patent_num
int64 3.93M
10.2M
| claim_num1
int64 1
519
| claim_num2
int64 2
520
| sentence1
stringlengths 40
15.9k
| sentence2
stringlengths 88
20k
| label
float64 0.5
0.99
|
|---|---|---|---|---|---|
9,442,930 | 1 | 2 |
1. A method of improving accuracy of computerized topic identification comprising: a domain independent, language independent, computer processor automated topic identification analysis method, the method comprising: a) deriving, by at least one computer processor, a lexicon from at least one hypertext corpus data set to associate at least one term with at least one topic, wherein said at least one term comprises at least one word, wherein at least one sense is derived from at least one hypertext link of the at least one hypertext corpus data set, and is associated with each of said at least one term, wherein each of said at least one sense refers to a single topic of said at least one term, wherein said at least one topic referred to by said at least one sense is to be used as a candidate topic at runtime, wherein a prior probability is associated with each of said at least one sense of said each said term, wherein each said prior probability is a fraction of occurrences of a given one of said at least one term as a relationship comprising a hypertext link that links to said single topic from said each of said at least one sense; b) receiving, by the at least one computer processor, at least one content document; c) searching for, by the at least one computer processor, at least one term from the lexicon derived from the at least one hypertext corpus data set, and finding the at least one term from the lexicon appearing in the at least one content document to determine at least one candidate topic of the at least one content document; d) lexically scoring, by the at least one computer processor, each of said at least one candidate topic found appearing in the at least one content document based on the at least one term found in said search of said (c) of the at least one content document to obtain a lexical score for each of said at least one candidate topic, and accumulating said lexical score for each of said at least one candidate topic for each occurrence in the at least one content document of a term, wherein the term has an associated sense, wherein the associated sense refers to said each said candidate topic, wherein said lexically scoring comprises lexically scoring, by the at least one computer processor, based on: a number of occurrences of the at least one term found in the at least one content document, a weighting factor representing a relative importance of the at least one term found, and the prior probability of the sense of the at least one term found; and e) semantically scoring, by the at least one computer processor, the at least one candidate topic found in the at least one content document, based on a degree to which any plurality of candidate topics are semantically related to each other comprising: i. quantifying a semantic relatedness score, by the at least one computer processor, of said any plurality of topics based on theoretical information content of co-adjacent links of a graph representation of the at least one hypertext corpus data set, ii. evaluating the semantic relatedness score, by the at least one computer processor, comprising evaluating said theoretical information content of an edge of said graph, wherein said theoretical information content comprises at least one of: A. self-information, or B. surprisal and; iii. semantically scoring, by the at least one computer processor, based on said lexical score of said each of said at least one candidate topic found appearing in the at least one content document, wherein said lexical score of said each of said at least one candidate topic was determined from the at least one term of said lexicon derived from the hypertext corpus data set, and based on said semantic relatedness score from said quantifying; and wherein said at least one data set comprises: a hypertext corpus comprising at least one graph, wherein said graph comprises: a plurality V of vertices, wherein each individual vertex v represents a page; and a plurality of edges, each edge representing a link; and wherein said each edge comprises: information content I(u,v), wherein said information content I comprises I ( u , v ) = - log b deg + ( u ) ∑ i ∈ V deg + ( i ) - log b deg - ( v ) ∑ i ∈ V deg - ( i ) wherein I is the information content, wherein v is the vertex associated with a term, wherein V is the total number of vertices in the graph, wherein {u,v} refer to an edge from vertex u to vertex v, wherein deg + (u) is the out-degree or number of outgoing edges from the vertex u or number of tail endpoints adjacent to a vertex, and wherein deg − (v) is the in-degree or number of incoming edges to the vertex v or number of head endpoints adjacent to a vertex.
|
1. A method of improving accuracy of computerized topic identification comprising: a domain independent, language independent, computer processor automated topic identification analysis method, the method comprising: a) deriving, by at least one computer processor, a lexicon from at least one hypertext corpus data set to associate at least one term with at least one topic, wherein said at least one term comprises at least one word, wherein at least one sense is derived from at least one hypertext link of the at least one hypertext corpus data set, and is associated with each of said at least one term, wherein each of said at least one sense refers to a single topic of said at least one term, wherein said at least one topic referred to by said at least one sense is to be used as a candidate topic at runtime, wherein a prior probability is associated with each of said at least one sense of said each said term, wherein each said prior probability is a fraction of occurrences of a given one of said at least one term as a relationship comprising a hypertext link that links to said single topic from said each of said at least one sense; b) receiving, by the at least one computer processor, at least one content document; c) searching for, by the at least one computer processor, at least one term from the lexicon derived from the at least one hypertext corpus data set, and finding the at least one term from the lexicon appearing in the at least one content document to determine at least one candidate topic of the at least one content document; d) lexically scoring, by the at least one computer processor, each of said at least one candidate topic found appearing in the at least one content document based on the at least one term found in said search of said (c) of the at least one content document to obtain a lexical score for each of said at least one candidate topic, and accumulating said lexical score for each of said at least one candidate topic for each occurrence in the at least one content document of a term, wherein the term has an associated sense, wherein the associated sense refers to said each said candidate topic, wherein said lexically scoring comprises lexically scoring, by the at least one computer processor, based on: a number of occurrences of the at least one term found in the at least one content document, a weighting factor representing a relative importance of the at least one term found, and the prior probability of the sense of the at least one term found; and e) semantically scoring, by the at least one computer processor, the at least one candidate topic found in the at least one content document, based on a degree to which any plurality of candidate topics are semantically related to each other comprising: i. quantifying a semantic relatedness score, by the at least one computer processor, of said any plurality of topics based on theoretical information content of co-adjacent links of a graph representation of the at least one hypertext corpus data set, ii. evaluating the semantic relatedness score, by the at least one computer processor, comprising evaluating said theoretical information content of an edge of said graph, wherein said theoretical information content comprises at least one of: A. self-information, or B. surprisal and; iii. semantically scoring, by the at least one computer processor, based on said lexical score of said each of said at least one candidate topic found appearing in the at least one content document, wherein said lexical score of said each of said at least one candidate topic was determined from the at least one term of said lexicon derived from the hypertext corpus data set, and based on said semantic relatedness score from said quantifying; and wherein said at least one data set comprises: a hypertext corpus comprising at least one graph, wherein said graph comprises: a plurality V of vertices, wherein each individual vertex v represents a page; and a plurality of edges, each edge representing a link; and wherein said each edge comprises: information content I(u,v), wherein said information content I comprises I ( u , v ) = - log b deg + ( u ) ∑ i ∈ V deg + ( i ) - log b deg - ( v ) ∑ i ∈ V deg - ( i ) wherein I is the information content, wherein v is the vertex associated with a term, wherein V is the total number of vertices in the graph, wherein {u,v} refer to an edge from vertex u to vertex v, wherein deg + (u) is the out-degree or number of outgoing edges from the vertex u or number of tail endpoints adjacent to a vertex, and wherein deg − (v) is the in-degree or number of incoming edges to the vertex v or number of head endpoints adjacent to a vertex. 2. The method according to claim 1 , further comprising: iterating through at least one of: each candidate topic, or a subset of candidate topics, identified by a lexical stage; and calculating a semantic score S, for each link from a first said candidate topic being evaluated that couples to another said candidate topic, wherein S=Σ(L x ×H), wherein L x describes the lexical score for each said candidate topic in a collection of said candidate topics.
| 0.740909 |
9,740,769 | 3 | 4 |
3. The method of claim 1 , further comprising: responsive to determining the no-answer response has the highest rank in the candidate answer ranking, determining whether n-grams and named entities in the input question are present in the corpus of source information above a predetermined frequency threshold; and responsive to determining the n-grams and named entities in the input question are present in the corpus of source information above the predetermined frequency threshold, marking the no-answer response to indicate the input question is unanswerable.
|
3. The method of claim 1 , further comprising: responsive to determining the no-answer response has the highest rank in the candidate answer ranking, determining whether n-grams and named entities in the input question are present in the corpus of source information above a predetermined frequency threshold; and responsive to determining the n-grams and named entities in the input question are present in the corpus of source information above the predetermined frequency threshold, marking the no-answer response to indicate the input question is unanswerable. 4. The method of claim 3 , further comprising: responsive to determining the n-grams and named entities in the input question are not present in the corpus of source information above the predetermined threshold, marking the no-answer response to indicate the corpus of source information does not contain an answer to the input question.
| 0.5 |
7,672,915 | 11 | 15 |
11. A computer program product, comprising a computer usable medium having a computer readable program code embodied therein, said computer readable program code adapted to be executed to implement a method of labeling data associated with data records stored in a database of a system, the method comprising: receiving data associated with said data records, said data comprising labeled data and unlabeled data; generating a self-organizing map of the data, said self-organizing map comprising a plurality of nodes each comprising one or more of said data records; identifying each node in the self-organizing map that comprises at least one data record having a label, and for each said node, assigning said label of said at least one data record of said node to each unlabeled data record in said node; self-labeling data records, said self-labeling comprising, for each selected node in the self-organizing map that comprises at least one data record having a label: identifying a first plurality of nodes in said self-organizing map, each node of said first plurality being adjacent to said selected node; identifying a second plurality of nodes in said self-organizing map, each node of said second plurality being either adjacent to said selected node or adjacent to a node of said first plurality other than said selected node; where there are no nodes in said first plurality or in said second plurality comprising at least one data record having a label different from the label of the at least one data record in said selected node, assigning the label of the at least one data record in said selected node to each unlabeled data record in each node of said first plurality; where there is at least one node in said first plurality comprising at least one data record having a label different from the label of the at least one data record in said selected node, not assigning the label of the at least one data record in said selected node to each unlabeled data record in each node of said first plurality; and where there are no nodes in said first plurality comprising at least one data record having a label different from the label of said selected node but there is at least one node in said second plurality comprising at least one data record having a label different from the label of said selected node, assigning the label of the at least one data record in said selected node to each unlabeled data record in each node of said first plurality that is not adjacent to the at least one node in said second plurality comprising at least one data record having a label different from the label of said selected node, and not assigning the label of the at least one data record in said selected node to each unlabeled data record in each node of said first plurality that is adjacent to the at least one node in said second plurality comprising at least one data record having a label different from the label of said selected node; and after said self-labeling, training a classifier for data classification in a customer relationship management system, wherein said classifier is trained based on data of data records having a label in said self-organizing map; wherein said method is executable by one or more processors.
|
11. A computer program product, comprising a computer usable medium having a computer readable program code embodied therein, said computer readable program code adapted to be executed to implement a method of labeling data associated with data records stored in a database of a system, the method comprising: receiving data associated with said data records, said data comprising labeled data and unlabeled data; generating a self-organizing map of the data, said self-organizing map comprising a plurality of nodes each comprising one or more of said data records; identifying each node in the self-organizing map that comprises at least one data record having a label, and for each said node, assigning said label of said at least one data record of said node to each unlabeled data record in said node; self-labeling data records, said self-labeling comprising, for each selected node in the self-organizing map that comprises at least one data record having a label: identifying a first plurality of nodes in said self-organizing map, each node of said first plurality being adjacent to said selected node; identifying a second plurality of nodes in said self-organizing map, each node of said second plurality being either adjacent to said selected node or adjacent to a node of said first plurality other than said selected node; where there are no nodes in said first plurality or in said second plurality comprising at least one data record having a label different from the label of the at least one data record in said selected node, assigning the label of the at least one data record in said selected node to each unlabeled data record in each node of said first plurality; where there is at least one node in said first plurality comprising at least one data record having a label different from the label of the at least one data record in said selected node, not assigning the label of the at least one data record in said selected node to each unlabeled data record in each node of said first plurality; and where there are no nodes in said first plurality comprising at least one data record having a label different from the label of said selected node but there is at least one node in said second plurality comprising at least one data record having a label different from the label of said selected node, assigning the label of the at least one data record in said selected node to each unlabeled data record in each node of said first plurality that is not adjacent to the at least one node in said second plurality comprising at least one data record having a label different from the label of said selected node, and not assigning the label of the at least one data record in said selected node to each unlabeled data record in each node of said first plurality that is adjacent to the at least one node in said second plurality comprising at least one data record having a label different from the label of said selected node; and after said self-labeling, training a classifier for data classification in a customer relationship management system, wherein said classifier is trained based on data of data records having a label in said self-organizing map; wherein said method is executable by one or more processors. 15. The method of claim 11 , wherein the assigning of a label to an unlabeled data record in a node of said self-organizing map comprises assigning a class to the data of the unlabeled data record.
| 0.5 |
9,176,952 | 9 | 15 |
9. A computerized method of statistical machine translation, the method comprising: training a statistical machine translation engine on a bilingual parallel corpus including source language documents and a corresponding target human translation of the source language documents; training a phrasal decoder, separate and distinct from the statistical machine translation engine, on a monolingual parallel corpus, the monolingual parallel corpus including a machine translation output of the source language documents of the bilingual parallel corpus and the corresponding target human translation output of the source language documents of the bilingual parallel corpus, to thereby learn mappings and build a phrase table by establishing phrase pairs between the machine translation output and the target human translation output, wherein the machine translation output is unedited by human translators, assigning to each phrase pair a statistical score representing a utility of each phrase pair; performing statistical machine translation via the statistical machine translation engine trained on the bilingual parallel corpus of a translation input to thereby produce a raw machine translation output; and processing the raw machine translation output to thereby produce a corrected translation output based on the learned mappings and the phrase table, programmatically correcting the raw machine translation output if a statistical score for correspondence of the phrase pair is above a predetermined threshold.
|
9. A computerized method of statistical machine translation, the method comprising: training a statistical machine translation engine on a bilingual parallel corpus including source language documents and a corresponding target human translation of the source language documents; training a phrasal decoder, separate and distinct from the statistical machine translation engine, on a monolingual parallel corpus, the monolingual parallel corpus including a machine translation output of the source language documents of the bilingual parallel corpus and the corresponding target human translation output of the source language documents of the bilingual parallel corpus, to thereby learn mappings and build a phrase table by establishing phrase pairs between the machine translation output and the target human translation output, wherein the machine translation output is unedited by human translators, assigning to each phrase pair a statistical score representing a utility of each phrase pair; performing statistical machine translation via the statistical machine translation engine trained on the bilingual parallel corpus of a translation input to thereby produce a raw machine translation output; and processing the raw machine translation output to thereby produce a corrected translation output based on the learned mappings and the phrase table, programmatically correcting the raw machine translation output if a statistical score for correspondence of the phrase pair is above a predetermined threshold. 15. The computerized method of claim 9 , wherein training the phrasal decoder occurs on a developer computing device on which the bilingual parallel corpus is stored; wherein performing statistical machine translation of a translation input to thereby produce a raw machine translation output and processing the raw machine translation output to thereby produce a corrected translation output occurs on a user computing device.
| 0.728372 |
8,438,142 | 27 | 28 |
27. The computer program product of claim 23 , wherein the first compound and the second compound are stored in an expansion/contraction table generated from at least one of a user input log and a user input database, and wherein the expansion/contraction table includes frequency values representing occurrences of sequences of words.
|
27. The computer program product of claim 23 , wherein the first compound and the second compound are stored in an expansion/contraction table generated from at least one of a user input log and a user input database, and wherein the expansion/contraction table includes frequency values representing occurrences of sequences of words. 28. The computer program product of claim 27 , wherein the expansion/contraction table is generated by determining frequent word sequences, filtering out non-phrasal word sequences, and associating counts with sequences of terms as the frequency values.
| 0.5 |
7,496,834 | 40 | 43 |
40. A method implemented by an apparatus for processing a response to a request for updating a previously supplied electronic document providing programming information about a plurality of television broadcast programs scheduled for broadcast in a program broadcasting system, wherein the previously supplied electronic document has a hierarchical structure based on a prescribed syntax, the hierarchical structure including an upper fragment and a plurality of lower fragments located below the upper fragment in the hierarchical structure to describe, for each of the scheduled television broadcast programs, a program identifier, a title, broadcast information and corresponding program content information, wherein the response to the request provides an update document having a structure based on the prescribed syntax and including the upper fragment and an invalid element which indicates that one of the lower fragments of the previously supplied electronic document is invalid, wherein said invalid fragment is related to one of the scheduled television broadcast programs, the method comprising: identifying said invalid fragment in the previously supplied electronic document by using the invalid element of the update document at said client; and controlling to delete said identified invalid fragment from the previously supplied electronic document at said client.
|
40. A method implemented by an apparatus for processing a response to a request for updating a previously supplied electronic document providing programming information about a plurality of television broadcast programs scheduled for broadcast in a program broadcasting system, wherein the previously supplied electronic document has a hierarchical structure based on a prescribed syntax, the hierarchical structure including an upper fragment and a plurality of lower fragments located below the upper fragment in the hierarchical structure to describe, for each of the scheduled television broadcast programs, a program identifier, a title, broadcast information and corresponding program content information, wherein the response to the request provides an update document having a structure based on the prescribed syntax and including the upper fragment and an invalid element which indicates that one of the lower fragments of the previously supplied electronic document is invalid, wherein said invalid fragment is related to one of the scheduled television broadcast programs, the method comprising: identifying said invalid fragment in the previously supplied electronic document by using the invalid element of the update document at said client; and controlling to delete said identified invalid fragment from the previously supplied electronic document at said client. 43. The method of claim 40 , wherein said invalid fragment is indicated to be invalid by an invalid attribute.
| 0.5 |
9,720,738 | 1 | 5 |
1. A computer-implemented machine learning method for scheduling a computational task by a scheduling entity in a datacenter environment, wherein the datacenter includes a plurality of computing entities and the scheduling entity includes a first and second parameter processing entities, the method comprising: receiving feedback information from the datacenter by the scheduling entity, wherein the feedback information includes: historical selections of the plurality of computing entities for processing historical computing tasks, and historical processing results of the historical computing tasks as processed by the historical computing entities; receiving and processing at the first parameter processing entity incoming data corresponding to a new computational task to be processed by the datacenter environment, wherein the incoming data includes: a set of computational task parameters specifying characteristics of the new computational task, and a set of computational tasks requirements specifying criteria which have to be fulfilled by a computer entity for processing the new computational task; receiving and processing at the second parameter processing entity a first and second set of computing entity parameters; wherein the first set of computing entity parameters specifies characteristics of the computing entities of the plurality of computing entities, and the second set of computing entity parameters specifies load parameters of the plurality of computing entities; providing processing result from the first and second parameter processing entities to machine logic of the scheduling entity; identifying, by a self-learning mechanism of the scheduling entity based on the processing result and the feedback information, features from one or more computing entities among the plurality of computing entities that have similar characteristics to the received set of computing entity parameters in the processing result; creating a contextual model that corresponds to the identified features of the one or more of the computing entities; responsive to detecting changes in the information deriving from the received feedback information, the processing result from the first and second parameter processing entities, and the created contextual model, modifying the set of identified features by increasing or decreasing the effect of one or more parameters; selecting, by machine logic of the scheduling entity, one or more computing entities of the plurality of computing entities for processing the new computational task based, at least in part, upon: the processing result from the first parameter processing entity, the processing result from the second parameter entity, the received feedback information, and the created contextual model; and processing the computational task on the selected one or more computing entities.
