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
1
|
|---|---|---|---|---|---|
9,690,771 | 1 | 6 | 1. A computer-implemented method for improving text annotation comprising: configuring a grammar and an ontology used for natural language understanding (NLU) processing by: obtaining, by a computing device, a plurality of annotations using the ontology and the grammar, each of the plurality of annotations corresponding to one of a plurality of text samples; performing, by the computing device, one or more quality assurance checks for at least one of the plurality of annotations, the ontology, or the grammar; generating, by the computing device, a list of one or more flagged annotations, each of the one or more flagged annotations corresponding to one of the plurality of annotations flagged during one of the one or more quality assurance checks; presenting, at a display device of the computing device, the list of flagged annotations; receiving, via a user input device of the computing device, user input modifying one of the one or more flagged annotations to obtain a modified annotation; and updating at least one of the grammar or the ontology based on the modified annotation; and wherein performance of the first one of the quality assurance checks comprises: comparing a first arrangement of a set of hypernyms in a first annotation of the plurality of annotations to a second arrangement of the set of hypernyms in a second annotation of the plurality of annotations; and configuring the list of flagged annotations to include the first annotation and the second annotation responsive to determine that the first arrangement of the set of hypernyms does not match the second arrangement of the set of hypernyms. | 1. A computer-implemented method for improving text annotation comprising: configuring a grammar and an ontology used for natural language understanding (NLU) processing by: obtaining, by a computing device, a plurality of annotations using the ontology and the grammar, each of the plurality of annotations corresponding to one of a plurality of text samples; performing, by the computing device, one or more quality assurance checks for at least one of the plurality of annotations, the ontology, or the grammar; generating, by the computing device, a list of one or more flagged annotations, each of the one or more flagged annotations corresponding to one of the plurality of annotations flagged during one of the one or more quality assurance checks; presenting, at a display device of the computing device, the list of flagged annotations; receiving, via a user input device of the computing device, user input modifying one of the one or more flagged annotations to obtain a modified annotation; and updating at least one of the grammar or the ontology based on the modified annotation; and wherein performance of the first one of the quality assurance checks comprises: comparing a first arrangement of a set of hypernyms in a first annotation of the plurality of annotations to a second arrangement of the set of hypernyms in a second annotation of the plurality of annotations; and configuring the list of flagged annotations to include the first annotation and the second annotation responsive to determine that the first arrangement of the set of hypernyms does not match the second arrangement of the set of hypernyms. 6. The method of claim 1 wherein performance of a second one of the quality assurance checks comprises: determining whether multiple grammar rules of the grammar parse an n-gram; and configuring the list of flagged annotations to include an annotation of the plurality of annotations corresponding to a text sample that includes the n-gram. | 0.807475 |
8,661,016 | 1 | 7 | 1. A method of processing descriptive queries for data sources in a system comprising a set of data providers, wherein each data provider is distinguished by a type of data that the data provider provides and a set of attributes, wherein each attribute has a unique name and a particular type of value, and wherein the data providers are grouped into provider kinds, wherein each provider kind has a name, and wherein the data providers that are grouped into a same provider kind provide a same type of data and have a same set of attributes, the method comprising: obtaining a descriptive query comprising the name of a given provider kind and a specification of a mapping from an assignment of one or more values for one or more attributes of one or more data providers to a true value or a false value; and resolving the descriptive query, wherein resolving the descriptive query comprises determining one or more data providers in the set of data providers that belong to the given provider kind specified in the descriptive query and for which the mapping specification in the descriptive query maps the one or more values of the one or more attributes of the one or more data providers to the true value, wherein the descriptive query further comprises a specification of a selection mechanism for selecting one or more data providers from a set of data providers, and wherein resolving the descriptive query further comprises applying the selection mechanism specified in the descriptive query to the data providers in the set of data providers that belong to the provider kind specified in the descriptive query and for which the mapping specified in the descriptive query maps the values of the attributes of the data provider to the true value, wherein the obtaining and resolving steps are performed at least in part by a processor coupled to a memory. | 1. A method of processing descriptive queries for data sources in a system comprising a set of data providers, wherein each data provider is distinguished by a type of data that the data provider provides and a set of attributes, wherein each attribute has a unique name and a particular type of value, and wherein the data providers are grouped into provider kinds, wherein each provider kind has a name, and wherein the data providers that are grouped into a same provider kind provide a same type of data and have a same set of attributes, the method comprising: obtaining a descriptive query comprising the name of a given provider kind and a specification of a mapping from an assignment of one or more values for one or more attributes of one or more data providers to a true value or a false value; and resolving the descriptive query, wherein resolving the descriptive query comprises determining one or more data providers in the set of data providers that belong to the given provider kind specified in the descriptive query and for which the mapping specification in the descriptive query maps the one or more values of the one or more attributes of the one or more data providers to the true value, wherein the descriptive query further comprises a specification of a selection mechanism for selecting one or more data providers from a set of data providers, and wherein resolving the descriptive query further comprises applying the selection mechanism specified in the descriptive query to the data providers in the set of data providers that belong to the provider kind specified in the descriptive query and for which the mapping specified in the descriptive query maps the values of the attributes of the data provider to the true value, wherein the obtaining and resolving steps are performed at least in part by a processor coupled to a memory. 7. The method of claim 1 , wherein the selection mechanism selects one arbitrary member of the set of data providers or all members of the set of data providers. | 0.906613 |
7,617,202 | 1 | 6 | 1. A system that facilitates analyzing a search query log, comprising: a computer readable storage medium having stored thereon: a component that obtains a set of queries from the query log; a distributional component that defines a respective probability distributional characteristic for the set of queries wherein the distributional characteristic is generated from at least one of a substring distribution algorithm that represents a distribution characteristic for a substring as a probability distribution over strings that include the substring or a string sequence distribution algorithm that represents a distribution characteristic for a query as a probability distribution of queries that a user queries subsequent the query; and a similarity component that suggests similar query terms by computing a distributional similarity measure between respective probability distributional characteristics of the query terms, wherein: the set of queries is obtained from the query log based on the substring distribution algorithm or the string sequence distribution algorithm; and the set of queries is selected based on one or more of: a distribution algorithm; a global unique identification (GUID); a substring; or a string. | 1. A system that facilitates analyzing a search query log, comprising: a computer readable storage medium having stored thereon: a component that obtains a set of queries from the query log; a distributional component that defines a respective probability distributional characteristic for the set of queries wherein the distributional characteristic is generated from at least one of a substring distribution algorithm that represents a distribution characteristic for a substring as a probability distribution over strings that include the substring or a string sequence distribution algorithm that represents a distribution characteristic for a query as a probability distribution of queries that a user queries subsequent the query; and a similarity component that suggests similar query terms by computing a distributional similarity measure between respective probability distributional characteristics of the query terms, wherein: the set of queries is obtained from the query log based on the substring distribution algorithm or the string sequence distribution algorithm; and the set of queries is selected based on one or more of: a distribution algorithm; a global unique identification (GUID); a substring; or a string. 6. The system of claim 1 , the string sequence distributional characteristic associated with an individual user. | 0.782946 |
8,868,562 | 1 | 2 | 1. A computer-implemented method for developing semantic relationships between elements distilled from content of a document to generate a semantic representation of the content, the method comprising: identifying, by way of a computing device having a processor and memory, a text portion of the document; determining semantic information for a plurality of elements identified in the text portion, the semantic information including one or more of meanings of the identified elements or grammatical functions of the identified elements; identifying at least one of the identified elements as a subject of the text portion; determining a plurality of levels of association from the text portion and identifying at least one of the identified elements as a reporting act corresponding to an attitude report for each of the plurality of levels of association, the reporting act identified based on a set of rules that utilizes, in part, surrounding text, wherein the attitude report describes the subject's attitude toward a particular topic of the text portion; based on the determined semantic information for the identified elements, associating the identified elements so that each association of identified elements represents a certain semantic relationship; generating, by way of the computing device, a semantic representation that represents the associations, by way of relational elements that describe the associations, of the identified elements to one another; and indexing the semantic representation, including the identified elements and the relational elements, in an index for retrieval, the index being searchable and including pointers from the semantic representation to its associated text portion. | 1. A computer-implemented method for developing semantic relationships between elements distilled from content of a document to generate a semantic representation of the content, the method comprising: identifying, by way of a computing device having a processor and memory, a text portion of the document; determining semantic information for a plurality of elements identified in the text portion, the semantic information including one or more of meanings of the identified elements or grammatical functions of the identified elements; identifying at least one of the identified elements as a subject of the text portion; determining a plurality of levels of association from the text portion and identifying at least one of the identified elements as a reporting act corresponding to an attitude report for each of the plurality of levels of association, the reporting act identified based on a set of rules that utilizes, in part, surrounding text, wherein the attitude report describes the subject's attitude toward a particular topic of the text portion; based on the determined semantic information for the identified elements, associating the identified elements so that each association of identified elements represents a certain semantic relationship; generating, by way of the computing device, a semantic representation that represents the associations, by way of relational elements that describe the associations, of the identified elements to one another; and indexing the semantic representation, including the identified elements and the relational elements, in an index for retrieval, the index being searchable and including pointers from the semantic representation to its associated text portion. 2. The method of claim 1 , wherein the text portion comprises at least one of one or more sentences, a table, a template, or a plurality of data. | 0.707661 |
9,563,399 | 1 | 9 | 1. A method of compiling a pattern into a non-deterministic finite automata (NFA) graph, the method comprising: examining the pattern for a plurality of elements and a plurality of node types, each node type corresponding with an element, each element of the pattern to be matched at least zero times, the element representing a character, character class or string; generating a plurality of nodes of the NFA graph, each node of the plurality of nodes configured to match with one of the plurality of elements and store the node type corresponding to the element, a next node address in the NFA graph, a count value, and the element, wherein the next node address and the count value are applicable as a function of the node type stored and wherein the plurality of nodes generated enable a graph walk engine to identify the pattern in a payload with less nodes relative to another NFA graph representing the pattern and employed by the graph walk engine to identify the pattern in the payload. | 1. A method of compiling a pattern into a non-deterministic finite automata (NFA) graph, the method comprising: examining the pattern for a plurality of elements and a plurality of node types, each node type corresponding with an element, each element of the pattern to be matched at least zero times, the element representing a character, character class or string; generating a plurality of nodes of the NFA graph, each node of the plurality of nodes configured to match with one of the plurality of elements and store the node type corresponding to the element, a next node address in the NFA graph, a count value, and the element, wherein the next node address and the count value are applicable as a function of the node type stored and wherein the plurality of nodes generated enable a graph walk engine to identify the pattern in a payload with less nodes relative to another NFA graph representing the pattern and employed by the graph walk engine to identify the pattern in the payload. 9. The method of claim 1 , wherein the plurality of node types includes a variable count node type and the variable count node type represents a portion of the pattern to match for the element a variable number of times. | 0.873853 |
9,176,958 | 8 | 14 | 8. An apparatus for searching music, the apparatus comprising: a tempo scale set generating unit, configured to: receive a query comprising a pulse train representable by a plurality of query values defining a tempo of music to be searched; generate a tempo scale set based on the query, wherein the generating comprises mapping each of the query values to a tempo scale representing a length of a note corresponding to the query value, wherein the tempo scale set is a set of tempo scales representing lengths of musical notes corresponding to the plurality of query values; a tempo word set constructing unit, configured to construct a tempo word set based on the tempo scale set generated by said tempo scale set generating unit, the tempo word set comprising a plurality of tempo words, wherein constructing each tempo word of the one or more tempo words comprises collecting one or more tempo scales of the tempo scale set, wherein the two or more tempo scales are positioned in the tempo scale set at a predetermined interval from one another; and a music identification unit, configured to identify the music based on the tempo word set constructed by said tempo word set constructing unit. | 8. An apparatus for searching music, the apparatus comprising: a tempo scale set generating unit, configured to: receive a query comprising a pulse train representable by a plurality of query values defining a tempo of music to be searched; generate a tempo scale set based on the query, wherein the generating comprises mapping each of the query values to a tempo scale representing a length of a note corresponding to the query value, wherein the tempo scale set is a set of tempo scales representing lengths of musical notes corresponding to the plurality of query values; a tempo word set constructing unit, configured to construct a tempo word set based on the tempo scale set generated by said tempo scale set generating unit, the tempo word set comprising a plurality of tempo words, wherein constructing each tempo word of the one or more tempo words comprises collecting one or more tempo scales of the tempo scale set, wherein the two or more tempo scales are positioned in the tempo scale set at a predetermined interval from one another; and a music identification unit, configured to identify the music based on the tempo word set constructed by said tempo word set constructing unit. 14. The apparatus according to claim 8 , further comprising an input device capable of receiving said query as a series of taps by a user on said input device. | 0.891542 |
7,991,129 | 33 | 34 | 33. The system of claim 32 , wherein the function includes a voice email service function. | 33. The system of claim 32 , wherein the function includes a voice email service function. 34. The system of claim 33 , wherein the service includes delivering, in the automated voice, information concerning at least one email message having a particular status. | 0.929046 |
9,996,626 | 1 | 10 | 1. A computer-implemented method for selecting promotional information for display, the method comprising: receiving, by a configured computing system of a content item selection service that communicates with computer systems of a separate online retailer over one or more computer networks, information about search results generated by the online retailer in response to a search request by a user, wherein the search results indicate multiple products available to be acquired from the online retailer and are included in a search results Web page from the online retailer to be displayed to the user on a client device of the user; automatically determining, by the configured computing system of the content item selection service, a product category to associate with the search request by analyzing information about multiple product categories of the indicated products in the search results; sending, by the configured computing system of the content item selection service, one or more electronic communications that have information about one or more additional products to include as part of the search results Web page displayed to the user, wherein the one or more additional products are distinct from the indicated products in the search results and are selected from the determined product category by the configured computing system; determining, by the configured computing system of the content item selection service and based at least in part on the determining of the product category to associate with the search request, to further associate the determined product category with one or more additional Web pages that are displayed to the user on the client device as a result of one or more interactions by the user with the search results included in the displayed search results Web page, including to send one or more additional electronic communications with information about one or more further products selected from the determined product category to include as part of the one or more additional Web pages displayed to the user; and updating, by the configured computing system of the content item selection service, and based at least in part on one or more further interactions by the user with the displayed one or more additional Web pages, an association for the search request from the determined product category to a different product category, for use with later searches by other users using the search request. | 1. A computer-implemented method for selecting promotional information for display, the method comprising: receiving, by a configured computing system of a content item selection service that communicates with computer systems of a separate online retailer over one or more computer networks, information about search results generated by the online retailer in response to a search request by a user, wherein the search results indicate multiple products available to be acquired from the online retailer and are included in a search results Web page from the online retailer to be displayed to the user on a client device of the user; automatically determining, by the configured computing system of the content item selection service, a product category to associate with the search request by analyzing information about multiple product categories of the indicated products in the search results; sending, by the configured computing system of the content item selection service, one or more electronic communications that have information about one or more additional products to include as part of the search results Web page displayed to the user, wherein the one or more additional products are distinct from the indicated products in the search results and are selected from the determined product category by the configured computing system; determining, by the configured computing system of the content item selection service and based at least in part on the determining of the product category to associate with the search request, to further associate the determined product category with one or more additional Web pages that are displayed to the user on the client device as a result of one or more interactions by the user with the search results included in the displayed search results Web page, including to send one or more additional electronic communications with information about one or more further products selected from the determined product category to include as part of the one or more additional Web pages displayed to the user; and updating, by the configured computing system of the content item selection service, and based at least in part on one or more further interactions by the user with the displayed one or more additional Web pages, an association for the search request from the determined product category to a different product category, for use with later searches by other users using the search request. 10. The method of claim 1 wherein the sending of the information about the one or more additional products includes sending multiple content pieces of a plurality of content types, the plurality of content types including text, images, video clips and audio clips. | 0.847399 |
8,301,392 | 10 | 15 | 10. Computer apparatus for automatically selecting and typing a subset of available family members for DNA profiling to a missing person to identify or exclude a typed unknown biological specimen, the computer apparatus comprising a processor, an input device coupled to the processor, an output device coupled to the processor and a memory for storing profile data obtained from said typed unknown biological specimen coupled to the processor, the method comprising: (a) the memory for storing relationships between said missing person and said available family members in a pedigree via said input device; (b) the processor using the relative discriminating power of the pedigree with at least two combinations of said available family members to automatically select a combination of available family members for DNA typing; (c) the input device using a selected DNA typing technology for typing the automatically selected combination of available family members to obtain DNA profile data and the memory for storing said DNA profile data for said automatically selected combination; (d) the processor using the pedigree and said stored DNA profile data of said automatically selected combination to calculate a likelihood function value using an adaptation of an Elston Stewart algorithm between the stored profile data obtained from said typed unknown biological specimen and said stored profile data for said automatically selected combination of available family members; and (e) the output device outputting a decision whether said typed unknown biological specimen originates from said missing person and said pedigree or to exclude said typed unknown biological specimen as unrelated to the pedigree. | 10. Computer apparatus for automatically selecting and typing a subset of available family members for DNA profiling to a missing person to identify or exclude a typed unknown biological specimen, the computer apparatus comprising a processor, an input device coupled to the processor, an output device coupled to the processor and a memory for storing profile data obtained from said typed unknown biological specimen coupled to the processor, the method comprising: (a) the memory for storing relationships between said missing person and said available family members in a pedigree via said input device; (b) the processor using the relative discriminating power of the pedigree with at least two combinations of said available family members to automatically select a combination of available family members for DNA typing; (c) the input device using a selected DNA typing technology for typing the automatically selected combination of available family members to obtain DNA profile data and the memory for storing said DNA profile data for said automatically selected combination; (d) the processor using the pedigree and said stored DNA profile data of said automatically selected combination to calculate a likelihood function value using an adaptation of an Elston Stewart algorithm between the stored profile data obtained from said typed unknown biological specimen and said stored profile data for said automatically selected combination of available family members; and (e) the output device outputting a decision whether said typed unknown biological specimen originates from said missing person and said pedigree or to exclude said typed unknown biological specimen as unrelated to the pedigree. 15. The computer apparatus of claim 10 wherein said relative discriminating power of the pedigree with at least two combinations of typed available family members is determined by a rank-ordered list. | 0.820467 |
7,664,730 | 5 | 6 | 5. The profile of claim 1 , wherein the tuning data further comprises: optimization information for the query statement; and an action to set a parameter of an optimizer based on the optimization information. | 5. The profile of claim 1 , wherein the tuning data further comprises: optimization information for the query statement; and an action to set a parameter of an optimizer based on the optimization information. 6. The profile of claim 5 , wherein the optimization information is related to an execution history of the query statement. | 0.960828 |
9,501,469 | 19 | 23 | 19. A non-transitory computer-readable medium comprising computer program instructions, wherein the computer program instructions are executable by a computer processor to perform a method comprising: (A) obtaining, from a user, first data representing a first part of a sentence, wherein the first part of the sentence comprises a first one of a subject, a predicate, and an object; (B) obtaining, from the user, second data representing a second part of the sentence, wherein the second part of the sentence comprises a second one of the subject, the predicate, and the object; (C) identifying a synonym of the first part of the sentence; (D) identifying a synonym of the second part of the sentence; (E) forming a first query from the synonym of the first part of the sentence and the synonym of the second part of the sentence, comprising: (E)(1) selecting a first form for the first query, wherein the first form specifies a first one of the following sequences: subject, predicate, object; subject, predicate; subject, object; predicate, object; (E)(2) forming the first query in the first form the forming comprising: (E)(2)(a) if the first form specifies the sequence subject, predicate, object, then forming the first query to include a subject followed by a predicate followed by an object, wherein the subject, predicate, and object are selected from the first part of the sentence, the second part of the sentence, the synonym of the first part of the sentence, and the synonym of the second part of the sentence; (E)(2)(b) if the first form specifies the sequence subject, predicate, then forming the first query to include a subject followed by a predicate, wherein the subject and predicate are selected from the first part of the sentence, the second part of the sentence, the synonym of the first part of the sentence, and the synonym of the second part of the sentence; (E)(2)(c) if the first form specifies the sequence subject, object, then forming the first query to include a subject followed by an object, wherein the subject and object are selected from the first part of the sentence, the second part of the sentence, the synonym of the first part of the sentence, and the synonym of the second part of the sentence; and (E)(2)(d) if the first form specifies the sequence predicate, object, then forming the first query to include a predicate followed by an object, wherein the predicate and object are selected from the first part of the sentence, the second part of the sentence, the synonym of the first part of the sentence, and the synonym of the second part of the sentence; and (F) searching a dataset in memory using the first query to identify a first subset of the dataset; (G) providing, to the user, executed by the computer processor, output representing the subset of the dataset wherein the sentence differs from the first query. | 19. A non-transitory computer-readable medium comprising computer program instructions, wherein the computer program instructions are executable by a computer processor to perform a method comprising: (A) obtaining, from a user, first data representing a first part of a sentence, wherein the first part of the sentence comprises a first one of a subject, a predicate, and an object; (B) obtaining, from the user, second data representing a second part of the sentence, wherein the second part of the sentence comprises a second one of the subject, the predicate, and the object; (C) identifying a synonym of the first part of the sentence; (D) identifying a synonym of the second part of the sentence; (E) forming a first query from the synonym of the first part of the sentence and the synonym of the second part of the sentence, comprising: (E)(1) selecting a first form for the first query, wherein the first form specifies a first one of the following sequences: subject, predicate, object; subject, predicate; subject, object; predicate, object; (E)(2) forming the first query in the first form the forming comprising: (E)(2)(a) if the first form specifies the sequence subject, predicate, object, then forming the first query to include a subject followed by a predicate followed by an object, wherein the subject, predicate, and object are selected from the first part of the sentence, the second part of the sentence, the synonym of the first part of the sentence, and the synonym of the second part of the sentence; (E)(2)(b) if the first form specifies the sequence subject, predicate, then forming the first query to include a subject followed by a predicate, wherein the subject and predicate are selected from the first part of the sentence, the second part of the sentence, the synonym of the first part of the sentence, and the synonym of the second part of the sentence; (E)(2)(c) if the first form specifies the sequence subject, object, then forming the first query to include a subject followed by an object, wherein the subject and object are selected from the first part of the sentence, the second part of the sentence, the synonym of the first part of the sentence, and the synonym of the second part of the sentence; and (E)(2)(d) if the first form specifies the sequence predicate, object, then forming the first query to include a predicate followed by an object, wherein the predicate and object are selected from the first part of the sentence, the second part of the sentence, the synonym of the first part of the sentence, and the synonym of the second part of the sentence; and (F) searching a dataset in memory using the first query to identify a first subset of the dataset; (G) providing, to the user, executed by the computer processor, output representing the subset of the dataset wherein the sentence differs from the first query. 23. The non-transitory computer-readable medium of claim 19 , wherein (B) comprises identifying a hyponym of the first part of the sentence. | 0.89521 |
