presenting of the video, the content relevant to the one or more entities identified. 18. The system of claim 16 , wherein the content is presented on a device from which the video is being presented or on a device different than the device from which the video is being presented. | 0.526699 |
9,280,562 | 17 | 18 | 17. The method according to claim 15 , wherein the hidden concept layer which connects a visual feature layer and a word layer which is discovered by fitting a generative model to a training set comprising images and annotation words, wherein the conditional probabilities of the visual content features and the annotation words given a hidden concept class are determined based on an Expectation-Maximization (EM) based iterative learning procedure. | 17. The method according to claim 15 , wherein the hidden concept layer which connects a visual feature layer and a word layer which is discovered by fitting a generative model to a training set comprising images and annotation words, wherein the conditional probabilities of the visual content features and the annotation words given a hidden concept class are determined based on an Expectation-Maximization (EM) based iterative learning procedure. 18. The method according to claim 17 , wherein f i ,iε[1,N] denotes a visual feature vector of images in a training database, where N is the size of the database, w j ,j ε[1,M] denotes the distinct textual words in a training annotation word set, where M is the size of annotation vocabulary in the training database, the visual features of images in the database, f i =[f i 1 ,f i 2 , . . . ,f i L ]iε[1, N] are known i.i.d. samples from an unknown distribution, having a visual feature dimension L, the specific visual feature annotation word pairs (f i ,w j ),i ε[1, N], jε[1,M] are known i.i.d. samples from an unknown distribution, associated with an unobserved semantic concept variable zε Z={z 1 , . . . z k }, in which each observation of one visual feature fεF={f i ,f 2 , . . . , f N } belongs to one or more concept classes z k and each observation of one word w εV={w 1 ,w 2 , . . . w M } in one image f i belongs to one concept class, in which the observation pairs (f i , w j ) are assumed to be generated independently, and the pairs of random variables (f i ,w j ) are assumed to be conditionally independent given the respective hidden concept z k , such that
P ( f i ,w j |z k )= ( f i |z k ) P V ( w j |z k ); the visual feature and word distribution is treated as a randomized data generation process, wherein a probability of a concept is represented as P z (z k ); a visual feature is selected f i εF with probability P ℑ (f i |z k ); and a textual word is selected w j εV with probability P V (w j |z k ), from which an observed pair (f i ,w j ) is obtained, such that a joint probability model is expressed as follows: P ( f i , w j ) = P ( w j ) P ( f i ❘ w j ) = P ( w j ) ∑ k = 1 K ( f i ❘ z k ) P ( z k ❘ w j ) = ∑ k = 1 K P z ( z k ) ( f i ❘ z k ) P V ( w j ❘ z k ) , and the visual features are generated from K Gaussian distributions, each one corresponding to a z k , such that for a specific semantic concept variable z k , the conditional probability density function of visual feature f i is expressed as: ( f i ❘ z k ) = 1 2 π L / 2 ❘ ∑ k 1 / 2 ⅇ - 1 2 ( f i - μ k ) T ∑ k - 1 ( f i - μ k ) where Σ k and μ k are the covariance matrix and mean of visual features belonging to z k , respectively; and word concept conditional probabilities P V (●|Z), i.e., P V (w j |z k ) for kε [1,K], are estimated through fitting the probabilistic model to the training set. | 0.680657 |
9,946,749 | 23 | 24 | 23. The computer program product of claim 19 , wherein the operations further comprise generating the multiple equality expressions including: initializing a variable n to be equal to 1, initializing a variable nbegin to be equal to a start of a range of values specified by the inequality expression, and initializing a variable nend to be equal to an end of the range of values specified by the inequality expression; and iteratively performing the following operations until the variable n is greater than or equal to a maximum interval size: determining whether nbegin is odd and adding an equality expression testing for an auxiliary attribute having a value belonging to a first interval of size n whenever nbegin is odd; determining whether nend is even and adding an equality expression testing for an auxiliary attribute having a value belonging to a last interval of size n whenever nend is odd; setting the variable n to be equal to n×2; setting nbegin to be equal to (nbegin+1)/2 and discarding any remainder; and setting nend to be equal to (nend−1)/2 and discarding any remainder. | 23. The computer program product of claim 19 , wherein the operations further comprise generating the multiple equality expressions including: initializing a variable n to be equal to 1, initializing a variable nbegin to be equal to a start of a range of values specified by the inequality expression, and initializing a variable nend to be equal to an end of the range of values specified by the inequality expression; and iteratively performing the following operations until the variable n is greater than or equal to a maximum interval size: determining whether nbegin is odd and adding an equality expression testing for an auxiliary attribute having a value belonging to a first interval of size n whenever nbegin is odd; determining whether nend is even and adding an equality expression testing for an auxiliary attribute having a value belonging to a last interval of size n whenever nend is odd; setting the variable n to be equal to n×2; setting nbegin to be equal to (nbegin+1)/2 and discarding any remainder; and setting nend to be equal to (nend−1)/2 and discarding any remainder. 24. The computer program product of claim 23 , wherein the operations further comprise performing the following operations until nbegin is greater than nend: adding an equality expression testing for the auxiliary attribute belonging to an interval identified by nbegin; and setting nbegin to be equal to nbegin+1. | 0.90638 |