|
1. A computer-implemented machine learning method for scheduling a computational task by a scheduling entity in a datacenter environment, wherein the datacenter includes a plurality of computing entities and the scheduling entity includes a first and second parameter processing entities, the method comprising: receiving feedback information from the datacenter by the scheduling entity, wherein the feedback information includes: historical selections of the plurality of computing entities for processing historical computing tasks, and historical processing results of the historical computing tasks as processed by the historical computing entities; receiving and processing at the first parameter processing entity incoming data corresponding to a new computational task to be processed by the datacenter environment, wherein the incoming data includes: a set of computational task parameters specifying characteristics of the new computational task, and a set of computational tasks requirements specifying criteria which have to be fulfilled by a computer entity for processing the new computational task; receiving and processing at the second parameter processing entity a first and second set of computing entity parameters; wherein the first set of computing entity parameters specifies characteristics of the computing entities of the plurality of computing entities, and the second set of computing entity parameters specifies load parameters of the plurality of computing entities; providing processing result from the first and second parameter processing entities to machine logic of the scheduling entity; identifying, by a self-learning mechanism of the scheduling entity based on the processing result and the feedback information, features from one or more computing entities among the plurality of computing entities that have similar characteristics to the received set of computing entity parameters in the processing result; creating a contextual model that corresponds to the identified features of the one or more of the computing entities; responsive to detecting changes in the information deriving from the received feedback information, the processing result from the first and second parameter processing entities, and the created contextual model, modifying the set of identified features by increasing or decreasing the effect of one or more parameters; selecting, by machine logic of the scheduling entity, one or more computing entities of the plurality of computing entities for processing the new computational task based, at least in part, upon: the processing result from the first parameter processing entity, the processing result from the second parameter entity, the received feedback information, and the created contextual model; and processing the computational task on the selected one or more computing entities. 5. The method according to claim 1 , wherein the scheduling entity includes a multi-armed bandit model for selecting the one or more computing entities.
| 0.868739 |
8,073,869 | 2 | 3 |
2. The method of claim 1 where returning the top-t records in R Q for a given value t, ranked by their relevancy to the query comprises finding a trie node corresponding to a keyword in a trie with inverted lists on leaf nodes by traversing the trie from the root; locating leaf descendants of the trie node corresponding to the keyword, and retrieving the corresponding predicted words and the predicted records on inverted lists.
|
2. The method of claim 1 where returning the top-t records in R Q for a given value t, ranked by their relevancy to the query comprises finding a trie node corresponding to a keyword in a trie with inverted lists on leaf nodes by traversing the trie from the root; locating leaf descendants of the trie node corresponding to the keyword, and retrieving the corresponding predicted words and the predicted records on inverted lists. 3. The method of claim 2 where returning the top-t records in R Q for a given value t, ranked by their relevancy to the query comprises tokenizing a query string into several keywords, k 1 ; k 2 ; : : : ; k i ; for each keyword k i (1≦i≦l−1) determining only a predicted word, k i , and one predicted-record list of a trie node corresponding to k i , denoted as l i , where q predicted words for k i , and their corresponding predicted record lists are l l1 ; l l2 ; : : : ; l lq , and determining the predicted records by ∩ i=1 l−1 l i ∩(∪ j=1 q l l j ) namely taking the union of the lists of predicted keywords for partial words, and intersecting the union of lists of predicted keywords for partial words with lists of the complete keywords.
| 0.601366 |
9,467,437 | 15 | 18 |
15. A system comprising: one or more hardware processors; and one or more memory devices comprising instructions that, when executed by the one or more processors, cause the one or more processors to authenticate users in a secure search system for searching a plurality of secure data sources by configuring the one or more processors to: receive user identification information from a user in a secure enterprise system (SES); provide the user identification information to a plurality of identity management systems in the SES, wherein each of the plurality of identity management systems receives the user identification information through a respective Application Program Interface (API); validate the user against at least one identity management system in the plurality of identity management systems; crawl at least one secure data source in the plurality of secure data sources residing on a plurality of different computer systems that is associated with the at least one identity management system; build an index of documents from the at least one secure data source based on the crawling; receive a query from the user; call back at query time into the at least one identity management system to obtain security attribute values for the user; append the security attribute values for the user to the query and using the appended query to query the index of documents; and determine one or more documents from the index of documents in the plurality of secure data sources, that are responsive to the query and accessible to the user based on the security attribute values for the user and respective security attributes of the one or more documents.
|
15. A system comprising: one or more hardware processors; and one or more memory devices comprising instructions that, when executed by the one or more processors, cause the one or more processors to authenticate users in a secure search system for searching a plurality of secure data sources by configuring the one or more processors to: receive user identification information from a user in a secure enterprise system (SES); provide the user identification information to a plurality of identity management systems in the SES, wherein each of the plurality of identity management systems receives the user identification information through a respective Application Program Interface (API); validate the user against at least one identity management system in the plurality of identity management systems; crawl at least one secure data source in the plurality of secure data sources residing on a plurality of different computer systems that is associated with the at least one identity management system; build an index of documents from the at least one secure data source based on the crawling; receive a query from the user; call back at query time into the at least one identity management system to obtain security attribute values for the user; append the security attribute values for the user to the query and using the appended query to query the index of documents; and determine one or more documents from the index of documents in the plurality of secure data sources, that are responsive to the query and accessible to the user based on the security attribute values for the user and respective security attributes of the one or more documents. 18. The system of claim 15 , comprising additional instructions that configure the one or more processors to: receive, using the one or more processors, user identification information from an additional user in the SES; determine that the additional user cannot be validated against the at least one identity management system; and deny the additional user access to the at least one secure data source.
| 0.5 |
8,452,783 | 2 | 5 |
2. The document processing apparatus according to claim 1 , further comprising: a filter storing unit configured to store a plurality of filters associated with application programs, respectively; and a filter selecting unit configured to select a filter associated with the second application program from the filter storing unit, wherein the character string extracting unit is configured to detect the one or more character strings using the filter selected by the filter selecting unit.
|
2. The document processing apparatus according to claim 1 , further comprising: a filter storing unit configured to store a plurality of filters associated with application programs, respectively; and a filter selecting unit configured to select a filter associated with the second application program from the filter storing unit, wherein the character string extracting unit is configured to detect the one or more character strings using the filter selected by the filter selecting unit. 5. The document processing apparatus according to claim 2 , wherein the filter selecting unit is configured to receive a designation of a filter to be used by the character string extracting unit from the second application program and select a designated filter from the filter storing unit in preference to a relationship between the application program and the filter determined by the filter storing unit.
| 0.559267 |
9,424,597 | 5 | 7 |
5. A method comprising: receiving, at an ecommerce service, text from a first user, the text in a first language and pertaining to a first listing on the ecommerce service; retrieving contextual information about the first listing; translating the text to a second language; locating, in a database, a plurality of text objects, in the second language, similar to the translated text, each text object comprising textual information pertaining to at least one listing; ranking the plurality of text objects similar to the translated text based on a comparison of the contextual information about the first listing and contextual information stored in the database for the listings corresponding to the plurality of text objects similar to the translated text; translating at least one of the ranked plurality of text objects to the first language; presenting a subset of the ranked plurality of text objects to the first user; receiving feedback from the first user; selecting one of the subset of the ranked plurality of text objects based on the feedback; and using the selected text object in the ecommerce service.
|
5. A method comprising: receiving, at an ecommerce service, text from a first user, the text in a first language and pertaining to a first listing on the ecommerce service; retrieving contextual information about the first listing; translating the text to a second language; locating, in a database, a plurality of text objects, in the second language, similar to the translated text, each text object comprising textual information pertaining to at least one listing; ranking the plurality of text objects similar to the translated text based on a comparison of the contextual information about the first listing and contextual information stored in the database for the listings corresponding to the plurality of text objects similar to the translated text; translating at least one of the ranked plurality of text objects to the first language; presenting a subset of the ranked plurality of text objects to the first user; receiving feedback from the first user; selecting one of the subset of the ranked plurality of text objects based on the feedback; and using the selected text object in the ecommerce service. 7. The method of claim 5 , wherein the text from the first user is a portion of an item description of the first listing and the using includes utilizing the selected text object as the portion of the item description of the first listing.
| 0.5 |
8,224,827 | 1 | 4 |
1. A method performed by one or more devices, the method comprising: determining, by at least one of the one or more devices, a first average amount of time that a first set of users spent accessing a document during a first time period; determining, by at least one of the one or more devices, a second average amount of time that a second set of users spent accessing the same document during a second time period, the second time period occurring after the first time period; comparing, by at least one of the one or more devices, the first average amount of time to the second average amount of time determining, by at least one of the one or more devices and based on the comparing, that the first average amount of time is different from the second average amount of time classifying, by at least one of the one or more devices, the document as stale or fresh based on determining that the first average amount of time is different from the second average amount of time, the classifying including: classifying the document as stale when the first average amount of time is greater than the second average amount of time, and classifying the document as fresh when the first average amount of time is less than the second average amount of time generating, by at least one of the one or more devices, a score for the document based on classifying the document as stale or fresh; and ranking, by at least one of the one or more devices, the document with regard to at least one other document based on the generated score.
|
1. A method performed by one or more devices, the method comprising: determining, by at least one of the one or more devices, a first average amount of time that a first set of users spent accessing a document during a first time period; determining, by at least one of the one or more devices, a second average amount of time that a second set of users spent accessing the same document during a second time period, the second time period occurring after the first time period; comparing, by at least one of the one or more devices, the first average amount of time to the second average amount of time determining, by at least one of the one or more devices and based on the comparing, that the first average amount of time is different from the second average amount of time classifying, by at least one of the one or more devices, the document as stale or fresh based on determining that the first average amount of time is different from the second average amount of time, the classifying including: classifying the document as stale when the first average amount of time is greater than the second average amount of time, and classifying the document as fresh when the first average amount of time is less than the second average amount of time generating, by at least one of the one or more devices, a score for the document based on classifying the document as stale or fresh; and ranking, by at least one of the one or more devices, the document with regard to at least one other document based on the generated score. 4. The method of claim 1 , further comprising: increasing a ranking of the document, with regard to the at least one other document, when the document is classified as fresh.
| 0.789855 |
10,089,387 | 11 | 12 |
11. A system comprising: at least one computing device operably coupled to at least one memory and configured to: determine conversion path data for a content provider, wherein the conversion path data comprises data relating to a plurality of conversion paths associated with the content provider leading to a plurality of conversions, and wherein each of the plurality of conversion paths comprises one or more user actions leading to one of the plurality of conversions, the conversion path data comprising paid keywords associated with bids submitted by the content provider and organic search keywords that are not associated with paid bids by the content provider; filter the conversion path data by removing the paid keywords from the conversion path data and determine, based on the filtered conversion path data, a plurality of organic keywords; analyze the plurality of organic search keywords within the conversion path data to generate an assist-to-last ratio for each of the plurality of organic search keywords, the assist-to-last ratio comprising a measure of a first number of times the organic search keyword is an assisting keyword not associated with a last selection of a content item prior to one of the plurality of conversions versus a second number of times the organic search keyword is a last-click keyword associated with the last selection prior to one of the plurality of conversions; determine, for each organic search keyword, a relative position within the conversion paths associated with the organic search keyword using the assist-to-last ratio for the organic search keyword; generate a recommendation and cause a display device to display the recommendation, the recommendation comprising an indication of one or more funnel position categories, each funnel position category comprising one or more of the organic search keywords that include a relative position associated with the funnel position category, wherein the recommendation prompts the content provider to accept or reject, via the display device, each of the one or more of the organic search keywords of the one or more funnel position categories; receive an indication of one or more accepted organic search keyword of the one or more organic search keywords from the display device, the accepted organic search keywords accepted by the content provider; and add the accepted organic search keywords to the content provider's paid keywords.
|
11. A system comprising: at least one computing device operably coupled to at least one memory and configured to: determine conversion path data for a content provider, wherein the conversion path data comprises data relating to a plurality of conversion paths associated with the content provider leading to a plurality of conversions, and wherein each of the plurality of conversion paths comprises one or more user actions leading to one of the plurality of conversions, the conversion path data comprising paid keywords associated with bids submitted by the content provider and organic search keywords that are not associated with paid bids by the content provider; filter the conversion path data by removing the paid keywords from the conversion path data and determine, based on the filtered conversion path data, a plurality of organic keywords; analyze the plurality of organic search keywords within the conversion path data to generate an assist-to-last ratio for each of the plurality of organic search keywords, the assist-to-last ratio comprising a measure of a first number of times the organic search keyword is an assisting keyword not associated with a last selection of a content item prior to one of the plurality of conversions versus a second number of times the organic search keyword is a last-click keyword associated with the last selection prior to one of the plurality of conversions; determine, for each organic search keyword, a relative position within the conversion paths associated with the organic search keyword using the assist-to-last ratio for the organic search keyword; generate a recommendation and cause a display device to display the recommendation, the recommendation comprising an indication of one or more funnel position categories, each funnel position category comprising one or more of the organic search keywords that include a relative position associated with the funnel position category, wherein the recommendation prompts the content provider to accept or reject, via the display device, each of the one or more of the organic search keywords of the one or more funnel position categories; receive an indication of one or more accepted organic search keyword of the one or more organic search keywords from the display device, the accepted organic search keywords accepted by the content provider; and add the accepted organic search keywords to the content provider's paid keywords. 12. The system of claim 11 , wherein the at least one computing device is configured to: select one of the plurality of organic search keywords by, for each of the plurality of organic search keywords: comparing the assist-to-last ratio for the organic search keyword to a particular threshold assist-to-last ratio; and determining whether to select the organic search keyword based on the comparison; and generate the recommendation, the recommendation comprising an indication to add the selected organic search keyword to the content provider's paid keywords.
| 0.5 |
8,364,300 | 13 | 14 |
13. A method, carried out by a computer system including a non-transitory computer-readable medium having computer-executable instructions for processing manufacturing information requests, the method comprising the steps of: receiving an information request to retrieve information based on a named data item defined by a configured manufacturing data model; in response to the receiving step, relating data coming from at least one backend system and adding context data according to a definition of the named data item in the configured manufacturing data model, the context data defining a relevant scope of data corresponding to the information request, wherein the at least one backend system comprises a time-series data buffer, said time-series data buffer providing time-series data; and wherein the named data item is of a dimension quantifiable by time; configuring the named data item using a dimension-based time-slicing capability comprising a time-defining dimension wherein the time-defining dimensions further comprises time-defining fields consisting of either a start time and an end time or a start time and a duration and said time-defining fields are absolute time stamps for the named data item; preparing a result, corresponding to the information request, according to a specified format and filtering criteria; producing an information response in a normalized format, the information response containing the requested information and the context data corresponding to the information request, the information response further containing the provided time-series data; and transmitting the information response in the normalized format to a requestor.
|
13. A method, carried out by a computer system including a non-transitory computer-readable medium having computer-executable instructions for processing manufacturing information requests, the method comprising the steps of: receiving an information request to retrieve information based on a named data item defined by a configured manufacturing data model; in response to the receiving step, relating data coming from at least one backend system and adding context data according to a definition of the named data item in the configured manufacturing data model, the context data defining a relevant scope of data corresponding to the information request, wherein the at least one backend system comprises a time-series data buffer, said time-series data buffer providing time-series data; and wherein the named data item is of a dimension quantifiable by time; configuring the named data item using a dimension-based time-slicing capability comprising a time-defining dimension wherein the time-defining dimensions further comprises time-defining fields consisting of either a start time and an end time or a start time and a duration and said time-defining fields are absolute time stamps for the named data item; preparing a result, corresponding to the information request, according to a specified format and filtering criteria; producing an information response in a normalized format, the information response containing the requested information and the context data corresponding to the information request, the information response further containing the provided time-series data; and transmitting the information response in the normalized format to a requestor. 14. The method of claim 13 wherein the definition of the named data item describes hierarchical relationships between a set of sub-data items.
| 0.797143 |
8,880,402 | 1 | 6 |
1. A speech recognition method comprising the steps of: (a) receiving input speech from a user; (b) processing said input speech to obtain at least one parameter value; and (c) determining the user's level of experience with using automatic speech recognition, using the at least one parameter value.
|
1. A speech recognition method comprising the steps of: (a) receiving input speech from a user; (b) processing said input speech to obtain at least one parameter value; and (c) determining the user's level of experience with using automatic speech recognition, using the at least one parameter value. 6. The speech recognition method of claim 1 , further comprising the step of maintaining a history of usage of automatic speech recognition by the user for determining said user's level of experience.
| 0.764151 |
8,862,455 | 5 | 6 |
5. The method of claim 1 , wherein one or more of the pre-generated resource IDs are sent to translators for translation into specific languages.
|
5. The method of claim 1 , wherein one or more of the pre-generated resource IDs are sent to translators for translation into specific languages. 6. The method of claim 5 , wherein translators for a given language are limited to using the pre-generated resource IDs for translation.
| 0.5 |
8,290,777 | 11 | 15 |
11. A method for synchronizing the playing and displaying of digital content in an electronic device, comprising: rendering a first portion of digital content; displaying the rendered first portion of digital content on the electronic device; playing a segment of digital content as audio using a text-to-speech engine while the rendered first portion of digital content is displayed on the electronic device; and rendering a second portion of digital content for display when a current bookmark that is associated with a particular position in the digital content is greater than a last position in the rendered first portion of digital content.
|
11. A method for synchronizing the playing and displaying of digital content in an electronic device, comprising: rendering a first portion of digital content; displaying the rendered first portion of digital content on the electronic device; playing a segment of digital content as audio using a text-to-speech engine while the rendered first portion of digital content is displayed on the electronic device; and rendering a second portion of digital content for display when a current bookmark that is associated with a particular position in the digital content is greater than a last position in the rendered first portion of digital content. 15. The method of claim 11 , wherein the current bookmark comprises a position of a text unit most recently compiled to be played as audio.
| 0.820876 |
8,996,564 | 14 | 15 |
14. A system including one or more processing devices and one or more computer-readable storage devices storing instructions which, when executed by the one or more processing devices, cause the one or more processing devices to: store a data file comprising a logic expression; execute application code comprising a plurality of executable components; in a first instance of executing the application code: receive a first query that does not include the logic expression and is not included in the data file; read the logic expression from the data file, the logic expression identifying a first executable component of the application code and a second executable component of the application code; process the first query using both the first executable component and the second executable component to determine a first logical result of the logic expression; and based on the first logical result of the logic expression, provide a first query result in response to the first query; and in a second instance of executing the application code without restarting the application code after the first instance: receive a second query that does not include the logic expression and is not included in the data file; read the logic expression from the data file again and detect that the logic expression no longer identifies the second executable component; responsive to a determination that the logic expression no longer identifies the second executable component, process the second query using the first executable component and not the second executable component to determine a second logical result of the logic expression; and based on the second logical result of the logic expression, provide a second query result in response to the second query, wherein the application code is executed without interpreting or compiling the logic expression.
|
14. A system including one or more processing devices and one or more computer-readable storage devices storing instructions which, when executed by the one or more processing devices, cause the one or more processing devices to: store a data file comprising a logic expression; execute application code comprising a plurality of executable components; in a first instance of executing the application code: receive a first query that does not include the logic expression and is not included in the data file; read the logic expression from the data file, the logic expression identifying a first executable component of the application code and a second executable component of the application code; process the first query using both the first executable component and the second executable component to determine a first logical result of the logic expression; and based on the first logical result of the logic expression, provide a first query result in response to the first query; and in a second instance of executing the application code without restarting the application code after the first instance: receive a second query that does not include the logic expression and is not included in the data file; read the logic expression from the data file again and detect that the logic expression no longer identifies the second executable component; responsive to a determination that the logic expression no longer identifies the second executable component, process the second query using the first executable component and not the second executable component to determine a second logical result of the logic expression; and based on the second logical result of the logic expression, provide a second query result in response to the second query, wherein the application code is executed without interpreting or compiling the logic expression. 15. The system of claim 14 , wherein the logic expression is provided in the data file in a markup language.
| 0.513514 |
8,204,749 | 1 | 2 |
1. A method for emotion detection, the method comprising: receiving an utterance from a user as part of a natural language dialog between the user and a computing device to yield a received utterance; receiving non-repetitive prompts generated by the computing device; and detecting an emotion of the user based at least in part on the received utterance and the non-repetitive prompts.