8,930,808 | 17 | 18 | 17. A system for storing rich text data, comprising: a processor; a plain text component operable by the processor to identify plain text in the rich text data and store the plain text in a first legacy data record; a rich text attribute component operable by the processor to identify rich text attributes in the rich text data and generate a second legacy data record which comprises the rich text attributes, wherein the rich text attributes specify richness features of the plain text, and the second legacy data record is prefixed by a token which includes a unique string of characters recognizable by a user and by an application capable of applying the rich text attributes to the plain text to present the rich text data; a record compressing component operable by the processor to compress the second data record, and a record storing component operable by the processor to store the second legacy data record inline with the first legacy data record in a legacy data repository of a data storage system, wherein the first legacy data record is separate from the second legacy data record so that the plain text appears without rich text, the plain text appearing in one or more plain text lines and the rich text appearing in one or more rich text lines prefixed by the token. | 17. A system for storing rich text data, comprising: a processor; a plain text component operable by the processor to identify plain text in the rich text data and store the plain text in a first legacy data record; a rich text attribute component operable by the processor to identify rich text attributes in the rich text data and generate a second legacy data record which comprises the rich text attributes, wherein the rich text attributes specify richness features of the plain text, and the second legacy data record is prefixed by a token which includes a unique string of characters recognizable by a user and by an application capable of applying the rich text attributes to the plain text to present the rich text data; a record compressing component operable by the processor to compress the second data record, and a record storing component operable by the processor to store the second legacy data record inline with the first legacy data record in a legacy data repository of a data storage system, wherein the first legacy data record is separate from the second legacy data record so that the plain text appears without rich text, the plain text appearing in one or more plain text lines and the rich text appearing in one or more rich text lines prefixed by the token. 18. The system of claim 17 , further comprising an encoder to encode the compressed second legacy data record in a readable text format. | 0.737452 |
8,196,090 | 1 | 4 | 1. A computerized method which, when executed by one or more processors, cause the one or more processors to perform operations comprising: creating a model, wherein the model comprises one or more associated profiles; reading a stereotype of a first profile, wherein the stereotype defines constraints to be applied to the model associated with the first profile; determining that the stereotype indicates a second profile and a third profile; accessing the second profile and the third profile; and aggregating a plurality of constraints from across the second and the third profiles for use as constraints for the stereotype of the first profile, wherein the profiles and the stereotype comport with semantics of a modeling language and wherein said aggregating comprises writing references to the plurality of constraints into a definition of the stereotype of the first profile. | 1. A computerized method which, when executed by one or more processors, cause the one or more processors to perform operations comprising: creating a model, wherein the model comprises one or more associated profiles; reading a stereotype of a first profile, wherein the stereotype defines constraints to be applied to the model associated with the first profile; determining that the stereotype indicates a second profile and a third profile; accessing the second profile and the third profile; and aggregating a plurality of constraints from across the second and the third profiles for use as constraints for the stereotype of the first profile, wherein the profiles and the stereotype comport with semantics of a modeling language and wherein said aggregating comprises writing references to the plurality of constraints into a definition of the stereotype of the first profile. 4. The computerized method of claim 1 further comprising aggregating attributes from across the second profile and the third profile for use as attributes for the stereotype of the first profile. | 0.694357 |
7,548,863 | 13 | 15 | 13. A machine-readable storage medium having executable instructions stored thereon, which when executed, causes a machine to perform operations comprising: receiving input data, the input data being data for conversion from a phonetic representation or speech; to written text characters of one or more words of a language, the input data having a plurality of data pieces; calculating by a processor of the machine, via an iterative process, an input vector for the input data in a textual format of the language, the iterative process including starting with a first data piece of the input data converted to a selected textual format to form a current vector of the input data and iteratively updating the current vector with a next data piece of the input data using elements of the current vector until all the data pieces are converted into the textual format; finding a subset of a plurality of documents based on the input vector, the plurality of documents having text being written in the language; determining a frequency of the text in the subset; and converting the input data to a representation of one or more written text characters of the language based on the frequency. | 13. A machine-readable storage medium having executable instructions stored thereon, which when executed, causes a machine to perform operations comprising: receiving input data, the input data being data for conversion from a phonetic representation or speech; to written text characters of one or more words of a language, the input data having a plurality of data pieces; calculating by a processor of the machine, via an iterative process, an input vector for the input data in a textual format of the language, the iterative process including starting with a first data piece of the input data converted to a selected textual format to form a current vector of the input data and iteratively updating the current vector with a next data piece of the input data using elements of the current vector until all the data pieces are converted into the textual format; finding a subset of a plurality of documents based on the input vector, the plurality of documents having text being written in the language; determining a frequency of the text in the subset; and converting the input data to a representation of one or more written text characters of the language based on the frequency. 15. The machine-readable storage medium of claim 13 , which when executed by the machine causes the machine to perform further operations comprising: creating a customized dictionary based on the frequency of words in the subset. | 0.793321 |
9,355,130 | 5 | 6 | 5. The method of claim 4 further comprising: displaying a list of selectable filters on a graphical user interface (GUI) for the user of the EDA software in response to a user activation by the user of the EDA software; and wherein receiving the query comprises retrieving one or more selected filters from the list of selectable filters. | 5. The method of claim 4 further comprising: displaying a list of selectable filters on a graphical user interface (GUI) for the user of the EDA software in response to a user activation by the user of the EDA software; and wherein receiving the query comprises retrieving one or more selected filters from the list of selectable filters. 6. The method of claim 5 wherein the list of selectable filters comprises one or more pre-defined filters provided by the EDA software. | 0.964323 |
9,601,111 | 2 | 9 | 2. The method of claim 1 further comprising processing the first result and the second result using a third speech model of a third model type to obtain a third result. | 2. The method of claim 1 further comprising processing the first result and the second result using a third speech model of a third model type to obtain a third result. 9. The method of claim 2 wherein the first model type is a Finite State Grammar model type, the second model type is a Statistical Language Model type, and the third model type is a Structural Equation Model type. | 0.888947 |
9,811,683 | 9 | 10 | 9. The computer system of claim 1 , wherein the method further comprises: associating a second non-contextual data object with a second context object to define a second synthetic context-based object, wherein the second non-contextual data object relates to multiple subject-matters, and wherein the second context object provides a context that identifies a specific subject-matter, from the multiple subject-matters, of the second non-contextual data object; associating the second synthetic context-based object with said at least one specific data store in the data structure; associating the second synthetic context-based object with the first synthetic context-based object; and accessing said at least one specific data store by accessing the second synthetic context-based object via the first synthetic context-based object. | 9. The computer system of claim 1 , wherein the method further comprises: associating a second non-contextual data object with a second context object to define a second synthetic context-based object, wherein the second non-contextual data object relates to multiple subject-matters, and wherein the second context object provides a context that identifies a specific subject-matter, from the multiple subject-matters, of the second non-contextual data object; associating the second synthetic context-based object with said at least one specific data store in the data structure; associating the second synthetic context-based object with the first synthetic context-based object; and accessing said at least one specific data store by accessing the second synthetic context-based object via the first synthetic context-based object. 10. The computer system of claim 9 , wherein said at least one specific data store is a text document, and wherein the method further comprises: searching the text document for text data that is part of the second synthetic context-based object; and associating the text document that contains said text data with the second synthetic context-based object. | 0.95768 |
7,856,375 | 37 | 55 | 37. A method for automatically preparing customized communications for a plurality of consumer entities, the method comprising the steps of: using data from a first electronic data file containing financial product and/or financial service data for the customized communications, which financial product and/or financial service data includes a plurality of separate descriptions, characteristics and/or identifications for at least a first financial product and/or financial service; using data from a second electronic data file containing customer information for at least certain of the plurality of consumer entities, said customer information including customer related data in addition to, but not excluding, any one or more of customer name, customer address and customer account information obtained for said certain of the plurality of consumer entities; performing an automated composition process using one or more computing systems configured to access said first electronic data file and second electronic data file to compose an electronic document file representing a customized communication for at least one of said certain of the plurality of the consumer entities; wherein said customized communication includes an identifying section adapted to present identifying content to identify a consumer entity, and a separate customized section adapted to present at least some customized content relating to an offering for said consumer entity of said first financial product and/or financial service; wherein at least some of said customized content included in said customized communication for said separate customized section is variable information that is determined for said consumer entity from said first electronic data file and said second electronic data file during creation of said electronic document file; delivering said customized communications to said at least one of said certain of the plurality of consumer entities based on said electronic document file. | 37. A method for automatically preparing customized communications for a plurality of consumer entities, the method comprising the steps of: using data from a first electronic data file containing financial product and/or financial service data for the customized communications, which financial product and/or financial service data includes a plurality of separate descriptions, characteristics and/or identifications for at least a first financial product and/or financial service; using data from a second electronic data file containing customer information for at least certain of the plurality of consumer entities, said customer information including customer related data in addition to, but not excluding, any one or more of customer name, customer address and customer account information obtained for said certain of the plurality of consumer entities; performing an automated composition process using one or more computing systems configured to access said first electronic data file and second electronic data file to compose an electronic document file representing a customized communication for at least one of said certain of the plurality of the consumer entities; wherein said customized communication includes an identifying section adapted to present identifying content to identify a consumer entity, and a separate customized section adapted to present at least some customized content relating to an offering for said consumer entity of said first financial product and/or financial service; wherein at least some of said customized content included in said customized communication for said separate customized section is variable information that is determined for said consumer entity from said first electronic data file and said second electronic data file during creation of said electronic document file; delivering said customized communications to said at least one of said certain of the plurality of consumer entities based on said electronic document file. 55. The method of claim 37 , wherein successive customized communications can also pertain to two or more different products or services. | 0.853004 |
8,849,797 | 1 | 2 | 1. A computer-implemented method for governing access of a query to a database on a computer system, the method comprising the steps of: receiving an estimated query execution time for the query; calculating a factor for the query, wherein the factor is one or more factors chosen from: a user factor, a query factor, a job priority factor, or a resource factor; dynamically generating a tailored threshold that is unique to the query, where the tailored threshold is determined by applying the factor to a fixed threshold; governing the query's access to the database based on the tailored threshold; and wherein the method steps are implemented in a computer software program stored in computer memory and executed by a computer processor. | 1. A computer-implemented method for governing access of a query to a database on a computer system, the method comprising the steps of: receiving an estimated query execution time for the query; calculating a factor for the query, wherein the factor is one or more factors chosen from: a user factor, a query factor, a job priority factor, or a resource factor; dynamically generating a tailored threshold that is unique to the query, where the tailored threshold is determined by applying the factor to a fixed threshold; governing the query's access to the database based on the tailored threshold; and wherein the method steps are implemented in a computer software program stored in computer memory and executed by a computer processor. 2. The method of claim 1 further comprising the step of: intelligently creating a user score for each user accessing the database that is used to calculate the user factor to determine the tailored threshold. | 0.502392 |
10,108,622 | 8 | 10 | 8. A computer-implemented method for managing a database, comprising: maintaining data in a computerized database, said computerized database having data organized in at least one database table; monitoring respective changes made to each database table of said at least one database table; responsive to said monitoring respective changes made to each database table of said at least one database table, determining a respective database table volatility state of each database table of said at least one database table; responsive to determining a respective database table volatility state of each database table of said at least one database table, generating a respective at least one volatility attribute representing the respective database table volatility state of each database table of said at least one database table, wherein the respective database table volatility state of each said database table is a property of the respective database table that is a function of changes to the respective database table with respect to time, independent of any queries against data in the respective database table; and using the respective at least one volatility attribute of each database table to manage data in the respective database table, wherein using the respective at least one volatility attribute of each database table to manage data in the respective database table comprises at least one of: (a) using the respective at least one volatility attribute to determine an optimum query execution strateay for a query against data in the respective database table, (b) using the respective at least one volatility attribute to determine whether to re-optimize a previously saved query execution strateay for a query against data in the respective database table, (c) using the respective at least one volatility attribute to determine whether to collect statistical data regarding the respective database table, and (d) using the respective at least one volatility attribute to manage storage and/or retrieval of data in the respective at least one database table. | 8. A computer-implemented method for managing a database, comprising: maintaining data in a computerized database, said computerized database having data organized in at least one database table; monitoring respective changes made to each database table of said at least one database table; responsive to said monitoring respective changes made to each database table of said at least one database table, determining a respective database table volatility state of each database table of said at least one database table; responsive to determining a respective database table volatility state of each database table of said at least one database table, generating a respective at least one volatility attribute representing the respective database table volatility state of each database table of said at least one database table, wherein the respective database table volatility state of each said database table is a property of the respective database table that is a function of changes to the respective database table with respect to time, independent of any queries against data in the respective database table; and using the respective at least one volatility attribute of each database table to manage data in the respective database table, wherein using the respective at least one volatility attribute of each database table to manage data in the respective database table comprises at least one of: (a) using the respective at least one volatility attribute to determine an optimum query execution strateay for a query against data in the respective database table, (b) using the respective at least one volatility attribute to determine whether to re-optimize a previously saved query execution strateay for a query against data in the respective database table, (c) using the respective at least one volatility attribute to determine whether to collect statistical data regarding the respective database table, and (d) using the respective at least one volatility attribute to manage storage and/or retrieval of data in the respective at least one database table. 10. The computer-implemented method for managing a database of claim 8 , wherein each respective at least one volatility attribute comprises a numerical value within a range. | 0.878999 |
7,805,455 | 3 | 4 | 3. The method according to claim 1 , wherein submitting the query to the one or more knowledge bases comprises formulating the query to be compatible with a knowledge search engine prior to submitting the query. | 3. The method according to claim 1 , wherein submitting the query to the one or more knowledge bases comprises formulating the query to be compatible with a knowledge search engine prior to submitting the query. 4. The method according to claim 3 , including formatting the query as either a natural language query or a key word query. | 0.969296 |
8,495,499 | 1 | 5 | 1. A method comprising: receiving a first search query that was entered by a user and includes one or more terms that define the first search query; performing a search using the first search query to identify search results that are responsive to the first search query; based on performing the search using the first search query, identifying a first list of search results that are responsive to the first search query, the first list of search results including at least a first search result that is responsive to the first search query and that links to first electronic content; causing display of the first list of search results identified based on performing the search using the first search query, the display of the first list of search results including the first search result; after causing display of the first list of search results identified based on performing the search using the first search query, receiving user input selecting the first search result included in the display of the first list of search results; receiving a second search query that was entered by the user and includes one or more terms that define the second search query, the second search query being different than the first search query; performing a search using the second search query to identify search results that are responsive to the second search query; based on performing the search using the second search query, identifying a second list of search results that are responsive to the second search query, the second list of search results including at least a second search result that is responsive to the second search query and that links to second electronic content, the second list of search results being different than the first list of search results and the second search result being different than the first search result; causing display of the second list of search results identified based on performing the search using the second search query, the display of the second list of search results including the second search result; after causing display of the second list of search results identified based on performing the search using the second search query, receiving user input selecting the second search result included in the display of the second list of search results; subsequent to receiving user input selecting the first search result included in the display of the first list of search results and subsequent to receiving user input selecting the second search result included in the display of the second list of search results, receiving a third search query that was entered by the user and includes one or more terms that define the third search query, the third search query being different than the first search query and being different than the second search query; performing a search using the third search query to identify search results that are responsive to the third search query; based on performing the search using the third search query, identifying a third list of search results that are responsive to the third search query, the third list of search results being different than the first list of search results and being different than the second list of search results, the third list of search results including the first search result that links to the first electronic content, the second search result that links to the second electronic content, and a third search result that links to third electronic content, that has not been selected by the user prior to receiving the third search query, and that is different than the first search result and the second search result; based on the selection, prior to receiving the third search query, of the first search result included in the display of the first list of search results, the selection, prior to receiving the third search query, of the second search result included in the display of the second list of search results, and the third list of search results including the first search result and the second search result, grouping, by at least one processor and within a display of the third list of search results identified based on performing the search using the third search query, a first representation of the first search result and a second representation of the second search result together in a group separate from a third representation of the third search result that has not been selected by the user, even though relevancy ratings, to the third search query, for the first search result, the second search result, and the third search result do not suggest grouping the first search result and the second search result together in the group separate from the third search result that has not been selected by the user. | 1. A method comprising: receiving a first search query that was entered by a user and includes one or more terms that define the first search query; performing a search using the first search query to identify search results that are responsive to the first search query; based on performing the search using the first search query, identifying a first list of search results that are responsive to the first search query, the first list of search results including at least a first search result that is responsive to the first search query and that links to first electronic content; causing display of the first list of search results identified based on performing the search using the first search query, the display of the first list of search results including the first search result; after causing display of the first list of search results identified based on performing the search using the first search query, receiving user input selecting the first search result included in the display of the first list of search results; receiving a second search query that was entered by the user and includes one or more terms that define the second search query, the second search query being different than the first search query; performing a search using the second search query to identify search results that are responsive to the second search query; based on performing the search using the second search query, identifying a second list of search results that are responsive to the second search query, the second list of search results including at least a second search result that is responsive to the second search query and that links to second electronic content, the second list of search results being different than the first list of search results and the second search result being different than the first search result; causing display of the second list of search results identified based on performing the search using the second search query, the display of the second list of search results including the second search result; after causing display of the second list of search results identified based on performing the search using the second search query, receiving user input selecting the second search result included in the display of the second list of search results; subsequent to receiving user input selecting the first search result included in the display of the first list of search results and subsequent to receiving user input selecting the second search result included in the display of the second list of search results, receiving a third search query that was entered by the user and includes one or more terms that define the third search query, the third search query being different than the first search query and being different than the second search query; performing a search using the third search query to identify search results that are responsive to the third search query; based on performing the search using the third search query, identifying a third list of search results that are responsive to the third search query, the third list of search results being different than the first list of search results and being different than the second list of search results, the third list of search results including the first search result that links to the first electronic content, the second search result that links to the second electronic content, and a third search result that links to third electronic content, that has not been selected by the user prior to receiving the third search query, and that is different than the first search result and the second search result; based on the selection, prior to receiving the third search query, of the first search result included in the display of the first list of search results, the selection, prior to receiving the third search query, of the second search result included in the display of the second list of search results, and the third list of search results including the first search result and the second search result, grouping, by at least one processor and within a display of the third list of search results identified based on performing the search using the third search query, a first representation of the first search result and a second representation of the second search result together in a group separate from a third representation of the third search result that has not been selected by the user, even though relevancy ratings, to the third search query, for the first search result, the second search result, and the third search result do not suggest grouping the first search result and the second search result together in the group separate from the third search result that has not been selected by the user. 5. The method of claim 1 , further comprising, based on the selection, prior to receiving the third search query, of the first search result included in the display of the first list of search results, the selection, prior to receiving the third search query, of the second search result included in the display of the second list of search results, and the third list of search results including the first search result and the second search result, causing display, within the display of the third list of search results identified based on performing the search using the third search query, of a first graphical indicator that is proximate to the first representation of the first search result, that distinguishes the first representation of the first search result from the third representation of the third search result, and that indicates that the first search result was previously selected, and a second graphical indicator that is proximate to the second representation of the second search result, that distinguishes the second representation of the second search result from the third representation of the third search result, and that indicates that the second search result was previously selected. | 0.500411 |