9,684,701 | 1 | 2 | 1. A method of replicating IP address assignment changes in a distributed database having a plurality of nodes, comprising: receiving a semantic command at a first node having a first local version of the database, wherein the semantic command comprises a semantically expressed request to modify one or more IP address assignments in the database that allows for provisional modification of the first local version of the database before sending the semantic command to a master node having a master version of the database, wherein a change request to modify the distributed database is expressed as the semantic command, the semantic command being expressed as one of a predefined set of commands, wherein the receiving of the semantic command at the first node comprises: receiving credentials of a user; and determining whether to authorize the user based on the received credentials, the user being authorized in the event that the user has not been previously authorized within a predetermined time period; and provisionally applying the semantic command to the first local version of the database using a processor before sending the semantic command to the master node, wherein the provisional applying the semantic command to the first local version of the database comprises modifying the first local version of the database before reconciling the modification with the master node, wherein the reconciling of the modification with the master comprises: forwarding the received credentials to the master; determining whether the modification would cause a conflict on the master, comprising: determining whether the same user has been authorized on a second node within the predetermined time period; and in the event that the modification would not cause a conflict, performing the modification on the master node. | 1. A method of replicating IP address assignment changes in a distributed database having a plurality of nodes, comprising: receiving a semantic command at a first node having a first local version of the database, wherein the semantic command comprises a semantically expressed request to modify one or more IP address assignments in the database that allows for provisional modification of the first local version of the database before sending the semantic command to a master node having a master version of the database, wherein a change request to modify the distributed database is expressed as the semantic command, the semantic command being expressed as one of a predefined set of commands, wherein the receiving of the semantic command at the first node comprises: receiving credentials of a user; and determining whether to authorize the user based on the received credentials, the user being authorized in the event that the user has not been previously authorized within a predetermined time period; and provisionally applying the semantic command to the first local version of the database using a processor before sending the semantic command to the master node, wherein the provisional applying the semantic command to the first local version of the database comprises modifying the first local version of the database before reconciling the modification with the master node, wherein the reconciling of the modification with the master comprises: forwarding the received credentials to the master; determining whether the modification would cause a conflict on the master, comprising: determining whether the same user has been authorized on a second node within the predetermined time period; and in the event that the modification would not cause a conflict, performing the modification on the master node. 2. A method as recited in claim 1 , wherein if the semantic command includes any IP address assignment changes that would result in an IP address assignment conflict with IP address assignment data stored in the master version of the database on the master node, then the semantic command is not applied to the master node. | 0.75192 |
8,230,338 | 3 | 7 | 3. The method of claim 1 , further comprising repeating the providing, comparing and applying for a sequence of content. | 3. The method of claim 1 , further comprising repeating the providing, comparing and applying for a sequence of content. 7. The method of claim 3 , further comprising determining the sequence of content by determining which content has fewer associated tags than a threshold number of tags. | 0.940743 |
4,888,823 | 3 | 8 | 3. A system for speech recognition comprising: means for extracting prescribed feature parameters including phonetic segment units and a label sequence composed of a series of labels each having its corresponding value and representing prescribed features of phonetic segment units from input continuous speech signals; means for obtaining similarities on the phonetic segment units extracted from input continuous speech signals by executing continuous matching of the extracted phonetic segment units with a voice dictionary compiled of the phonetic segment units having prescribed phonetic meanings so that a sequence of a plurality of similarities is obtained; means for converting the similarity of phonetic segment obtained by the similarity obtaining means into a normalized similarity having a normalized standard value; means for extracting a sequence of a plurality of phonetic segment likelihoods up to a prescribed placing of order based on the normalized standard values of the normalized similarities; means for selectively scoring the values of the labels with respect to predetermined phonetic segments except for transitional phonetic segments obtained in said feature parameter extracting means by accumulating the values of the labels; plurality of transition networks formed for each word included in the input speech by use of standard phonetic segment sequence; means for passing the extracted phonetic segment likelihood sequence through said transition networks by referring to a selectively scored value obtained in said scoring means for performing word-by-word matching; and means for continuously combining results of the word-by-word matching to obtain recognition outputs of the input speech. | 3. A system for speech recognition comprising: means for extracting prescribed feature parameters including phonetic segment units and a label sequence composed of a series of labels each having its corresponding value and representing prescribed features of phonetic segment units from input continuous speech signals; means for obtaining similarities on the phonetic segment units extracted from input continuous speech signals by executing continuous matching of the extracted phonetic segment units with a voice dictionary compiled of the phonetic segment units having prescribed phonetic meanings so that a sequence of a plurality of