|
1. A method for emotion detection, the method comprising: receiving an utterance from a user as part of a natural language dialog between the user and a computing device to yield a received utterance; receiving non-repetitive prompts generated by the computing device; and detecting an emotion of the user based at least in part on the received utterance and the non-repetitive prompts. 2. The method of claim 1 , wherein at least one prompt of the non-repetitive prompts comprises a greeting prompt, a re-prompt, a specification prompt, an acknowledgement prompt, and an informative prompt.
| 0.803846 |
7,548,491 | 17 | 18 |
17. The method of claim 15 wherein the stored messages include at least one user preference.
|
17. The method of claim 15 wherein the stored messages include at least one user preference. 18. The method of claim 17 wherein the at least one user preference is preloaded into a memory.
| 0.5 |
8,527,260 | 17 | 20 |
17. A computer system for selectively modifying an electronic document, the system comprising: one or more processors, one or more computer-readable memories and one or more computer-readable, tangible storage devices; program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to receive a request from a user to view a user selection of text of an electronic document in an original language instead of a second translated language, the electronic document having substantially all of its text in the second translated language; program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to analyze the request to determine an expanded selection to be changed into original replacement language, and to decide on the amount of text to modify, the expanded selection including at least the user selection; program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to determine original replacement language associated with the determined expanded selection; program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to choose text that best communicates the meaning of the associated original replacement language, and to selectively modify the electronic document by permanently replacing text of the electronic document in the second translated language with chosen text that communicates the meaning of the associated original replacement language; and program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to store the modified electronic document.
|
17. A computer system for selectively modifying an electronic document, the system comprising: one or more processors, one or more computer-readable memories and one or more computer-readable, tangible storage devices; program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to receive a request from a user to view a user selection of text of an electronic document in an original language instead of a second translated language, the electronic document having substantially all of its text in the second translated language; program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to analyze the request to determine an expanded selection to be changed into original replacement language, and to decide on the amount of text to modify, the expanded selection including at least the user selection; program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to determine original replacement language associated with the determined expanded selection; program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to choose text that best communicates the meaning of the associated original replacement language, and to selectively modify the electronic document by permanently replacing text of the electronic document in the second translated language with chosen text that communicates the meaning of the associated original replacement language; and program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to store the modified electronic document. 20. The system of claim 17 , further comprising: program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to store an indication of selections requested to have original replacement language by a plurality of users; program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to analyze the selections by the plurality of users to determine frequency of requests for a particular section; and in response to the program instructions to determine that the frequency of requests for the particular section exceeds a threshold, program instructions, stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, to perform a remedial action for the particular selection.
| 0.5 |
9,690,851 | 11 | 12 |
11. A computer-readable memory comprising a set of instructions stored therein which, when executed by a processor, causes the processor to perform a search string expansion by: receiving a list of reserved phrases, each reserved phrase in the list being related to a content and wherein each reserved phrase is associated with a portion of the content; categorizing each reserved phrase according to linguistic characteristics; generating a candidate list of synonyms for each reserved phrase in the list; filtering the candidate list of synonyms by: removing synonym duplicates; and comparing synonyms to a synonym rule and removing synonyms that do not comply with the synonym rule; categorizing each synonym in the filtered candidate list of synonyms according to linguistic characteristics of the associated reserved phrase; receiving a query string; identifying a matching synonym from the filtered list of candidate synonyms that matches a part of the query string; and determining if the part of the query string matches the linguistic characteristics of the matching synonym.
|
11. A computer-readable memory comprising a set of instructions stored therein which, when executed by a processor, causes the processor to perform a search string expansion by: receiving a list of reserved phrases, each reserved phrase in the list being related to a content and wherein each reserved phrase is associated with a portion of the content; categorizing each reserved phrase according to linguistic characteristics; generating a candidate list of synonyms for each reserved phrase in the list; filtering the candidate list of synonyms by: removing synonym duplicates; and comparing synonyms to a synonym rule and removing synonyms that do not comply with the synonym rule; categorizing each synonym in the filtered candidate list of synonyms according to linguistic characteristics of the associated reserved phrase; receiving a query string; identifying a matching synonym from the filtered list of candidate synonyms that matches a part of the query string; and determining if the part of the query string matches the linguistic characteristics of the matching synonym. 12. The computer-readable memory from claim 11 , further comprising: transmitting the portion of the content associated with the reserved phrase of the matching synonym.
| 0.619369 |
4,612,532 | 1 | 14 |
1. A system for the dynamic encoding of a stream of characters, the system comprising: an input for receiving the stream of characters; an output for providing encoded data; means, hereinafter referred to as "followset means," connected to the input, for storing, accessing, and updating, for each given character of a plurality of characters, a table listing the set of candidates for the character which may follow the given character in the stream, such table hereinafter referred to as a "followset table"; followset identification means, connected to the input, for identifying the followset table, hereinafter the "given followset table," for that character which immediately precedes a given character in the stream at the input; position encoding means for providing at the output, for the given character, a signal indicative of the position, occupied by the given character, in the followset table identified by the followset identification means; and updating means for updating a given followset table periodically when the position of a candidate therein has been signaled by the encoding means at the output, by interchanging the position of such candidate, unless such candidate is at the top of the followset table, with the position of a candidate thereabove in the given followset table.
|
1. A system for the dynamic encoding of a stream of characters, the system comprising: an input for receiving the stream of characters; an output for providing encoded data; means, hereinafter referred to as "followset means," connected to the input, for storing, accessing, and updating, for each given character of a plurality of characters, a table listing the set of candidates for the character which may follow the given character in the stream, such table hereinafter referred to as a "followset table"; followset identification means, connected to the input, for identifying the followset table, hereinafter the "given followset table," for that character which immediately precedes a given character in the stream at the input; position encoding means for providing at the output, for the given character, a signal indicative of the position, occupied by the given character, in the followset table identified by the followset identification means; and updating means for updating a given followset table periodically when the position of a candidate therein has been signaled by the encoding means at the output, by interchanging the position of such candidate, unless such candidate is at the top of the followset table, with the position of a candidate thereabove in the given followset table. 14. A system according to claim 1, wherein the updating means updates a given followset table whenever the position of a candidate therein has been signaled by the encoding means at the output, by interchanging the position of such candidate, unless such candidate is at the top of the followset table, with the position of the candidate immediately thereabove in the given followset table.
| 0.855662 |
9,461,945 | 10 | 11 |
10. The apparatus of claim 9 , wherein the question determination module is configured to determine the question type by identifying one or more predefined terms associated with the question type.
|
10. The apparatus of claim 9 , wherein the question determination module is configured to determine the question type by identifying one or more predefined terms associated with the question type. 11. The apparatus of claim 10 , wherein at least one of the predefined terms comprises one or more of an abbreviation, a typo, and a slang term within the message.
| 0.5 |
8,521,581 | 10 | 17 |
10. A system comprising: a text message server operable to transmit a first SMS message for an advertisement to a device, the first SMS message comprising text outlining navigation instructions for retrieval of one or more text content options available for delivery to the device, each navigation instruction including a corresponding text response for transmission from the device to a communications interface for requesting delivery of one of the one or more text content options, each text response corresponding to one of the text content options, each text content option being associated with the advertisement; a response parsing engine operable to receive a response from the device through the communications interface, the response comprising a second SMS message that includes a text response that corresponds to a selected navigation instruction, and to parse the second SMS message to identify an indicator of the text response; and the text message server further operable to: identify a selected text message from among text messages based upon the indicator identified by the response parsing engine, each of the text messages comprising text content associated with one of the one or more text content options, the selected text message including text content associated with the text content option that corresponds to the text response included in the second SMS message; and transmit a third SMS message comprising the text content included in the selected text message to the device through the communications interface.
|
10. A system comprising: a text message server operable to transmit a first SMS message for an advertisement to a device, the first SMS message comprising text outlining navigation instructions for retrieval of one or more text content options available for delivery to the device, each navigation instruction including a corresponding text response for transmission from the device to a communications interface for requesting delivery of one of the one or more text content options, each text response corresponding to one of the text content options, each text content option being associated with the advertisement; a response parsing engine operable to receive a response from the device through the communications interface, the response comprising a second SMS message that includes a text response that corresponds to a selected navigation instruction, and to parse the second SMS message to identify an indicator of the text response; and the text message server further operable to: identify a selected text message from among text messages based upon the indicator identified by the response parsing engine, each of the text messages comprising text content associated with one of the one or more text content options, the selected text message including text content associated with the text content option that corresponds to the text response included in the second SMS message; and transmit a third SMS message comprising the text content included in the selected text message to the device through the communications interface. 17. The system of claim 10 , further comprising a web interface engine operable to transmit information associated with the device to a web server based upon the response.
| 0.828657 |
6,044,365 | 6 | 11 |
6. A system for indexing and retrieving information for social expression cards comprising: means for indexing each social expression card by building a thesaurus of descriptors and equivalent words by: (a) allowing a user to specify at least one descriptor for each social expression card; (b) allowing a user to specify a number of equivalent words for each descriptor; (c) incorporating the descriptors and equivalent words into the thesaurus as a meaning referent set; and (d) linking said social expression card to the meaning referent set in the thesaurus; and means for retrieving said information for selected social expression cards by: (a) allowing a user to specify at least one descriptor; (b) locating said descriptors in the thesaurus; (c) locating any equivalent words for said descriptors in the thesaurus; and (d) retrieving said information for social expression cards linked to said descriptors or equivalent words for said descriptors in the thesaurus.
|
6. A system for indexing and retrieving information for social expression cards comprising: means for indexing each social expression card by building a thesaurus of descriptors and equivalent words by: (a) allowing a user to specify at least one descriptor for each social expression card; (b) allowing a user to specify a number of equivalent words for each descriptor; (c) incorporating the descriptors and equivalent words into the thesaurus as a meaning referent set; and (d) linking said social expression card to the meaning referent set in the thesaurus; and means for retrieving said information for selected social expression cards by: (a) allowing a user to specify at least one descriptor; (b) locating said descriptors in the thesaurus; (c) locating any equivalent words for said descriptors in the thesaurus; and (d) retrieving said information for social expression cards linked to said descriptors or equivalent words for said descriptors in the thesaurus. 11. The system of claim 6, wherein after the user has specified descriptors, the indexing means searchers the thesaurus for each descriptor and asks the user to verify that any existing meaning referent sets in the thesaurus that include a specified descriptor are appropriate for said social expression cards.
| 0.5 |
9,367,605 | 13 | 14 |
13. The system of claim 5 , further comprising an output module configured to output the abstract as a search result.
|
13. The system of claim 5 , further comprising an output module configured to output the abstract as a search result. 14. The system of claim 13 , wherein the output module: ranks abstracts of multiple determined documents based on a number of the keywords from the inquiry word that are matched in each of the documents; and outputs the ranked abstracts according to a predetermined data format.
| 0.5 |
9,678,949 | 17 | 19 |
17. The one or more non-transitory computer-readable media including computer instructions for performing a method of claim 16 comprising creating, using the CPU, a topology from the one or more identified risk elements and explications to construct an ontology and one or more alternate ontologies.
|
17. The one or more non-transitory computer-readable media including computer instructions for performing a method of claim 16 comprising creating, using the CPU, a topology from the one or more identified risk elements and explications to construct an ontology and one or more alternate ontologies. 19. The one or more non-transitory computer-readable media including computer instructions for performing a method of claim 17 comprising displaying, using a CPU, a plurality of explications as text; selecting, using a CPU, one of the plurality of explications to replace a risk element in the one or more requirements documents within training materials.
| 0.515027 |
8,249,356 | 1 | 2 |
1. A method of performing physical page layout analysis via tab stop detection, the method comprising: receiving an input image; determining the physical page layout of the input image, comprising: determining connected components from the input image, wherein the connected components comprise connected groups of pixels that may be text; identifying a subset of the connected components that are candidates for being located at a tab-stop by, for each respective connected component in question: establishing a vertical gutter to a side of the connected component in question; determining if neighboring connected components are in the gutter; determining if the neighboring connected components are edge-aligned with the connected component in question; and identifying the connected component in question as a candidate connected component responsive to no neighboring connected components being in the gutter and neighboring connected components being edge-aligned with the connected component in question; forming a plurality of tab-stop lines from the candidate connected components, wherein a tab-stop line defines a position of a tab-stop for a vertical span of a respective tab-stop line; creating column partitions from the positions of the plurality of tab-stop lines; and forming chains of column partitions to identify regions of the physical page layout of the input image; determining a reading order of the identified regions of the physical page layout of the input image; and outputting metadata describing at least one selected from a group consisting of the regions and the reading order for use in optical character recognition.
|
1. A method of performing physical page layout analysis via tab stop detection, the method comprising: receiving an input image; determining the physical page layout of the input image, comprising: determining connected components from the input image, wherein the connected components comprise connected groups of pixels that may be text; identifying a subset of the connected components that are candidates for being located at a tab-stop by, for each respective connected component in question: establishing a vertical gutter to a side of the connected component in question; determining if neighboring connected components are in the gutter; determining if the neighboring connected components are edge-aligned with the connected component in question; and identifying the connected component in question as a candidate connected component responsive to no neighboring connected components being in the gutter and neighboring connected components being edge-aligned with the connected component in question; forming a plurality of tab-stop lines from the candidate connected components, wherein a tab-stop line defines a position of a tab-stop for a vertical span of a respective tab-stop line; creating column partitions from the positions of the plurality of tab-stop lines; and forming chains of column partitions to identify regions of the physical page layout of the input image; determining a reading order of the identified regions of the physical page layout of the input image; and outputting metadata describing at least one selected from a group consisting of the regions and the reading order for use in optical character recognition. 2. The method of claim 1 , further comprising: performing optical character recognition on text in the determined reading order; and outputting the recognized text.
| 0.792405 |
8,195,036 | 3 | 4 |
3. The non-transitory storage medium as claimed in claim 1 , wherein the text-based subtitle data is provided as a single file.
|
3. The non-transitory storage medium as claimed in claim 1 , wherein the text-based subtitle data is provided as a single file. 4. The non-transitory storage medium as claimed in claim 3 , wherein the single file of the text-based subtitle data is an extended markup language (XML) file.
| 0.5 |
8,406,384 | 1 | 9 |
1. A computer-implemented method employing at least one hardware based computer processor to develop query tags for classification of user queries to a call routing application, the method comprising: accessing a plurality of user query corpuses containing user queries from a plurality of call routing applications in a plurality of different vertical domains; selecting a set of frequent user queries that appear in a plurality of different query corpuses in a plurality of different vertical domains; developing frequent query tags for semantic classification of the frequent user queries; and storing the frequent query tags in a query tag database; wherein the user queries and the query tags are in a first language, and the method further comprises: automatically translating the user queries into a second language; and storing the translated user queries with the frequent query tags in a call routing database for a call routing application in the second language.
|
1. A computer-implemented method employing at least one hardware based computer processor to develop query tags for classification of user queries to a call routing application, the method comprising: accessing a plurality of user query corpuses containing user queries from a plurality of call routing applications in a plurality of different vertical domains; selecting a set of frequent user queries that appear in a plurality of different query corpuses in a plurality of different vertical domains; developing frequent query tags for semantic classification of the frequent user queries; and storing the frequent query tags in a query tag database; wherein the user queries and the query tags are in a first language, and the method further comprises: automatically translating the user queries into a second language; and storing the translated user queries with the frequent query tags in a call routing database for a call routing application in the second language. 9. A method according to claim 1 , further comprising: using the stored query tags to build a statistical language model for audio clustering in an automatic speech recognition application.
| 0.75 |
9,460,238 | 14 | 15 |
14. A method for determining a form for a headphone part using a three-dimensional object, the method comprising: receiving at least one data file for at least one ear model, wherein the at least one data file comprises information on rendering the at least one ear model on a display; receiving comfort level information for at least one location on the at least one ear model, wherein the comfort level information comprises at least one value for comfort level for the at least one location of an ear associated with the at least one ear model when wearing the three-dimensional object; using a data processing device, orienting the at least one ear model within a coordinate system using the at least one data file, wherein orienting the at least one ear model is based at least in part on alignment with respect to at least one area of an ear; using the data processing device, determining a representative model having at least one surface common to at least two ear models of a plurality of oriented ear models including the oriented at least one ear model; associating at least one visual indicator for comfort information for the plurality of oriented ear models with the at least one location on the at least one surface of the representative model; rendering a visualization for the representative model on the display; displaying visual indicators for the comfort level information for the plurality of oriented ear models on the at least one surface of the rendered visualization for the representative model; and determining a shape for the headphone part based on the rendered visualization for the representative model and the visual indicators.
|
14. A method for determining a form for a headphone part using a three-dimensional object, the method comprising: receiving at least one data file for at least one ear model, wherein the at least one data file comprises information on rendering the at least one ear model on a display; receiving comfort level information for at least one location on the at least one ear model, wherein the comfort level information comprises at least one value for comfort level for the at least one location of an ear associated with the at least one ear model when wearing the three-dimensional object; using a data processing device, orienting the at least one ear model within a coordinate system using the at least one data file, wherein orienting the at least one ear model is based at least in part on alignment with respect to at least one area of an ear; using the data processing device, determining a representative model having at least one surface common to at least two ear models of a plurality of oriented ear models including the oriented at least one ear model; associating at least one visual indicator for comfort information for the plurality of oriented ear models with the at least one location on the at least one surface of the representative model; rendering a visualization for the representative model on the display; displaying visual indicators for the comfort level information for the plurality of oriented ear models on the at least one surface of the rendered visualization for the representative model; and determining a shape for the headphone part based on the rendered visualization for the representative model and the visual indicators. 15. The method of claim 14 , wherein the visual indicators comprise a heat map indicating the comfort level information for the plurality of oriented ear models.
| 0.807876 |
8,885,797 | 1 | 2 |
1. A method comprising: receiving, by a network device, a request to perform a transaction, the request being received from a device of a user, the transaction corresponding to a particular type of transaction; determining, by the network device, an authentication level, of a plurality of authentication levels, associated with the request, the authentication level being determined based on the transaction corresponding to the particular type of transaction; selecting, by the network device, an access telephone number, the access telephone number being selected, from a plurality of access numbers, based on the authentication level, each of the plurality of access numbers being associated with a different authentication level of the plurality of authentication levels; transferring, by the network device, the request and information regarding the authentication level to a different network device to perform speaker verification of the user for the transaction, the request being transferred using the selected access telephone number, the information regarding the authentication level being transferred after determining the authentication level, the different network device performing, based on the authentication level, the speaker verification of the user to generate a verification score associated with the transaction; receiving, by the network device, the verification score from the different network device; and performing, by the network device, the transaction based on the verification score.
|
1. A method comprising: receiving, by a network device, a request to perform a transaction, the request being received from a device of a user, the transaction corresponding to a particular type of transaction; determining, by the network device, an authentication level, of a plurality of authentication levels, associated with the request, the authentication level being determined based on the transaction corresponding to the particular type of transaction; selecting, by the network device, an access telephone number, the access telephone number being selected, from a plurality of access numbers, based on the authentication level, each of the plurality of access numbers being associated with a different authentication level of the plurality of authentication levels; transferring, by the network device, the request and information regarding the authentication level to a different network device to perform speaker verification of the user for the transaction, the request being transferred using the selected access telephone number, the information regarding the authentication level being transferred after determining the authentication level, the different network device performing, based on the authentication level, the speaker verification of the user to generate a verification score associated with the transaction; receiving, by the network device, the verification score from the different network device; and performing, by the network device, the transaction based on the verification score. 2. The method of claim 1 , where the network device remains in a call path associated with the request after transferring the request to the different network device, and where the verification score is received via the call path.