8,751,507 | 4 | 29 | 4. A computer-implemented method of providing item recommendations to a target user, the method comprising: by a computer system comprising computer hardware, the computer system configured to generate recommendations by at least: by a computer system comprising computer hardware, the computer system configured to generate recommendations by at least: providing a user interface that provides functionality for users of an electronic catalog of items to create and assign arbitrary textual tags to individual items represented in the electronic catalog after selecting said items, such that the textual tags categorize the items represented in the electronic catalog, the user interface comprising a tag entry field on item detail pages of the electronic catalog, the tag entry field enabling the users to create the tags via entry of text strings into the tag entry field; programmatically identifying at least one tagged item that was selected by a target user, the at least one tagged item being represented in the electronic catalog, without receiving a designation from the target user of the at least one tagged item; subsequent to and in response to said identifying, selecting one or more user-defined textual tags associated with the at least one tagged item by at least identifying a threshold number of most popular tags assigned to the at least one tagged item, without any input from the target user, each tag comprising a descriptor selectively associated with the at least one tagged item by one or more users; subsequent to said selecting the one or more user-defined tags, automatically searching the electronic catalog using the one or more tags as one or more keywords to identify a corresponding set of related items; selecting at least a portion of the set of related items to provide as the recommendations to the target user; and outputting the recommendations with associated textual reasons for recommending the items. | 4. A computer-implemented method of providing item recommendations to a target user, the method comprising: by a computer system comprising computer hardware, the computer system configured to generate recommendations by at least: by a computer system comprising computer hardware, the computer system configured to generate recommendations by at least: providing a user interface that provides functionality for users of an electronic catalog of items to create and assign arbitrary textual tags to individual items represented in the electronic catalog after selecting said items, such that the textual tags categorize the items represented in the electronic catalog, the user interface comprising a tag entry field on item detail pages of the electronic catalog, the tag entry field enabling the users to create the tags via entry of text strings into the tag entry field; programmatically identifying at least one tagged item that was selected by a target user, the at least one tagged item being represented in the electronic catalog, without receiving a designation from the target user of the at least one tagged item; subsequent to and in response to said identifying, selecting one or more user-defined textual tags associated with the at least one tagged item by at least identifying a threshold number of most popular tags assigned to the at least one tagged item, without any input from the target user, each tag comprising a descriptor selectively associated with the at least one tagged item by one or more users; subsequent to said selecting the one or more user-defined tags, automatically searching the electronic catalog using the one or more tags as one or more keywords to identify a corresponding set of related items; selecting at least a portion of the set of related items to provide as the recommendations to the target user; and outputting the recommendations with associated textual reasons for recommending the items. 29. The method of claim 4 , further comprising limiting a scope of said searching to a category of items in the electronic catalog. | 0.675743 |
8,849,829 | 1 | 2 | 1. A method for generating trending search magazines comprising: receiving an identification of at least one trending topic in response to a request from a user; generating an edition, specified by a publisher, for a trending topic, wherein the trending topic for the edition is selected based on characteristics related to news sources generating articles and characteristics related to sharing activities of different users on a plurality of social media platforms; selecting a search magazine layout format of the edition based on a configurable application data model, wherein the publisher configures the application data model differently based on edition content of the edition; adjusting the search magazine layout format of the edition, wherein the search magazine layout format is adjusted based on a specific mobile device type of a mobile device associated with the request, and the search magazine layout format includes a news section for display of news articles retrieved for the trending topic and additional sections are included based on the configured application data model; searching news servers for the news articles on the trending topic; automatically, without user intervention, transforming search results into the adjusted search magazine layout format of the edition; and transmitting the edition in the adjusted search magazine layout format that includes the news section with the transformed search results for display in a current display view of the mobile device, matching the specific mobile device type, wherein the adjusted search magazine layout format further comprises: a user-generated media section that includes media related to the trending topic and shared by the user and additional users, the user-generated media section including a user video upload portion, a user photo upload portion and a user social media portion, wherein layout of the section user-generated media is dynamically adjusted based on the user-generated media related to the trending topic; and an about section that includes descriptive information retrieved for the trending topic. | 1. A method for generating trending search magazines comprising: receiving an identification of at least one trending topic in response to a request from a user; generating an edition, specified by a publisher, for a trending topic, wherein the trending topic for the edition is selected based on characteristics related to news sources generating articles and characteristics related to sharing activities of different users on a plurality of social media platforms; selecting a search magazine layout format of the edition based on a configurable application data model, wherein the publisher configures the application data model differently based on edition content of the edition; adjusting the search magazine layout format of the edition, wherein the search magazine layout format is adjusted based on a specific mobile device type of a mobile device associated with the request, and the search magazine layout format includes a news section for display of news articles retrieved for the trending topic and additional sections are included based on the configured application data model; searching news servers for the news articles on the trending topic; automatically, without user intervention, transforming search results into the adjusted search magazine layout format of the edition; and transmitting the edition in the adjusted search magazine layout format that includes the news section with the transformed search results for display in a current display view of the mobile device, matching the specific mobile device type, wherein the adjusted search magazine layout format further comprises: a user-generated media section that includes media related to the trending topic and shared by the user and additional users, the user-generated media section including a user video upload portion, a user photo upload portion and a user social media portion, wherein layout of the section user-generated media is dynamically adjusted based on the user-generated media related to the trending topic; and an about section that includes descriptive information retrieved for the trending topic. 2. The method of claim 1 , further comprising searching for extended media and synchronizing a representation of the extended media on the mobile device, the extended media including additional images, videos, audio, and maps related to the trending topic. | 0.787728 |
8,601,397 | 17 | 23 | 17. A non-transitory computer readable storage medium having stored thereon computer executable instructions which, when executed on a computer, configure the computer to perform a method comprising: displaying a plurality of document series display sections; and displaying a plurality of document entries in each of the at least two document series display sections, the document entries in each of the document series display sections corresponding to a different document series, each of the document entries having a presentation state selected from a group comprising an expanded state and a contracted state, the presentation state of the document entries being selectable by a user, each of the document entries corresponding to a different document, wherein when the presentation state of one of the one or more document entries is the expanded state, the method further comprising displaying at least a name of an author of a document associated with the one of the document entries and a plurality of user-selectable icons, wherein when the presentation state of the one of the document entries is in the contracted state, the method further comprising hiding the user-selectable icons, wherein when one of the user-selectable icons is selected, the method further comprising opening an email window, the email window including a link to the document, and wherein when one of the user-selectable icons is selected, the method further comprising displaying the document entries corresponding to the document series, wherein each document series display section has a different document series publisher, wherein each publisher maintains editing control of one of the plurality of document entries corresponding to the document series. | 17. A non-transitory computer readable storage medium having stored thereon computer executable instructions which, when executed on a computer, configure the computer to perform a method comprising: displaying a plurality of document series display sections; and displaying a plurality of document entries in each of the at least two document series display sections, the document entries in each of the document series display sections corresponding to a different document series, each of the document entries having a presentation state selected from a group comprising an expanded state and a contracted state, the presentation state of the document entries being selectable by a user, each of the document entries corresponding to a different document, wherein when the presentation state of one of the one or more document entries is the expanded state, the method further comprising displaying at least a name of an author of a document associated with the one of the document entries and a plurality of user-selectable icons, wherein when the presentation state of the one of the document entries is in the contracted state, the method further comprising hiding the user-selectable icons, wherein when one of the user-selectable icons is selected, the method further comprising opening an email window, the email window including a link to the document, and wherein when one of the user-selectable icons is selected, the method further comprising displaying the document entries corresponding to the document series, wherein each document series display section has a different document series publisher, wherein each publisher maintains editing control of one of the plurality of document entries corresponding to the document series. 23. The non-transitory computer readable storage medium of claim 17 , wherein when one of the user-selectable icons is selected, the method further comprises deleting the document entry. | 0.772616 |
9,390,174 | 2 | 4 | 2. The method of claim 1 , the method further comprising causing to be presented, using one or more processors, search results in an arrangement according to user input. | 2. The method of claim 1 , the method further comprising causing to be presented, using one or more processors, search results in an arrangement according to user input. 4. The method of claim 2 , wherein user input comprises a selection of a presentation technique. | 0.962294 |
6,104,989 | 1 | 4 | 1. A method for real time identification of topics in text, comprising the steps of: forming a battery of topics from training data; detecting topic changes in said text, using said battery and a first threshold ratio; identifying topics in said text, using said battery and a second threshold ratio; wherein said detecting and identifying steps are applied to said text in real time, wherein said first and second threshold ratios compare a metric having a likelihood measure, and wherein a topic can be identified for a current segment of said text prior to detecting a topic change in said segment. | 1. A method for real time identification of topics in text, comprising the steps of: forming a battery of topics from training data; detecting topic changes in said text, using said battery and a first threshold ratio; identifying topics in said text, using said battery and a second threshold ratio; wherein said detecting and identifying steps are applied to said text in real time, wherein said first and second threshold ratios compare a metric having a likelihood measure, and wherein a topic can be identified for a current segment of said text prior to detecting a topic change in said segment. 4. The method of claim 1 wherein said identifying step performs translation of segmented text using topic labeling of each segment. | 0.941256 |
8,423,349 | 29 | 31 | 29. One or more computer-readable media storing computer-executable instructions that, when executed on one or more processors, perform acts comprising: receiving a first corpus of phrases, each of the phrases comprising at least one word; determining, for each phrase of the first corpus of phrases: (i) a commonality of the phrase; (ii) a commonality of each word of the phrase; (iii) a readability of the phrase; (iv) a number of words in the phrase, or (v) a source of the phrase; scoring each phrase of the first corpus of phrases based on the commonality of the phrase, the commonality of each word of the phrase, the readability of the phrase, the number of words in the phrase, or the source of the phrase; and based at least in part on the scoring of the phrases, filtering out at least a portion of the first corpus of phrases to define a second corpus of phrases; outputting phrases of the second corpus of phrases to a user; and responsive to receiving a selection of a phrase by the user, associating the selected phrase with at least one of a shipping address, a device or location data in a server that is associated with the user. | 29. One or more computer-readable media storing computer-executable instructions that, when executed on one or more processors, perform acts comprising: receiving a first corpus of phrases, each of the phrases comprising at least one word; determining, for each phrase of the first corpus of phrases: (i) a commonality of the phrase; (ii) a commonality of each word of the phrase; (iii) a readability of the phrase; (iv) a number of words in the phrase, or (v) a source of the phrase; scoring each phrase of the first corpus of phrases based on the commonality of the phrase, the commonality of each word of the phrase, the readability of the phrase, the number of words in the phrase, or the source of the phrase; and based at least in part on the scoring of the phrases, filtering out at least a portion of the first corpus of phrases to define a second corpus of phrases; outputting phrases of the second corpus of phrases to a user; and responsive to receiving a selection of a phrase by the user, associating the selected phrase with at least one of a shipping address, a device or location data in a server that is associated with the user. 31. One or more computer-readable as recited in claim 29 , wherein the source of the phrase comprises a manner in which the phrase was generated. | 0.799169 |
8,639,505 | 11 | 18 | 11. A computer system configured to simplify the pasting of textual transcriptions from a transcription engine into an application by loading the textual transcription directly to a clipboard without launching a word processing application, comprising: a processor; memory in electronic communication with the processor; the processor configure to: send an audio file to a transcription engine remote from the processor; receive a textual transcription file of the audio file from the transcription engine; load in a first instance the textual transcription file into the clipboard without invoking the word processing application; and paste the textual transcription file from the clipboard into an application. | 11. A computer system configured to simplify the pasting of textual transcriptions from a transcription engine into an application by loading the textual transcription directly to a clipboard without launching a word processing application, comprising: a processor; memory in electronic communication with the processor; the processor configure to: send an audio file to a transcription engine remote from the processor; receive a textual transcription file of the audio file from the transcription engine; load in a first instance the textual transcription file into the clipboard without invoking the word processing application; and paste the textual transcription file from the clipboard into an application. 18. The computer system of claim 11 , wherein the computer system operates in a call center environment. | 0.873171 |
9,135,255 | 9 | 14 | 9. A system for identifying interests comprising one or more processors and one or more memory devices operably coupled to the one or more processors, the one or more memory devices storing executable and operational data effective to cause the one or more processors to: evaluate social media data of a user; identify from the social media data a plurality of communities each community of the plurality of communities including a plurality of friends of the user, each community of the plurality of communities identified according to social media interconnections and interactions among friends of the user in each community; for each community of the plurality of communities, identify common interests from personal interests of the plurality of friends of the user of the each community; identify, for each community of the plurality of communities, at least one characterizing interest of the common interests identified for the each community, the at least one characterizing interest being absent from any self-identified interests of the user in the social media data; and select one or more inferred interests for the user from among the at least one characterizing interests of the plurality of communities; wherein the executable and operational data further effective to cause the one or more processors to identify, for each community of the plurality of communities, at least one characterizing interest of the common interests identified for each community of the plurality of communities by, for each community: performing a first ranking of the common interests identified for the each community according to popularity of the common interests identified within the each community; and selecting the at least one characterizing interest for the each community according to the first ranking; and wherein the executable and operational data are further effective to cause the one or more processors to select the one or more inferred interests for the user from among the at least one characterizing interests of the plurality of communities by: evaluating frequency of interaction of the user with the plurality of friends of the user corresponding to each community of the plurality of communities; ranking the at least one characterizing interests of the plurality of communities according to the frequency of interaction of the user with the plurality of friends of the user corresponding to each community of the plurality of communities; and selecting the one or more inferred interests according to the ranking. | 9. A system for identifying interests comprising one or more processors and one or more memory devices operably coupled to the one or more processors, the one or more memory devices storing executable and operational data effective to cause the one or more processors to: evaluate social media data of a user; identify from the social media data a plurality of communities each community of the plurality of communities including a plurality of friends of the user, each community of the plurality of communities identified according to social media interconnections and interactions among friends of the user in each community; for each community of the plurality of communities, identify common interests from personal interests of the plurality of friends of the user of the each community; identify, for each community of the plurality of communities, at least one characterizing interest of the common interests identified for the each community, the at least one characterizing interest being absent from any self-identified interests of the user in the social media data; and select one or more inferred interests for the user from among the at least one characterizing interests of the plurality of communities; wherein the executable and operational data further effective to cause the one or more processors to identify, for each community of the plurality of communities, at least one characterizing interest of the common interests identified for each community of the plurality of communities by, for each community: performing a first ranking of the common interests identified for the each community according to popularity of the common interests identified within the each community; and selecting the at least one characterizing interest for the each community according to the first ranking; and wherein the executable and operational data are further effective to cause the one or more processors to select the one or more inferred interests for the user from among the at least one characterizing interests of the plurality of communities by: evaluating frequency of interaction of the user with the plurality of friends of the user corresponding to each community of the plurality of communities; ranking the at least one characterizing interests of the plurality of communities according to the frequency of interaction of the user with the plurality of friends of the user corresponding to each community of the plurality of communities; and selecting the one or more inferred interests according to the ranking. 14. The system of claim 9 , wherein the executable and operational data are further effective to cause the one or more processors to identify from the social media data the plurality of communities by at least one of: identifying friends of the plurality of friends of the user that have each other as friends; identifying friends of the plurality of friends of the user that have at least one other person as a common friend; and identifying friends of the plurality of friends of the user that have at least one attribute in common. | 0.584759 |
8,006,138 | 1 | 6 | 1. A method for software processing, comprising: accepting quality information comprising names of elements of software code and respective quality indications regarding tested acceptability of the elements; processing the names to extract a list of substrings that occur in the names; assigning respective metrics to the substrings responsively to the quality indications of the elements in whose names the substrings occur; and presenting at least some of the substrings to a user in accordance with the assigned metrics, wherein the quality indications are indicative of known faults in the respective elements, and wherein the quality indications are indicative of the known faults in the respective elements that were expected to be initially found by a first testing phase but were initially found by a second testing phase subsequent to the first testing phase. | 1. A method for software processing, comprising: accepting quality information comprising names of elements of software code and respective quality indications regarding tested acceptability of the elements; processing the names to extract a list of substrings that occur in the names; assigning respective metrics to the substrings responsively to the quality indications of the elements in whose names the substrings occur; and presenting at least some of the substrings to a user in accordance with the assigned metrics, wherein the quality indications are indicative of known faults in the respective elements, and wherein the quality indications are indicative of the known faults in the respective elements that were expected to be initially found by a first testing phase but were initially found by a second testing phase subsequent to the first testing phase. 6. The method according to claim 1 , and comprising: selecting one of the presented substrings; re-extracting the substrings from only the elements in whose names the selected substring occurs; re-assigning the metrics to the re-extracted substrings; and presenting at least some of the re-extracted substrings to the user in accordance with the re-assigned metrics. | 0.641176 |
9,773,045 | 14 | 18 | 14. A system for providing search results, the system comprising: a memory; a processor; and a component that is stored in said memory, that executes on said processor, that receives a query from a user, that determines that said query is one to be asked to a person in addition to, or instead of, a search engine, that obtains objective results corresponding to the query from a corpus of information, that identifies one or more people in a social graph whose relationship to said user meets a closeness condition and who have an aspect of relevance to said query, that creates person results that comprise a portion of said one or more people, that creates, for each of said one or more people, an explanation of each person's relevance to said query, and that provides, to said user, a set of results that comprise said objective results and said person results. | 14. A system for providing search results, the system comprising: a memory; a processor; and a component that is stored in said memory, that executes on said processor, that receives a query from a user, that determines that said query is one to be asked to a person in addition to, or instead of, a search engine, that obtains objective results corresponding to the query from a corpus of information, that identifies one or more people in a social graph whose relationship to said user meets a closeness condition and who have an aspect of relevance to said query, that creates person results that comprise a portion of said one or more people, that creates, for each of said one or more people, an explanation of each person's relevance to said query, and that provides, to said user, a set of results that comprise said objective results and said person results. 18. The system of claim 14 , said aspect of relevance being based on a determination that words in said query and an annotation of a person in said social graph are both associated with a concept in a concept graph. | 0.859109 |
9,449,530 | 11 | 20 | 11. Non-transitory computer readable media embodying executable instructions for controlling a computer for an automatic diet tracking method, comprising: receiving text from a user that describes a food that is to be tracked; parsing the received text into text segments; identifying automatically in each parsed text segment a food quantity value and a food quantity unit for said food that is to be tracked, said identifying comprising searching said parsed text segment for a quantity value followed directly by a quantity unit, and assigning said quantity value and said quantity unit to be said food quantity value and said food quantity unit for the food to be tracked, and upon not finding a quantity value followed directly by a quantity unit, selecting as said food quantity value and said food quantity unit a most frequently occurring quantity value and quantity unit for said food to be tracked; cleaning the parsed text segments to identify and remove words, connected spaces, and punctuation that are not used to identify food and produce parsed cleaned text; processing the parsed cleaned text segments using a text match algorithm to find said food that is to be tracked in each parsed cleaned text segment comprising ranking each food text match found using a ranking process, and selecting the food with a predetermined rank to be the food that is to be tracked; and reporting diet tracking information for said food to be tracked. | 11. Non-transitory computer readable media embodying executable instructions for controlling a computer for an automatic diet tracking method, comprising: receiving text from a user that describes a food that is to be tracked; parsing the received text into text segments; identifying automatically in each parsed text segment a food quantity value and a food quantity unit for said food that is to be tracked, said identifying comprising searching said parsed text segment for a quantity value followed directly by a quantity unit, and assigning said quantity value and said quantity unit to be said food quantity value and said food quantity unit for the food to be tracked, and upon not finding a quantity value followed directly by a quantity unit, selecting as said food quantity value and said food quantity unit a most frequently occurring quantity value and quantity unit for said food to be tracked; cleaning the parsed text segments to identify and remove words, connected spaces, and punctuation that are not used to identify food and produce parsed cleaned text; processing the parsed cleaned text segments using a text match algorithm to find said food that is to be tracked in each parsed cleaned text segment comprising ranking each food text match found using a ranking process, and selecting the food with a predetermined rank to be the food that is to be tracked; and reporting diet tracking information for said food to be tracked. 20. The non-transitory computer readable media of claim 11 further comprising providing diet recommendations based food availability within a predetermined distance of a location of said user and target nutrient values of said user. | 0.808896 |