similarities is obtained; means for converting the similarity of phonetic segment obtained by the similarity obtaining means into a normalized similarity having a normalized standard value; means for extracting a sequence of a plurality of phonetic segment likelihoods up to a prescribed placing of order based on the normalized standard values of the normalized similarities; means for selectively scoring the values of the labels with respect to predetermined phonetic segments except for transitional phonetic segments obtained in said feature parameter extracting means by accumulating the values of the labels; plurality of transition networks formed for each word included in the input speech by use of standard phonetic segment sequence; means for passing the extracted phonetic segment likelihood sequence through said transition networks by referring to a selectively scored value obtained in said scoring means for performing word-by-word matching; and means for continuously combining results of the word-by-word matching to obtain recognition outputs of the input speech. 8. A system according to claim 3, wherein the voice dictionary includes phonetic segments expressed in prescribed labels, and said similarity obtaining means includes means for matching the prescribed label sequence with labels stored in said voice dictionary. | 0.588608 |
9,378,194 | 12 | 16 | 12. A system operating in a communication network, comprising: a computer comprising a memory to store a program code, and a processor to execute the program code to: receive a request from a user of the computer to preview an e-document template; invoke a content of the e-document template, wherein the content include a placeholder for a variable; determine a correspondence language of the user; identify, by the computer, a markup language element for the placeholder in the e-document template; determine, by the computer, a descriptive name for the identified markup language element by invoking metadata information pertaining to the place holder, and based on the metadata information, deriving the descriptive name associated with the identified markup language element for the placeholder in the correspondence language of the user; replace the markup language element for the placeholder with the selected descriptive name; and render a preview of the e-document template. | 12. A system operating in a communication network, comprising: a computer comprising a memory to store a program code, and a processor to execute the program code to: receive a request from a user of the computer to preview an e-document template; invoke a content of the e-document template, wherein the content include a placeholder for a variable; determine a correspondence language of the user; identify, by the computer, a markup language element for the placeholder in the e-document template; determine, by the computer, a descriptive name for the identified markup language element by invoking metadata information pertaining to the place holder, and based on the metadata information, deriving the descriptive name associated with the identified markup language element for the placeholder in the correspondence language of the user; replace the markup language element for the placeholder with the selected descriptive name; and render a preview of the e-document template. 16. The system of claim 12 , wherein the variable includes at least one of name, title, designation, customer ID, social security number, and reference number. | 0.799242 |
8,538,754 | 7 | 9 | 7. The method of claim 2 , wherein the second set of suggestions comprises a predetermined number of suggestions from the first set of suggestions. | 7. The method of claim 2 , wherein the second set of suggestions comprises a predetermined number of suggestions from the first set of suggestions. 9. The method of claim 7 , wherein the predetermined number of suggestions is selected from the first set based on an order of appearance of the suggestions in a ranked list derived from the first set. | 0.89607 |
8,160,362 | 8 | 14 | 8. At least one computer storage media storing computer-executable instructions that, when executed by a computer, cause the computer to perform a method for recognizing handwritten input data, the method comprising combining, in response to recognizing the handwritten input data, a first set of scores provided by an offline recognizer with a second set of scores provided by an online recognizer in response to recognizing the handwritten input data, the combining based on a repeated base learning algorithm. | 8. At least one computer storage media storing computer-executable instructions that, when executed by a computer, cause the computer to perform a method for recognizing handwritten input data, the method comprising combining, in response to recognizing the handwritten input data, a first set of scores provided by an offline recognizer with a second set of scores provided by an online recognizer in response to recognizing the handwritten input data, the combining based on a repeated base learning algorithm. 14. The at least one computer storage media of claim 8 further comprising returning, in response to the combining, a result set with each result in the set scored, each scored result representing the recognized handwritten input data. | 0.502128 |
7,702,650 | 6 | 10 | 6. A computer-implemented method for generating a user interface, the method comprising: receiving, by a processor associated with the computer, a business object containing first business data functionality; receiving, by the processor, a model framework comprising a meta model including second business data functionality and a meta model node, the meta model node containing a data record having a unique key associating the meta model node with the business object containing the first business data functionality; and generating, by the processor, the user interface based on the meta model, the generating including: determining whether a predetermined event occurs; based on a result of the determination, adapting the meta model to include the first business data functionality of the business object using the unique key; and incorporating the adapted meta model into the user interface. | 6. A computer-implemented method for generating a user interface, the method comprising: receiving, by a processor associated with the computer, a business object containing first business data functionality; receiving, by the processor, a model framework comprising a meta model including second business data functionality and a meta model node, the meta model node containing a data record having a unique key associating the meta model node with the business object containing the first business data functionality; and generating, by the processor, the user interface based on the meta model, the generating including: determining whether a predetermined event occurs; based on a result of the determination, adapting the meta model to include the first business data functionality of the business object using the unique key; and incorporating the adapted meta model into the user interface. 10. The method of claim 6 , wherein the models framework further includes: a model for implementing a user action on a meta model node. | 0.664179 |