| 0.720874 |
8,041,669 | 8 | 10 |
8. A method comprising: processing text in electronic content to identify a topical expression and a polar expression in the electronic content; determining a topic of the topical expression and a polarity of the polar expression; identifying a relevance of the polar expression to the topical expression; generating a confidence score associated with the relevance of the polar expression to the topical expression; and displaying the topical expression with the polar expression and the confidence score to a user.
|
8. A method comprising: processing text in electronic content to identify a topical expression and a polar expression in the electronic content; determining a topic of the topical expression and a polarity of the polar expression; identifying a relevance of the polar expression to the topical expression; generating a confidence score associated with the relevance of the polar expression to the topical expression; and displaying the topical expression with the polar expression and the confidence score to a user. 10. The method of claim 8 , further comprising displaying an aggregated plurality of topical expression and corresponding polar expressions identified from a plurality of electronic content.
| 0.775943 |
8,503,715 | 1 | 3 |
1. A method implemented by one or more computing devices, the method comprising: identifying values representing individual text characters in a string of one or more text characters to determine which human writing system is associated with the individual text characters; comparing the values to a table that associates subsets of values with individual human writing systems; determining that the values representing the individual text characters are within a particular subset of values in the table that correspond to a particular said human writing system; and designating that the particular said human writing system is associated with the string based on the values associated with the individual text characters being within the particular subset of values that corresponds with the particular said human writing system.
|
1. A method implemented by one or more computing devices, the method comprising: identifying values representing individual text characters in a string of one or more text characters to determine which human writing system is associated with the individual text characters; comparing the values to a table that associates subsets of values with individual human writing systems; determining that the values representing the individual text characters are within a particular subset of values in the table that correspond to a particular said human writing system; and designating that the particular said human writing system is associated with the string based on the values associated with the individual text characters being within the particular subset of values that corresponds with the particular said human writing system. 3. The method of claim 1 , further comprising: indicating a range of positions in a text for the one or more text characters; and forming a communication that associates the particular said human writing system with the range of positions.
| 0.5 |
9,971,790 | 1 | 2 |
1. A method performed by data processing apparatus, the method comprising: identifying a set of one or more seed descriptors for a given image in a given document that is hosted on a website, wherein each seed descriptor has been identified as being a correct description of the given image; for each seed descriptor: identifying a location of at least one word of the seed descriptor in the given document by comparing each word of the seed descriptor to text included in the given document; in response to identifying the location of the at least one word of the seed descriptor in the given document, generating, based on the identified location of the at least one word of the seed descriptor in the given document, structure information that specifies a structure of the given document with respect to the given image and the seed descriptor, the structure specifying a location of a string of text that includes the at least one word of the seed descriptor within the given document with respect to a location of the given image within the given document; and generating one or more templates using the structure information for the seed descriptor, each template including: image location information specifying a location of the given image within the given document; document structure information specifying the structure of the given document with respect to the given image and the seed descriptor, including the location of the string of text that includes the at least one word of the seed descriptor within the given document with respect to the location of the given image within the given document; image feature information specifying one or more feature values of the given image, each feature value representing a respective visual characteristic of the given image or data regarding an image file in which the given image is stored; and a generative rule that generates descriptive text for other images in other documents; and for each generated template: identifying a set of documents that have (i) document structure information that matches the document structure information specified by the generated template and (ii) an image that has image feature information that matches the image feature information of the given image; and for each document in the set of documents: generating descriptive text for the image of the document using the generative rule of the generated template and the document; and associating the descriptive text with the image.
|
1. A method performed by data processing apparatus, the method comprising: identifying a set of one or more seed descriptors for a given image in a given document that is hosted on a website, wherein each seed descriptor has been identified as being a correct description of the given image; for each seed descriptor: identifying a location of at least one word of the seed descriptor in the given document by comparing each word of the seed descriptor to text included in the given document; in response to identifying the location of the at least one word of the seed descriptor in the given document, generating, based on the identified location of the at least one word of the seed descriptor in the given document, structure information that specifies a structure of the given document with respect to the given image and the seed descriptor, the structure specifying a location of a string of text that includes the at least one word of the seed descriptor within the given document with respect to a location of the given image within the given document; and generating one or more templates using the structure information for the seed descriptor, each template including: image location information specifying a location of the given image within the given document; document structure information specifying the structure of the given document with respect to the given image and the seed descriptor, including the location of the string of text that includes the at least one word of the seed descriptor within the given document with respect to the location of the given image within the given document; image feature information specifying one or more feature values of the given image, each feature value representing a respective visual characteristic of the given image or data regarding an image file in which the given image is stored; and a generative rule that generates descriptive text for other images in other documents; and for each generated template: identifying a set of documents that have (i) document structure information that matches the document structure information specified by the generated template and (ii) an image that has image feature information that matches the image feature information of the given image; and for each document in the set of documents: generating descriptive text for the image of the document using the generative rule of the generated template and the document; and associating the descriptive text with the image. 2. The method of claim 1 , wherein each template further includes text information that specifies a first portion of the string of text and a wildcard for a second portion of the string of text, the second portion of the string of text corresponding to at least one term of the string of text that matches at least one of the terms of the template's corresponding seed descriptor, and the first portion of the string of text being text that does not match a term of the seed descriptor.
| 0.77583 |
4,647,276 | 10 | 21 |
10. A rosary card comprising a planar sheet-like body having an outer periphery including side edges, a plurality of protrusions on said side edges lying in the same plane as said planar sheet-like body, said protrusions representing various portions of the rosary, and a plurality of cutouts extending entirely through said planar sheet-like body and representing additional portions of the rosary.
|
10. A rosary card comprising a planar sheet-like body having an outer periphery including side edges, a plurality of protrusions on said side edges lying in the same plane as said planar sheet-like body, said protrusions representing various portions of the rosary, and a plurality of cutouts extending entirely through said planar sheet-like body and representing additional portions of the rosary. 21. A rosary card as set forth in claim 10 including numerals printed on said card proximate said protrusions for designating the steps in reciting the rosary.
| 0.509259 |
9,645,817 | 20 | 21 |
20. The system of claim 18 , wherein the operations further comprise receiving a request from the developer for developer metrics for the developer; and providing, in response to the request, a user interface presentation that presents a contextual rank of the developer relative to the other developers in the context group.
|
20. The system of claim 18 , wherein the operations further comprise receiving a request from the developer for developer metrics for the developer; and providing, in response to the request, a user interface presentation that presents a contextual rank of the developer relative to the other developers in the context group. 21. The system of claim 20 , wherein the user interface presentation presents a change in a rank of the developer within the context group.
| 0.5 |
7,957,968 | 1 | 5 |
1. A computer based method for automatically generating a hierarchical grammar associated with a plurality of tasks comprising the steps of: creating a sub-grammar for each of the plurality of tasks, wherein creating a sub-grammar for a task comprises: receiving data representing the task based from responses received from a distributed database; automatically tagging the data into parts of speech to form tagged data using a processor executing instructions included in a memory coupled to the processor; identifying filler words and core words from said tagged data by applying rules to differentiate between filler words and core words using the processor executing instructions included in the memory coupled to the processor; automatically modeling sentence structure based upon said tagged data using a set of modeling rules retrieved from the memory coupled to the processor; automatically identifying synonyms of said core words; and automatically creating a sub-grammar using the modeled sentence structure, the tagged data, the identified synonyms, and the identified core words; and creating a high-level grammar by combining filler words identified in the creation of the sub-grammars.
|
1. A computer based method for automatically generating a hierarchical grammar associated with a plurality of tasks comprising the steps of: creating a sub-grammar for each of the plurality of tasks, wherein creating a sub-grammar for a task comprises: receiving data representing the task based from responses received from a distributed database; automatically tagging the data into parts of speech to form tagged data using a processor executing instructions included in a memory coupled to the processor; identifying filler words and core words from said tagged data by applying rules to differentiate between filler words and core words using the processor executing instructions included in the memory coupled to the processor; automatically modeling sentence structure based upon said tagged data using a set of modeling rules retrieved from the memory coupled to the processor; automatically identifying synonyms of said core words; and automatically creating a sub-grammar using the modeled sentence structure, the tagged data, the identified synonyms, and the identified core words; and creating a high-level grammar by combining filler words identified in the creation of the sub-grammars. 5. The method of claim 1 , wherein said data represents a request to perform said first task.
| 0.633858 |
7,895,225 | 2 | 3 |
2. The information-processing system of claim 1 , wherein the document score is generated based on the identified document's ordinal position in the results set in which the identified document is found, and the total number of documents contained in the results set in which the identified document is found.
|
2. The information-processing system of claim 1 , wherein the document score is generated based on the identified document's ordinal position in the results set in which the identified document is found, and the total number of documents contained in the results set in which the identified document is found. 3. The information-processing system of claim 2 , wherein the document score is generated according to the formula score=rank*log(count), where rank corresponds to the identified document's ordinal position in the results set in which the identified document is found, and count corresponds to the total number of documents contained in the results set in which the identified document is found.
| 0.5 |
9,535,983 | 7 | 10 |
7. The method in accordance with claim 5 , wherein the act of performing a search comprises an act of performing a search for a text sample that includes the first particular text component and a second particular text component, the act of scanning also performed in search of a second particular text component identifier that identifies the second particular text component; whenever upon finding the second particular text component identifier during the act of scanning, an act of using the corresponding text sample identifier to identify the text sample that includes the second particular text component, the method further comprising: an act of identifying a result of the search as including at least some of the text samples that include the first particular text component and the second particular text component.
|
7. The method in accordance with claim 5 , wherein the act of performing a search comprises an act of performing a search for a text sample that includes the first particular text component and a second particular text component, the act of scanning also performed in search of a second particular text component identifier that identifies the second particular text component; whenever upon finding the second particular text component identifier during the act of scanning, an act of using the corresponding text sample identifier to identify the text sample that includes the second particular text component, the method further comprising: an act of identifying a result of the search as including at least some of the text samples that include the first particular text component and the second particular text component. 10. The method in accordance with claim 7 , wherein the act of performing a search comprises an act of performing a search for a text sample that includes the first particular text component and a second particular text component, but which does not include a third particular text component, the act of scanning also performed in search of a third particular text component identifier that identifies the third particular text component; whenever upon finding the third particular text component identifier during the act of scanning, an act of using the corresponding text sample identifier to identify the text sample that includes the third particular text component, the method further comprising: an act of identifying a result of the search as including at least some of the text samples that include the first particular text component and the second particular text component, but which does not include the third particular text component.
| 0.5 |
7,792,830 | 1 | 10 |
1. A method for document analysis, comprising the steps of: designating a subset of relevant documents from a document collection; using a greedy algorithm to establish a query coverage set of words or terms, wherein at each stage thereof a single word or term from the subset of relevant documents is included in the query coverage set, wherein the single word or term minimizes a distance measurement between the document collection and the query coverage set, wherein the distance measurement is determined by constructing a difficulty model for a topic by computing a plurality of distances comprising a first distance between the query coverage set and the document collection (d(Q,C)), a second distance among the query coverage set (d(Q,Q)); a third distance between the subset of relevant documents and the document collection (d(R,C)), a fourth distance among the subset of relevant documents (d(R,R)), and a fifth distance between the query coverage set and the subset of relevant documents (d(Q,R)); storing the query coverage set in a database; constructing a set of queries from the query coverage set, each of the queries having a number of terms; executing the queries in a search engine to generate respective results; responsively to the respective results determining an average precision for each of the queries by considering the subset of relevant documents as representing the document collection; categorizing the queries by analyzing the average precision against the number of terms thereof; and reporting respective abilities of the categorized queries to find information in the subset of relevant documents.
|
1. A method for document analysis, comprising the steps of: designating a subset of relevant documents from a document collection; using a greedy algorithm to establish a query coverage set of words or terms, wherein at each stage thereof a single word or term from the subset of relevant documents is included in the query coverage set, wherein the single word or term minimizes a distance measurement between the document collection and the query coverage set, wherein the distance measurement is determined by constructing a difficulty model for a topic by computing a plurality of distances comprising a first distance between the query coverage set and the document collection (d(Q,C)), a second distance among the query coverage set (d(Q,Q)); a third distance between the subset of relevant documents and the document collection (d(R,C)), a fourth distance among the subset of relevant documents (d(R,R)), and a fifth distance between the query coverage set and the subset of relevant documents (d(Q,R)); storing the query coverage set in a database; constructing a set of queries from the query coverage set, each of the queries having a number of terms; executing the queries in a search engine to generate respective results; responsively to the respective results determining an average precision for each of the queries by considering the subset of relevant documents as representing the document collection; categorizing the queries by analyzing the average precision against the number of terms thereof; and reporting respective abilities of the categorized queries to find information in the subset of relevant documents. 10. The method according to claim 1 , further comprising the steps of: partitioning the document collection into a plurality of domains; for each of the domains performing the steps of designating a subset of relevant documents, using a greedy algorithm, storing the query coverage set, constructing a set of queries, and executing the queries; determining an average overlap among the terms in the queries at a plurality of cut-off points; and determining from the average overlap the most similar domains.
| 0.5 |
9,483,452 | 5 | 7 |
5. The method of claim 4 , wherein the method further comprises: determining a degree of similarity between one or more instances of previous application data and current application data based on a comparison of a geographic location associated with each of the previous application data and the current application data; and selecting at least one of the one or more instances of previous application data based on the degree of similarity between the one or more instances of previous application data and the current application data.
|
5. The method of claim 4 , wherein the method further comprises: determining a degree of similarity between one or more instances of previous application data and current application data based on a comparison of a geographic location associated with each of the previous application data and the current application data; and selecting at least one of the one or more instances of previous application data based on the degree of similarity between the one or more instances of previous application data and the current application data. 7. The method of claim 5 , wherein the degree of similarity is further determined based on a difference between dates associated with the one or more instances of previous application data and current application data being compared.
| 0.5 |
8,335,755 | 11 | 12 |
11. A computer-aided reasoning system comprising˜ a plurality of atomic contexts, that each atomic context have an abstract data type and comprise: a reference to at least one mathematical model that defines a computational experiment and that computes answers to the computational experiment, wherein the mathematical model is located externally to the atomic context and is capable of computing answers to the computational experiment without being associated with the atomic context; at least one interface to the mathematical model; and at least one method configured to run operations on the mathematical model; wherein each atomic context is configured to drive experiments using the mathematical model at least partially by providing inputs to the mathematical model, changing input values to the mathematical model, and iteratively instructing the mathematical model to run a computational experiment and to return at least one output of the computational experiment and wherein the atomic context does not alter the mathematical model; a plurality of derived contexts that each have the same abstract data type as an atomic context and is configured to cause the performance of at least one computation at least partially by invoking at least one other context; and, a results store processor configured to: receive at least one input into a data processing algorithm; initiate, using the data processing algorithm, a number of queries to a result store within a base context; receive, at the data processing algorithm, responses to the queries from the result store; and summarize the responses and produce an output based on the summarized responses.
|
11. A computer-aided reasoning system comprising˜ a plurality of atomic contexts, that each atomic context have an abstract data type and comprise: a reference to at least one mathematical model that defines a computational experiment and that computes answers to the computational experiment, wherein the mathematical model is located externally to the atomic context and is capable of computing answers to the computational experiment without being associated with the atomic context; at least one interface to the mathematical model; and at least one method configured to run operations on the mathematical model; wherein each atomic context is configured to drive experiments using the mathematical model at least partially by providing inputs to the mathematical model, changing input values to the mathematical model, and iteratively instructing the mathematical model to run a computational experiment and to return at least one output of the computational experiment and wherein the atomic context does not alter the mathematical model; a plurality of derived contexts that each have the same abstract data type as an atomic context and is configured to cause the performance of at least one computation at least partially by invoking at least one other context; and, a results store processor configured to: receive at least one input into a data processing algorithm; initiate, using the data processing algorithm, a number of queries to a result store within a base context; receive, at the data processing algorithm, responses to the queries from the result store; and summarize the responses and produce an output based on the summarized responses. 12. The reasoning system of claim 11 , wherein the results store processor is a derived context.
| 0.5 |
9,794,423 | 8 | 13 |
8. A service comprising: at least one processor; memory coupled to the at least one processor, and a matching engine implemented at least in part by the at least one processor and via which the service is configured to perform actions comprising: receiving a vocal rendition that originates live from a user; generating a melody representation of the received vocal rendition; selecting, based on the melody representation, at least one candidate based on aligning features of the melody representation with features of the at least one candidate; adding, based on a weighting of the at least one candidate, the selected at least one candidate to a list; and returning the list to the user.
|
8. A service comprising: at least one processor; memory coupled to the at least one processor, and a matching engine implemented at least in part by the at least one processor and via which the service is configured to perform actions comprising: receiving a vocal rendition that originates live from a user; generating a melody representation of the received vocal rendition; selecting, based on the melody representation, at least one candidate based on aligning features of the melody representation with features of the at least one candidate; adding, based on a weighting of the at least one candidate, the selected at least one candidate to a list; and returning the list to the user. 13. The service of claim 8 where the aligning is according to a Viterbi alignment on notes.
| 0.751366 |
8,117,535 | 1 | 3 |
1. A system for creating a dynamic folder hierarchy, comprising: a network; a host server connected to said network via a communications link; and a computer in communication with said network wherein said computer including a processor capable of executing instructions comprising: creating a design-time folder group definition specifying an organization of a hierarchy of design-time folder groups with each design-time folder group consisting of a plurality of design-time folders arranged in parent-child relationships, said definition including a set of variable binding expressions for associating a plurality of documents with a plurality of dynamic folders; generating a query with predicates from said set of variable binding expressions; searching a collection of documents to identify documents which match said query, each of said documents in said collection of documents containing self-describing data; dynamically creating a hierarchy of dynamic folders including child dynamic folders within said hierarchy of design-time folder groups by using a combination of said design-time folder groups definition and said identified documents; and associating each of said identified documents with at least one dynamic folder in said hierarchy of dynamic folders.
|
1. A system for creating a dynamic folder hierarchy, comprising: a network; a host server connected to said network via a communications link; and a computer in communication with said network wherein said computer including a processor capable of executing instructions comprising: creating a design-time folder group definition specifying an organization of a hierarchy of design-time folder groups with each design-time folder group consisting of a plurality of design-time folders arranged in parent-child relationships, said definition including a set of variable binding expressions for associating a plurality of documents with a plurality of dynamic folders; generating a query with predicates from said set of variable binding expressions; searching a collection of documents to identify documents which match said query, each of said documents in said collection of documents containing self-describing data; dynamically creating a hierarchy of dynamic folders including child dynamic folders within said hierarchy of design-time folder groups by using a combination of said design-time folder groups definition and said identified documents; and associating each of said identified documents with at least one dynamic folder in said hierarchy of dynamic folders. 3. The system of claim 1 , further comprising generating additional dynamic folder hierarchies, each of which associates a collection of documents wherein overlapping occurs between said dynamic folder hierarchies.
| 0.609489 |
9,813,879 | 26 | 27 |
26. The mobile face-to-face interaction monitoring method according to claim 24 , wherein the creating the volume topography based on the sound signals is performed by using a normalized vector P′(t), wherein the normalized feature vector P′(t) is defined as P′(t)=P(t)/E(t), where E(t) is an average of the feature vector P(t).
|
26. The mobile face-to-face interaction monitoring method according to claim 24 , wherein the creating the volume topography based on the sound signals is performed by using a normalized vector P′(t), wherein the normalized feature vector P′(t) is defined as P′(t)=P(t)/E(t), where E(t) is an average of the feature vector P(t). 27. The mobile face-to-face interaction monitoring method according to claim 26 , wherein the creating the volume topography based on the sound signals is performed by using a transformed vector P″(t), wherein the transformed vector P″(t) is defined as
P ″( t )= D ( t )* P ′( t )={ D ( t, 1)* p ( t, 1)/ E ( t ), D ( t, 2)* p ( t, 2)/ E ( t ), . . . , D ( t,np )* p ( t,np )/ E ( t )}, where a decibel D(t) measured on the mobile device i, at the given time t, is defined as D(t,i)=20*log 10 (p(t,i)/p.ref), where p.ref is a standard reference sound pressure level.