9,483,768 | 1 | 9 | 1. A computer-implemented method, comprising: receiving, by a processor, interaction data corresponding to one or more interactions between a customer and a customer support representative and storing said interaction data in a memory; extracting the stored interaction data from the memory and detecting, by the processor, the presence of at least one language associated with the interaction data by comparing whole text strings or portions of text in the interaction data with available language detection models for different languages, and predicting a best matching language corresponding to the interaction data; generating, by the processor, textual content in a plurality of languages corresponding to the interaction data based at least in part on translating the interaction data using two or more languages different than the at least one language; determining, by the processor, at least one emotion score for text corresponding to each language from among the plurality of languages; determining, by the processor, an aggregate emotion score using the at least one emotion score for the text corresponding to the each language; modeling, by the processor, an interaction experience of the customer based at least in part on the aggregate emotion score; and providing at least one recommendation to the customer based on said modeled interaction experience. | 1. A computer-implemented method, comprising: receiving, by a processor, interaction data corresponding to one or more interactions between a customer and a customer support representative and storing said interaction data in a memory; extracting the stored interaction data from the memory and detecting, by the processor, the presence of at least one language associated with the interaction data by comparing whole text strings or portions of text in the interaction data with available language detection models for different languages, and predicting a best matching language corresponding to the interaction data; generating, by the processor, textual content in a plurality of languages corresponding to the interaction data based at least in part on translating the interaction data using two or more languages different than the at least one language; determining, by the processor, at least one emotion score for text corresponding to each language from among the plurality of languages; determining, by the processor, an aggregate emotion score using the at least one emotion score for the text corresponding to the each language; modeling, by the processor, an interaction experience of the customer based at least in part on the aggregate emotion score; and providing at least one recommendation to the customer based on said modeled interaction experience. 9. The method of claim 1 , wherein the aggregate emotion score is determined to be one of a highest emotion score or a lowest emotion score from among the at least one emotion score. | 0.886957 |
8,055,298 | 7 | 8 | 7. A communication device comprising: a microphone; a speaker; an input device; a display; a camera; a wireless communicating system; a voice communicating implementer to implement voice communication by utilizing said microphone and said speaker; an OCR implementer, wherein an image data is input via said camera and alphanumeric data is extracted from said image data; a caller ID implementer which retrieves a predetermined color data and/or sound data which is specific to the caller of the incoming call received by said communication device, and outputs the color and/or sound corresponding to said predetermined color data and/or sound data from said communication device; an auto time adjusting implementer which automatically adjusts the clock of said communication device in accordance with a wireless signal received by said wireless communication system; a calculating implementer which implements mathematical calculation by utilizing digits input via said input device; a word processing implementer which includes a bold formatting implementer, an italic formatting implementer, and/or a font formatting implementer, wherein said bold formatting implementer changes alphanumeric data to bold, said italic formatting implementer changes alphanumeric data to italic, and said font formatting implementer changes alphanumeric data to a selected font; a startup software implementer, wherein a startup software identification data storage area stores a startup software identification data which is an identification of a certain software program selected by the user, and when the power of said communication device is turned on, said startup software implementer retrieves said startup software identification data from said startup software identification data storage area and activates said certain software program; a stereo audio data playback implementer which playbacks and outputs in a stereo fashion the audio data selected by the user of said communication device; a digital camera implementer, wherein a photo quality identifying command is input via said input device, and when a photo taking command is input via said input device, a photo data retrieved via said camera is stored in a photo data storage area with the quality indicated by said photo quality identifying command; a multiple language displaying implementer, wherein a specific language is selected from a plurality of languages, and the interface to operate said communication device is displayed with said specific language; a caller's information displaying implementer which displays a personal information regarding caller on said display when said communication device receives a phone call; a communication device remote controlling implementer, wherein said communication device is remotely controlled by a computer via a network; a shortcut icon displaying implementer, wherein a shortcut icon is displayed on said display, and a software program indicated by said shortcut icon is activated when said shortcut icon is selected; and a multiple channel processing implementer which sends data in a wireless fashion by utilizing multiple channels. | 7. A communication device comprising: a microphone; a speaker; an input device; a display; a camera; a wireless communicating system; a voice communicating implementer to implement voice communication by utilizing said microphone and said speaker; an OCR implementer, wherein an image data is input via said camera and alphanumeric data is extracted from said image data; a caller ID implementer which retrieves a predetermined color data and/or sound data which is specific to the caller of the incoming call received by said communication device, and outputs the color and/or sound corresponding to said predetermined color data and/or sound data from said communication device; an auto time adjusting implementer which automatically adjusts the clock of said communication device in accordance with a wireless signal received by said wireless communication system; a calculating implementer which implements mathematical calculation by utilizing digits input via said input device; a word processing implementer which includes a bold formatting implementer, an italic formatting implementer, and/or a font formatting implementer, wherein said bold formatting implementer changes alphanumeric data to bold, said italic formatting implementer changes alphanumeric data to italic, and said font formatting implementer changes alphanumeric data to a selected font; a startup software implementer, wherein a startup software identification data storage area stores a startup software identification data which is an identification of a certain software program selected by the user, and when the power of said communication device is turned on, said startup software implementer retrieves said startup software identification data from said startup software identification data storage area and activates said certain software program; a stereo audio data playback implementer which playbacks and outputs in a stereo fashion the audio data selected by the user of said communication device; a digital camera implementer, wherein a photo quality identifying command is input via said input device, and when a photo taking command is input via said input device, a photo data retrieved via said camera is stored in a photo data storage area with the quality indicated by said photo quality identifying command; a multiple language displaying implementer, wherein a specific language is selected from a plurality of languages, and the interface to operate said communication device is displayed with said specific language; a caller's information displaying implementer which displays a personal information regarding caller on said display when said communication device receives a phone call; a communication device remote controlling implementer, wherein said communication device is remotely controlled by a computer via a network; a shortcut icon displaying implementer, wherein a shortcut icon is displayed on said display, and a software program indicated by said shortcut icon is activated when said shortcut icon is selected; and a multiple channel processing implementer which sends data in a wireless fashion by utilizing multiple channels. 8. The communication device of claim 7 , wherein said communication device is a handheld device. | 0.902041 |
10,114,899 | 1 | 4 | 1. A method of analyzing data, comprising: generating, by an entity, a query based at least in part on a topic of interest; executing the query on a plurality of data sources, at least one of the plurality of data sources comprising customer information or financial information; selecting, by the entity, a data source from the plurality of data sources for monitoring based on a correlation between the data source and the topic of interest, the correlation determined based on results of the executed query; monitoring, based on a set schedule, the data source for matches to the query to extract data from the data source when at least an update to stored data matches the query, newly added data matches the query, or a combination thereof; extracting data from the data source when at least an update to stored data matches the query, newly added data matches the query, or a combination thereof; determining an extraction rate for extracting the data, the extraction rate indicating an amount of the data that is extracted over a first time period; determining a first processing rate for processing the extracted data with a number of parallel processors, the first processing rate indicating an amount of extracted data that is processed over a second time period; dynamically adjusting the number of parallel processors for analyzing the extracted data based on the extraction rate to obtain a second processing rate that is greater than the first processing rate; analyzing, with the parallel processors, the extracted data to determine at least one of a sentiment, an index, a pattern, or a combination thereof; establishing a two-way communication channel, between at least the entity that selected the data source for monitoring and a user device of a user that provided data to the data source, based on the analysis of the extracted data; transmitting, from the entity via the two-way communication channel, a first message directed to the user device based on the analysis of the extracted data; and receiving, from the user device via the two-way communication channel, a second message in response to the first message directed to the user device. | 1. A method of analyzing data, comprising: generating, by an entity, a query based at least in part on a topic of interest; executing the query on a plurality of data sources, at least one of the plurality of data sources comprising customer information or financial information; selecting, by the entity, a data source from the plurality of data sources for monitoring based on a correlation between the data source and the topic of interest, the correlation determined based on results of the executed query; monitoring, based on a set schedule, the data source for matches to the query to extract data from the data source when at least an update to stored data matches the query, newly added data matches the query, or a combination thereof; extracting data from the data source when at least an update to stored data matches the query, newly added data matches the query, or a combination thereof; determining an extraction rate for extracting the data, the extraction rate indicating an amount of the data that is extracted over a first time period; determining a first processing rate for processing the extracted data with a number of parallel processors, the first processing rate indicating an amount of extracted data that is processed over a second time period; dynamically adjusting the number of parallel processors for analyzing the extracted data based on the extraction rate to obtain a second processing rate that is greater than the first processing rate; analyzing, with the parallel processors, the extracted data to determine at least one of a sentiment, an index, a pattern, or a combination thereof; establishing a two-way communication channel, between at least the entity that selected the data source for monitoring and a user device of a user that provided data to the data source, based on the analysis of the extracted data; transmitting, from the entity via the two-way communication channel, a first message directed to the user device based on the analysis of the extracted data; and receiving, from the user device via the two-way communication channel, a second message in response to the first message directed to the user device. 4. The method of claim 1 , in which the plurality of data sources include open-source data sources that are publically available and closed-source data sources that are not publically accessible. | 0.844 |
8,694,483 | 1 | 2 | 1. A method for assisting a user to develop a query in a natural language, comprising: storing logs comprising information derived from prior user search sessions in which user queries were input to a search engine for retrieving responsive instances from a knowledge base; storing a collection of query suggestions, each of the query suggestions formulated to retrieve at least one responsive instance in the knowledge base, each query suggestion being constructed from an index of the knowledge base and comprising a linguistically coherent expression which includes one or a group of syntactically related words, the query suggestion having a surface form which is presented to a user and an underlying form, and wherein at least one instance of each query suggestion is present in the knowledge base; ranking the query suggestions in the collection, based at least in part on the stored logs and the frequency of instances of the query suggestion in the knowledge base; receiving a user's query in a natural language; and while the user's query is being entered, with a computer processor, generating a subset of the ranked collection of query suggestions and presenting at least one of the subset to the user as a candidate for a user query, the subset being based on that portion of the user's query already entered, the presentation of query suggestions in the subset of query suggestions being based on their respective rankings in the collection, whereby at least some of the presented query suggestions are alternate queries rather than extensions of the user's query. | 1. A method for assisting a user to develop a query in a natural language, comprising: storing logs comprising information derived from prior user search sessions in which user queries were input to a search engine for retrieving responsive instances from a knowledge base; storing a collection of query suggestions, each of the query suggestions formulated to retrieve at least one responsive instance in the knowledge base, each query suggestion being constructed from an index of the knowledge base and comprising a linguistically coherent expression which includes one or a group of syntactically related words, the query suggestion having a surface form which is presented to a user and an underlying form, and wherein at least one instance of each query suggestion is present in the knowledge base; ranking the query suggestions in the collection, based at least in part on the stored logs and the frequency of instances of the query suggestion in the knowledge base; receiving a user's query in a natural language; and while the user's query is being entered, with a computer processor, generating a subset of the ranked collection of query suggestions and presenting at least one of the subset to the user as a candidate for a user query, the subset being based on that portion of the user's query already entered, the presentation of query suggestions in the subset of query suggestions being based on their respective rankings in the collection, whereby at least some of the presented query suggestions are alternate queries rather than extensions of the user's query. 2. The method of claim 1 , wherein each of the responsive instances comprises a text string. | 0.850649 |
8,249,915 | 17 | 18 | 17. A system for user evaluation of at least one product that has a plurality of features whereby users operate user devices that are operably coupled to the system over network communication therebetween, the system comprising: a computer processing system which includes first software logic, second software logic, third software logic, fourth software logic, fifth software logic and sixth software logic, each operating on the computer processing system; the first software logic for interacting with a plurality of users via network communication between the user devices and the computer processing system to assign user-supplied ratings, wherein the features of the at least one product are logically organized into a hierarchical structure of feature levels, and wherein each user supplied rating corresponds to a respective feature level and represents a user derived assessment of at least one feature of the corresponding feature level of the at least one product; the second software logic for processing the user-supplied ratings to associate moderation scores to the user-supplied ratings; the third software logic for interacting with a given user via network communication between a given user device and the system to specify a threshold moderation score that defines a first range of acceptable moderation scores; the fourth software logic for computing a composite rating for a given feature level of the at least one product based upon a set of the user-supplied ratings corresponding to the given feature level of the at least one product, wherein each user-supplied rating within the set is associated with a moderation score that falls within the first range of acceptable moderation scores, thereby omitting from the composite rating contributions of user-supplied ratings for the at least one product whose associated moderation scores fall outside the first range of acceptable moderation scores; the fifth software logic for outputting the composite rating for supply to the given user via network communication between the system and the given user device; and the sixth software logic for selectively enabling particular users to evaluate the user-supplied ratings and associated moderation scores with the user-supplied ratings whereby the particular users are selectively enabled based upon moderation points earned by the particular users, and wherein the moderation points are limited in number and distributed to a respective user by an automatic process that accounts for positive evaluation of the respective user's own user-supplied ratings. | 17. A system for user evaluation of at least one product that has a plurality of features whereby users operate user devices that are operably coupled to the system over network communication therebetween, the system comprising: a computer processing system which includes first software logic, second software logic, third software logic, fourth software logic, fifth software logic and sixth software logic, each operating on the computer processing system; the first software logic for interacting with a plurality of users via network communication between the user devices and the computer processing system to assign user-supplied ratings, wherein the features of the at least one product are logically organized into a hierarchical structure of feature levels, and wherein each user supplied rating corresponds to a respective feature level and represents a user derived assessment of at least one feature of the corresponding feature level of the at least one product; the second software logic for processing the user-supplied ratings to associate moderation scores to the user-supplied ratings; the third software logic for interacting with a given user via network communication between a given user device and the system to specify a threshold moderation score that defines a first range of acceptable moderation scores; the fourth software logic for computing a composite rating for a given feature level of the at least one product based upon a set of the user-supplied ratings corresponding to the given feature level of the at least one product, wherein each user-supplied rating within the set is associated with a moderation score that falls within the first range of acceptable moderation scores, thereby omitting from the composite rating contributions of user-supplied ratings for the at least one product whose associated moderation scores fall outside the first range of acceptable moderation scores; the fifth software logic for outputting the composite rating for supply to the given user via network communication between the system and the given user device; and the sixth software logic for selectively enabling particular users to evaluate the user-supplied ratings and associated moderation scores with the user-supplied ratings whereby the particular users are selectively enabled based upon moderation points earned by the particular users, and wherein the moderation points are limited in number and distributed to a respective user by an automatic process that accounts for positive evaluation of the respective user's own user-supplied ratings. 18. A system according to claim 17 , further comprising: seventh software logic operating on the computer processing system for processing user-supplied feedback at the computer processing system to derive a moderation score associated with a particular user-supplied rating. | 0.560703 |
8,612,468 | 4 | 5 | 4. The method of claim 3 , wherein the new aspects of the object model are logically derived associations. | 4. The method of claim 3 , wherein the new aspects of the object model are logically derived associations. 5. The method of claim 4 , further comprising defining the new aspects of the object model in terms of logically derived associations that have been previously defined. | 0.958188 |
9,565,521 | 1 | 6 | 1. A method comprising: recognizing an activity performed at a first place based on sensor data of an electronic device, wherein the first place comprises an unlabeled semantic place without an assigned semantic place label; determining a location for the first place by performing localization for the electronic device; determining an observed mapping between the activity and the location for the first place; determining a typical mapping between the activity and a second place, wherein the second place comprises a labeled semantic place with an assigned semantic place label; based on the observed mapping and the typical mapping, assigning the same sematic place label assigned to the labeled semantic place to the location for the first place; and updating a semantic place map to include the semantic place label assigned to the location for the first place. | 1. A method comprising: recognizing an activity performed at a first place based on sensor data of an electronic device, wherein the first place comprises an unlabeled semantic place without an assigned semantic place label; determining a location for the first place by performing localization for the electronic device; determining an observed mapping between the activity and the location for the first place; determining a typical mapping between the activity and a second place, wherein the second place comprises a labeled semantic place with an assigned semantic place label; based on the observed mapping and the typical mapping, assigning the same sematic place label assigned to the labeled semantic place to the location for the first place; and updating a semantic place map to include the semantic place label assigned to the location for the first place. 6. The method of claim 1 , further comprising: identifying one of a franchise name or a subcategory of the first place based on at least one of: a unique activity pattern, device identification information for the electronic device, or a layout of the first place. | 0.775891 |
7,685,105 | 15 | 17 | 15. The system of claim 8 wherein the controlled vocabulary contains terms of metadata and an electronic thesaurus relates one metadata term to search terms, specific values or other user-specified indicators, wherein the electronic thesaurus comprises equivalent terms. | 15. The system of claim 8 wherein the controlled vocabulary contains terms of metadata and an electronic thesaurus relates one metadata term to search terms, specific values or other user-specified indicators, wherein the electronic thesaurus comprises equivalent terms. 17. The system of claim 15 wherein the electronic thesaurus contains place names related to latitude and longitude, or other geographic identifier. | 0.968099 |
8,203,767 | 9 | 10 | 9. The apparatus according to claim 7 , wherein adjusting the first conveyance mode is in accordance with a slip of the original document in the first conveying path or the second conveying path. | 9. The apparatus according to claim 7 , wherein adjusting the first conveyance mode is in accordance with a slip of the original document in the first conveying path or the second conveying path. 10. The apparatus according to claim 9 , wherein adjusting the first conveyance mode on the first conveying path side is with measuring a slip of the original document passing through the first conveying path, or adjusting the first conveyance mode on the second conveying path side is with measuring a slip of the original document passing through the second conveying path. | 0.905779 |
7,519,568 | 1 | 5 | 1. A computer-implemented method comprising: selecting at least one rule included in an operational knowledge associated with an application being monitored; associating, one or more playbook-based tasks, playbook-based views, or playbook-based links with the at least one rule, the playbook-based tasks, playbook-based views, and playbook-based links being for one or more of diagnosing, resolving, and verifying a problem associated with the application; and generating an integrated management pack responsive to the selecting and the associating, the integrated management pack enabling sharing of information between the operational knowledge and the one or more playbook-based tasks, the playbook-based views, or the playbook-based links. | 1. A computer-implemented method comprising: selecting at least one rule included in an operational knowledge associated with an application being monitored; associating, one or more playbook-based tasks, playbook-based views, or playbook-based links with the at least one rule, the playbook-based tasks, playbook-based views, and playbook-based links being for one or more of diagnosing, resolving, and verifying a problem associated with the application; and generating an integrated management pack responsive to the selecting and the associating, the integrated management pack enabling sharing of information between the operational knowledge and the one or more playbook-based tasks, the playbook-based views, or the playbook-based links. 5. A method as recited in claim 1 , wherein information and resources associated with the integrated management pack are represented in a markup language. | 0.876404 |
9,681,016 | 13 | 14 | 13. The method of claim 12 , further comprising injecting a reference number corresponding to the identified portion in the printed, scanned or photocopied document, wherein the reference number relates to the hand-written annotation. | 13. The method of claim 12 , further comprising injecting a reference number corresponding to the identified portion in the printed, scanned or photocopied document, wherein the reference number relates to the hand-written annotation. 14. The method of claim 13 , wherein the hand-written annotation is included as a footnote, or a part of appendix. | 0.972195 |
9,562,781 | 1 | 16 | 1. A mapping system implemented on a computer processor and configured to provide at least one of route guidance and reassurance from an origin to a destination, comprising: a database implemented on a one or more computer storage devices containing: landmark information including for each landmark: a text description; a location; a category of landmark; and a route guidance and reassurance ratings comprising at least one of: multiple user defined rating of at least one of: accuracy; and usefulness; single user defined rating of at least one of: accuracy; and usefulness; fee based ratings; and a road-based routing information including for a plurality of road segments: a text description of each road segment and attribution; and a location of each road segment; and the computer processor controlling a route guidance generator to accept a user input with respect to how to implement route guidance and reassurance based on one or more of the route guidance and reassurance ratings and a preference for landmark based route guidance and reassurance or road-based route guidance and reassurance. | 1. A mapping system implemented on a computer processor and configured to provide at least one of route guidance and reassurance from an origin to a destination, comprising: a database implemented on a one or more computer storage devices containing: landmark information including for each landmark: a text description; a location; a category of landmark; and a route guidance and reassurance ratings comprising at least one of: multiple user defined rating of at least one of: accuracy; and usefulness; single user defined rating of at least one of: accuracy; and usefulness; fee based ratings; and a road-based routing information including for a plurality of road segments: a text description of each road segment and attribution; and a location of each road segment; and the computer processor controlling a route guidance generator to accept a user input with respect to how to implement route guidance and reassurance based on one or more of the route guidance and reassurance ratings and a preference for landmark based route guidance and reassurance or road-based route guidance and reassurance. 16. The mapping system of claim 1 wherein the user can input landmark information and one or more of: control access to the inputted landmark information for other users, rate the inputted landmarks and categorize the landmarks as being personal. | 0.651558 |