8,793,119 | 10 | 11 | 10. The system of claim 7 , wherein the set of allowed dialog actions incorporates a set of business rules. | 10. The system of claim 7 , wherein the set of allowed dialog actions incorporates a set of business rules. 11. The system of claim 10 , wherein prompt wordings in the generated natural language spoken dialog system are tailored to a current context while following the set of business rules. | 0.886139 |
7,613,731 | 71 | 72 | 71. The computer system for presenting an electronic document of claim 70 wherein the knowledge database comprises at least one of a cognitive cluster database, a graphical similarity database, a part of speech database, and a context database. | 71. The computer system for presenting an electronic document of claim 70 wherein the knowledge database comprises at least one of a cognitive cluster database, a graphical similarity database, a part of speech database, and a context database. 72. The computer system for presenting an electronic document of claim 71 wherein the computer program product further comprises program instructions that compare each word with the rarity database to assign a rarity value to each word. | 0.933144 |
10,019,708 | 17 | 18 | 17. The system as recited in claim 16 , wherein the transaction phrase token processing service further causes a display of a user interface for obtaining a target transaction phrase token and determining whether the target transaction phrase token is unambiguous. | 17. The system as recited in claim 16 , wherein the transaction phrase token processing service further causes a display of a user interface for obtaining a target transaction phrase token and determining whether the target transaction phrase token is unambiguous. 18. The system as recited in claim 17 , wherein the transaction phrase token service further suggests at least one available transaction phrase token via the user interface. | 0.966408 |
8,874,555 | 1 | 8 | 1. A computer-implemented method comprising: calculating one or more time trend statistics for a plurality of quality of result statistics for a first document as a search result for a first query, each of the quality of result statistics corresponding to a different time period, the one or more time trend statistics estimating changes in the quality of result statistics over time, wherein each of the one or more time trend statistics comprises a quality of result difference between a first quality of result statistic for the first document as a search result for the first query during a first time period and a second quality of result statistic for the first document as a search result for the first query during a second time period; generating a first modified quality of result statistic by modifying a first quality of result statistic for the first document as a search result for the first query by a factor, where the factor is based on the one or more time trend statistics; and providing the first modified quality of result statistic as an input to a document ranking process for the first document and the first query. | 1. A computer-implemented method comprising: calculating one or more time trend statistics for a plurality of quality of result statistics for a first document as a search result for a first query, each of the quality of result statistics corresponding to a different time period, the one or more time trend statistics estimating changes in the quality of result statistics over time, wherein each of the one or more time trend statistics comprises a quality of result difference between a first quality of result statistic for the first document as a search result for the first query during a first time period and a second quality of result statistic for the first document as a search result for the first query during a second time period; generating a first modified quality of result statistic by modifying a first quality of result statistic for the first document as a search result for the first query by a factor, where the factor is based on the one or more time trend statistics; and providing the first modified quality of result statistic as an input to a document ranking process for the first document and the first query. 8. The method of claim 1 , wherein each quality of result statistic is an estimate of a respective percentage of users that found the first document relevant to the first query out of a first total number of users who viewed the first document as a search result for the first query during a respective time period. | 0.793307 |
10,148,600 | 16 | 17 | 16. The computer implemented method of claim 10 further comprising comparing a second converted media to the vocabulary and generating a second plurality of intents and a second plurality of sub-entities. | 16. The computer implemented method of claim 10 further comprising comparing a second converted media to the vocabulary and generating a second plurality of intents and a second plurality of sub-entities. 17. The computer implemented method of claim 16 where the first and the second plurality of intents and the first and the second plurality of sub-entities identify speech included in the media. | 0.911711 |