| 0.5 |
9,501,696 | 8 | 9 |
8. The computer-readable storage medium of claim 7 , wherein the instructions further configure the computer to: notify a second plugin service associated to the one or more zones in the plurality of zones; 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; and, invoke the one or more triggers assigned to the one or more zones in the plurality of zones.
|
8. The computer-readable storage medium of claim 7 , wherein the instructions further configure the computer to: notify a second plugin service associated to the one or more zones in the plurality of zones; 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; and, invoke the one or more triggers assigned to the one or more zones in the plurality of zones. 9. The computer-readable storage medium of claim 8 , wherein the document associated with the document type comprises text and wherein extracting the first metadata element or the second metadata element associated to each zone in the plurality of zones comprises apply optical character recognition to the document associated with the document type.
| 0.5 |
9,159,324 | 1 | 6 |
1. A method for identifying a speaker with a mobile device, the method comprising: capturing, via a microphone on the mobile device, audio data comprising a speech signal; inferring, via the mobile device, a context of a user of the mobile device; identifying, via the mobile device, a social graph based at least partly on the inferred context, the social graph comprising a list of potential speakers; and identifying, via the mobile device, a speaker determined to have vocally contributed to the speech signal, the speaker identification based at least partly on the identified social graph.
|
1. A method for identifying a speaker with a mobile device, the method comprising: capturing, via a microphone on the mobile device, audio data comprising a speech signal; inferring, via the mobile device, a context of a user of the mobile device; identifying, via the mobile device, a social graph based at least partly on the inferred context, the social graph comprising a list of potential speakers; and identifying, via the mobile device, a speaker determined to have vocally contributed to the speech signal, the speaker identification based at least partly on the identified social graph. 6. The method of claim 1 , wherein inferring the context of the user is based at least partly on an activity of the user.
| 0.841207 |
9,189,361 | 3 | 4 |
3. The system of claim 1 , wherein the model further defines the one or more quotas in accord with a class to which the user belongs.
|
3. The system of claim 1 , wherein the model further defines the one or more quotas in accord with a class to which the user belongs. 4. The system of claim 3 , wherein the quotas include any of (i) maximum permitted usage of the application by users in each of one or more classes, and (ii) expected usage of the application by users in each of one or more classes.
| 0.5 |
7,827,190 | 1 | 16 |
1. The integrated circuit chip comprising programmable intelligent search memory for content search wherein said programmable intelligent search memory performs regular expression based search and wherein said regular expression comprises complex symbols, said programmable intelligent search memory for content search using one or more regular expressions, said one or more regular expressions comprising one or more symbols or characters and further comprising one or more complex symbols, said one or more regular expressions converted into one or more finite state automata representing the functionality of said one or more regular expressions for programming in said programmable intelligent search memory, said one or more finite state automata comprising a plurality of states, said plurality of states derived from said one or more symbols or characters of said one or more regular expressions, said content comprising one or more input symbols provided as input to said programmable intelligent search memory, said programmable intelligent search memory comprising at least one of each of: a. a symbol memory circuit to store said one or more symbols; b. a complex symbol memory circuit to store said one or more complex symbols; c. a complex symbol evaluation circuit coupled to said complex symbol memory circuit to evaluate match of said one or more complex symbols stored in said complex symbol memory circuit with said one or more input symbols of said content; d. a symbol evaluation circuit coupled to said symbol memory circuit to evaluate match of said one or more symbols stored in said symbol memory circuit with said one or more input symbols of said content; e. a state dependent vector memory circuit to store state transition controls for said one or more finite state automata; f. a current state vector memory circuit to store said plurality of states; and g. a state transition circuit coupled to said symbol evaluation circuit, said complex symbol evaluation circuit, said current state vector memory circuit and said state dependent vector memory circuit to perform state transition from one or more first states to one or more second states of said plurality of states of said one or more finite state automata.
|
1. The integrated circuit chip comprising programmable intelligent search memory for content search wherein said programmable intelligent search memory performs regular expression based search and wherein said regular expression comprises complex symbols, said programmable intelligent search memory for content search using one or more regular expressions, said one or more regular expressions comprising one or more symbols or characters and further comprising one or more complex symbols, said one or more regular expressions converted into one or more finite state automata representing the functionality of said one or more regular expressions for programming in said programmable intelligent search memory, said one or more finite state automata comprising a plurality of states, said plurality of states derived from said one or more symbols or characters of said one or more regular expressions, said content comprising one or more input symbols provided as input to said programmable intelligent search memory, said programmable intelligent search memory comprising at least one of each of: a. a symbol memory circuit to store said one or more symbols; b. a complex symbol memory circuit to store said one or more complex symbols; c. a complex symbol evaluation circuit coupled to said complex symbol memory circuit to evaluate match of said one or more complex symbols stored in said complex symbol memory circuit with said one or more input symbols of said content; d. a symbol evaluation circuit coupled to said symbol memory circuit to evaluate match of said one or more symbols stored in said symbol memory circuit with said one or more input symbols of said content; e. a state dependent vector memory circuit to store state transition controls for said one or more finite state automata; f. a current state vector memory circuit to store said plurality of states; and g. a state transition circuit coupled to said symbol evaluation circuit, said complex symbol evaluation circuit, said current state vector memory circuit and said state dependent vector memory circuit to perform state transition from one or more first states to one or more second states of said plurality of states of said one or more finite state automata. 16. The integrated circuit chip of claim 1 , wherein the complex symbols comprises a range detect symbol or a complement symbol or a symbol with some bits masked or a combination of the foregoing.
| 0.930889 |
7,853,596 | 13 | 14 |
13. A computing device for determining topics and locations associated with a target document, comprising: a document store having a collection of documents of words, each word of a document associated with a location; a memory storing computer-implemented instructions of a component that generates collection level parameters for a latent Dirichlet allocation model for the collection of documents based on latent topics and the location of each document, the collection level parameters relating to probabilities of latent topics, locations, and words of the collection including a probability that each location of the collection relates to each latent topic, the collection level parameters indicating a probability that a document in the collection relates to each latent topic, a probability that each word of the collection relates to each latent topic, and a probability that each location of the collection relates to each latent topic, wherein a variational expectation maximization algorithm is used to estimate the collection level parameters that are a maximization of a lower bound on the collection level parameters represented by a summation for each document in the collection of the log of the conditional probability of the document and its location given the collection level parameters; and a component that estimates using the collection level parameters probabilities of topics and locations being associated with the target document; and a component that selects the location with the highest estimated probability for a target document as the location associated with that target document; and a processor for executing the computer-implemented instructions stored in memory.
|
13. A computing device for determining topics and locations associated with a target document, comprising: a document store having a collection of documents of words, each word of a document associated with a location; a memory storing computer-implemented instructions of a component that generates collection level parameters for a latent Dirichlet allocation model for the collection of documents based on latent topics and the location of each document, the collection level parameters relating to probabilities of latent topics, locations, and words of the collection including a probability that each location of the collection relates to each latent topic, the collection level parameters indicating a probability that a document in the collection relates to each latent topic, a probability that each word of the collection relates to each latent topic, and a probability that each location of the collection relates to each latent topic, wherein a variational expectation maximization algorithm is used to estimate the collection level parameters that are a maximization of a lower bound on the collection level parameters represented by a summation for each document in the collection of the log of the conditional probability of the document and its location given the collection level parameters; and a component that estimates using the collection level parameters probabilities of topics and locations being associated with the target document; and a component that selects the location with the highest estimated probability for a target document as the location associated with that target document; and a processor for executing the computer-implemented instructions stored in memory. 14. The computing device of claim 13 wherein each word of a document in the document store is associated with the same document.
| 0.5 |
8,024,327 | 17 | 18 |
17. The method according to claim 1 , wherein the act of analyzing the set to obtain a statistical distribution further comprises an act of approximating the distribution.
|
17. The method according to claim 1 , wherein the act of analyzing the set to obtain a statistical distribution further comprises an act of approximating the distribution. 18. The method according to claim 17 , wherein the act of approximating the distribution includes an act of employing sampling to calculate the statistical distribution for a set of documents.
| 0.764128 |
7,752,197 | 8 | 9 |
8. A database system, comprising: a processor; a memory storing a plurality of executable components, including at least: a graphical user interface component configured to receive, from a user: a plurality of reusable query components for use in building a database query, each reusable query component having one or more associated query conditions to associate with the reusable query component, wherein each condition provides a portion of a database query in a query language, wherein the condition operates to filter results to be returned by the database query, wherein each query condition is an abstract query condition defined by one or more logical fields and wherein each condition includes: a field name referencing a field in a database, a comparison operator, and a comparison value to which one or more database values for the field are compared, based on the comparison operator, to determine whether the condition is satisfied for a given database value corresponding to the field; wherein each of the reusable query components includes: a name to identify the reusable query component; and an access level to assign to the reusable query component, wherein the access level indicates a privilege level required for a user in order for the reusable query component to be made available to the user for use in building in a database query, and wherein the access level is specific to the reusable query component, and wherein XML representations of the one or more query conditions, the specified access level, and the specified name are stored a database for later retrieval; and a query building component configured to, via operation of the one or more computer processors: receive a request to include at least two of the plurality of reusable query component in an abstract query, wherein the request specifies the respective name of the at least two reusable query components; and upon determining that the request satisfies the respective specified access level for the reusable query components, include all of the respective query conditions of the reusable query components in the abstract query, responsive to the request.
|
8. A database system, comprising: a processor; a memory storing a plurality of executable components, including at least: a graphical user interface component configured to receive, from a user: a plurality of reusable query components for use in building a database query, each reusable query component having one or more associated query conditions to associate with the reusable query component, wherein each condition provides a portion of a database query in a query language, wherein the condition operates to filter results to be returned by the database query, wherein each query condition is an abstract query condition defined by one or more logical fields and wherein each condition includes: a field name referencing a field in a database, a comparison operator, and a comparison value to which one or more database values for the field are compared, based on the comparison operator, to determine whether the condition is satisfied for a given database value corresponding to the field; wherein each of the reusable query components includes: a name to identify the reusable query component; and an access level to assign to the reusable query component, wherein the access level indicates a privilege level required for a user in order for the reusable query component to be made available to the user for use in building in a database query, and wherein the access level is specific to the reusable query component, and wherein XML representations of the one or more query conditions, the specified access level, and the specified name are stored a database for later retrieval; and a query building component configured to, via operation of the one or more computer processors: receive a request to include at least two of the plurality of reusable query component in an abstract query, wherein the request specifies the respective name of the at least two reusable query components; and upon determining that the request satisfies the respective specified access level for the reusable query components, include all of the respective query conditions of the reusable query components in the abstract query, responsive to the request. 9. The database system of claim 8 , wherein the executable components further comprise: a data repository abstraction component comprising mapping rules which map the one or more logical fields to physical entities of data; and a runtime component for transforming the abstract query into a query consistent with the physical entities of data according to the mapping rules.
| 0.5 |
8,473,404 | 12 | 13 |
12. The method of claim 1 , further comprising recording the portion of the market data and a time the portion of the market data was flagged.
|
12. The method of claim 1 , further comprising recording the portion of the market data and a time the portion of the market data was flagged. 13. The method of claim 12 where the portion of the market data and the time the portion of the market data was flagged is recorded in response to a first user command.
| 0.5 |
9,552,816 | 5 | 12 |
5. A method, comprising: providing a command to an audio device to perform an activity, wherein the command identifies a responsible application from among multiple applications; receiving an event message from the audio device regarding sound presented by the audio device, the event message identifying the responsible application; if the event message indicates that the sound is part of a user interaction, designating the responsible application as being primarily active; receiving speech captured by the audio device; determining a meaning of the speech; and if there is a primarily active application among the multiple applications that can respond to the meaning, requesting the primarily active application to respond to the meaning.
|
5. A method, comprising: providing a command to an audio device to perform an activity, wherein the command identifies a responsible application from among multiple applications; receiving an event message from the audio device regarding sound presented by the audio device, the event message identifying the responsible application; if the event message indicates that the sound is part of a user interaction, designating the responsible application as being primarily active; receiving speech captured by the audio device; determining a meaning of the speech; and if there is a primarily active application among the multiple applications that can respond to the meaning, requesting the primarily active application to respond to the meaning. 12. The method of claim 5 , wherein: the command specifies an application identifier that identifies the responsible application; and the event message specifies the application identifier to identify the responsible application.
| 0.72343 |
7,882,127 | 2 | 3 |
2. The method of claim 1 , further comprising the step of: validating the received scoring data to ensure active attributes and a target attribute specified for the data mining model are present in the received scoring data and the source attributes specified for the multi-category apply output are present in the input data.
|
2. The method of claim 1 , further comprising the step of: validating the received scoring data to ensure active attributes and a target attribute specified for the data mining model are present in the received scoring data and the source attributes specified for the multi-category apply output are present in the input data. 3. The method of claim 2 , wherein the selection criterion comprises one of: a topmost category including a class value or predicted value having a highest associated probability, top N categories including N class values having highest associated probabilities, bottom N categories including N class values having lowest associated probabilities, or all categories including all class values with associated probabilities.
| 0.5 |
8,141,139 | 1 | 8 |
1. A method, operative within a federated environment in which a token service fulfills requests by executing a module chain comprising a set of modules, comprising: responsive to receipt of a token, initiating processing of the module chain within a data processing system; during processing of the module chain within the data processing system, attempting to validate a value of a name-value pair based on a rule, wherein the rule is determined based on one or more invocation parameters of the module chain; and returning a response.
|
1. A method, operative within a federated environment in which a token service fulfills requests by executing a module chain comprising a set of modules, comprising: responsive to receipt of a token, initiating processing of the module chain within a data processing system; during processing of the module chain within the data processing system, attempting to validate a value of a name-value pair based on a rule, wherein the rule is determined based on one or more invocation parameters of the module chain; and returning a response. 8. The method as described in claim 1 wherein the rule identifies a condition that must be satisfied by a user of a first entity attempting to execute a federated single sign-on (F-SSO) to the federated environment.
| 0.683824 |
8,719,425 | 11 | 13 |
11. A wireless communication device comprising: a network adapter configured to establish an Extensible Messaging and Presence Protocol (XMPP) connection between the wireless communication device and a first server, and configured to establish a closed connection between the wireless communication device and a second server; wherein the XMPP connection and the closed connection are not established at the same time; and a processor configured to: receive a first message pushed from the first server through the XMPP connection, wherein the first message is generated at the first server and is pushed from the first server to the wireless communication device in response to the second server receiving specific data; request, in response to receiving the first message, the specific data from the second server through the closed connection; and receive the specific data from the second server through the closed connection in response to requesting the specific data from the second server.
|
11. A wireless communication device comprising: a network adapter configured to establish an Extensible Messaging and Presence Protocol (XMPP) connection between the wireless communication device and a first server, and configured to establish a closed connection between the wireless communication device and a second server; wherein the XMPP connection and the closed connection are not established at the same time; and a processor configured to: receive a first message pushed from the first server through the XMPP connection, wherein the first message is generated at the first server and is pushed from the first server to the wireless communication device in response to the second server receiving specific data; request, in response to receiving the first message, the specific data from the second server through the closed connection; and receive the specific data from the second server through the closed connection in response to requesting the specific data from the second server. 13. The wireless communication device according to claim 11 , wherein the closed connection is a Hypertext Transfer Protocol (HTTP) connection.
| 0.5 |
8,296,123 | 8 | 9 |
8. The system of claim 6 , wherein: the scoring information comprises statistical data for each mapping between the source language and the target language.
|
8. The system of claim 6 , wherein: the scoring information comprises statistical data for each mapping between the source language and the target language. 9. The system of claim 8 , wherein: the statistical data comprises a probability of occurrence of each mapping between the source language and the target language.
| 0.5 |
6,112,021 | 9 | 11 |
9. A method according to claim 5, wherein the established Markov Model parameters are first Markov Model parameters and further comprising the step of: establishing second Markov Model parameters for the second classification based on the first and the second training sets.
|
9. A method according to claim 5, wherein the established Markov Model parameters are first Markov Model parameters and further comprising the step of: establishing second Markov Model parameters for the second classification based on the first and the second training sets. 11. A method according to claim 9, wherein the establishing of first and the second Markov Model parameters includes: generating Markov Model parameters corresponding to both the first sequences and the second sequences; increasing those of the generated Markov Model parameters which correspond primarily to the first sequences to form first increased parameters and decreasing those of the generated Markov Model parameters which correspond primarily to the second sequences to form first decreased parameters; and increasing those of the generated Markov Model parameters which correspond primarily to the second sequences to form second increased parameters and decreasing those of the generated Markov Model parameters which correspond primarily to the first sequences to form second decreased parameters.
| 0.5 |
8,738,355 | 51 | 59 |
51. An apparatus for use in a mobile station, the apparatus comprising: means for generating a request for translation information from a translation information service, wherein said translation information is associated with a location and one or more written and/or spoken languages; means for transmitting said request for translation information to said translation information service; means for receiving a response from said translation information service comprising requested translation information, said requested translation information being based, at least in part, on said request for translation information, said location, and predicted information, wherein the predicted information is associated with the request for translation information, the location, and at least one other request for translation information associated with at least one other location and previously transmitted to said translation information service by at least one other mobile station; and means for generating a presentation for a user based, at least in part, on said response.
|
51. An apparatus for use in a mobile station, the apparatus comprising: means for generating a request for translation information from a translation information service, wherein said translation information is associated with a location and one or more written and/or spoken languages; means for transmitting said request for translation information to said translation information service; means for receiving a response from said translation information service comprising requested translation information, said requested translation information being based, at least in part, on said request for translation information, said location, and predicted information, wherein the predicted information is associated with the request for translation information, the location, and at least one other request for translation information associated with at least one other location and previously transmitted to said translation information service by at least one other mobile station; and means for generating a presentation for a user based, at least in part, on said response. 59. The apparatus as recited in claim 51 , wherein said location is associated with at least one of: a region, a structure, a point of interest, an estimated position of said mobile station, and/or an estimated orientation of said mobile station.
| 0.666667 |
9,256,736 | 1 | 6 |
1. A method for monitoring a malicious attribute of a webpage, comprising: acquiring webpage query requests submitted by a plurality of clients; crawling the webpage based on the acquired webpage query requests and acquiring crawled webpage contents; counting up a referenced value of a uniform resource locator (URL) based on the crawled webpage contents; and calling a predetermined detection program based on the referenced value of the URL to perform malicious attribute detection of the URL.
|
1. A method for monitoring a malicious attribute of a webpage, comprising: acquiring webpage query requests submitted by a plurality of clients; crawling the webpage based on the acquired webpage query requests and acquiring crawled webpage contents; counting up a referenced value of a uniform resource locator (URL) based on the crawled webpage contents; and calling a predetermined detection program based on the referenced value of the URL to perform malicious attribute detection of the URL. 6. The method of claim 1 , wherein the crawled web page is specified in a plurality of the acquired webpage query requests.
| 0.889982 |
8,332,787 | 1 | 4 |
1. A method comprising: removing, based on execution of an instruction by a processor, a part of a logic from a hardware description language design to create a modified hardware language design, the part of the logic comprising at least one time sensitive path in the hardware description language design; comparing the modified hardware language design to a physical representation that is logically equivalent to the hardware description language design to create a delta list of differences; creating a modified physical representation that includes a part of the physical representation that includes the logical equivalent of the part of the logic comprising the at least one time sensitive path, using the delta list of differences; creating a structured hardware description language design of the part of the logic comprising the at least one time sensitive path using the modified physical representation, the structured hardware description language design comprising a physical implementation requirement of at least one component; and modifying the structured hardware description language design, wherein the modifying updates the structured hardware description language design to comply with requirements of a synthesis operation of a synthesis tool, and wherein the modifying precludes the synthesis tool from modifying the physical implementation requirement of the at least one component.