9,760,933 | 19 | 22 | 19. An interactive shopping advisor comprising: one or more processors for executing programs; a non-transitory memory operatively coupled to the one or more processors; a network interface device operatively coupled to the one or more processors for communicating with a user via a communications network; and a program stored in the memory for: configuring a plurality of long short term memory modules as a recurrent neural network, wherein the plurality of long short term memory modules are each provided with one or more respective gates for determining when a corresponding input value of a plurality of input values should persist in memory, and when the corresponding input value should comprise an output value; receiving a natural language query for a product search, wherein each respective long short term memory module of the plurality of long short term memory modules receives a corresponding word from the natural language query; generating an initial product recommendation from the natural language query; receiving a natural language preference parameter for refining the product search; mapping the natural language preference parameter to a product attribute value for the product search; identifying an adjustment orientation of the product attribute value from the natural language preference parameter; and applying the adjustment orientation to the natural language query to provide a refined product recommendation for the product search by determining, for each of the one or more respective gates, when the corresponding input value should persist in memory, and when the corresponding input value should comprise an output value. | 19. An interactive shopping advisor comprising: one or more processors for executing programs; a non-transitory memory operatively coupled to the one or more processors; a network interface device operatively coupled to the one or more processors for communicating with a user via a communications network; and a program stored in the memory for: configuring a plurality of long short term memory modules as a recurrent neural network, wherein the plurality of long short term memory modules are each provided with one or more respective gates for determining when a corresponding input value of a plurality of input values should persist in memory, and when the corresponding input value should comprise an output value; receiving a natural language query for a product search, wherein each respective long short term memory module of the plurality of long short term memory modules receives a corresponding word from the natural language query; generating an initial product recommendation from the natural language query; receiving a natural language preference parameter for refining the product search; mapping the natural language preference parameter to a product attribute value for the product search; identifying an adjustment orientation of the product attribute value from the natural language preference parameter; and applying the adjustment orientation to the natural language query to provide a refined product recommendation for the product search by determining, for each of the one or more respective gates, when the corresponding input value should persist in memory, and when the corresponding input value should comprise an output value. 22. The interactive shopping advisor of claim 19 further configured for identifying the adjustment orientation by: identifying a product attribute corresponding to the natural language preference parameter; and when the product attribute is not a numerical attribute: obtaining a current product attribute value for a current product that was identified during a most recent product search, wherein the current product attribute value is associated with one or more respective comment words, and wherein each of the one or more respective comment words is associated with a corresponding support value; and comparing the current product attribute value with the product attribute value to which the natural language preference parameter is mapped, to identify a linear adjustment orientation comprising a direction and a distance along an axis that defines the product attribute corresponding to the natural language preference parameter. | 0.500532 |
8,601,016 | 7 | 10 | 7. The method of claim 1 , further comprising tuning the database based on the generated static SQL query before executing the source code; generating a dynamic SQL query based on the static SQL query by the ORM during execution of the source code; and executing the dynamic SQL query against the tuned database during execution of the source code. | 7. The method of claim 1 , further comprising tuning the database based on the generated static SQL query before executing the source code; generating a dynamic SQL query based on the static SQL query by the ORM during execution of the source code; and executing the dynamic SQL query against the tuned database during execution of the source code. 10. The method of claim 7 , wherein tuning the database comprises adding an index to a table based on the static SQL query. | 0.971262 |
8,117,191 | 8 | 9 | 8. The XDBMS of claim 1 , wherein the XDBMS is further configured to generate an access privilege index from the structure-based and/or instance-based access privileges, and wherein the execution engine is configured to scan the access privilege index. | 8. The XDBMS of claim 1 , wherein the XDBMS is further configured to generate an access privilege index from the structure-based and/or instance-based access privileges, and wherein the execution engine is configured to scan the access privilege index. 9. The XDBMS of claim 8 , wherein the access privilege index comprises one or more index entries each comprising a reference to a user and/or group, a reference to an XML document, an access privilege and/or an identifier. | 0.883403 |
8,249,352 | 8 | 10 | 8. A document image processing method comprising; extracting at least one of character row images included in the specified sentence region; recognizing respective characters included in the extracted character row image; interpreting an original sentence character row comprising the recognized characters and generates an interpreted sentence character row; arranging the respective character row images in the sentence region by contracting the respective character row images, the respective character row images each including an image of an original character row, and the generated respective interpreted sentence character rows in a vacant region except a region arranging the respective character row images from the sentence region; and generating a data of an output document arranged with the respective character row images and the respective interpreted sentence character rows in the sentence region, wherein the arranging includes arranging the interpreted sentence character row and the original sentence character row by first aligning the interpreted sentence character row and the original sentence character row, then by correcting a size of the longer of the interpreted sentence character row and the original sentence character row to match the length of the shorter, and then by confining the interpreted sentence character row and the original sentence character row in the sentence region. | 8. A document image processing method comprising; extracting at least one of character row images included in the specified sentence region; recognizing respective characters included in the extracted character row image; interpreting an original sentence character row comprising the recognized characters and generates an interpreted sentence character row; arranging the respective character row images in the sentence region by contracting the respective character row images, the respective character row images each including an image of an original character row, and the generated respective interpreted sentence character rows in a vacant region except a region arranging the respective character row images from the sentence region; and generating a data of an output document arranged with the respective character row images and the respective interpreted sentence character rows in the sentence region, wherein the arranging includes arranging the interpreted sentence character row and the original sentence character row by first aligning the interpreted sentence character row and the original sentence character row, then by correcting a size of the longer of the interpreted sentence character row and the original sentence character row to match the length of the shorter, and then by confining the interpreted sentence character row and the original sentence character row in the sentence region. 10. The document image processing method according to claim 8 , further comprising: determining a size of the character row image and a character size of the interpreted sentence character row such that a length of connecting the respective character row images and a length of connecting the respective interpreted sentence character rows are substantially equal to each other. | 0.734923 |
8,380,840 | 1 | 13 | 1. A method comprising: receiving, by a Session Initiation Protocol (SIP) server, first call event information, associated with a first call event, from a first module, the first call event information from the first module being processed by the first module using a first type of proprietary application; receiving, by the SIP server, second call event information, associated with a second call event, from a second module, the second call event information from the second module being processed by the second module using a second type of proprietary application, the second type of proprietary application being different from the first type of proprietary application; converting, by the SIP server, the first call event information from a first format associated with the first type of proprietary application into a first Extensible Markup Language (XML) document to generate a first call event record for the first call event information; converting, by the SIP server, the second call event information from a second format associated with the second type of proprietary application into a second XML document to generate a second call event record for the second call event information; creating, by the SIP server, an XML call event file based on the first XML document and the second XML document, creating the XML call event file including: generating a first section that includes data identifying relationships associated with one or more tags included in the XML call event file, generating a second section that includes data identifying the SIP server, generating third section that identifies a type of a first message associated with the first call event and a type of a second message associated with the second call event record, and generating a fourth section that includes information associated with a processing of the first message and information associated with a processing of the second message; and monitoring, by the SIP server, network traffic associated with the SIP server based on the XML call event file using a third type of proprietary application that is different than the first type of proprietary application and the second type of proprietary application. | 1. A method comprising: receiving, by a Session Initiation Protocol (SIP) server, first call event information, associated with a first call event, from a first module, the first call event information from the first module being processed by the first module using a first type of proprietary application; receiving, by the SIP server, second call event information, associated with a second call event, from a second module, the second call event information from the second module being processed by the second module using a second type of proprietary application, the second type of proprietary application being different from the first type of proprietary application; converting, by the SIP server, the first call event information from a first format associated with the first type of proprietary application into a first Extensible Markup Language (XML) document to generate a first call event record for the first call event information; converting, by the SIP server, the second call event information from a second format associated with the second type of proprietary application into a second XML document to generate a second call event record for the second call event information; creating, by the SIP server, an XML call event file based on the first XML document and the second XML document, creating the XML call event file including: generating a first section that includes data identifying relationships associated with one or more tags included in the XML call event file, generating a second section that includes data identifying the SIP server, generating third section that identifies a type of a first message associated with the first call event and a type of a second message associated with the second call event record, and generating a fourth section that includes information associated with a processing of the first message and information associated with a processing of the second message; and monitoring, by the SIP server, network traffic associated with the SIP server based on the XML call event file using a third type of proprietary application that is different than the first type of proprietary application and the second type of proprietary application. 13. The method of claim 1 , where one of the first call event or the second call event is associated with a network fault condition. | 0.909091 |
8,381,095 | 1 | 2 | 1. A method for automated comparison of Darwin Information Typing Architecture (DITA) documents for revision mark-up, the method comprising: reading via a processing unit document data from a first DITA document into a first document object model tree comprising a plurality of nodes, and from a second DITA document into a second document object model tree comprising a plurality of nodes; identifying and collapsing via the processing unit emphasis subtree nodes in the first document object model tree into their parent nodes in the first document object model tree, and emphasis subtree nodes in the second document object model tree into their parent nodes in the second document object model tree, the collapsing comprising caching emphasis data from the identified subtree nodes; the processing unit transforming via a preorder traversal the first document object model tree into a first pre-order node list, and the second document object model tree into a second pre-order node list, the listed nodes each comprising primary sort key information and document structure metadata, and wherein the transforming captures adjacent sibling emphasis subtree nodes as single text nodes; merging via the processing unit the first and second node lists into a merged node list via a first phase longest common subsequence process that recognizes matches of node pairs from each list that have primary sort key information and document structure metadata meeting a threshold percentage of match, and that saves differences between matching tokens of the node pairs; refining table comparisons in the merged node list via at least one additional longest common subsequence process; building a merged document object model from the merged node list and the refined table comparisons; and transforming the built merged document object model into a hypertext mark-up language document that displays the saved differences between the matching tokens as word-level highlighting mark-ups within the refined table comparisons. | 1. A method for automated comparison of Darwin Information Typing Architecture (DITA) documents for revision mark-up, the method comprising: reading via a processing unit document data from a first DITA document into a first document object model tree comprising a plurality of nodes, and from a second DITA document into a second document object model tree comprising a plurality of nodes; identifying and collapsing via the processing unit emphasis subtree nodes in the first document object model tree into their parent nodes in the first document object model tree, and emphasis subtree nodes in the second document object model tree into their parent nodes in the second document object model tree, the collapsing comprising caching emphasis data from the identified subtree nodes; the processing unit transforming via a preorder traversal the first document object model tree into a first pre-order node list, and the second document object model tree into a second pre-order node list, the listed nodes each comprising primary sort key information and document structure metadata, and wherein the transforming captures adjacent sibling emphasis subtree nodes as single text nodes; merging via the processing unit the first and second node lists into a merged node list via a first phase longest common subsequence process that recognizes matches of node pairs from each list that have primary sort key information and document structure metadata meeting a threshold percentage of match, and that saves differences between matching tokens of the node pairs; refining table comparisons in the merged node list via at least one additional longest common subsequence process; building a merged document object model from the merged node list and the refined table comparisons; and transforming the built merged document object model into a hypertext mark-up language document that displays the saved differences between the matching tokens as word-level highlighting mark-ups within the refined table comparisons. 2. The method of claim 1 , further comprising: separating out table segments from the merged node list into a table node list; recovering the cached emphasis data for the table segments in the table node list, wherein the step of refining the table comparisons in the merged node list via the at least one additional longest common subsequence process is performed on the table node list table segments comprising the recovered cached emphasis data; and embedding the refined table comparisons into the merged node list in combination with the non-table node list prior to the building of the merged document object model from the merged node list. | 0.604396 |
7,673,295 | 1 | 3 | 1. A method for compile-time detection of non-concurrency in a parallel program having a base language and a parallel programming language, the method comprising: modeling a program control flow of a subroutine of the parallel program, utilizing at least one processing unit, in a control flow graph having plural phases, wherein each phase having plural nodes; modeling a program hierarchical loop structure of the subroutine of the base language and a parallel programming language constructs in a region tree of the subroutine, utilizing the at least one processing unit, wherein the region tree comprises at least one construct edge defining a cycle between at least one end construct directive node and at least one begin construct directive node, wherein the at least one construct edge does not reflect control transfer of a the subroutine; analyzing the control flow graph and the region tree, utilizing the at least one processing unit, to identify plural parallel regions; analyzing a parallel region, utilizing the at least one processing unit, to identify plural static phases, each static phases having one or more nodes; and comparing nodes of the control flow graph and nodes of the static phases, utilizing the at least one processing unit, to determine non-concurrency at compile time for nodes in the same phase. | 1. A method for compile-time detection of non-concurrency in a parallel program having a base language and a parallel programming language, the method comprising: modeling a program control flow of a subroutine of the parallel program, utilizing at least one processing unit, in a control flow graph having plural phases, wherein each phase having plural nodes; modeling a program hierarchical loop structure of the subroutine of the base language and a parallel programming language constructs in a region tree of the subroutine, utilizing the at least one processing unit, wherein the region tree comprises at least one construct edge defining a cycle between at least one end construct directive node and at least one begin construct directive node, wherein the at least one construct edge does not reflect control transfer of a the subroutine; analyzing the control flow graph and the region tree, utilizing the at least one processing unit, to identify plural parallel regions; analyzing a parallel region, utilizing the at least one processing unit, to identify plural static phases, each static phases having one or more nodes; and comparing nodes of the control flow graph and nodes of the static phases, utilizing the at least one processing unit, to determine non-concurrency at compile time for nodes in the same phase. 3. The method of claim 1 wherein analyzing a parallel region further comprises: performing a forward depth-first-search of the parallel region from an each barrier node through subsequent nodes to a subsequent barrier node; performing a backward depth-first-search of the parallel region from each barrier node through preceding nodes to a preceding barrier node; and determining one or more phases associated with each non-barrier node of the parallel region. | 0.706633 |
4,430,726 | 5 | 6 | 5. The method of claim 4 wherein said assigning step (c) further includes the steps of: determining the busy status of each assigned transcriber terminal in said assignment table and assigning a transcriber terminal to a voice dictation segment from a dictator terminal having no assigned transcriber terminal by selecting a non-busy transcriber terminal presently assigned to another dictation terminal. | 5. The method of claim 4 wherein said assigning step (c) further includes the steps of: determining the busy status of each assigned transcriber terminal in said assignment table and assigning a transcriber terminal to a voice dictation segment from a dictator terminal having no assigned transcriber terminal by selecting a non-busy transcriber terminal presently assigned to another dictation terminal. 6. The method of claim 5 wherein said assigning step (c) further includes the steps of: checking and storing in a done time table entries identifying the time when each assigned transcriber terminal is no longer busy transcribing and assigning a transcriber terminal to a voice dictation segment from a dictation terminal having no assigned transcriber by selecting a transcriber terminal having the earliest time entry in said done time table. | 0.814846 |
9,152,652 | 14 | 19 | 14. A system comprising: a data store; and one or more data processing apparatus that interact with the data store and execute instructions that cause the one or more computers to perform operations comprising: identifying responsive images for a search phrase that includes two or more terms; determining interaction rankings for each of the responsive images based on a number of user interactions with the responsive image; creating two or more sub-queries based on the search phrase, the sub-queries each being a proper subset of the two or more terms; for each sub-query from the two or more sub-queries: determining sub-query model rankings for the responsive images based on a sub-query model for the sub-query and visual features of the responsive images, the sub-query model being an image relevance model for the sub-query; and determining a search phrase score for the sub-query model, the search phrase score being based on a measure of similarity between positions of the responsive images in each of the interaction rankings and the sub-query model rankings; and selecting, based on the search phrase scores for the sub-queries, one of the sub-query models as a model for the search phrase, the selected sub-query model having a search phrase score that meets a threshold search phrase score. | 14. A system comprising: a data store; and one or more data processing apparatus that interact with the data store and execute instructions that cause the one or more computers to perform operations comprising: identifying responsive images for a search phrase that includes two or more terms; determining interaction rankings for each of the responsive images based on a number of user interactions with the responsive image; creating two or more sub-queries based on the search phrase, the sub-queries each being a proper subset of the two or more terms; for each sub-query from the two or more sub-queries: determining sub-query model rankings for the responsive images based on a sub-query model for the sub-query and visual features of the responsive images, the sub-query model being an image relevance model for the sub-query; and determining a search phrase score for the sub-query model, the search phrase score being based on a measure of similarity between positions of the responsive images in each of the interaction rankings and the sub-query model rankings; and selecting, based on the search phrase scores for the sub-queries, one of the sub-query models as a model for the search phrase, the selected sub-query model having a search phrase score that meets a threshold search phrase score. 19. The system of claim 14 , wherein the instructions cause the one or more data processing apparatus to perform operations comprising: obtaining, for the selected sub-query model, an additional search phrase score specifying a measure of similarity between interaction rankings of other images responsive to another search phrase and sub-query model rankings of the other images based on the selected sub-query model; and determining a global search phrase score for the selected sub-query model, the global search phrase score being determined based on an aggregate measure of the search phrase score and the additional search phrase score. | 0.518741 |
9,201,959 | 11 | 14 | 11. The system of claim 4 , wherein the logic that performs the analysis of text from the closed captioning data further comprises: logic that ranks a plurality of performers appearing within the video content feature according to prominence; and logic that ranks a respective scene according to a ranking of a performer from the plurality of performers appearing within the respective scene. | 11. The system of claim 4 , wherein the logic that performs the analysis of text from the closed captioning data further comprises: logic that ranks a plurality of performers appearing within the video content feature according to prominence; and logic that ranks a respective scene according to a ranking of a performer from the plurality of performers appearing within the respective scene. 14. The system of claim 11 wherein the logic that ranks the plurality of performers appearing within the video content feature according to prominence comprises logic that ranks the plurality of performers based at least in part upon data obtained from a network site containing information about the plurality of performers appearing within video content feature. | 0.691525 |
8,401,252 | 1 | 5 | 1. A method for processing video data, comprising: detecting human faces in a plurality of video frames in said video data using a processor; for at least one detected human face, identifying a face-specific set of video frames using said processor, irrespective of whether said detected human face is present in said face-specific set of video frames in a substantially temporally continuous manner; grouping video frames in said face-specific set of video frames into a plurality of face tracks using said processor, wherein each face track contains corresponding one or more video frames having at least a substantial temporal continuity therebetween; using said processor, merging two or more of said plurality of face tracks that are disjoint in time using a face recognition method based on a Bayesian Network based classifier, wherein the Bayesian Network based classifier is constructed based on a ratio of a plurality of Bayesian networks and each of said Bayesian networks is a probability distribution representation derived from dependencies among video input variables that statistically depend upon each other; and enabling a user to view on an electronic display face-specific video segments of said at least one detected human face in said video data based on said merging of temporally disjoint face tracks. | 1. A method for processing video data, comprising: detecting human faces in a plurality of video frames in said video data using a processor; for at least one detected human face, identifying a face-specific set of video frames using said processor, irrespective of whether said detected human face is present in said face-specific set of video frames in a substantially temporally continuous manner; grouping video frames in said face-specific set of video frames into a plurality of face tracks using said processor, wherein each face track contains corresponding one or more video frames having at least a substantial temporal continuity therebetween; using said processor, merging two or more of said plurality of face tracks that are disjoint in time using a face recognition method based on a Bayesian Network based classifier, wherein the Bayesian Network based classifier is constructed based on a ratio of a plurality of Bayesian networks and each of said Bayesian networks is a probability distribution representation derived from dependencies among video input variables that statistically depend upon each other; and enabling a user to view on an electronic display face-specific video segments of said at least one detected human face in said video data based on said merging of temporally disjoint face tracks. 5. The method of claim 1 , further comprising: allowing said user to manually override a match between respective grouped video frames in said face-specific set of video frames and an image entry stored in a database using said processor. | 0.769826 |
8,775,844 | 1 | 10 | 1. A method comprising: detecting, by a computing device, a change to a power mode of the computing device; responsive to detecting the change to the power mode, processing, by the computing device and from among a set of elements within a page of content that each specify a respective portion of the content in accordance with a markup language, the page of content to identify one or more elements of the set of elements that each have at least one respective attribute designated to be modified in response to the change to the power mode; modifying, by the computing device and based on the change to the power mode, at least a portion of the at least one respective attribute of each of the identified one or more elements to associate the respective portion of the content specified by each of the identified one or more elements with a set of presentation properties; and rendering, by the computing device and for display in accordance with the set of presentation properties, the respective portion of the content specified by each of the identified one or more elements. | 1. A method comprising: detecting, by a computing device, a change to a power mode of the computing device; responsive to detecting the change to the power mode, processing, by the computing device and from among a set of elements within a page of content that each specify a respective portion of the content in accordance with a markup language, the page of content to identify one or more elements of the set of elements that each have at least one respective attribute designated to be modified in response to the change to the power mode; modifying, by the computing device and based on the change to the power mode, at least a portion of the at least one respective attribute of each of the identified one or more elements to associate the respective portion of the content specified by each of the identified one or more elements with a set of presentation properties; and rendering, by the computing device and for display in accordance with the set of presentation properties, the respective portion of the content specified by each of the identified one or more elements. 10. The method of claim 1 , wherein the set of elements each specify respective content in accordance with hypertext markup language, and wherein the set of presentation properties are defined by one or more cascaded style sheets. | 0.806071 |
9,734,261 | 6 | 7 | 6. The method of claim 1 further comprising: storing the received client query in a query database; receiving a second client query entered from the client-retrieved instance of the web page; and storing the second client query in the query database and associating the second client query with the client query. | 6. The method of claim 1 further comprising: storing the received client query in a query database; receiving a second client query entered from the client-retrieved instance of the web page; and storing the second client query in the query database and associating the second client query with the client query. 7. The method of claim 6 wherein said retrieving from the database the plurality of queries comprises: determining a query identifier associated with the client query that distinguishes the client query from other client queries; and retrieving from the query database a plurality of queries associated with the query identifier. | 0.879487 |