8,745,051 | 1 | 2 | 1. A computer-implemented method comprising: receiving Roman character inputs representing at least one word of a uniform resource locator for a desired webpage, using the Roman character inputs to determine candidate sets of non-Roman characters; determining candidate sets of uniform resource locators, each candidate set of uniform resource locators associated with a corresponding candidate set of non-Roman characters, wherein determining candidate sets of uniform resource locators includes, for each candidate set of non-Roman characters: determining sets of keywords that have the candidate set of non-Roman characters as a prefix by conducting a prefix keyword search using the determined candidate set of non-Roman characters as the prefix to expand the prefix into the determined sets of keywords such that at least one set of the determined sets of keywords includes a greater number of words than the prefix, for each determined set of keywords, conducting another search to determine a set of associated uniform resource locators, and associating the candidate set of uniform resource locators with the candidate set of non-Roman characters; and providing the candidate sets of uniform resource locators and candidate sets of non-Roman characters to the user interface for display, wherein each candidate set of uniform resource locators is displayed in association with a respective corresponding candidate set of non-Roman characters. | 1. A computer-implemented method comprising: receiving Roman character inputs representing at least one word of a uniform resource locator for a desired webpage, using the Roman character inputs to determine candidate sets of non-Roman characters; determining candidate sets of uniform resource locators, each candidate set of uniform resource locators associated with a corresponding candidate set of non-Roman characters, wherein determining candidate sets of uniform resource locators includes, for each candidate set of non-Roman characters: determining sets of keywords that have the candidate set of non-Roman characters as a prefix by conducting a prefix keyword search using the determined candidate set of non-Roman characters as the prefix to expand the prefix into the determined sets of keywords such that at least one set of the determined sets of keywords includes a greater number of words than the prefix, for each determined set of keywords, conducting another search to determine a set of associated uniform resource locators, and associating the candidate set of uniform resource locators with the candidate set of non-Roman characters; and providing the candidate sets of uniform resource locators and candidate sets of non-Roman characters to the user interface for display, wherein each candidate set of uniform resource locators is displayed in association with a respective corresponding candidate set of non-Roman characters. 2. The method of claim 1 , further comprising: receiving a selection of one of the candidate sets of non-Roman characters; and in response to receiving the selection, displaying the candidate set of uniform resource locators associated with the candidate set of non-Roman characters. | 0.828485 |
10,089,585 | 2 | 6 | 2. The computer readable medium of claim 1 , wherein determining the PD-document-to-RFP-segment relevance further comprises: creating using said one or more processors a PD-document decomposition by decomposing a said PD document into PD segments; establishing using said one or more processors hierarchical relationships for said PD-document decomposition comprising contains-as-a-segment relationships, is-a-segment-of relationships, or both said relationships between said PD document and said PD segments; determining using said one or more processors a PD-segment-to-RFP-segment relevance for an RFP segment of said RFP-document decomposition and a PD segment of said PD-document decomposition using said document similarity processing and said metric, wherein said determining said PD-segment-to-RFP-segment relevance comprises creating a second relevance matrix R 2 of dimensions N by M, where N is a number of said PD segments in said PD-document decomposition and M is the number of said RFP segments, and populating each element R 2 [n, m] of said relevance matrix R 2 with a relevance value produced by said document similarity processing and said metric that represents a similarity of a PD segment n of said PD segments and said RFP segment m of said RFP segments; aggregating using said one or more processors said PD-segment-to-RFP-segment relevance of said PD document by said hierarchical relationships for said PD-document decomposition to produce said PD-document-to-RFP-segment relevance, wherein said aggregating said PD-segment-to-RFP-segment relevance comprises performing a summation by said dimension N of said element R 2 [n, m] relevance values of said relevance matrix R 2 to produce said relevance matrix R 1 ; and transmitting using at least one of said output devices, said communications bus, or said network interface said PD-document-to-RFP-segment relevance to said originator of said RFP document. | 2. The computer readable medium of claim 1 , wherein determining the PD-document-to-RFP-segment relevance further comprises: creating using said one or more processors a PD-document decomposition by decomposing a said PD document into PD segments; establishing using said one or more processors hierarchical relationships for said PD-document decomposition comprising contains-as-a-segment relationships, is-a-segment-of relationships, or both said relationships between said PD document and said PD segments; determining using said one or more processors a PD-segment-to-RFP-segment relevance for an RFP segment of said RFP-document decomposition and a PD segment of said PD-document decomposition using said document similarity processing and said metric, wherein said determining said PD-segment-to-RFP-segment relevance comprises creating a second relevance matrix R 2 of dimensions N by M, where N is a number of said PD segments in said PD-document decomposition and M is the number of said RFP segments, and populating each element R 2 [n, m] of said relevance matrix R 2 with a relevance value produced by said document similarity processing and said metric that represents a similarity of a PD segment n of said PD segments and said RFP segment m of said RFP segments; aggregating using said one or more processors said PD-segment-to-RFP-segment relevance of said PD document by said hierarchical relationships for said PD-document decomposition to produce said PD-document-to-RFP-segment relevance, wherein said aggregating said PD-segment-to-RFP-segment relevance comprises performing a summation by said dimension N of said element R 2 [n, m] relevance values of said relevance matrix R 2 to produce said relevance matrix R 1 ; and transmitting using at least one of said output devices, said communications bus, or said network interface said PD-document-to-RFP-segment relevance to said originator of said RFP document. 6. The computer readable medium of claim 2 , wherein aggregating the PD-segment-to-RFP-segment relevance further comprises: ranking using said PD-segment-to-RFP-segment relevance said PD segment from said PD document using said one or more processors. | 0.959214 |