|
1. A method comprising: removing, based on execution of an instruction by a processor, a part of a logic from a hardware description language design to create a modified hardware language design, the part of the logic comprising at least one time sensitive path in the hardware description language design; comparing the modified hardware language design to a physical representation that is logically equivalent to the hardware description language design to create a delta list of differences; creating a modified physical representation that includes a part of the physical representation that includes the logical equivalent of the part of the logic comprising the at least one time sensitive path, using the delta list of differences; creating a structured hardware description language design of the part of the logic comprising the at least one time sensitive path using the modified physical representation, the structured hardware description language design comprising a physical implementation requirement of at least one component; and modifying the structured hardware description language design, wherein the modifying updates the structured hardware description language design to comply with requirements of a synthesis operation of a synthesis tool, and wherein the modifying precludes the synthesis tool from modifying the physical implementation requirement of the at least one component. 4. The method of claim 1 , wherein the physical implementation requirement comprises at least one of a placement of a component and a requirement of a wire coupling components together.
| 0.5 |
7,818,378 | 29 | 42 |
29. A computer readable storage medium storing one or more programs for execution by one or more processors of a computer system, the computer readable storage medium being non-transitory, the one or more programs in the computer readable storage medium comprising: instructions for receiving a plurality of messages directed to a user, each message having a unique message identifier; instructions for associating each of the plurality of messages with a respective conversation, each conversation having a respective conversation identifier, wherein each conversation comprises a set of one or more messages sharing a common set of characteristics that meet first predefined criteria and the respective conversation identifier is distinct from a subject reference of the one or more messages in the respective conversation; instructions for associating with each conversation a set of senders of messages included in the conversation; and instructions for displaying a list of conversations in an order determined in accordance with second predefined criteria, each conversation being represented as a single item in the list, wherein a plurality of conversations in the list of conversations each include a plurality of messages that share a common set of characteristics that meet the first predefined criteria; wherein the list of conversations comprises a set of rows, each row corresponding to one of the listed conversations and including at least a sender list, a conversation topic and a date/time value, wherein the sender list of a row in the list of conversations includes identifiers of one or more senders of at least one message in the corresponding conversation, including identifiers of a plurality of the senders in the set of senders, but less than all of the senders in the set of senders, when the set of senders exceeds a predefined limit.
|
29. A computer readable storage medium storing one or more programs for execution by one or more processors of a computer system, the computer readable storage medium being non-transitory, the one or more programs in the computer readable storage medium comprising: instructions for receiving a plurality of messages directed to a user, each message having a unique message identifier; instructions for associating each of the plurality of messages with a respective conversation, each conversation having a respective conversation identifier, wherein each conversation comprises a set of one or more messages sharing a common set of characteristics that meet first predefined criteria and the respective conversation identifier is distinct from a subject reference of the one or more messages in the respective conversation; instructions for associating with each conversation a set of senders of messages included in the conversation; and instructions for displaying a list of conversations in an order determined in accordance with second predefined criteria, each conversation being represented as a single item in the list, wherein a plurality of conversations in the list of conversations each include a plurality of messages that share a common set of characteristics that meet the first predefined criteria; wherein the list of conversations comprises a set of rows, each row corresponding to one of the listed conversations and including at least a sender list, a conversation topic and a date/time value, wherein the sender list of a row in the list of conversations includes identifiers of one or more senders of at least one message in the corresponding conversation, including identifiers of a plurality of the senders in the set of senders, but less than all of the senders in the set of senders, when the set of senders exceeds a predefined limit. 42. The computer readable storage medium of claim 29 , wherein a row in the set of rows includes a recipient indicator that indicates whether the user is a primary recipient or secondary recipient of any message in the conversation.
| 0.822358 |
8,249,877 | 10 | 12 |
10. A computer-implemented method performed by a client device, the method comprising: (A) receiving a request from a requester to apply automatic speech recognition to an audio signal; (B) providing the audio signal to a first automatic speech recognition engine in the client device; (C) receiving first speech recognition results from the first automatic speech recognition engine; (D) providing the audio signal to a second automatic speech recognition engine in a server device; (E) receiving second speech recognition results from the second automatic speech recognition engine; (F) producing hybrid speech recognition results based on the first speech recognition results and the second speech recognition results; and (G) providing the hybrid speech recognition results to the requester in response to the request.
|
10. A computer-implemented method performed by a client device, the method comprising: (A) receiving a request from a requester to apply automatic speech recognition to an audio signal; (B) providing the audio signal to a first automatic speech recognition engine in the client device; (C) receiving first speech recognition results from the first automatic speech recognition engine; (D) providing the audio signal to a second automatic speech recognition engine in a server device; (E) receiving second speech recognition results from the second automatic speech recognition engine; (F) producing hybrid speech recognition results based on the first speech recognition results and the second speech recognition results; and (G) providing the hybrid speech recognition results to the requester in response to the request. 12. The method of claim 10 , wherein the client device is configured to treat one of the first and second automatic speech recognition engines as a preferred speech recognition engine, and: wherein (C) comprises receiving the first speech recognition results at an arbitration engine in the client device at a first time; wherein (E) comprises receiving the second speech recognition results at the arbitration engine in the client device at a second time that is later than the first time; and wherein (F) comprises: (F)(1) including the first recognition results in the hybrid speech recognition results; and (F)(2) including the second speech recognition results in the hybrid speech recognition results only if the second automatic speech recognition engine is the preferred speech recognition engine.
| 0.5 |
9,600,806 | 74 | 75 |
74. A non-transitory computer readable medium encoded with a computer program including instructions to cause a processor to: analyze electronic messages of a first user with respect to one or more features associated with the electronic messages; associate descriptive tags with the electronic messages of the first user based on analysis results; and perform tasks with respect to the electronic messages of the first user, on behalf of the first user, based on the descriptive tags associated with the respective electronic messages, including to organize the electronic messages of the first user into a first organization based on metadata associated with the electronic messages and contents of the electronic messages, and present a first body of information associated with the electronic messages through a graphical user interface in accordance with the first organization.
|
74. A non-transitory computer readable medium encoded with a computer program including instructions to cause a processor to: analyze electronic messages of a first user with respect to one or more features associated with the electronic messages; associate descriptive tags with the electronic messages of the first user based on analysis results; and perform tasks with respect to the electronic messages of the first user, on behalf of the first user, based on the descriptive tags associated with the respective electronic messages, including to organize the electronic messages of the first user into a first organization based on metadata associated with the electronic messages and contents of the electronic messages, and present a first body of information associated with the electronic messages through a graphical user interface in accordance with the first organization. 75. The non-transitory computer readable medium of claim 74 , further including instructions to cause the processor to: organize the electronic messages of the first user into a second organization based on the metadata and contents; and present a second body of information associated with the electronic messages through the graphical user interface in accordance with the second organization.
| 0.5 |
8,140,337 | 4 | 7 |
4. A text mining apparatus which, when one or more of input texts as subjects of text mining is entered, outputs at least one set of a characteristic word and a characteristic measure representing a characteristic of the input text, the apparatus comprising: a statistical information database that records and holds statistical information representing a property of an input text presupposed to be entered; a characteristic word count unit that normalizes a confidence measure provided in the input text, except for a case where the confidence measure is already normalized to a correct solution probability of a character string in the input text with the confidence measure attached, and, using at least one of the normalized value of the confidence measure and a probability value regarding tendency to errors in the input text stored in the statistical information database, calculates an estimation value of the number of occurrence times of a correct solution of a characteristic word from a count value of occurrence times of the characteristic word included in the input text, wherein in calculating the estimation value of the number of occurrence times of a correct solution of the characteristic ward, the characteristic word count unit performs one of: a processing that compares one of the normalized value and a correct solution probability value calculated from the probability value regarding tendency to errors in the input text stored in the statistical information database, with a predetermined truncation threshold value and counts the occurrence times of the characteristic word in the input text, except for a text with a correct solution probability value having a predetermined truncation threshold value or less to adopt the count value as the estimation value of the number of occurrence times of a correct solution of the characteristic word; a processing that, using one of the normalized value and a correct solution probability value calculated from the probability value regarding tendency to errors in the input text stored in the statistical information database, calculates a correct solution probability at each location at which the characteristic word occurs in the input text, and calculates an expectation value of occurrence times of the characteristic word from a calculation result of the correct solution probability to adopt the expectation value as the estimation value of the number of occurrence times of a correct solution of the characteristic word; and a processing that, using one of the normalized value and a correct solution probability value calculated from the probability value regarding tendency to errors in the input text stored in the statistical information database, calculates a correct solution probability at each location at which the characteristic word occurs in the input text, and calculates a most frequent value of the occurrence times of the characteristic word from a calculation result of the correct solution probability to adopt the most frequent value as the estimation value of the number of occurrence times of a correct solution of the characteristic word; a characteristic measure calculation unit that calculates the characteristic measure of the characteristic word, based on the estimation value of the occurrence times of the characteristic word; and an output device that outputs the characteristic measure of the characteristic word calculated by characteristic measure calculation unit.
|
4. A text mining apparatus which, when one or more of input texts as subjects of text mining is entered, outputs at least one set of a characteristic word and a characteristic measure representing a characteristic of the input text, the apparatus comprising: a statistical information database that records and holds statistical information representing a property of an input text presupposed to be entered; a characteristic word count unit that normalizes a confidence measure provided in the input text, except for a case where the confidence measure is already normalized to a correct solution probability of a character string in the input text with the confidence measure attached, and, using at least one of the normalized value of the confidence measure and a probability value regarding tendency to errors in the input text stored in the statistical information database, calculates an estimation value of the number of occurrence times of a correct solution of a characteristic word from a count value of occurrence times of the characteristic word included in the input text, wherein in calculating the estimation value of the number of occurrence times of a correct solution of the characteristic ward, the characteristic word count unit performs one of: a processing that compares one of the normalized value and a correct solution probability value calculated from the probability value regarding tendency to errors in the input text stored in the statistical information database, with a predetermined truncation threshold value and counts the occurrence times of the characteristic word in the input text, except for a text with a correct solution probability value having a predetermined truncation threshold value or less to adopt the count value as the estimation value of the number of occurrence times of a correct solution of the characteristic word; a processing that, using one of the normalized value and a correct solution probability value calculated from the probability value regarding tendency to errors in the input text stored in the statistical information database, calculates a correct solution probability at each location at which the characteristic word occurs in the input text, and calculates an expectation value of occurrence times of the characteristic word from a calculation result of the correct solution probability to adopt the expectation value as the estimation value of the number of occurrence times of a correct solution of the characteristic word; and a processing that, using one of the normalized value and a correct solution probability value calculated from the probability value regarding tendency to errors in the input text stored in the statistical information database, calculates a correct solution probability at each location at which the characteristic word occurs in the input text, and calculates a most frequent value of the occurrence times of the characteristic word from a calculation result of the correct solution probability to adopt the most frequent value as the estimation value of the number of occurrence times of a correct solution of the characteristic word; a characteristic measure calculation unit that calculates the characteristic measure of the characteristic word, based on the estimation value of the occurrence times of the characteristic word; and an output device that outputs the characteristic measure of the characteristic word calculated by characteristic measure calculation unit. 7. The text mining apparatus according to claim 4 , wherein the text input device inputs the input text, a part or all of the input text being described in a word graph or N-best, wherein a recognition result of a specific location in the input text includes a plurality of recognition candidate character strings, each with the confidence measure attached.
| 0.743534 |
9,356,941 | 2 | 3 |
2. The computer-implemented method of claim 1 , wherein classifying the first web page as suspicious using the classification model comprises classifying the first web page as suspicious based on at least one of the following: a content feature of the first web page; a uniform resource locator feature of the first web page; a link between the first web page and the plurality of malicious web pages, wherein the link is represented by an edge within the web-page link graph.
|
2. The computer-implemented method of claim 1 , wherein classifying the first web page as suspicious using the classification model comprises classifying the first web page as suspicious based on at least one of the following: a content feature of the first web page; a uniform resource locator feature of the first web page; a link between the first web page and the plurality of malicious web pages, wherein the link is represented by an edge within the web-page link graph. 3. The computer-implemented method of claim 2 , wherein: classifying the first web page as suspicious using the classification model comprises classifying the first web page as suspicious based on the link between the first web page and the plurality of malicious web pages; classifying the first web page as suspicious based on the link between the first web page and the plurality of malicious web pages comprises: using the web-page link graph to identify a set of direct links between the first web page and the plurality of malicious web pages; using the web-page link graph to identify a set of indirect links between the first web page and the plurality of malicious web pages; calculating a suspicious link score for the first web page based on the set of direct links and the set of indirect links; classifying the first web page as suspicious based at least in part on the suspicious link score.
| 0.5 |
6,167,328 | 4 | 29 |
4. A robot language processing apparatus for describing operation details of a teaching-playback robot and teaching the robot, comprising: display means for graphically displaying a picture and capable of designating a position in the displayed picture with pointing means; storage means for storing said robot program as intermediate code; and language processing means for decoding said intermediate codes and connecting orthogonal space positions of a group of motion commands stored in a time-series manner with straight lines or curved lines, converting an obtained group of lines into coordinates in the displayed picture as viewed from an arbitrary viewpoint, graphically displaying the converted group of lines on said display means, and displaying time-series numbers of points in the group of motion commands in superimposed relation to the group of lines on said display means.
|
4. A robot language processing apparatus for describing operation details of a teaching-playback robot and teaching the robot, comprising: display means for graphically displaying a picture and capable of designating a position in the displayed picture with pointing means; storage means for storing said robot program as intermediate code; and language processing means for decoding said intermediate codes and connecting orthogonal space positions of a group of motion commands stored in a time-series manner with straight lines or curved lines, converting an obtained group of lines into coordinates in the displayed picture as viewed from an arbitrary viewpoint, graphically displaying the converted group of lines on said display means, and displaying time-series numbers of points in the group of motion commands in superimposed relation to the group of lines on said display means. 29. The robot language processing apparatus according to claim 4, wherein said language processing apparatus displays all orientations of a tool at teaching positions of the motion commands which correspond to the time-series numbers of the motion commands on said display means.
| 0.797826 |
8,892,488 | 1 | 3 |
1. A method for document classification, comprising: embedding n-grams from an input text in a latent space; embedding the input text in the latent space based on the embedded n-grams and weighting the n-grams according to a non-linear function q j = 1 Q ∑ k = 1 K sigmoid ( a k · j N + b k ) , using a mixture model on a relative position of the n-grams in the input text, where a k and b k are parameters to be learned, Q = ∑ j = 1 N q j and K specify a number of mixture quantities, sigmoid (•) is a non-linear transfer function, q j is the weight associated with a j th n-gram, j signifies the position of an n-gram in the input text, and N is the position of a final n-gram in the input text; classifying the document along one or more axes using a processor; and adjusting weights used to weight the n-grams based on the output of the classifying step.
|
1. A method for document classification, comprising: embedding n-grams from an input text in a latent space; embedding the input text in the latent space based on the embedded n-grams and weighting the n-grams according to a non-linear function q j = 1 Q ∑ k = 1 K sigmoid ( a k · j N + b k ) , using a mixture model on a relative position of the n-grams in the input text, where a k and b k are parameters to be learned, Q = ∑ j = 1 N q j and K specify a number of mixture quantities, sigmoid (•) is a non-linear transfer function, q j is the weight associated with a j th n-gram, j signifies the position of an n-gram in the input text, and N is the position of a final n-gram in the input text; classifying the document along one or more axes using a processor; and adjusting weights used to weight the n-grams based on the output of the classifying step. 3. The method of claim 1 , wherein a weight of each n-gram is modeled as a function of a relative position of each n-gram in the input text and an embedding representation of each n-gram.
| 0.728198 |
8,523,572 | 1 | 8 |
1. A method for communicating visual images to a handicapped person, said method comprising the steps of: providing at least one device for physically transmitting information to said handicapped person; providing information about said visual images to said handicapped person using said at least one device; and said information providing step comprising delivering a physical signal representative of a key word describing a portion of a visual image to a first part of a body of said handicapped person using said at least one device and further comprising transmitting at least one physical input describing a dynamic element associated with said key word to a second part of the body of said handicapped person; and wherein Dividing the fingers of a hand of said handicapped person into a first group consisting of a pointer finger and a middle finger and into a second group consisting of a ring finger and a pinky and said transmitting step comprises transmitting information about a bad character to one of said fingers of said first group and transmitting information about a good character to one of said fingers of said second group.
|
1. A method for communicating visual images to a handicapped person, said method comprising the steps of: providing at least one device for physically transmitting information to said handicapped person; providing information about said visual images to said handicapped person using said at least one device; and said information providing step comprising delivering a physical signal representative of a key word describing a portion of a visual image to a first part of a body of said handicapped person using said at least one device and further comprising transmitting at least one physical input describing a dynamic element associated with said key word to a second part of the body of said handicapped person; and wherein Dividing the fingers of a hand of said handicapped person into a first group consisting of a pointer finger and a middle finger and into a second group consisting of a ring finger and a pinky and said transmitting step comprises transmitting information about a bad character to one of said fingers of said first group and transmitting information about a good character to one of said fingers of said second group. 8. A method according to claim 1 , further comprising delivering information about a musical background associated with said visual image to said handicapped person.
| 0.851083 |
9,767,801 | 1 | 7 |
1. A computer-implemented method, comprising: receiving, by a dialog cancellation detector of a system that includes the dialog cancellation detector, a cancellation score database, and a dialog engine, a request that is input at a user device in response to a prompt; identifying, by the dialog cancellation detector, an expected input type that is associated with the prompt; identifying, by the dialog cancellation detector and using the cancellation score database, a cancellation score that is predefined for a potential cancellation term included in the request that is input at the user device in response to the prompt, based on the identified expected input type that is associated with the prompt, wherein the cancellation score that is predefined for the potential cancellation term is different for different expected input types that are associated with different prompts; determining, by the dialog cancellation detector, that the identified cancellation score satisfies a first threshold score; in response to determining that the identified cancellation score satisfies the first threshold score, identifying, by the dialog cancellation detector, the potential cancellation term included in the request that is input at the user device in response to the prompt as a cancellation command; and in response to identifying the potential cancellation term included in the request that is input at the user device as a cancellation command, outputting, by the dialog cancellation detector and to the dialog engine, an indication to the user device that the potential cancellation term included in the request that is input at the user device is a cancellation command rather than a refinement of a previous input.
|
1. A computer-implemented method, comprising: receiving, by a dialog cancellation detector of a system that includes the dialog cancellation detector, a cancellation score database, and a dialog engine, a request that is input at a user device in response to a prompt; identifying, by the dialog cancellation detector, an expected input type that is associated with the prompt; identifying, by the dialog cancellation detector and using the cancellation score database, a cancellation score that is predefined for a potential cancellation term included in the request that is input at the user device in response to the prompt, based on the identified expected input type that is associated with the prompt, wherein the cancellation score that is predefined for the potential cancellation term is different for different expected input types that are associated with different prompts; determining, by the dialog cancellation detector, that the identified cancellation score satisfies a first threshold score; in response to determining that the identified cancellation score satisfies the first threshold score, identifying, by the dialog cancellation detector, the potential cancellation term included in the request that is input at the user device in response to the prompt as a cancellation command; and in response to identifying the potential cancellation term included in the request that is input at the user device as a cancellation command, outputting, by the dialog cancellation detector and to the dialog engine, an indication to the user device that the potential cancellation term included in the request that is input at the user device is a cancellation command rather than a refinement of a previous input. 7. The method of claim 1 , wherein the cancellation score is stored in a data triple that comprises data identifying the expected input type, data identifying the potential cancellation term, and data identifying the cancellation score.