7,542,979 | 38 | 39 | 38. A method as in claim 37 , wherein the entity rule comprises a condition and an attributelist. | 38. A method as in claim 37 , wherein the entity rule comprises a condition and an attributelist. 39. A method as in claim 38 , wherein the entity rule includes an endcondition clause. | 0.979897 |
7,996,462 | 1 | 19 | 1. A method comprising: maintaining, by an agent comprising an autonomous bot implemented by one or more processors, a topic list of topics previously identified in a plurality of collaboration data sources that are monitored by the agent, the plurality of collaboration data sources comprising a collaborative work environment that comprises a portal facilitating communication between a plurality of users who contribute collaboration information to the collaborative work environment via one or more user interfaces; monitoring, automatically and autonomously by the agent, the collaborative work environment, the monitoring comprising the collaboration information provided within the collaborative work environment; extracting, by the agent using a text-mining tool upon detection via the monitoring of new collaboration information contributed by one of the plurality of users in the collaborative work environment for use by at least one other of the plurality of users in the collaborative work environment, an extracted topic of the new collaboration information, the extracted topic comprising at least one of a theme, a question, a problem, and a subject matter of the new collaboration information; identifying an information resource within the plurality of collaboration data sources that is related to the extracted topic, the identifying comprising comparing the extracted topic to the topic list and determining that previously contributed collaboration information available from the information resource has a topic related to the extracted topic; creating, by the agent, a link insertable into a collaborative process, through which the plurality of users can send communications, of the collaborative work environment, the link being to the information resource identified as related to the extracted topic of the new collaboration information contributed by the one of the plurality of users for use by the at least one other of the plurality of users in the collaborative work environment; and inserting, automatically by the agent, the link into the collaborative process through which the plurality of users can send communications and into the user interface in a close proximal relation to the new collaboration information. | 1. A method comprising: maintaining, by an agent comprising an autonomous bot implemented by one or more processors, a topic list of topics previously identified in a plurality of collaboration data sources that are monitored by the agent, the plurality of collaboration data sources comprising a collaborative work environment that comprises a portal facilitating communication between a plurality of users who contribute collaboration information to the collaborative work environment via one or more user interfaces; monitoring, automatically and autonomously by the agent, the collaborative work environment, the monitoring comprising the collaboration information provided within the collaborative work environment; extracting, by the agent using a text-mining tool upon detection via the monitoring of new collaboration information contributed by one of the plurality of users in the collaborative work environment for use by at least one other of the plurality of users in the collaborative work environment, an extracted topic of the new collaboration information, the extracted topic comprising at least one of a theme, a question, a problem, and a subject matter of the new collaboration information; identifying an information resource within the plurality of collaboration data sources that is related to the extracted topic, the identifying comprising comparing the extracted topic to the topic list and determining that previously contributed collaboration information available from the information resource has a topic related to the extracted topic; creating, by the agent, a link insertable into a collaborative process, through which the plurality of users can send communications, of the collaborative work environment, the link being to the information resource identified as related to the extracted topic of the new collaboration information contributed by the one of the plurality of users for use by the at least one other of the plurality of users in the collaborative work environment; and inserting, automatically by the agent, the link into the collaborative process through which the plurality of users can send communications and into the user interface in a close proximal relation to the new collaboration information. 19. A method in accordance with claim 1 , further comprising: adding the extracted topic to the topic list. | 0.884449 |
9,591,014 | 1 | 2 | 1. A computer program product for identifying correlations between events recorded in a system log of a computer, the recorded events generated by a plurality of processes executing on the computer, the computer program product comprising one or more non-transitory computer readable storage medium and program instructions stored on at least one of the one or more non-transitory computer readable storage medium, the program instructions comprising: program instructions to partition, by the computer, a system log into a plurality of segments, each segment associated with a characteristic found in an event, each segment including one or more events having a same characteristic value; program instructions to select, by the computer, a plurality of attributes of the one or more events in a segment, wherein the plurality of attributes do not describe an action of the event; program instructions to generate, by the computer, one or more distinct n-grams, each distinct n-gram including the selected attributes from successive events within the segment, wherein a distinct n-gram is distinct from all other generated n-grams; program instructions to identify, by the computer, a correlation for each first selected attribute of each of the successive events of an n-gram with all other second selected attributes from each of the successive events of the n-gram; program instructions to generate, by the computer, a correlation metric as a function of the number of correlated first selected attributes and the total number of selected attributes of each of the successive events of the n-gram, wherein the program instructions to generate the correlation metric include: program instructions to increment, by the computer, a count of n-gram instances in which the first selected attribute of each of the successive events of the n-gram correlates with one of the second selected attributes of each of the successive events of the n-gram; and program instructions to divide, by the computer, the count by a total number of possible correlations between the first selected attributes and the second selected attributes; and program instructions to record, by the computer, the correlations for each first selected attribute. | 1. A computer program product for identifying correlations between events recorded in a system log of a computer, the recorded events generated by a plurality of processes executing on the computer, the computer program product comprising one or more non-transitory computer readable storage medium and program instructions stored on at least one of the one or more non-transitory computer readable storage medium, the program instructions comprising: program instructions to partition, by the computer, a system log into a plurality of segments, each segment associated with a characteristic found in an event, each segment including one or more events having a same characteristic value; program instructions to select, by the computer, a plurality of attributes of the one or more events in a segment, wherein the plurality of attributes do not describe an action of the event; program instructions to generate, by the computer, one or more distinct n-grams, each distinct n-gram including the selected attributes from successive events within the segment, wherein a distinct n-gram is distinct from all other generated n-grams; program instructions to identify, by the computer, a correlation for each first selected attribute of each of the successive events of an n-gram with all other second selected attributes from each of the successive events of the n-gram; program instructions to generate, by the computer, a correlation metric as a function of the number of correlated first selected attributes and the total number of selected attributes of each of the successive events of the n-gram, wherein the program instructions to generate the correlation metric include: program instructions to increment, by the computer, a count of n-gram instances in which the first selected attribute of each of the successive events of the n-gram correlates with one of the second selected attributes of each of the successive events of the n-gram; and program instructions to divide, by the computer, the count by a total number of possible correlations between the first selected attributes and the second selected attributes; and program instructions to record, by the computer, the correlations for each first selected attribute. 2. The computer program product according to claim 1 , wherein a correlations is one of: always, never, or sometimes. | 0.877615 |
4,503,426 | 10 | 11 | 10. The device claimed in claim 9 wherein said predicate phrase area comprises: a means for accepting the addresses of a series of auxiliary verb forms, and for coupling said forms with the infinitive, past participle and gerund modes of verbs to create present, past and future conjugations of said verbs. | 10. The device claimed in claim 9 wherein said predicate phrase area comprises: a means for accepting the addresses of a series of auxiliary verb forms, and for coupling said forms with the infinitive, past participle and gerund modes of verbs to create present, past and future conjugations of said verbs. 11. The device claimed in claim 10 wherein said dedicated keys include keys for entering auxiliary verb forms, articles, prepositions, personal pronouns, demonstrative pronouns and interrogative adverbs. | 0.962615 |
9,898,935 | 1 | 3 | 1. A method of translating from one language selected by a user to another language selected by the user and using a processing system, the system having: an image library with a plurality of usage based image groupings wherein each image grouping has a plurality of images therein, each such image representing a word or phrase in such image grouping; and wherein each such image grouping has one or more translation text sub-libraries for each translation language the user can select; and wherein, each such translation text sub-library for an image grouping, contains a generic text for each image in the image grouping in such translation language and which is coupled to its respective image when the user selects such specific language as a ‘translation from’ language; and one or more user devices coupled to the processing system wherein the display screens of the user devices are configured by the processing system with: a menu window for displaying a listing of the usage based image groupings from which the user can select, an identifier symbol coupled to each such image grouping and a language selection option for the user to select a language for the listing of the image groupings displayed in the menu window; one or more display windows for the usage based image groupings selected by the user; and one or more user work windows; the method of translating comprising the steps of: selecting a translation language for the listing of usage based groupings displayed in the menu window and the ‘translation from’ language for the generic text coupled to the images in the image groupings to be displayed in the display windows; selecting, in the menu window, one or more usage based image groupings, the images of each such selected image grouping thereby being displayed in the display windows with the generic text for each image in the selected ‘translation from’ language coupled thereto; selecting, in the display windows, one or more images with generic text in the selected ‘translation from’ language coupled thereto, for placement and display in one or more of the user work windows and wherein the usage based image grouping identifier symbol for each such selected image is also coupled to such image and displayed in the user work windows; arranging, in one or more of the user work windows, the order of the selected usage based images; and selecting, in one or more user work windows, a ‘translation to’ language wherein the text language in such work windows is changed from the ‘translation from’ language to the ‘translation to’ language and wherein, in response to the ordering of the images and the presence of images from certain image groupings in such user work windows, the system grammatically adjusts the text to an applied ‘translation to’ language. | 1. A method of translating from one language selected by a user to another language selected by the user and using a processing system, the system having: an image library with a plurality of usage based image groupings wherein each image grouping has a plurality of images therein, each such image representing a word or phrase in such image grouping; and wherein each such image grouping has one or more translation text sub-libraries for each translation language the user can select; and wherein, each such translation text sub-library for an image grouping, contains a generic text for each image in the image grouping in such translation language and which is coupled to its respective image when the user selects such specific language as a ‘translation from’ language; and one or more user devices coupled to the processing system wherein the display screens of the user devices are configured by the processing system with: a menu window for displaying a listing of the usage based image groupings from which the user can select, an identifier symbol coupled to each such image grouping and a language selection option for the user to select a language for the listing of the image groupings displayed in the menu window; one or more display windows for the usage based image groupings selected by the user; and one or more user work windows; the method of translating comprising the steps of: selecting a translation language for the listing of usage based groupings displayed in the menu window and the ‘translation from’ language for the generic text coupled to the images in the image groupings to be displayed in the display windows; selecting, in the menu window, one or more usage based image groupings, the images of each such selected image grouping thereby being displayed in the display windows with the generic text for each image in the selected ‘translation from’ language coupled thereto; selecting, in the display windows, one or more images with generic text in the selected ‘translation from’ language coupled thereto, for placement and display in one or more of the user work windows and wherein the usage based image grouping identifier symbol for each such selected image is also coupled to such image and displayed in the user work windows; arranging, in one or more of the user work windows, the order of the selected usage based images; and selecting, in one or more user work windows, a ‘translation to’ language wherein the text language in such work windows is changed from the ‘translation from’ language to the ‘translation to’ language and wherein, in response to the ordering of the images and the presence of images from certain image groupings in such user work windows, the system grammatically adjusts the text to an applied ‘translation to’ language. 3. The method of claim 1 wherein the menu window and the display windows are enabled by the processing system with a language option, such that the user may select different languages for the menu window and the text in the display windows. | 0.863481 |
8,601,023 | 1 | 14 | 1. A computer implemented method performed by a processor, comprising: observing usage patterns by one or more users in an on-line community in connection with an on-line asset; identifying usefulness of the on-line asset by observing user implicit behaviors in connection with the usage patterns of the on-line asset and by extracting behavioral patterns from the user implicit behaviors; refining the identified on-line asset usefulness by context, wherein a context of the on-line asset is automatically detected based on one or more observed terms obtained by observing user implicit behaviors with respect to the identified on-line asset, wherein the identified on-line asset has a plurality of term vectors; assigning a term vector entry of a term vector of the plurality of term vectors that describes a degree to which the identified on-line asset has an affinity with the observed terms, wherein each term vector of the plurality of term vectors is associated with a different user of the one or more users; identifying for each user of the one or more users an expertise vector by identifying on-line assets with respect to which the each user has engaged in one or more of the user implicit behaviors; and generating the expertise vector by summing the plurality of term vectors for the identified on-line assets; receive query including query terms; obtaining search result documents; determining that at least one search result document is an identified on-line asset for an expert user of the one or more users; and ranking the at least one search result based on a relationship of the query terms to the expertise vector of the expert user. | 1. A computer implemented method performed by a processor, comprising: observing usage patterns by one or more users in an on-line community in connection with an on-line asset; identifying usefulness of the on-line asset by observing user implicit behaviors in connection with the usage patterns of the on-line asset and by extracting behavioral patterns from the user implicit behaviors; refining the identified on-line asset usefulness by context, wherein a context of the on-line asset is automatically detected based on one or more observed terms obtained by observing user implicit behaviors with respect to the identified on-line asset, wherein the identified on-line asset has a plurality of term vectors; assigning a term vector entry of a term vector of the plurality of term vectors that describes a degree to which the identified on-line asset has an affinity with the observed terms, wherein each term vector of the plurality of term vectors is associated with a different user of the one or more users; identifying for each user of the one or more users an expertise vector by identifying on-line assets with respect to which the each user has engaged in one or more of the user implicit behaviors; and generating the expertise vector by summing the plurality of term vectors for the identified on-line assets; receive query including query terms; obtaining search result documents; determining that at least one search result document is an identified on-line asset for an expert user of the one or more users; and ranking the at least one search result based on a relationship of the query terms to the expertise vector of the expert user. 14. The computer implemented method of claim 1 , further comprising: determining a current context interest of the user based on the on-line asset used and searches performed; identifying relationships between the one or more users based on similarity between identified contexts/interests; and detecting which of the on-line assets of a plurality of on-line assets are most used and useful to the on-line community. | 0.645656 |
9,760,838 | 23 | 24 | 23. A system for analyzing trend data, which comprises: a processor; an analysis module, wherein the analysis module is a non-transitory computer readable medium operably connected to the processor, wherein the non-transitory computer readable medium comprises a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, wherein the plurality of instructions when executed: analyze a first plurality of communications occurring over a first time period and determine a first plurality of terms; analyze a second plurality of communications occurring over a second time period and determine a second plurality of terms; determine a frequency that each term of the first plurality of terms and the second plurality of terms respectively occurs during the first and second plurality of communications; compare the frequency of each of the terms in the first plurality of terms to the frequency of each of the terms in the second plurality of terms; identify one or more trend parameters; determine one or more trend factors based on application of the identified one or more trend parameters to the comparison of terms of the first plurality of terms to the terms of the second plurality of terms without reference to a library of pre-defined terms; identify a subset of communications from the first and the second plurality of communications in which a determined term is absent; analyze the subset of communications to determine a signal associated with the determined absent term; and communicate the determined one or more trend factors, the determined absent term, and the signal associated with the determined absent term to a display; and a display device configured to display at least the determined one or more trend factors, the determined absent term, and the signal associated with the determined absent term to a user. | 23. A system for analyzing trend data, which comprises: a processor; an analysis module, wherein the analysis module is a non-transitory computer readable medium operably connected to the processor, wherein the non-transitory computer readable medium comprises a plurality of instructions stored in association therewith that are accessible to, and executable by, the processor, wherein the plurality of instructions when executed: analyze a first plurality of communications occurring over a first time period and determine a first plurality of terms; analyze a second plurality of communications occurring over a second time period and determine a second plurality of terms; determine a frequency that each term of the first plurality of terms and the second plurality of terms respectively occurs during the first and second plurality of communications; compare the frequency of each of the terms in the first plurality of terms to the frequency of each of the terms in the second plurality of terms; identify one or more trend parameters; determine one or more trend factors based on application of the identified one or more trend parameters to the comparison of terms of the first plurality of terms to the terms of the second plurality of terms without reference to a library of pre-defined terms; identify a subset of communications from the first and the second plurality of communications in which a determined term is absent; analyze the subset of communications to determine a signal associated with the determined absent term; and communicate the determined one or more trend factors, the determined absent term, and the signal associated with the determined absent term to a display; and a display device configured to display at least the determined one or more trend factors, the determined absent term, and the signal associated with the determined absent term to a user. 24. The system of claim 23 , wherein the determined one or more trend factors further comprise an emergence of a trend, a length of a trend, a popularity of a trend, and a geographic spread of a trend. | 0.736911 |
8,135,715 | 9 | 12 | 9. A non-transitory computer-readable storage medium storing one or more sequences of instructions which, when executed by one or more processors, cause the one or more processors to perform: for each target word of target words, determining a set of overcorrelated pairs of said each target word and words in a plurality of documents, wherein an overcorrelated pair occurs when said each target word and a particular word in said plurality of documents have a first actual co-occurrence in portions of said plurality of documents that exceeds an expected probabilistic co-occurrence by at least a first threshold; wherein the first actual co-occurrence is determined by counting a number of blocks in said plurality of documents that contain both said each target word and the particular word; for said each target word of said target words and the set of the overcorrelated pairs determined for said each target word, determining overcorrelated triplets comprising said each target word and two words from said set of overcorrelated pairs, wherein an overcorrelated triplet occurs when said each target word and particular two words in said set of overcorrelated pairs have a second actual co-occurrence in said portions of said plurality of documents that exceeds said expected probabilistic co-occurrence by at least a second threshold; wherein said second actual co-occurrence is determined by counting a number of blocks in said plurality of documents that contain said each target word and the particular two words; wherein determining an overcorrelated triplet for said each target word and the particular two words comprises computing a ratio between said second actual co-occurence and a predicted proportion of blocks having said each target word and the particular two words; and storing data representing the overcorrelated triplets determined for said target words on one or more storage devices. | 9. A non-transitory computer-readable storage medium storing one or more sequences of instructions which, when executed by one or more processors, cause the one or more processors to perform: for each target word of target words, determining a set of overcorrelated pairs of said each target word and words in a plurality of documents, wherein an overcorrelated pair occurs when said each target word and a particular word in said plurality of documents have a first actual co-occurrence in portions of said plurality of documents that exceeds an expected probabilistic co-occurrence by at least a first threshold; wherein the first actual co-occurrence is determined by counting a number of blocks in said plurality of documents that contain both said each target word and the particular word; for said each target word of said target words and the set of the overcorrelated pairs determined for said each target word, determining overcorrelated triplets comprising said each target word and two words from said set of overcorrelated pairs, wherein an overcorrelated triplet occurs when said each target word and particular two words in said set of overcorrelated pairs have a second actual co-occurrence in said portions of said plurality of documents that exceeds said expected probabilistic co-occurrence by at least a second threshold; wherein said second actual co-occurrence is determined by counting a number of blocks in said plurality of documents that contain said each target word and the particular two words; wherein determining an overcorrelated triplet for said each target word and the particular two words comprises computing a ratio between said second actual co-occurence and a predicted proportion of blocks having said each target word and the particular two words; and storing data representing the overcorrelated triplets determined for said target words on one or more storage devices. 12. The non-transitory computer-readable storage medium of claim 9 , further comprising instructions which, when executed by the one or more processors, cause the one or more processors to perform determining a set of highly overcorrelated words based on said set of overcorrelated pairs from which said overcorrelated triplets were determined. | 0.581509 |
9,092,523 | 1 | 16 | 1. A method comprising: a) managing a database by an Internet search application in response to user input provided by one or more first users in response to one of visiting a Web site and performing a first search query, wherein the user input comprises relevance feedback; b) ranking documents for including in a results list in response to a search query, by a search engine, an order in which the documents are ranked being influenced by the user input; and c) presenting web page search results from the database in response to a second search query initiated by a second user different from the one or more first users, wherein the web page search results includes supplemental information related to the search query and a results list comprising a list of web pages related to the second search query, multiple users are able to modify the same portions of the supplemental information, which pages are presented and the order in which the web pages are presented are influenced by the relevance feedback received prior to the second search query, the relevance feedback is different from selection of a link to a web page in the results list, the supplemental information includes a user-entered description of a first concept related to the search query, and the user-entered description of the first concept contains one of a link to a description of a second concept related to the first concept or links to documents related to the first concept. | 1. A method comprising: a) managing a database by an Internet search application in response to user input provided by one or more first users in response to one of visiting a Web site and performing a first search query, wherein the user input comprises relevance feedback; b) ranking documents for including in a results list in response to a search query, by a search engine, an order in which the documents are ranked being influenced by the user input; and c) presenting web page search results from the database in response to a second search query initiated by a second user different from the one or more first users, wherein the web page search results includes supplemental information related to the search query and a results list comprising a list of web pages related to the second search query, multiple users are able to modify the same portions of the supplemental information, which pages are presented and the order in which the web pages are presented are influenced by the relevance feedback received prior to the second search query, the relevance feedback is different from selection of a link to a web page in the results list, the supplemental information includes a user-entered description of a first concept related to the search query, and the user-entered description of the first concept contains one of a link to a description of a second concept related to the first concept or links to documents related to the first concept. 16. The method of claim 1 , wherein the relevance feedback comprises information related to relevancies of elements in the results list or an indication of the user=s assessment of the quality or interest of the web page corresponding to an entry in the results list. | 0.675182 |