8,774,392 | 11 | 16 | 11. A computer-implemented method for processing calls in a call center, comprising: receiving within a call center, a call comprising an inquiry from a caller; assigning the call to a human agent; executing via an automated voice response system, a script in response to the inquiry, wherein the script is selected by the human agent; altering a flow of the script via a sliding control that varies a level of participation by the automated voice response system and the human agent by receiving input from the human agent; and providing the altered script and input from the human agent as a response to the caller inquiry. | 11. A computer-implemented method for processing calls in a call center, comprising: receiving within a call center, a call comprising an inquiry from a caller; assigning the call to a human agent; executing via an automated voice response system, a script in response to the inquiry, wherein the script is selected by the human agent; altering a flow of the script via a sliding control that varies a level of participation by the automated voice response system and the human agent by receiving input from the human agent; and providing the altered script and input from the human agent as a response to the caller inquiry. 16. A method according to claim 11 , further comprising: assigning an identifier to the call; and storing a record of the call with the identifier in a database. | 0.798246 |
8,830,200 | 1 | 15 | 1. A method of controlling an electronic device having a touch-sensitive display, the method comprising: detecting a touch at an area associated with a character displayed on the touch-sensitive display; adding the character to a character string; identifying, from stored data, objects that at least partially match the character string; determining a next character of ones of the objects identified to yield a set of next characters; increasing a size of an area associated with each character of the set of next characters; and in response to detecting entry of a space or a period that ends the character string, reducing the size of the area associated with each character of the set of next characters. | 1. A method of controlling an electronic device having a touch-sensitive display, the method comprising: detecting a touch at an area associated with a character displayed on the touch-sensitive display; adding the character to a character string; identifying, from stored data, objects that at least partially match the character string; determining a next character of ones of the objects identified to yield a set of next characters; increasing a size of an area associated with each character of the set of next characters; and in response to detecting entry of a space or a period that ends the character string, reducing the size of the area associated with each character of the set of next characters. 15. A non-transitory computer-readable storage device having stored thereon computer-readable code executable by at least one processor in an electronic device comprising a touch-sensitive display connected to the at least one processor and a memory connected to the at least one processor, to perform the method of claim 1 . | 0.512012 |
8,990,134 | 1 | 8 | 1. A computer-implemented method for training video location classifiers, the method comprising: storing a set of locations, each location uniquely corresponding to a geographic area having a unique geographic placement; providing a user interface for uploading a video, the user interface comprising a user interface element for specifying locations from the stored set of locations; receiving, from users via the user interface, a set of uploaded videos, each uploaded video labeled with a location from the stored set of locations, the location specified using the user interface; selecting, for each of a plurality of the locations, a location training set comprising ones of the uploaded videos that are labeled with the location; for each of a plurality of video location classifiers, each video location classifier associated with one of the locations: for each uploaded video of the location training set for the associated location, deriving a set of features associated with the uploaded video, the set of features comprising: audiovisual features extracted from content of the uploaded video; upload location information derived from an internet protocol (IP) address from which the uploaded video was uploaded; landmark scores indicating whether the uploaded video contains landmark features, the landmark scores being produced by applying trained landmark classifiers to the uploaded video; category scores indicating whether the uploaded video represents predetermined categories, the category scores produced by category classifiers that are trained based at least in part on a set of videos considered to represent the categories; and textual features derived from metadata of the uploaded video; training the video location classifier based at least in part on the features derived from the uploaded videos in the location training set; for an unlabeled video not labeled with a location from the stored set of locations, and for a first one of the trained video location classifiers: deriving a set of features comprising audiovisual features extracted from content of the unlabeled video, upload location information derived from the IP address from which the video was uploaded, landmark scores indicating whether the unlabeled video contains landmark features, category scores indicating whether the unlabeled video represents predetermined categories, and textual features derived from metadata of the unlabeled video; applying the first one of the trained video location classifiers to the set of features derived for the unlabeled video, thereby producing a location score indicating how strongly the unlabeled video represents the location associated with the first one of the trained video location classifiers; predicting based on the location score, that the unlabeled video represents the location associated with the first one of the trained