| 0.510373 |
8,274,520 | 28 | 31 |
28. The computer-readable storage medium of claim 25 , wherein the resources are identifiers for images, wherein the identifiers include: addresses of a location in a memory or on a disk where the images are stored; MD5 tags, a size value, and/or an age value.
|
28. The computer-readable storage medium of claim 25 , wherein the resources are identifiers for images, wherein the identifiers include: addresses of a location in a memory or on a disk where the images are stored; MD5 tags, a size value, and/or an age value. 31. The computer-readable storage medium of claim 28 , wherein returning the subcache in response to the filtering query involves indicating a maximum size allowed for the subcache and indicting a maximum age and a purgeable age for identifiers for images in the subcache.
| 0.580247 |
9,563,695 | 10 | 11 |
10. The system of claim 1 , wherein the instructions when executed by the processor further cause the processor to: generate output content items for presentation by a client computer based on the profile topics.
|
10. The system of claim 1 , wherein the instructions when executed by the processor further cause the processor to: generate output content items for presentation by a client computer based on the profile topics. 11. The system of claim 10 , wherein the output content items are divided into a plurality of sets including a first set of output content items and a second set of output content items, the second set of output content items displayed, by the client computer, after the first set of output content items.
| 0.5 |
8,346,331 | 2 | 3 |
2. The method of claim 1 , further comprising: positioning a sensing device proximate to a scalp of the subject; providing near-infrared optical energy directed toward the scalp; and in response to providing near-infrared optical energy, receiving reflected near-infrared optical energy indicative of the oxygenation level in the blood supply of the cortex of the brain of the subject.
|
2. The method of claim 1 , further comprising: positioning a sensing device proximate to a scalp of the subject; providing near-infrared optical energy directed toward the scalp; and in response to providing near-infrared optical energy, receiving reflected near-infrared optical energy indicative of the oxygenation level in the blood supply of the cortex of the brain of the subject. 3. The method of claim 2 , wherein the near-infrared optical energy is provided at a relatively constant intensity.
| 0.5 |
9,043,195 | 30 | 35 |
30. The system of claim 1 , wherein the spatial context comprises a plurality of distinct display piece collections, each display piece collection comprising a plurality of individual display pieces such that the entire plurality of sets of display pieces are contained within the plurality of display piece collections, and wherein the predefined rules comprise rules for acquisition of the display pieces from the distinct display piece collections.
|
30. The system of claim 1 , wherein the spatial context comprises a plurality of distinct display piece collections, each display piece collection comprising a plurality of individual display pieces such that the entire plurality of sets of display pieces are contained within the plurality of display piece collections, and wherein the predefined rules comprise rules for acquisition of the display pieces from the distinct display piece collections. 35. The system of claim 30 , wherein individual display pieces of each given set of display pieces are all grouped together in one of the plurality of display piece collections.
| 0.78777 |
7,949,647 | 15 | 16 |
15. The system of claim 10 , wherein the search assistance list is configured to be transmitted to a computer of the user to be displayed to the user in a drop down list under a search query entry box.
|
15. The system of claim 10 , wherein the search assistance list is configured to be transmitted to a computer of the user to be displayed to the user in a drop down list under a search query entry box. 16. The system of claim 15 , wherein the at least a partial search query is input by the user in the search query entry box; wherein the user is enabled to modify the at least a partial search query in the search query entry box; and wherein the suggested search manager is configured to receive the modified at least a partial search query.
| 0.5 |
8,903,858 | 1 | 5 |
1. A computer implemented method for composing a target keyphrase to be searched, wherein the target keyphrase comprises a plurality of keywords, the computer implemented method comprising: receiving, in a search bar, one or more textual characters following at least one previously existing keyword; providing a plurality of keyword results associated only with the one or more textual characters irrespective of the at least one previously existing keyword; receiving a selection input for selecting a keyword result from amongst the plurality of keyword results associated only with the one or more textual characters irrespective of the at least one previously existing keyword; and word by word composing the target keyphrase by appending the selected keyword result associated only with the one or more textual characters irrespective of the at least one previously existing keyword to the at least one previously existing keyword in the search bar without launching search.
|
1. A computer implemented method for composing a target keyphrase to be searched, wherein the target keyphrase comprises a plurality of keywords, the computer implemented method comprising: receiving, in a search bar, one or more textual characters following at least one previously existing keyword; providing a plurality of keyword results associated only with the one or more textual characters irrespective of the at least one previously existing keyword; receiving a selection input for selecting a keyword result from amongst the plurality of keyword results associated only with the one or more textual characters irrespective of the at least one previously existing keyword; and word by word composing the target keyphrase by appending the selected keyword result associated only with the one or more textual characters irrespective of the at least one previously existing keyword to the at least one previously existing keyword in the search bar without launching search. 5. The computer implemented method as claimed in claim 1 , wherein the providing is based on the at least one previously existing keyword.
| 0.655 |
8,204,182 | 1 | 21 |
1. A method comprising acts of: establishing a real-time communication session between a text exchange client and a speech enabled application; identifying a translation table that includes a plurality of entries, each entry including a text exchange item and a corresponding conversational translation item; receiving a text exchange message that was entered into a text exchange client; detecting at least one text exchange item in the text exchange message, which corresponds to an entry included in the translation table; in the text exchange message, substituting a corresponding conversational translation item for each detected text exchange item; sending the substitute message to a text input interface of a voice server to be processed; receiving, from the speech enabled application, an automatic output message responsive to the text entered into the text exchange client; and sending output text related to the automatic output message to the text exchange client, wherein the substituting act occurs in a manner transparent to the text exchange client and to the speech enabled application.
|
1. A method comprising acts of: establishing a real-time communication session between a text exchange client and a speech enabled application; identifying a translation table that includes a plurality of entries, each entry including a text exchange item and a corresponding conversational translation item; receiving a text exchange message that was entered into a text exchange client; detecting at least one text exchange item in the text exchange message, which corresponds to an entry included in the translation table; in the text exchange message, substituting a corresponding conversational translation item for each detected text exchange item; sending the substitute message to a text input interface of a voice server to be processed; receiving, from the speech enabled application, an automatic output message responsive to the text entered into the text exchange client; and sending output text related to the automatic output message to the text exchange client, wherein the substituting act occurs in a manner transparent to the text exchange client and to the speech enabled application. 21. The method of claim 1 , further comprising acts of: receiving the substitute message at the voice server; and at the voice server, matching the substitute message against a speech grammar.
| 0.73842 |
7,792,780 | 13 | 18 |
13. An engine for use by an electronic user device, comprising: an engine update module configured to: receive engine logic from a service, wherein the engine logic is expressed in a description language; and update an engine parse tree based on the engine logic; and an engine module configured to process events using the parse tree.
|
13. An engine for use by an electronic user device, comprising: an engine update module configured to: receive engine logic from a service, wherein the engine logic is expressed in a description language; and update an engine parse tree based on the engine logic; and an engine module configured to process events using the parse tree. 18. The engine of claim 13 , wherein the engine module is operable for controlling the user device's processing of incoming electronic messages.
| 0.529412 |
4,712,242 | 10 | 11 |
10. A method for recognizing speech comprising: receiving an analog input speech signal; processing said analog input speech signal to provide a sequence of feature vectors from said input speech signal at predetermined speech frame intervals; associating at least one mask vector with each sequence of a plurality of reference vectors which have been organized in sequence with each of said reference vector sequences corresponding to a word which can be recognized, with said mask vector being indicative of the significance of portions of the reference vector sequence with which it is associated in establishing the identity of the word to which the respective reference vector sequence corresponds; comparing each of said feature vectors with each of said plurality of reference vectors in relation to the status of the respective mask vector associated with each said reference vector sequence; determining a distance measure with respect to each of said reference vectors for each successive feature vector in said sequence of feature vectors in response to the comparison therebetween wherein portions of each said reference vector sequence indicated by the associated at least one mask vector corresponding thereto to be insignificant are ignored such that said distance measure is based upon significant portions of the reference vector sequence; and recognizing words in accordance with the distance measures between each of said reference vector sequences and successively received feature vectors corresponding to respective speech frames.
|
10. A method for recognizing speech comprising: receiving an analog input speech signal; processing said analog input speech signal to provide a sequence of feature vectors from said input speech signal at predetermined speech frame intervals; associating at least one mask vector with each sequence of a plurality of reference vectors which have been organized in sequence with each of said reference vector sequences corresponding to a word which can be recognized, with said mask vector being indicative of the significance of portions of the reference vector sequence with which it is associated in establishing the identity of the word to which the respective reference vector sequence corresponds; comparing each of said feature vectors with each of said plurality of reference vectors in relation to the status of the respective mask vector associated with each said reference vector sequence; determining a distance measure with respect to each of said reference vectors for each successive feature vector in said sequence of feature vectors in response to the comparison therebetween wherein portions of each said reference vector sequence indicated by the associated at least one mask vector corresponding thereto to be insignificant are ignored such that said distance measure is based upon significant portions of the reference vector sequence; and recognizing words in accordance with the distance measures between each of said reference vector sequences and successively received feature vectors corresponding to respective speech frames. 11. A method as set forth in claim 10, wherein said feature vectors, said reference vectors and said mask vectors are binary, and wherein said distance measure-determining step comprises a Hamming distance measurement between said feature vector and a corresponding reference vector as modified in accordance with a respective mask vector associated therewith.
| 0.5 |
9,900,498 | 2 | 11 |
2. The glass-type mobile terminal of claim 1 , wherein the controller is further configured to: prompt the user to share object interest information about the object captured by the camera, when image capturing is terminated, the object has moved out of an angle view of the camera, or a current position of the glass-type terminal is changed, and wherein the object interest information includes the captured image, the emotion information and the linguistic expression information.
|
2. The glass-type mobile terminal of claim 1 , wherein the controller is further configured to: prompt the user to share object interest information about the object captured by the camera, when image capturing is terminated, the object has moved out of an angle view of the camera, or a current position of the glass-type terminal is changed, and wherein the object interest information includes the captured image, the emotion information and the linguistic expression information. 11. The glass-type mobile terminal of claim 2 , wherein the controller is further configured to: transmit the object interest information using one or more of a Social Networking Service (SNS), a Short Message Service (SMS), a Long Message Service (LMS) and a Multi Message Service (MMS) based on a positive response to the prompt to the user to share the object interest information.
| 0.5 |
8,930,337 | 32 | 36 |
32. A computer-readable storage device storing a computer program for presenting visual feedback in an interface, the computer program including instructions for causing a computer to carry out operations including: receiving one or more mapped relationships between a given output and one or more inputs represented by input variables, at least one of the mapped relationships including a transformational expression executable on a data processing system, the transformational expression defining an output of a mapped relationship based on at least one input variable mapped to an element of an input dataset; receiving identification of elements of an output dataset mapped to outputs of respective mapped relationships; generating output data from the data processing system according to the transformational expression based on input data from the input dataset associated with the element of the input dataset mapped to the input variable, including applying the transformational expressions to input values in respective fields of input records of the input dataset and storing output values in respective fields of output records of the output dataset, including executing a dataflow graph including nodes representing data processing components, links representing data flows between the data processing components, a node representing the input dataset providing a data flow of the input records, and a node representing the output dataset receiving a data flow of the output records; determining validation information in response to the generated output data based on validation criteria defining one or more characteristics of valid values associated with one or more of the identified elements of the output dataset; and presenting in the interface visual feedback based on the determined validation information.
|
32. A computer-readable storage device storing a computer program for presenting visual feedback in an interface, the computer program including instructions for causing a computer to carry out operations including: receiving one or more mapped relationships between a given output and one or more inputs represented by input variables, at least one of the mapped relationships including a transformational expression executable on a data processing system, the transformational expression defining an output of a mapped relationship based on at least one input variable mapped to an element of an input dataset; receiving identification of elements of an output dataset mapped to outputs of respective mapped relationships; generating output data from the data processing system according to the transformational expression based on input data from the input dataset associated with the element of the input dataset mapped to the input variable, including applying the transformational expressions to input values in respective fields of input records of the input dataset and storing output values in respective fields of output records of the output dataset, including executing a dataflow graph including nodes representing data processing components, links representing data flows between the data processing components, a node representing the input dataset providing a data flow of the input records, and a node representing the output dataset receiving a data flow of the output records; determining validation information in response to the generated output data based on validation criteria defining one or more characteristics of valid values associated with one or more of the identified elements of the output dataset; and presenting in the interface visual feedback based on the determined validation information. 36. The computer readable storage device of claim 32 , further including presenting in the interface a value representing the generated output data.
| 0.851107 |
9,002,908 | 9 | 12 |
9. A system for routing and managing stored documents based on document content, comprising: at least one processor; a data repository; at least one memory coupled to the at least one processor and comprising computer readable program code embodied in the at least one memory that when executed by the at least one processor causes the at least one processor to perform operations comprising: determining a classification for a document based on the document content; determining a storage location for the document based on the document classification and storage rules of a storage provider policy; determining whether the document is stored in the determined storage location; when the document is stored in the determined storage location, updating the document in the determined storage location; when the document is not stored in the determined storage location, determining whether the document is stored in another storage location; when the document is stored in another storage location, adding the document to the determined storage location and deleting the document from the other storage location; when the document is not stored in the determined storage location or the other storage location, adding the document to the determined storage location; reclassifying the document with a new document classification responsive to an update of the document; determining a new storage provider for the document responsive to a comparison of the new document classification and the storage provider policy; adding the document to a new storage location of the new storage provider; deleting the document from the determined storage location; and updating an entry of the document in a storage map responsive to the adding the document to the new storage location.
|
9. A system for routing and managing stored documents based on document content, comprising: at least one processor; a data repository; at least one memory coupled to the at least one processor and comprising computer readable program code embodied in the at least one memory that when executed by the at least one processor causes the at least one processor to perform operations comprising: determining a classification for a document based on the document content; determining a storage location for the document based on the document classification and storage rules of a storage provider policy; determining whether the document is stored in the determined storage location; when the document is stored in the determined storage location, updating the document in the determined storage location; when the document is not stored in the determined storage location, determining whether the document is stored in another storage location; when the document is stored in another storage location, adding the document to the determined storage location and deleting the document from the other storage location; when the document is not stored in the determined storage location or the other storage location, adding the document to the determined storage location; reclassifying the document with a new document classification responsive to an update of the document; determining a new storage provider for the document responsive to a comparison of the new document classification and the storage provider policy; adding the document to a new storage location of the new storage provider; deleting the document from the determined storage location; and updating an entry of the document in a storage map responsive to the adding the document to the new storage location. 12. The system according to claim 9 , wherein the document is added to the determined storage location and the document is updated in the determined storage location using a storage gateway performing on the document one or more of caching, encrypting, de-duplicating, and compressing.
| 0.733146 |
8,103,737 | 3 | 4 |
3. A method for assisting a user during web navigation, as per claim 1 , wherein said at least one remote terminal is operatively connected to a browser.
|
3. A method for assisting a user during web navigation, as per claim 1 , wherein said at least one remote terminal is operatively connected to a browser. 4. A method for assisting a user during web navigation, as per claim 3 , wherein said browser provides for visually previewing said associated hyperlink in a web page by rendering said textual abstract.
| 0.5 |
7,580,429 | 12 | 13 |
12. A data compressor which compresses a digital stream of data, the compressor comprising: an encoder which matches strings of digital data with fixed size code words; a transmitter coupled to the encoder which sends the fixed size code word; a memory coupled to the encoder to store a dictionary with nodes representing strings of digital data, the fixed size code word and an index relating to the frequency that the fixed size code words are sent, wherein the fixed size code words are deleted from the dictionary as additional fixed sized code words are added; and a processor which, when a string is matched, creates a copy of the node representing the string, creates a new node to store the copy of the node, and deletes the node representing the string from the dictionary.
|
12. A data compressor which compresses a digital stream of data, the compressor comprising: an encoder which matches strings of digital data with fixed size code words; a transmitter coupled to the encoder which sends the fixed size code word; a memory coupled to the encoder to store a dictionary with nodes representing strings of digital data, the fixed size code word and an index relating to the frequency that the fixed size code words are sent, wherein the fixed size code words are deleted from the dictionary as additional fixed sized code words are added; and a processor which, when a string is matched, creates a copy of the node representing the string, creates a new node to store the copy of the node, and deletes the node representing the string from the dictionary. 13. The data compressor of claim 12 wherein the memory includes a linked list with entries to record nodes in the order they are used to represent subsequent strings of data; and wherein the entry relating to a node is updated by moving the entry to the start of the linked list when the node is used.
| 0.5 |
7,644,069 | 10 | 11 |
10. The file system search engine according to claim 9 , wherein, in response to a click on a file item performed by a user, said energy tree updating module increases the energy score of a node corresponding to said file item recorded in said file system energy tree index, and at least partially transfers the increased energy score of said node to other related nodes along paths on said file system energy tree.
|
10. The file system search engine according to claim 9 , wherein, in response to a click on a file item performed by a user, said energy tree updating module increases the energy score of a node corresponding to said file item recorded in said file system energy tree index, and at least partially transfers the increased energy score of said node to other related nodes along paths on said file system energy tree. 11. The file system search engine according to claim 10 , wherein said energy tree updating module calculates the energy scores of nodes which need to be updated in said file system energy tree index with the following expression:
E ′( n )= E ( n )+ p dist(n,d) wherein, d represents the node corresponding to said clicked file item; n represents any node within a predetermined transfer depth; E(n) represents the original energy score of the node n; E′(n) represents the updated energy score of the node n; p is the energy transfer rate with a value ranging from 0 to 1; and dist(n, d) represents a distance from the node n to the node d along a tree path(s) on the structure of said file system energy tree, wherein dist(n, d) is smaller than or equal to the predetermined transfer depth.
| 0.5 |
9,098,584 | 2 | 5 |
2. A method of controlling access to visual query results, the method comprising the steps of: obtaining, by a search entity, from a requester, a visual query comprising at least a first facial image; identifying, by the search entity, via facial recognition on the at least first facial image, general web content, from web pages not under control of the search entity, the general web content being associated with an individual subject of the first facial image; obtaining, by the search entity, from the individual subject of the at least first facial image, via a mechanism for expressing privacy preferences, at least one privacy preference comprising a visual query preference of the individual subject of the at least first facial image, wherein the mechanism provides for an individual subject of the visual query to express the privacy preferences to suppress the requester from receiving a visual query result that is associated with the individual, for the requester not being logged into an online account of the unified search entity, or the requester not being in a social group of the individual; and for the visual query preference of the individual subject permitting the requester to receive the visual query result, communicating at least a portion of the general web content, from the search entity, to the requester; and for the visual query preference of the individual subject suppressing the requester from receiving the visual query result, providing the requester with one of an indication that the individual exists but does not permit the visual query of his or her facial image by a user not in the social group of the individual, and an option to invite the individual to a sharing group.
|
2. A method of controlling access to visual query results, the method comprising the steps of: obtaining, by a search entity, from a requester, a visual query comprising at least a first facial image; identifying, by the search entity, via facial recognition on the at least first facial image, general web content, from web pages not under control of the search entity, the general web content being associated with an individual subject of the first facial image; obtaining, by the search entity, from the individual subject of the at least first facial image, via a mechanism for expressing privacy preferences, at least one privacy preference comprising a visual query preference of the individual subject of the at least first facial image, wherein the mechanism provides for an individual subject of the visual query to express the privacy preferences to suppress the requester from receiving a visual query result that is associated with the individual, for the requester not being logged into an online account of the unified search entity, or the requester not being in a social group of the individual; and for the visual query preference of the individual subject permitting the requester to receive the visual query result, communicating at least a portion of the general web content, from the search entity, to the requester; and for the visual query preference of the individual subject suppressing the requester from receiving the visual query result, providing the requester with one of an indication that the individual exists but does not permit the visual query of his or her facial image by a user not in the social group of the individual, and an option to invite the individual to a sharing group. 5. The method of claim 2 , wherein the identifying comprises: performing the facial recognition to compare the at least first facial image to an index database of general web content, obtained by a crawler, the index database of general web content including facial image data for comparison with the at least first facial image during the facial recognition.