8,670,997 | 1 | 10 | 1. A system for editing medical related quality metric information, the system comprising: a user input device; at least one memory operable to store at least one medical patient record; and a processor configured to: extract at least a first quality metric from the at least one medical patient record, the first quality metric being a value indicating a quality of care related to a guideline or requirement and the first quality metric calculated from patient treatment information in the at least one medical patient record, the patient treatment information comprising a fact, inferred conclusion, or report value for a patient based on the at least one medical patient record; receive a change request relative to the first quality metric from the user input device, the change request being for editing the patient treatment information in the at least one medical patient record used to determine the first quality metric; change the fact, inferred conclusion, or report value to a different fact, different conclusion, or different report value in the at least one medical patient record for the editing; and output the first quality metric modified as a function of the different fact, different conclusion, or different report value. | 1. A system for editing medical related quality metric information, the system comprising: a user input device; at least one memory operable to store at least one medical patient record; and a processor configured to: extract at least a first quality metric from the at least one medical patient record, the first quality metric being a value indicating a quality of care related to a guideline or requirement and the first quality metric calculated from patient treatment information in the at least one medical patient record, the patient treatment information comprising a fact, inferred conclusion, or report value for a patient based on the at least one medical patient record; receive a change request relative to the first quality metric from the user input device, the change request being for editing the patient treatment information in the at least one medical patient record used to determine the first quality metric; change the fact, inferred conclusion, or report value to a different fact, different conclusion, or different report value in the at least one medical patient record for the editing; and output the first quality metric modified as a function of the different fact, different conclusion, or different report value. 10. The system of claim 1 wherein the at least one memory is operable to store medical patient records for a plurality of patients, and wherein the processor is configured to select a sub-set of the plurality of patients associated with a condition, configured to extract a plurality of quality metrics for each patient of the sub-set, the plurality of quality metrics including the first quality metric, and configured to generate a report as a function of the plurality of quality metrics. | 0.542831 |
8,756,515 | 11 | 13 | 11. The method of claim 1 , further comprising instantiating a data flow engine to support transformations of collections, records and atoms and track dependencies across declarative model data items. | 11. The method of claim 1 , further comprising instantiating a data flow engine to support transformations of collections, records and atoms and track dependencies across declarative model data items. 13. The method of claim 11 , wherein the instantiated editors are configured to build queries which are processed by the data flow engine. | 0.981751 |
9,684,690 | 24 | 25 | 24. The computer storage medium of claim 21 , wherein determining that the received search query is a flights-related query is based on processing the received search query. | 24. The computer storage medium of claim 21 , wherein determining that the received search query is a flights-related query is based on processing the received search query. 25. The computer storage medium of claim 24 , wherein one or more of the values associated with the dimensions is adjustable based on user input. | 0.954574 |
7,689,526 | 10 | 12 | 10. An article as in claim 1 , wherein the article is further operable to cause one or more machines to result in operations comprising: reasoning over at least a portion of the symbols if it is determined that the bins indicate that the knowledge base contains knowledge requested in the query. | 10. An article as in claim 1 , wherein the article is further operable to cause one or more machines to result in operations comprising: reasoning over at least a portion of the symbols if it is determined that the bins indicate that the knowledge base contains knowledge requested in the query. 12. An article as in claim 10 , wherein the portion of the symbols reasoned over are specified by a plan. | 0.90675 |
8,880,492 | 9 | 10 | 9. A method of suggesting keywords for a litigation hold, comprising: receiving a seed set of keywords; identifying, by one or more processing devices, a set of documents corresponding to the seed set of keywords; determining an indication of relevance for each document in a subset of the set of documents, wherein the subset comprises a plurality of documents, and wherein the indication of relevance for each document comprises a specified percentage degree of relevance of the document to the seed set of keywords; computing a discriminatory power of the keywords based on the indication of relevance for each document, wherein the discriminatory power of each keyword is determined based on how many relevant and non-relevant documents are yielded based on the respective keyword; generating, by one or more processing devices, a suggested set of keywords with a highest computed discriminatory power, wherein each keyword in the suggested set of keywords occurs within a same portion of a particular one of the plurality of documents that is textually subdivided into two or more portions; and providing the suggested set of keywords to a client, wherein the suggested set is used to perform a further search of the set of documents. | 9. A method of suggesting keywords for a litigation hold, comprising: receiving a seed set of keywords; identifying, by one or more processing devices, a set of documents corresponding to the seed set of keywords; determining an indication of relevance for each document in a subset of the set of documents, wherein the subset comprises a plurality of documents, and wherein the indication of relevance for each document comprises a specified percentage degree of relevance of the document to the seed set of keywords; computing a discriminatory power of the keywords based on the indication of relevance for each document, wherein the discriminatory power of each keyword is determined based on how many relevant and non-relevant documents are yielded based on the respective keyword; generating, by one or more processing devices, a suggested set of keywords with a highest computed discriminatory power, wherein each keyword in the suggested set of keywords occurs within a same portion of a particular one of the plurality of documents that is textually subdivided into two or more portions; and providing the suggested set of keywords to a client, wherein the suggested set is used to perform a further search of the set of documents. 10. The method of claim 9 , wherein each keyword in the suggested set of keywords is linguistically related to one or more keyword in the seed set of keywords. | 0.901973 |
7,730,395 | 47 | 66 | 47. A computer program product for transforming a dynamically changing electronic document comprising: means for providing a visual representation of one or more instances of a dynamically changing electronic document to a user; means for receiving feedback from interaction by the user with the visual representation, said feedback identifying one or more portions of said visual representation, said feedback being used to generate one or more virtual tags, said virtual tags identifying features of said one or more portions of said visual representation; means for constructing one or more transformation rules using said feedback and said one or more virtual tags; and means for applying said one or more transformation rules to said one or more instances of said electronic document, a second electronic document or future versions of said one or more instances of said electronic document to extract customized content identified from said one or more virtual tags and generate a virtual page of said customized content. | 47. A computer program product for transforming a dynamically changing electronic document comprising: means for providing a visual representation of one or more instances of a dynamically changing electronic document to a user; means for receiving feedback from interaction by the user with the visual representation, said feedback identifying one or more portions of said visual representation, said feedback being used to generate one or more virtual tags, said virtual tags identifying features of said one or more portions of said visual representation; means for constructing one or more transformation rules using said feedback and said one or more virtual tags; and means for applying said one or more transformation rules to said one or more instances of said electronic document, a second electronic document or future versions of said one or more instances of said electronic document to extract customized content identified from said one or more virtual tags and generate a virtual page of said customized content. 66. The computer program product of claim 47 wherein said graphical user interface includes a proxy for monitoring actions of said user. | 0.909695 |
9,798,391 | 1 | 7 | 1. A device comprising: a housing; a display device supported by the housing and configured operable to display a play of a game; at least one gyroscope supported by the housing and configured to detect motion of the housing during the play of the game; and a controller configured to: analyze the detected motion of the housing and determine whether the detected motion of the housing corresponds to any of a plurality of different designated gestures; and responsive to determining that the detected motion of the housing corresponds to one of the plurality of designated gestures: determine a game input associated with said designated gesture, said determined game input being one of a plurality of different game inputs; determine at least one aspect of the play of the game to modify based on the determined game input; and cause a modification of the determined at least one aspect of the play of the game. | 1. A device comprising: a housing; a display device supported by the housing and configured operable to display a play of a game; at least one gyroscope supported by the housing and configured to detect motion of the housing during the play of the game; and a controller configured to: analyze the detected motion of the housing and determine whether the detected motion of the housing corresponds to any of a plurality of different designated gestures; and responsive to determining that the detected motion of the housing corresponds to one of the plurality of designated gestures: determine a game input associated with said designated gesture, said determined game input being one of a plurality of different game inputs; determine at least one aspect of the play of the game to modify based on the determined game input; and cause a modification of the determined at least one aspect of the play of the game. 7. The device of claim 1 , wherein the determined at least one aspect of the play of the game includes a location of at least one displayed game object during the play of the game. | 0.933234 |
8,375,046 | 1 | 2 | 1. A method for retrieving query results comprising: receiving, at a first device coupled to a network, an abstract query from a requesting entity, wherein the abstract query comprises one or more logical fields defined in a first data abstraction model comprising a plurality of first logical field definitions mapping to physical fields of a first database; wherein the one or more logical fields in the abstract query each have a respective concept code relating corresponding logical field definitions of a plurality of data abstraction models including the first data abstraction model, a second data abstraction model and a third data abstraction model, the second data abstraction model being resident on the first device and comprising a plurality of second logical field definitions mapping to physical fields of a second database; modifying the abstract query to include one or more of the second logical field definitions from the second data abstraction model based on the respective concept codes; issuing the modified abstract query against the second database to retrieve a first set of results for the modified abstract query; sending the abstract query to at least one second device coupled to the network, the second device comprising the third data abstraction model comprising a plurality of third logical field definitions mapping to physical fields of a third database, wherein the first, second and third data abstraction models, and their respective logical field definitions, are distinct from one another, and wherein the second device is configured to modify the abstract query to include one or more of the third logical field definitions from the third data abstraction model based on the respective concept codes; receiving a second set of results for the abstract query from the at least one second device; and providing the first and second set of results to the requesting entity. | 1. A method for retrieving query results comprising: receiving, at a first device coupled to a network, an abstract query from a requesting entity, wherein the abstract query comprises one or more logical fields defined in a first data abstraction model comprising a plurality of first logical field definitions mapping to physical fields of a first database; wherein the one or more logical fields in the abstract query each have a respective concept code relating corresponding logical field definitions of a plurality of data abstraction models including the first data abstraction model, a second data abstraction model and a third data abstraction model, the second data abstraction model being resident on the first device and comprising a plurality of second logical field definitions mapping to physical fields of a second database; modifying the abstract query to include one or more of the second logical field definitions from the second data abstraction model based on the respective concept codes; issuing the modified abstract query against the second database to retrieve a first set of results for the modified abstract query; sending the abstract query to at least one second device coupled to the network, the second device comprising the third data abstraction model comprising a plurality of third logical field definitions mapping to physical fields of a third database, wherein the first, second and third data abstraction models, and their respective logical field definitions, are distinct from one another, and wherein the second device is configured to modify the abstract query to include one or more of the third logical field definitions from the third data abstraction model based on the respective concept codes; receiving a second set of results for the abstract query from the at least one second device; and providing the first and second set of results to the requesting entity. 2. The method of claim 1 , wherein the respective concept codes associated with each of the one or more logical fields of the abstract query are included in the abstract query, wherein the concept code associates a respective logical field to metadata describing the logical field, the metadata conforming to a predefined format. | 0.661523 |
9,317,595 | 1 | 9 | 1. A method for generating shorter versions of one or more sentences, the method comprising: receiving, by a computing device, the one or more sentences from a text; based on the one or more sentences, generating, by the computing device within a memory of the computing device, a tree comprising a plurality of nodes; wherein the tree represents the one or more sentences; wherein each node, of the plurality of nodes, represents a grammatical element of the one or more sentences; using a named entity recognizer, automatically recognizing which nodes, of the plurality of nodes, represent grammatical elements that correspond to recognized named entities; selecting a particular node from the tree based, at least in part, on (a) which nodes in the tree correspond to recognized named entities, (b) a position of the particular node within the tree, and (c) a node type of the particular node; wherein the particular node has no children within the tree that correspond to recognized named entities identified by the named entity recognizer; after said selecting, modifying the tree within the memory of the computing device by removing the particular node and children of the particular node from the tree; after removing, from the tree, the particular node and the children of the particular node, generating, from remaining nodes of the tree a first set of one or more sub-sentences; wherein the first set of one or more sub-sentences are shorter in length than said one or more sentences; causing display of at least one sub-sentence generated from remaining nodes of the tree to a user. | 1. A method for generating shorter versions of one or more sentences, the method comprising: receiving, by a computing device, the one or more sentences from a text; based on the one or more sentences, generating, by the computing device within a memory of the computing device, a tree comprising a plurality of nodes; wherein the tree represents the one or more sentences; wherein each node, of the plurality of nodes, represents a grammatical element of the one or more sentences; using a named entity recognizer, automatically recognizing which nodes, of the plurality of nodes, represent grammatical elements that correspond to recognized named entities; selecting a particular node from the tree based, at least in part, on (a) which nodes in the tree correspond to recognized named entities, (b) a position of the particular node within the tree, and (c) a node type of the particular node; wherein the particular node has no children within the tree that correspond to recognized named entities identified by the named entity recognizer; after said selecting, modifying the tree within the memory of the computing device by removing the particular node and children of the particular node from the tree; after removing, from the tree, the particular node and the children of the particular node, generating, from remaining nodes of the tree a first set of one or more sub-sentences; wherein the first set of one or more sub-sentences are shorter in length than said one or more sentences; causing display of at least one sub-sentence generated from remaining nodes of the tree to a user. 9. The method of claim 1 , wherein the one or more sentences are from an article associated with an image, the method further comprising: returning the one or more sub-sentences as a title for said image. | 0.905904 |
8,775,409 | 28 | 32 | 28. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving a request to identify trending search queries in a search system; grouping a plurality of search queries into a plurality of clusters of search queries; associating each cluster of search queries with a respective representative category; determining, by one or more computers and for each cluster of search queries, a cluster score based on a cluster performance score or a category popularity score, wherein the category popularity score of a particular category is a score whose value correlates with the number of clusters associated with the particular category, and wherein the cluster performance score of a particular cluster is a score whose value correlates with a respective rank of one or more pages that are identified for one or more of search queries that are grouped into the particular cluster; generating a ranking of the clusters of search queries based on the cluster scores; and presenting, as a representation of the trending search queries in the search system, information identifying a subset of the clusters of search queries as ranked according to the ranking. | 28. A system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: receiving a request to identify trending search queries in a search system; grouping a plurality of search queries into a plurality of clusters of search queries; associating each cluster of search queries with a respective representative category; determining, by one or more computers and for each cluster of search queries, a cluster score based on a cluster performance score or a category popularity score, wherein the category popularity score of a particular category is a score whose value correlates with the number of clusters associated with the particular category, and wherein the cluster performance score of a particular cluster is a score whose value correlates with a respective rank of one or more pages that are identified for one or more of search queries that are grouped into the particular cluster; generating a ranking of the clusters of search queries based on the cluster scores; and presenting, as a representation of the trending search queries in the search system, information identifying a subset of the clusters of search queries as ranked according to the ranking. 32. The system of claim 28 , wherein presenting information identifying a subset of the clusters of search queries further comprises: selecting a representative query from each cluster of search queries; and presenting the representative queries of the clusters in the subset of the clusters of search queries as ranked according to the cluster ranking. | 0.606027 |
8,239,562 | 1 | 8 | 1. In a computing environment, a method implemented by computing system having a processor, the method comprising: receiving a canonical enveloped message from a computer implemented application, wherein the canonical enveloped message comprises payload data and is associated with a context store that stores context information of the canonical enveloped message in a form that is independent of one or more protocols employed by the canonical enveloped message; at a protocol pipeline comprising a plurality of protocol components processing the canonical enveloped message including using two or more of the protocol components to process the canonical enveloped message, and by at least adding, removing or modifying context entries in the context store, wherein each portion of the context information is stored in the context store as a context entry that comprises at least a name element to identify the portion, a value element representing the value of the identified portion, and optionally a metadata element that defines any additional information about the identified portion, such that context information for a plurality of protocol components is aggregated using a common format in the context store; converting the canonical enveloped message to a raw message that does not include the context store; and sending at least a portion of the raw message on a computer implemented communication medium. | 1. In a computing environment, a method implemented by computing system having a processor, the method comprising: receiving a canonical enveloped message from a computer implemented application, wherein the canonical enveloped message comprises payload data and is associated with a context store that stores context information of the canonical enveloped message in a form that is independent of one or more protocols employed by the canonical enveloped message; at a protocol pipeline comprising a plurality of protocol components processing the canonical enveloped message including using two or more of the protocol components to process the canonical enveloped message, and by at least adding, removing or modifying context entries in the context store, wherein each portion of the context information is stored in the context store as a context entry that comprises at least a name element to identify the portion, a value element representing the value of the identified portion, and optionally a metadata element that defines any additional information about the identified portion, such that context information for a plurality of protocol components is aggregated using a common format in the context store; converting the canonical enveloped message to a raw message that does not include the context store; and sending at least a portion of the raw message on a computer implemented communication medium. 8. The method of claim 1 , wherein the name element and value element are defined as strings. | 0.84396 |
9,922,087 | 1 | 3 | 1. A computer-implemented method comprising: receiving a query for execution in a database; building a logical intersection of query structures associated with the received query; identifying, as selected, a set of structure elements associated with the query structures, wherein the set of structure elements is identified by the query, and wherein the set of structure elements is used for accessing, from the database, data tuples associated with the set of structure elements; determining that query execution is set to retrieve data only for structure elements identified as selected; setting an object creation property for each element of the logical intersection, where all structure elements associated with the element are object creating; setting a selection property for each of the elements of the logical intersection where all structure elements associated with the element are identified as selected; setting a contributing structure element property for each element of the logical intersection where the element lacks the object creation property; and requesting data needed for each element of the logical intersection with a set selection property and contributing structure element property. | 1. A computer-implemented method comprising: receiving a query for execution in a database; building a logical intersection of query structures associated with the received query; identifying, as selected, a set of structure elements associated with the query structures, wherein the set of structure elements is identified by the query, and wherein the set of structure elements is used for accessing, from the database, data tuples associated with the set of structure elements; determining that query execution is set to retrieve data only for structure elements identified as selected; setting an object creation property for each element of the logical intersection, where all structure elements associated with the element are object creating; setting a selection property for each of the elements of the logical intersection where all structure elements associated with the element are identified as selected; setting a contributing structure element property for each element of the logical intersection where the element lacks the object creation property; and requesting data needed for each element of the logical intersection with a set selection property and contributing structure element property. 3. The method of claim 1 , wherein a structure element contains either a key figure and selections on multiple characteristics or a formula, wherein operands of the formula are other structure elements, and wherein the formula includes exception aggregation. | 0.695755 |
8,226,416 | 16 | 18 | 16. The non-transitory computer readable medium of claim 3 , further comprising: displaying the text to the reader prior to receiving the utterance. | 16. The non-transitory computer readable medium of claim 3 , further comprising: displaying the text to the reader prior to receiving the utterance. 18. The non-transitory computer readable medium of claim 16 , wherein the displaying comprises: formatting the text for display. | 0.915007 |
7,873,499 | 1 | 3 | 1. A computer program product on a computer readable storage medium comprising computer code for i) encoding two or more biological molecules into initial character strings to provide a collection of two or more different initial character strings wherein each of said biological molecules comprises at least about ten subunits; ii) selecting at least two substrings from said initial character strings; iii) concatenating said at least two substrings to form one or more product strings about the same length as one or more of the initial character strings; iv) determining sequence identities of at least one of the product strings relative to at least one initial character string; and v) selecting one or more product biological molecules for production, wherein the one or more product biological molecules correspond to one or more of the product strings. | 1. A computer program product on a computer readable storage medium comprising computer code for i) encoding two or more biological molecules into initial character strings to provide a collection of two or more different initial character strings wherein each of said biological molecules comprises at least about ten subunits; ii) selecting at least two substrings from said initial character strings; iii) concatenating said at least two substrings to form one or more product strings about the same length as one or more of the initial character strings; iv) determining sequence identities of at least one of the product strings relative to at least one initial character string; and v) selecting one or more product biological molecules for production, wherein the one or more product biological molecules correspond to one or more of the product strings. 3. The computer program product of claim 1 , wherein said two or more biological molecules are nucleic acid sequences encoding naturally occurring proteins. | 0.845238 |
9,747,264 | 3 | 4 | 3. The method of claim 2 , wherein adding the one or more items to the subsumed update list further comprises: comparing, by one or more processors, a first target node of a first item of the one or more items to a second target node of a second item of the second plurality of items, wherein the second item is a tail of the subsumed update list; and responsive to determining, by one or more processors, that the first target node is before the second target node in the document order: adding, by one or more processors, the first item to the subsumed update list based, at least in part, on the document order and on the first target node; identifying, by one or more processors, a non-attribute node by searching backwards through the subsumed update list from the tail; and applying, by one or more processors, the subsume logic to the first item and a preceding item in the subsumed update list, wherein the preceding item is associated with the non-attribute node. | 3. The method of claim 2 , wherein adding the one or more items to the subsumed update list further comprises: comparing, by one or more processors, a first target node of a first item of the one or more items to a second target node of a second item of the second plurality of items, wherein the second item is a tail of the subsumed update list; and responsive to determining, by one or more processors, that the first target node is before the second target node in the document order: adding, by one or more processors, the first item to the subsumed update list based, at least in part, on the document order and on the first target node; identifying, by one or more processors, a non-attribute node by searching backwards through the subsumed update list from the tail; and applying, by one or more processors, the subsume logic to the first item and a preceding item in the subsumed update list, wherein the preceding item is associated with the non-attribute node. 4. The method of claim 3 , further comprising: determining, by one or more processors, a subsume rule of the one or more subsume rules to apply based, at least in part, on an operation type of an incoming item of the one or more items and an operation type of a previous item of the subsumed update list. | 0.912794 |
8,577,131 | 12 | 13 | 12. The method of claim 1 , further comprising: clustering the unmatched query images into groups of similar query images; determining that a group of similar query images describes a given object; and adding the group of similar query images to the training corpus as training images associated with the given object. | 12. The method of claim 1 , further comprising: clustering the unmatched query images into groups of similar query images; determining that a group of similar query images describes a given object; and adding the group of similar query images to the training corpus as training images associated with the given object. 13. The method of claim 12 , further comprising filtering the groups by removing groups having an amount of similar query images below a threshold. | 0.965017 |