video location classifiers; and providing, to a user, a visual representation of a map, the map including a visual indication of the unlabeled video on a portion of the map corresponding to the location associated with the first one of the trained video location classifiers. | 1. A computer-implemented method for training video location classifiers, the method comprising: storing a set of locations, each location uniquely corresponding to a geographic area having a unique geographic placement; providing a user interface for uploading a video, the user interface comprising a user interface element for specifying locations from the stored set of locations; receiving, from users via the user interface, a set of uploaded videos, each uploaded video labeled with a location from the stored set of locations, the location specified using the user interface; selecting, for each of a plurality of the locations, a location training set comprising ones of the uploaded videos that are labeled with the location; for each of a plurality of video location classifiers, each video location classifier associated with one of the locations: for each uploaded video of the location training set for the associated location, deriving a set of features associated with the uploaded video, the set of features comprising: audiovisual features extracted from content of the uploaded video; upload location information derived from an internet protocol (IP) address from which the uploaded video was uploaded; landmark scores indicating whether the uploaded video contains landmark features, the landmark scores being produced by applying trained landmark classifiers to the uploaded video; category scores indicating whether the uploaded video represents predetermined categories, the category scores produced by category classifiers that are trained based at least in part on a set of videos considered to represent the categories; and textual features derived from metadata of the uploaded video; training the video location classifier based at least in part on the features derived from the uploaded videos in the location training set; for an unlabeled video not labeled with a location from the stored set of locations, and for a first one of the trained video location classifiers: deriving a set of features comprising audiovisual features extracted from content of the unlabeled video, upload location information derived from the IP address from which the video was uploaded, landmark scores indicating whether the unlabeled video contains landmark features, category scores indicating whether the unlabeled video represents predetermined categories, and textual features derived from metadata of the unlabeled video; applying the first one of the trained video location classifiers to the set of features derived for the unlabeled video, thereby producing a location score indicating how strongly the unlabeled video represents the location associated with the first one of the trained video location classifiers; predicting based on the location score, that the unlabeled video represents the location associated with the first one of the trained video location classifiers; and providing, to a user, a visual representation of a map, the map including a visual indication of the unlabeled video on a portion of the map corresponding to the location associated with the first one of the trained video location classifiers. 8. The computer-implemented method of claim 1 , further comprising: receiving a query from a user for videos, the query comprising text associated with the location; responsive to determining that the unlabeled video represents the location associated with the first one of the video location classifiers, adding the video to a query result set; and providing the query result set to the user. | 0.628544 |
8,738,384 | 7 | 10 | 7. A machine comprising: a) a set of memories storing data from a plurality of knowledge sources; and b) a means for automatically defining a hierarchically organized grammar using the data from the plurality of knowledge sources, wherein the hierarchically organized grammar comprises: i) a lowest-level grammar comprising a vocabulary comprising words corresponding to natural language statements; and ii) one or more higher level grammars, wherein each higher level grammar comprises one or more elements provided by a grammar from the next lower level. | 7. A machine comprising: a) a set of memories storing data from a plurality of knowledge sources; and b) a means for automatically defining a hierarchically organized grammar using the data from the plurality of knowledge sources, wherein the hierarchically organized grammar comprises: i) a lowest-level grammar comprising a vocabulary comprising words corresponding to natural language statements; and ii) one or more higher level grammars, wherein each higher level grammar comprises one or more elements provided by a grammar from the next lower level. 10. The machine of claim 7 , wherein the means for automatically defining the hierarchically organized grammar using the data from the plurality of knowledge sources comprises a means for bootstrapping creation of the grammar using data comprising website data. | 0.779933 |
8,171,013 | 11 | 14 | 11. A method for indexing a product identifier and logical parts thereof, comprising: receiving a product identifier; splitting the product identifier into logical parts; storing the product identifiers and logical parts into a document; indexing the product identifier and the individual logical parts in an index; and storing the index, wherein different weights are assigned to separate fields based on the field types such as product identifier or logical parts of the product identifier field, wherein the fields and weights are encoded to word positions in the document, wherein the weights affect a score generated upon performing a query using the index. | 11. A method for indexing a product identifier and logical parts thereof, comprising: receiving a product identifier; splitting the product identifier into logical parts; storing the product identifiers and logical parts into a document; indexing the product identifier and the individual logical parts in an index; and storing the index, wherein different weights are assigned to separate fields based on the field types such as product identifier or logical parts of the product identifier field, wherein the fields and weights are encoded to word positions in the document, wherein the weights affect a score generated upon performing a query using the index. 14. The method as recited in claim 11 , wherein if the product identifier comprises multiple logical parts separated by a space, further comprising indexing at least some of the logical parts as a single consecutive character string. | 0.726526 |