| 0.57565 |
9,116,885 | 3 | 5 |
3. The computer-implemented method of claim 2 , wherein the determining of the coefficient indicative of the gender of the recipient includes retrieving, based on information associated with the recipient, a base coefficient.
|
3. The computer-implemented method of claim 2 , wherein the determining of the coefficient indicative of the gender of the recipient includes retrieving, based on information associated with the recipient, a base coefficient. 5. The computer-implemented method of claim 3 , wherein the determining of the coefficient indicative of the gender of the recipient further includes retrieving, from a network, the information associated with the recipient, wherein the information retrieved from the network includes at least one of search engine results and a social network profile.
| 0.5 |
8,280,959 | 13 | 14 |
13. The method of claim 9 , wherein the request for information comprises one or more parameters for selecting the requested information, and wherein the requested information received from the social networking system is selected based on the one or more parameters and on the one or more actions performed by one or more other users with which the user has established a connection in the social networking system.
|
13. The method of claim 9 , wherein the request for information comprises one or more parameters for selecting the requested information, and wherein the requested information received from the social networking system is selected based on the one or more parameters and on the one or more actions performed by one or more other users with which the user has established a connection in the social networking system. 14. The method of claim 13 , wherein the one or more parameters comprise at least one domain.
| 0.5 |
8,990,889 | 2 | 4 |
2. A system, comprising: at least one processor, and at least one memory storing instructions that, when executed by the at least one processor, cause the system to: receive, by a credentialing and access control system, identity and authentication information associated with at least one internal identity from at least one electronic identity provider that is external to the credentialing and access control system, based on permission granted by at least one user; generate at least a first physical resource token for permitting physical access to a first physical resource; associate the first physical resource token with the at least one internal identity; receive, by the credentialing and access control system and from the at least one user, identity and authentication information derived from the external identity provider that is associated with the at least one internal identity; receive, by the credentialing and access control system, an indication of interaction of the first physical resource token with the first physical resource; and grant access to the first physical resource.
|
2. A system, comprising: at least one processor, and at least one memory storing instructions that, when executed by the at least one processor, cause the system to: receive, by a credentialing and access control system, identity and authentication information associated with at least one internal identity from at least one electronic identity provider that is external to the credentialing and access control system, based on permission granted by at least one user; generate at least a first physical resource token for permitting physical access to a first physical resource; associate the first physical resource token with the at least one internal identity; receive, by the credentialing and access control system and from the at least one user, identity and authentication information derived from the external identity provider that is associated with the at least one internal identity; receive, by the credentialing and access control system, an indication of interaction of the first physical resource token with the first physical resource; and grant access to the first physical resource. 4. The system of claim 2 wherein the received identity and authentication information is defined through an outside party electronic identity provider, wherein the credentialing and access control system receives the identity and authentication information associated with the at least one internal user based on permission granted by the at least one internal user, wherein the credentialing and access control system receives at least one condition from a first physical resource access owner for the at least one user to access it first physical resource, wherein the credentialing and access control system receives an indication that the at least one user has met the at least one condition.
| 0.5 |
7,945,904 | 14 | 16 |
14. The method of claim 13 , wherein the content is embedded in an expression or a statement block, wherein the embedded expression or statement block within the at least one XML literal is non-static within a static structure of the at least one XML literal.
|
14. The method of claim 13 , wherein the content is embedded in an expression or a statement block, wherein the embedded expression or statement block within the at least one XML literal is non-static within a static structure of the at least one XML literal. 16. The method of claim 14 , further comprising escaping a domain-specific identifier to create a valid identifier for the computer language.
| 0.716867 |
9,483,447 | 7 | 8 |
7. The device according to claim 6 , wherein the second processing unit comprises: a first processing sub-unit, configured to collect a variety of texts; and a second processing sub-unit, configured to carry out the word segmentation processing for each of the texts, and extract all unrepeated words obtained by the word segmentation processing as the characteristic words to form a characteristic word list; or, remove high-frequency words, stop words and low-frequency words from all the unrepeated words obtained by the word segmentation processing, and consider remaining words as the characteristic words to form a characteristic word list; for each characteristic word y and each hyperlink word x, calculating the co-occurrence frequency P(x/y) using a formula of: P(x/y)=a quantity of xy co-occurrences/a quantity of y occurrences; wherein, the quantity of xy co-occurrences represents the number of texts appearing the characteristic words y and the hyperlink words x in the same time; the quantity of y occurrences represents the number of texts appearing the characteristic words y; or, for each characteristic word y and each hyperlink word x, calculating the co-occurrence frequency P(x/y) using a formula of: P(x/y)=H(x,y)/l(x,y)=H(x,y)/(H(x)+H(y)−H(x,y)), wherein, H represents information entropy; I represents mutual information.
|
7. The device according to claim 6 , wherein the second processing unit comprises: a first processing sub-unit, configured to collect a variety of texts; and a second processing sub-unit, configured to carry out the word segmentation processing for each of the texts, and extract all unrepeated words obtained by the word segmentation processing as the characteristic words to form a characteristic word list; or, remove high-frequency words, stop words and low-frequency words from all the unrepeated words obtained by the word segmentation processing, and consider remaining words as the characteristic words to form a characteristic word list; for each characteristic word y and each hyperlink word x, calculating the co-occurrence frequency P(x/y) using a formula of: P(x/y)=a quantity of xy co-occurrences/a quantity of y occurrences; wherein, the quantity of xy co-occurrences represents the number of texts appearing the characteristic words y and the hyperlink words x in the same time; the quantity of y occurrences represents the number of texts appearing the characteristic words y; or, for each characteristic word y and each hyperlink word x, calculating the co-occurrence frequency P(x/y) using a formula of: P(x/y)=H(x,y)/l(x,y)=H(x,y)/(H(x)+H(y)−H(x,y)), wherein, H represents information entropy; I represents mutual information. 8. The device according to claim 7 , wherein the adding module comprises: a third processing unit, configured to carry out the word segmentation processing to the text X and obtain a segmentation result; and a fourth processing unit, configured to extract the hyperlink words occurred in the hyperlink word list and the characteristic words occurred in the characteristic word list from the segmentation result, compute a weight of each of the hyperlink words that are occurred in the hyperlink word list, and compute a weight of each of the characteristic words that are occurred in the characteristic word list, and determine the final weight of each of the hyperlink word according to each co-occurrence frequency and the weights of the hyperlink words; and a fifth processing unit, configured to descendingly sort the hyperlink words occurred in the hyperlink word list according to the final weights of the hyperlink words, and obtain K numbers of hyperlink words that are arranged in first, and add hyperlinks to the K numbers of hyperlink words, wherein, K is a positive integer.
| 0.5 |
8,285,049 | 1 | 3 |
1. A machine-implemented method for correcting a recognized handwritten mathematical expression, the machine-implemented method comprising: receiving input indicating a region having at least one atom of a plurality of atoms corresponding to a misrecognized production of a recognized mathematical expression; providing a menu of possible recognition results based on the at least one atom in the region; receiving a selection of one of the possible recognition results as a correction hint; shrinking the region and linearly transforming the at least one atom corresponding to the correction hint such that the at least one atom is horizontally or vertically separate from other atoms of the plurality of atoms; and re-recognizing the plurality of atoms, including the at least one linearly transformed atom, to produce a correctly recognized mathematical expression.
|
1. A machine-implemented method for correcting a recognized handwritten mathematical expression, the machine-implemented method comprising: receiving input indicating a region having at least one atom of a plurality of atoms corresponding to a misrecognized production of a recognized mathematical expression; providing a menu of possible recognition results based on the at least one atom in the region; receiving a selection of one of the possible recognition results as a correction hint; shrinking the region and linearly transforming the at least one atom corresponding to the correction hint such that the at least one atom is horizontally or vertically separate from other atoms of the plurality of atoms; and re-recognizing the plurality of atoms, including the at least one linearly transformed atom, to produce a correctly recognized mathematical expression. 3. The machine-implemented method of claim 1 , wherein: the region is indicated by a bounding box, and the shrinking of the region produces a minimal bounding box.
| 0.93954 |
8,126,742 | 25 | 29 |
25. A tangible computer readable media comprising: computer program code stored on the tangible computer readable media, the computer program code executable on a computer processor, wherein the computer program code includes: an instruction to retrieve insurance claim data; an instruction to segment the insurance claim data based upon a plurality of predetermined insurance business rules; an instruction to associate a segment of the insurance claim data with an insurance claim pattern type, wherein the insurance claim pattern type includes a first insurance claim pattern and a second insurance claim pattern; an instruction to assign first discrete information to the first insurance claim pattern for pattern analysis based upon the segment of the insurance claim data associated with the insurance claim pattern type; an instruction to assign second discrete information to the second insurance claim pattern for pattern analysis based upon the segment of the insurance claim data associated with the insurance claim pattern type; an instruction to generate a first insurance claim pattern outcome for the first insurance claim pattern based upon a logic based analysis of a first combination of the first discrete information; an instruction to generate a second insurance claim pattern outcome for the first insurance claim pattern based upon a logic based analysis of a second combination of the first discrete information; an instruction to generate a first insurance claim pattern outcome for the second insurance claim pattern based upon a logic based analysis of a first combination of the second discrete information; an instruction to store the first insurance claim pattern outcome of the first insurance claim pattern, the second insurance claim pattern outcome of the first insurance claim pattern, and the first insurance claim pattern outcome of the second insurance claim pattern in the memory as stored insurance claim pattern outcomes associated with the insurance claim pattern type; an instruction to pattern analyze a first combination of the stored insurance claim pattern outcomes to generate a first insurance claim pattern result for the insurance claim pattern type; an instruction to pattern analyze a second combination of the stored insurance claim pattern outcomes to generate a second insurance claim pattern result for the pattern type; an instruction to store the first pattern result and the second pattern result in association with the pattern type; and an instruction to assign at least a portion of the insurance claim to at least one target organizational entity based upon a combination of the first insurance claim pattern result and the second insurance claim pattern result.
|
25. A tangible computer readable media comprising: computer program code stored on the tangible computer readable media, the computer program code executable on a computer processor, wherein the computer program code includes: an instruction to retrieve insurance claim data; an instruction to segment the insurance claim data based upon a plurality of predetermined insurance business rules; an instruction to associate a segment of the insurance claim data with an insurance claim pattern type, wherein the insurance claim pattern type includes a first insurance claim pattern and a second insurance claim pattern; an instruction to assign first discrete information to the first insurance claim pattern for pattern analysis based upon the segment of the insurance claim data associated with the insurance claim pattern type; an instruction to assign second discrete information to the second insurance claim pattern for pattern analysis based upon the segment of the insurance claim data associated with the insurance claim pattern type; an instruction to generate a first insurance claim pattern outcome for the first insurance claim pattern based upon a logic based analysis of a first combination of the first discrete information; an instruction to generate a second insurance claim pattern outcome for the first insurance claim pattern based upon a logic based analysis of a second combination of the first discrete information; an instruction to generate a first insurance claim pattern outcome for the second insurance claim pattern based upon a logic based analysis of a first combination of the second discrete information; an instruction to store the first insurance claim pattern outcome of the first insurance claim pattern, the second insurance claim pattern outcome of the first insurance claim pattern, and the first insurance claim pattern outcome of the second insurance claim pattern in the memory as stored insurance claim pattern outcomes associated with the insurance claim pattern type; an instruction to pattern analyze a first combination of the stored insurance claim pattern outcomes to generate a first insurance claim pattern result for the insurance claim pattern type; an instruction to pattern analyze a second combination of the stored insurance claim pattern outcomes to generate a second insurance claim pattern result for the pattern type; an instruction to store the first pattern result and the second pattern result in association with the pattern type; and an instruction to assign at least a portion of the insurance claim to at least one target organizational entity based upon a combination of the first insurance claim pattern result and the second insurance claim pattern result. 29. The tangible computer readable media of claim 25 , further comprising: an instruction to detect a failure to assign the at least one target exception organizational entity to the insurance claim; an instruction to identify, in response to detection of the failure to assign the at least one target exception organizational entity to the insurance claim, identifying a default target organizational entity to the insurance claim; and an instruction to assign the insurance claim to the default target organizational entity based upon identification of the default target organizational entity.
| 0.595109 |
8,977,618 | 9 | 13 |
9. A processor-executed job matching server-implemented method, comprising: receiving a job-matching request from a job seeker, said job-matching request including profile parameters of the job seeker; generating job-matching key terms based on the job seeker's profile parameters; if available, retrieving from a database via the server a previous search history and previous job-matching key terms for the job seeker; forming a query via the server on a list of previously stored jobs based on the generated key terms and the previous key terms; adding to the query the previous search history; generating via the server from the query a list of previously stored jobs related to the generated key terms, previous key terms, and the job seeker's previous search history; receiving a selection to view a job from the list of previously stored jobs; storing the selection in the user's previous search history; tracking correlation data between the job seeker and other job seekers by comparing the job seeker's job seeking activities and other job seekers' job seeking activities; calculating an affinity metric indicating similarities between the first job and a second job based on the tracked correlation data, said first job and said second job having been both interacted with by at least one common job seeker; determining the second job as an alternative job recommendation to the first job seeker when the calculated affinity metric indicates a satisfactory level of similarity between the first job and the second job; and providing an output displaying the first job and the second job as the alternative job in response to the received job matching request.
|
9. A processor-executed job matching server-implemented method, comprising: receiving a job-matching request from a job seeker, said job-matching request including profile parameters of the job seeker; generating job-matching key terms based on the job seeker's profile parameters; if available, retrieving from a database via the server a previous search history and previous job-matching key terms for the job seeker; forming a query via the server on a list of previously stored jobs based on the generated key terms and the previous key terms; adding to the query the previous search history; generating via the server from the query a list of previously stored jobs related to the generated key terms, previous key terms, and the job seeker's previous search history; receiving a selection to view a job from the list of previously stored jobs; storing the selection in the user's previous search history; tracking correlation data between the job seeker and other job seekers by comparing the job seeker's job seeking activities and other job seekers' job seeking activities; calculating an affinity metric indicating similarities between the first job and a second job based on the tracked correlation data, said first job and said second job having been both interacted with by at least one common job seeker; determining the second job as an alternative job recommendation to the first job seeker when the calculated affinity metric indicates a satisfactory level of similarity between the first job and the second job; and providing an output displaying the first job and the second job as the alternative job in response to the received job matching request. 13. The method of claim 9 , wherein the affinity metric is derived from J12 divided by J1, wherein J12 is the number of applicants who applied to the first and second job, and wherein J1 is the number of applicants who only applied to the first job.
| 0.610938 |
9,973,381 | 2 | 3 |
2. The method of claim 1 , wherein the set of nodes includes at least one of a leaf node that is a standalone node with no child node and a nugget node that includes one or more child nodes, each child node being a nugget or leaf node.
|
2. The method of claim 1 , wherein the set of nodes includes at least one of a leaf node that is a standalone node with no child node and a nugget node that includes one or more child nodes, each child node being a nugget or leaf node. 3. The method of claim 2 , wherein the child node in the nodal structure represents a next operation following an operation of the nugget node, wherein the next operation is conditioned on a determined value of the nugget node when executed in the linked list.
| 0.5 |
9,384,287 | 1 | 3 |
1. A method comprising: receiving, by a processing device, data representing a plurality of corpora, each of the plurality of corpora including a set of documents; receiving, by a processing device, data representing terms that appear in the corpora; for each one of the terms, determining, by a processing device, a plurality of inverse document frequency values each associated with a respective one of the plurality of corpora; receiving, by a processing device, data representing a subset of the terms that also appear in a document; for each term in the subset of the terms, determining, by a processing device, a term frequency for the term in the document; for each term in the subset of the terms, determining, by a processing device, an augmented term frequency-inverse document frequency value based on: (i) the term frequency, and (ii) the plurality of inverse document frequency values that were determined for the term in the subset of the terms; and for each term in the subset of the terms that also appear in the document, selecting, by a processing device, as a combined inverse document frequency value, a minimum value of a plurality of normalized inverse document frequency values determined for the term in the subset of the terms.
|
1. A method comprising: receiving, by a processing device, data representing a plurality of corpora, each of the plurality of corpora including a set of documents; receiving, by a processing device, data representing terms that appear in the corpora; for each one of the terms, determining, by a processing device, a plurality of inverse document frequency values each associated with a respective one of the plurality of corpora; receiving, by a processing device, data representing a subset of the terms that also appear in a document; for each term in the subset of the terms, determining, by a processing device, a term frequency for the term in the document; for each term in the subset of the terms, determining, by a processing device, an augmented term frequency-inverse document frequency value based on: (i) the term frequency, and (ii) the plurality of inverse document frequency values that were determined for the term in the subset of the terms; and for each term in the subset of the terms that also appear in the document, selecting, by a processing device, as a combined inverse document frequency value, a minimum value of a plurality of normalized inverse document frequency values determined for the term in the subset of the terms. 3. The method of claim 1 , wherein the determining, for each one of the terms, a plurality of inverse document frequency values each associated with a respective one of the plurality of corpora comprises: determining, for each one of the terms, a plurality of inverse document frequency values each associated with a respective one of the plurality of corpora and inversely proportional to a count of documents that are in the respective one of the plurality of corpora and include the one of the terms.
| 0.5 |
8,433,556 | 1 | 2 |
1. A method for aligning words in parallel segments, the method comprising: calculating a first probability distribution, utilizing a processor and a memory, according to a model estimate of word alignments within a first corpus comprising word-level unaligned parallel segments, the model estimate comprising an N-best list of one or more sub-models; modifying the model estimate according to the first probability distribution; discriminatively re-ranking one or more sub-models associated with the modified model estimate according to word-level annotated parallel segments; and calculating a second probability distribution of the word alignments within the first corpus according to the re-ranked sub-models associated with the modified model estimate; wherein discriminatively re-ranking one or more sub-models within the modified model estimate according to manual alignments further comprises: adding manual alignments to hypothesized alignments within the first corpus; comparing the manual alignments to the hypothesized alignments; and weighting the one or more sub-models according to the comparison; and wherein the comparing of the manual alignments to the hypothesized alignments comprises: comparing an updated weighting factor for each sub-model derived using the first corpus to randomly generated weighting factors; and selecting one of the updated weighting factor and the randomly generated weighting factor that generates a least amount of error.
|
1. A method for aligning words in parallel segments, the method comprising: calculating a first probability distribution, utilizing a processor and a memory, according to a model estimate of word alignments within a first corpus comprising word-level unaligned parallel segments, the model estimate comprising an N-best list of one or more sub-models; modifying the model estimate according to the first probability distribution; discriminatively re-ranking one or more sub-models associated with the modified model estimate according to word-level annotated parallel segments; and calculating a second probability distribution of the word alignments within the first corpus according to the re-ranked sub-models associated with the modified model estimate; wherein discriminatively re-ranking one or more sub-models within the modified model estimate according to manual alignments further comprises: adding manual alignments to hypothesized alignments within the first corpus; comparing the manual alignments to the hypothesized alignments; and weighting the one or more sub-models according to the comparison; and wherein the comparing of the manual alignments to the hypothesized alignments comprises: comparing an updated weighting factor for each sub-model derived using the first corpus to randomly generated weighting factors; and selecting one of the updated weighting factor and the randomly generated weighting factor that generates a least amount of error. 2. The method recited in claim 1 , wherein the word-level annotated parallel segments comprise annotations indicating manual alignments.
| 0.874074 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.