9,355,169 | 1 | 9 | 1. A computer implemented method of extracting a set of phrases from a plurality of documents, the method comprising: for each document: identifying a plurality of candidate phrases occurring within the document, wherein a candidate phrase includes two or more consecutive words that are determined to occur in the document, and scoring candidate phrases in the document to produce respective document phrase scores for the candidate phrases for the document, the document phrase score for a candidate phrase being based on attributes of individual occurrences of the candidate phrase in the document, with at least some candidate phrases appearing repeatedly having a higher document phrase score than candidate phrases appearing once; for a candidate phrase of the plurality of the candidate phrases: creating, via a processor, a combined score for the candidate phrase based on a plurality of different document phrase scores for the candidate phrase for respective different documents; and selecting the candidate phrase for inclusion in the extracted set based on the combined score for the candidate phrase and based on the document phrase scores for the candidate phrase. | 1. A computer implemented method of extracting a set of phrases from a plurality of documents, the method comprising: for each document: identifying a plurality of candidate phrases occurring within the document, wherein a candidate phrase includes two or more consecutive words that are determined to occur in the document, and scoring candidate phrases in the document to produce respective document phrase scores for the candidate phrases for the document, the document phrase score for a candidate phrase being based on attributes of individual occurrences of the candidate phrase in the document, with at least some candidate phrases appearing repeatedly having a higher document phrase score than candidate phrases appearing once; for a candidate phrase of the plurality of the candidate phrases: creating, via a processor, a combined score for the candidate phrase based on a plurality of different document phrase scores for the candidate phrase for respective different documents; and selecting the candidate phrase for inclusion in the extracted set based on the combined score for the candidate phrase and based on the document phrase scores for the candidate phrase. 9. The method of claim 1 , wherein selecting the candidate phrase for inclusion in the extracted set based on the combined score and based on the document phrase scores includes: selecting the candidate phrase when the number of documents for which the candidate phrase had at least a minimum document phrase score exceeds a third threshold. | 0.749265 |
7,792,832 | 34 | 35 | 34. An article of manufacture comprising a machine-readable storage medium with instruction code stored in the medium, said instruction code, when executed by a data processing system comprising a processor, causes the processor to perform the following steps to identify products potentially infringing a patent: determining frequencies of occurrence within the text of the patent of each word of a plurality of words in a claim of the patent to obtain a plurality of first frequencies; determining frequencies of occurrence of said each word in a neutral text unrelated to the patent and to technology of the patent to obtain a plurality of second frequencies; for said each word, calculating a ratio of the first frequency associated with said each word to the second frequency associated with said each word, thereby obtaining a plurality of ratios, a ratio of the plurality of ratios per said each word; comparing each ratio of the plurality of ratios to a first parameter to obtain a plurality of key terms, each key term of the plurality of key terms comprising a word corresponding to a ratio of the plurality of ratios that exceeds the first parameter; formulating at least one query to search for data items that include the key terms; launching the at least one query; receiving search results responsive to the at least one query; and organizing the search results according to a criterion of relevance to the patent. | 34. An article of manufacture comprising a machine-readable storage medium with instruction code stored in the medium, said instruction code, when executed by a data processing system comprising a processor, causes the processor to perform the following steps to identify products potentially infringing a patent: determining frequencies of occurrence within the text of the patent of each word of a plurality of words in a claim of the patent to obtain a plurality of first frequencies; determining frequencies of occurrence of said each word in a neutral text unrelated to the patent and to technology of the patent to obtain a plurality of second frequencies; for said each word, calculating a ratio of the first frequency associated with said each word to the second frequency associated with said each word, thereby obtaining a plurality of ratios, a ratio of the plurality of ratios per said each word; comparing each ratio of the plurality of ratios to a first parameter to obtain a plurality of key terms, each key term of the plurality of key terms comprising a word corresponding to a ratio of the plurality of ratios that exceeds the first parameter; formulating at least one query to search for data items that include the key terms; launching the at least one query; receiving search results responsive to the at least one query; and organizing the search results according to a criterion of relevance to the patent. 35. The article of manufacture of claim 34 , wherein the code further causes the processor to perform the steps of: determining a number of distinct search results; comparing the number of distinct search results to a limit of quantity of search results; and repeating the steps of formulating at least one query, launching the at least one query, and receiving search results before the step of organizing if the number of distinct search results exceeds the limit of quantity of search results. | 0.845 |
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