8,515,939 | 4 | 5 | 4. The method of claim 1 , wherein the input rule can be a text-based input rule or an element-based input rule. | 4. The method of claim 1 , wherein the input rule can be a text-based input rule or an element-based input rule. 5. The method of claim 4 , wherein the text-based input rule can be a text-analysis rule or a tag-syntax rule; wherein a text-analysis rule can include one or more of: a line rule, a fixed-length rule, a regular expression rule, a hypertext-tag rule, and a text-matching rule; and wherein a tag-syntax rule can include one or more of: a sequence rule, a block OR rule, a repetition rule, a template-object rule, a symbolic tag rule, a numeric tag rule, a string rule, an existence rule, a non-existence rule, a word rule, a word-capitalization rule, and an empty rule. | 0.840896 |
8,041,694 | 26 | 32 | 26. A system comprising: one or more computers, the one or more computers implementing: a dataset tool to identify a comparison vector x having processed features and non-processed features, a first set of vectors, each vector in the first set of vectors having processed features and non-processed features corresponding to the processed features and non-processed features of the comparison vector x, and a candidate vector y from the first set of vectors; and a similarity tool to determine a similarity threshold, and a maximum similarity between the non-processed features of x and the non-processed features of y; wherein the dataset tool removes the vector y from the first set of vectors if the maximum similarity does not meet the similarity threshold. | 26. A system comprising: one or more computers, the one or more computers implementing: a dataset tool to identify a comparison vector x having processed features and non-processed features, a first set of vectors, each vector in the first set of vectors having processed features and non-processed features corresponding to the processed features and non-processed features of the comparison vector x, and a candidate vector y from the first set of vectors; and a similarity tool to determine a similarity threshold, and a maximum similarity between the non-processed features of x and the non-processed features of y; wherein the dataset tool removes the vector y from the first set of vectors if the maximum similarity does not meet the similarity threshold. 32. The system of claim 26 in which each vector in the set of vectors represents a corresponding user in a community, and each feature of each vector represents a preference of the corresponding user towards an object in a set of objects. | 0.879433 |
8,781,829 | 28 | 38 | 28. A non-transitory computer readable medium having instructions stored thereon, wherein the instructions are executable by at least one computer processor to perform a method, the method comprising: (A) applying automatic speech recognition to an audio signal to produce a structured document representing contents of the audio signal; (B) determining whether the structured document includes an indication of compliance for each of a plurality of best practices to produce a conclusion; (C) inserting content into the structured document, based on the conclusion, to produce a modified structured document; (D) generating a first indication that a user should provide additional input of a first type to conform the structured document to a first best practice in the plurality of best practices; and (E) generating a second indication that the user should provide additional input of a second type to conform the structured document to a second best practice in the plurality of best practices. | 28. A non-transitory computer readable medium having instructions stored thereon, wherein the instructions are executable by at least one computer processor to perform a method, the method comprising: (A) applying automatic speech recognition to an audio signal to produce a structured document representing contents of the audio signal; (B) determining whether the structured document includes an indication of compliance for each of a plurality of best practices to produce a conclusion; (C) inserting content into the structured document, based on the conclusion, to produce a modified structured document; (D) generating a first indication that a user should provide additional input of a first type to conform the structured document to a first best practice in the plurality of best practices; and (E) generating a second indication that the user should provide additional input of a second type to conform the structured document to a second best practice in the plurality of best practices. 38. The computer readable medium of claim 28 , wherein the method further comprises generating an indication that a user should provide additional input to conform the structured document to the best practice. | 0.688988 |
7,587,319 | 23 | 34 | 23. A speech recognition circuit comprising: an input buffer for receiving processed speech parameters; lexical memory containing lexical data for word recognition, said lexical data comprising a plurality of lexical tree data structures, each lexical tree data structure comprising a model of words having common prefix components, the initial component of each lexical tree data structure being unique; a plurality of lexical tree processors connected in parallel to said input buffer for processing the speech parameters in parallel to perform parallel lexical tree processing for word recognition by accessing said lexical data in said lexical memory; results memory connected to said lexical tree processors for storing processing results from said lexical tree processors and lexical tree identifiers to identify lexical trees to be processed by said lexical tree processors; and a control processor for controlling said lexical tree processors to process lexical trees identified in said results memory by performing parallel processing on a plurality of said lexical tree data structures. | 23. A speech recognition circuit comprising: an input buffer for receiving processed speech parameters; lexical memory containing lexical data for word recognition, said lexical data comprising a plurality of lexical tree data structures, each lexical tree data structure comprising a model of words having common prefix components, the initial component of each lexical tree data structure being unique; a plurality of lexical tree processors connected in parallel to said input buffer for processing the speech parameters in parallel to perform parallel lexical tree processing for word recognition by accessing said lexical data in said lexical memory; results memory connected to said lexical tree processors for storing processing results from said lexical tree processors and lexical tree identifiers to identify lexical trees to be processed by said lexical tree processors; and a control processor for controlling said lexical tree processors to process lexical trees identified in said results memory by performing parallel processing on a plurality of said lexical tree data structures. 34. A speech recognition circuit according to claim 23 , wherein said lexical tree processors determine and output scores in the processing results during the processing of said speech parameters. | 0.854815 |
8,140,321 | 1 | 13 | 1. A method comprising: calculating, by one or more processors, a first value derived from coherence of terms in a sequence, where the coherence of the terms in the sequence is calculated relative to a first collection of documents; calculating, by one or more processors, a second value derived from a variation of context in which the sequence occurs, where the variation of context is calculated relative to a second collection of documents that differs from the first collection of documents; determining, by one or more processors, that the sequence is a semantic unit based on the first value, the second value, and one or more rules for excluding particular sequences; and labeling, by one or more processors, the sequence as a semantic unit. | 1. A method comprising: calculating, by one or more processors, a first value derived from coherence of terms in a sequence, where the coherence of the terms in the sequence is calculated relative to a first collection of documents; calculating, by one or more processors, a second value derived from a variation of context in which the sequence occurs, where the variation of context is calculated relative to a second collection of documents that differs from the first collection of documents; determining, by one or more processors, that the sequence is a semantic unit based on the first value, the second value, and one or more rules for excluding particular sequences; and labeling, by one or more processors, the sequence as a semantic unit. 13. The method of claim 1 , where determining, based on the first and second values, that the sequence is the semantic unit includes: determining whether the first value exceeds a first threshold value; determining whether the second value exceeds a second threshold value; and identifying the sequence as the semantic unit in response to determining that the first value and exceeds the first threshold value and the second value exceeds the second threshold value. | 0.817969 |
8,065,316 | 26 | 27 | 26. A system, comprising: a memory to store instructions; and a processor to execute the stored instructions to: receive, from one or more client devices, a plurality of refinement query suggestions that are refinements of a particular search query, where each refinement query suggestion, of the plurality of refinement query suggestions, has been received as a refinement of the particular search query a particular quantity of times; cluster the received plurality of refinement query suggestions to form a plurality of clusters; identify, for each refinement query suggestion, of the plurality of refinement query suggestions, the particular quantity of times that the refinement query suggestion was received as a refinement of the particular search query; identify, for each clustered received plurality of refinement query suggestions, of the formed plurality of clusters, a particular refinement query suggestion that corresponds to a highest identified quantity, from the identified particular quantity of times that the refinement query suggestion, of the refinement query suggestions of the cluster, has been received as the refinement of the particular search query; and present the identified particular refinement query suggestion from each of the clusters in response to receiving the particular search query. | 26. A system, comprising: a memory to store instructions; and a processor to execute the stored instructions to: receive, from one or more client devices, a plurality of refinement query suggestions that are refinements of a particular search query, where each refinement query suggestion, of the plurality of refinement query suggestions, has been received as a refinement of the particular search query a particular quantity of times; cluster the received plurality of refinement query suggestions to form a plurality of clusters; identify, for each refinement query suggestion, of the plurality of refinement query suggestions, the particular quantity of times that the refinement query suggestion was received as a refinement of the particular search query; identify, for each clustered received plurality of refinement query suggestions, of the formed plurality of clusters, a particular refinement query suggestion that corresponds to a highest identified quantity, from the identified particular quantity of times that the refinement query suggestion, of the refinement query suggestions of the cluster, has been received as the refinement of the particular search query; and present the identified particular refinement query suggestion from each of the clusters in response to receiving the particular search query. 27. The system of claim 26 , where the processor is further configured to: obtain search results in response to receiving the particular search query; and where, when presenting the identified particular refinement query suggestion from each of the clusters, the processor is configured to: present the identified particular refinement query suggestion from each of the clusters on a same graphical user interface as search results. | 0.863291 |
9,823,824 | 7 | 9 | 7. The method of claim 6 wherein the text is stretched or shrunk to change the size of the text. | 7. The method of claim 6 wherein the text is stretched or shrunk to change the size of the text. 9. The method of claim 7 wherein the text is stretched or shrunk automatically to adapt to the content of the image. | 0.963636 |
7,970,764 | 18 | 20 | 18. The article of manufacture of claim 17 , wherein the plurality of search terms correspond to a neural network and determining the plurality of positions and the plurality of search results comprises exciting the neural network. | 18. The article of manufacture of claim 17 , wherein the plurality of search terms correspond to a neural network and determining the plurality of positions and the plurality of search results comprises exciting the neural network. 20. The article of manufacture of claim 18 , wherein modifying the plurality of positions based on a new search term comprises increasing the importance of the new search term in the neural network. | 0.882283 |
8,103,703 | 1 | 4 | 1. A computer program embodied on a computer-readable storage medium and comprising code, that, when executed by a computer, enables the computer to perform the following: generate, in a mind map, a first type of topic that primarily provide a blank space in which text and graphic elements can be inserted by a user; and generate, in a mind map, one or more of a second type of topic, wherein the second type of topic is content-specific such that it displays and processes a select type of content according to a predefined behavior and enables a user to view and manipulate the select type of content within the content-specific topic within the mind map, and wherein each kind of content-specific topic is associated with a different class that defines common behavior across instances of the class and generating a content-specific topic within the mind map comprises creating an instance of the class for the content-specific topic within the mind map. | 1. A computer program embodied on a computer-readable storage medium and comprising code, that, when executed by a computer, enables the computer to perform the following: generate, in a mind map, a first type of topic that primarily provide a blank space in which text and graphic elements can be inserted by a user; and generate, in a mind map, one or more of a second type of topic, wherein the second type of topic is content-specific such that it displays and processes a select type of content according to a predefined behavior and enables a user to view and manipulate the select type of content within the content-specific topic within the mind map, and wherein each kind of content-specific topic is associated with a different class that defines common behavior across instances of the class and generating a content-specific topic within the mind map comprises creating an instance of the class for the content-specific topic within the mind map. 4. The computer program of claim 1 , wherein, for one of the content-specific topics, the select type of content is audio. | 0.864745 |
8,606,573 | 1 | 2 | 1. A system comprising: a) a microphone of a voice over internet protocol (VoIP) phone, the microphone being internal or external to the VoIP phone; b) delivery of an input frame from the microphone to a voice activity detection (VAD) system, the VAD system determining, using one or more processors, whether the input frame is speech or non-speech and the VAD system performing the steps of: i. directing an input frame of non-speech to a noise spectrum estimation component, wherein the input frame is averaged to overcome artifacts; ii. obtaining an estimation of an expected value of a noise magnitude spectrum by exponential averaging of the noise magnitude spectrum during non-speech activity for a particular band wherein:
E[N ( k )]=σ× E[N ( k )]+(1−σ)×| N ( k )| and the optimum value for σ is between 0.75 and 0.95; and iii. directing an input frame of speech to a spectral subtraction block which subtracts background noise from the input frame of speech, the input frame of speech then enters a time domain conversion component and is converted to time domain and them becomes a speech input and the speech input travels to a sampler block; c) the sampler block accepts the speech input; d) a feature extractor block accepts input from the sampler block, the feature extractor block extracts time and domain and spectral domain parameters from the input speech into a feature vector; e) a polynomial expansion block accepts input from the feature extractor block, the polynomial expansion block generates polynomial coefficients; f) a correlator block accepts input from the polynomial expansion block and from a speech unit table, the correlator block directs output to a sequence vector block and to a hidden markov model (HMM) table block; e) a viterbi block accepts input from the HMM table block and the sequence vector block; and f) the correlator block, sequence vector block, HMM table block and Viterbi block perform speech recognition based on speech units stored in the speech unit table and HMM word models stored in the HMM table block, the HMM word model that produces the highest probability is determined to be the word that was spoken. | 1. A system comprising: a) a microphone of a voice over internet protocol (VoIP) phone, the microphone being internal or external to the VoIP phone; b) delivery of an input frame from the microphone to a voice activity detection (VAD) system, the VAD system determining, using one or more processors, whether the input frame is speech or non-speech and the VAD system performing the steps of: i. directing an input frame of non-speech to a noise spectrum estimation component, wherein the input frame is averaged to overcome artifacts; ii. obtaining an estimation of an expected value of a noise magnitude spectrum by exponential averaging of the noise magnitude spectrum during non-speech activity for a particular band wherein:
E[N ( k )]=σ× E[N ( k )]+(1−σ)×| N ( k )| and the optimum value for σ is between 0.75 and 0.95; and iii. directing an input frame of speech to a spectral subtraction block which subtracts background noise from the input frame of speech, the input frame of speech then enters a time domain conversion component and is converted to time domain and them becomes a speech input and the speech input travels to a sampler block; c) the sampler block accepts the speech input; d) a feature extractor block accepts input from the sampler block, the feature extractor block extracts time and domain and spectral domain parameters from the input speech into a feature vector; e) a polynomial expansion block accepts input from the feature extractor block, the polynomial expansion block generates polynomial coefficients; f) a correlator block accepts input from the polynomial expansion block and from a speech unit table, the correlator block directs output to a sequence vector block and to a hidden markov model (HMM) table block; e) a viterbi block accepts input from the HMM table block and the sequence vector block; and f) the correlator block, sequence vector block, HMM table block and Viterbi block perform speech recognition based on speech units stored in the speech unit table and HMM word models stored in the HMM table block, the HMM word model that produces the highest probability is determined to be the word that was spoken. 2. The system of claim 1 wherein the spectral subtraction block works as follows: a) an input signal is divided into frames; b) a decision on whether the frame is composed of speech or non-speech is made within a speech noise selection component; c) a power spectrum is then calculated and then classified as speech power or noise power; d) noise power and speech power are used to calculate gain; e) gain is modified; f) a Fourier Transform of the signal from the signal frame component is calculated and multiplied with the modified gain; g) the resulting product of the multiplication is then used to calculate an Inverse Fourier Transform; and h) the resulting noise reduced frame is the desired output. | 0.500706 |
8,538,157 | 1 | 18 | 1. A device for detecting characters in an image, comprising: a Hough transformer arranged to identify, as identified elements of writing, circular arcs or elliptical arcs in the image or in a preprocessed version of the image; a character description generator arranged to acquire, on the basis of the identified circular arcs or elliptical arcs, a character description which describes locations of the identified circular arcs or elliptical arcs; and a database comparator arranged to compare the character description with a plurality of comparative character descriptions which have character codes associated with them, so as to provide, as a result of the comparison, a character code of a detected character; wherein the Hough transformer is arranged to identify, as identified elements of writing, individual circular arcs or elliptical arcs, which approximate a course of line of a character within a surrounding of local extremes, in the image or in a preprocessed version of the image, and to provide information about an orientation of an identified circular arc or elliptical arc; and the character description generator is arranged to acquire, based on the identified circular arcs or elliptical arcs and the information about the orientation, a character description which describes locations of the individual identified circular arcs or elliptical arcs. | 1. A device for detecting characters in an image, comprising: a Hough transformer arranged to identify, as identified elements of writing, circular arcs or elliptical arcs in the image or in a preprocessed version of the image; a character description generator arranged to acquire, on the basis of the identified circular arcs or elliptical arcs, a character description which describes locations of the identified circular arcs or elliptical arcs; and a database comparator arranged to compare the character description with a plurality of comparative character descriptions which have character codes associated with them, so as to provide, as a result of the comparison, a character code of a detected character; wherein the Hough transformer is arranged to identify, as identified elements of writing, individual circular arcs or elliptical arcs, which approximate a course of line of a character within a surrounding of local extremes, in the image or in a preprocessed version of the image, and to provide information about an orientation of an identified circular arc or elliptical arc; and the character description generator is arranged to acquire, based on the identified circular arcs or elliptical arcs and the information about the orientation, a character description which describes locations of the individual identified circular arcs or elliptical arcs. 18. The device according to claim 1 , wherein the character description generator is arranged to generate a description of the character by joining together selected adjacent identified character elements, wherein the character description generator is arranged to select the selected adjacent identified character elements employed for the description of the character from a totality of identified character elements such that the selected adjacent identified character elements describe a continuous course of line from a predefined starting point to a predefined end point. | 0.556154 |
8,504,599 | 1 | 11 | 1. A method for intelligent database retrieval, comprising the steps of: providing a system comprised of a storage device, an input device, a computing device, and a display device that is operated by a user for conducting at least one of a user initiated free-form inquiry and a user initiated structured inquiry; logging into the system with the input device; entering the free-form inquiry by the user; structuring a search for related knowledge initiated by the system that is based upon sensory information, the sensory information including the free-form inquiry, prior inquiries by the user, and also the user's role within an organization, wherein the search for related knowledge is not the same as the free-form inquiry; searching the storage device for results to the free-form inquiry and the related knowledge; returning the results of the free-form inquiry and the related knowledge search to the computing device; formatting the results of the free-form inquiry and the related knowledge search with the computing device; displaying the formatted results on the display device; and adapting the search structure for related knowledge based upon follow-up free-form inquiries. | 1. A method for intelligent database retrieval, comprising the steps of: providing a system comprised of a storage device, an input device, a computing device, and a display device that is operated by a user for conducting at least one of a user initiated free-form inquiry and a user initiated structured inquiry; logging into the system with the input device; entering the free-form inquiry by the user; structuring a search for related knowledge initiated by the system that is based upon sensory information, the sensory information including the free-form inquiry, prior inquiries by the user, and also the user's role within an organization, wherein the search for related knowledge is not the same as the free-form inquiry; searching the storage device for results to the free-form inquiry and the related knowledge; returning the results of the free-form inquiry and the related knowledge search to the computing device; formatting the results of the free-form inquiry and the related knowledge search with the computing device; displaying the formatted results on the display device; and adapting the search structure for related knowledge based upon follow-up free-form inquiries. 11. The method for intelligent database retrieval of claim 1 , wherein the prior inquiries by the user are retrieved from a lookup table containing information previously accessed by the user. | 0.775701 |
8,302,029 | 1 | 11 | 1. A method comprising: at least partially dividing at least one object into a plurality of sub-objects; causing presentation of said plurality of sub-objects in a first representation, determining at least one sub-object of said plurality of sub-objects to be made an active sub-object; making said at least one sub-object of said plurality of sub-objects an active sub-object; and in response to a user operation on said at least one active sub-object, causing presentation of at least one of said at least one active sub-objects in a second representation, wherein said at least partially dividing at least one object into a plurality of sub-objects comprises element-wise rendering elements contained in said at least one object to obtain a rendered object with a height, a width, and a depth, checking if a product of the height, the width, and the depth size of said rendered object exceeds a threshold, and forming one or more parallelepiped from said rendered object in an instance in which said threshold is exceeded. | 1. A method comprising: at least partially dividing at least one object into a plurality of sub-objects; causing presentation of said plurality of sub-objects in a first representation, determining at least one sub-object of said plurality of sub-objects to be made an active sub-object; making said at least one sub-object of said plurality of sub-objects an active sub-object; and in response to a user operation on said at least one active sub-object, causing presentation of at least one of said at least one active sub-objects in a second representation, wherein said at least partially dividing at least one object into a plurality of sub-objects comprises element-wise rendering elements contained in said at least one object to obtain a rendered object with a height, a width, and a depth, checking if a product of the height, the width, and the depth size of said rendered object exceeds a threshold, and forming one or more parallelepiped from said rendered object in an instance in which said threshold is exceeded. 11. The method according to claim 1 , wherein sub-objects of said plurality of sub-objects with a size that is above a size threshold, or that contain an amount of information that is above an information threshold are made active sub-objects, or both. | 0.837419 |
9,928,234 | 13 | 17 | 13. A computer-readable non-transitory storage medium comprising executable instructions that, when executed by a computer system, cause the computer system to: perform semantico-syntactic analysis of a natural language text to produce a semantic structure representing a set of semantic classes; associate a first semantic class of the set of semantic classes with a first value reflecting a specified semantic class attribute; identify a second semantic class associated with the first semantic class by a pre-defined semantic relationship; associate the second semantic class with a second value reflecting the specified semantic class attribute, wherein the second value is determined by applying a pre-defined transformation to the first value; identify a third semantic class associated with the second semantic class by the pre-defined semantic relationship; and associate the third semantic class with a third value reflecting the specified semantic class attribute, wherein the third value is determined by applying the pre-defined transformation to the second value; evaluate a feature of the natural language text based on the first value and the second value; determine, by a classifier model using the evaluated feature of the natural language text, a degree of association of the natural language text with a particular category of a pre-defined set of categories; and perform, using the degree of association, a natural language processing operation. | 13. A computer-readable non-transitory storage medium comprising executable instructions that, when executed by a computer system, cause the computer system to: perform semantico-syntactic analysis of a natural language text to produce a semantic structure representing a set of semantic classes; associate a first semantic class of the set of semantic classes with a first value reflecting a specified semantic class attribute; identify a second semantic class associated with the first semantic class by a pre-defined semantic relationship; associate the second semantic class with a second value reflecting the specified semantic class attribute, wherein the second value is determined by applying a pre-defined transformation to the first value; identify a third semantic class associated with the second semantic class by the pre-defined semantic relationship; and associate the third semantic class with a third value reflecting the specified semantic class attribute, wherein the third value is determined by applying the pre-defined transformation to the second value; evaluate a feature of the natural language text based on the first value and the second value; determine, by a classifier model using the evaluated feature of the natural language text, a degree of association of the natural language text with a particular category of a pre-defined set of categories; and perform, using the degree of association, a natural language processing operation. 17. The computer-readable non-transitory storage medium of claim 13 , wherein an instance of the second semantic class is an ancestor of the first semantic class in a semantic hierarchy associated with the set of semantic classes. | 0.621711 |
6,055,498 | 1 | 16 | 1. In an automatic speech processing system, a method for assessing pronunciation of a student speech sample using a computerized acoustic segmentation system, the method comprising: accepting said student speech sample which comprises a sequence of words spoken by a student speaker; operating said computerized acoustic segmentation system to define sample acoustic units within said student speech sample based on speech acoustic models within said segmentation system, said speech acoustic models being established using training speech data from at least one speaker, said training speech data not necessarily including said sequence of spoken words; measuring duration of said sample acoustic units; and comparing said durations of sample acoustic units to a model of exemplary acoustic unit duration to compute a duration score indicative of similarity between said sample acoustic unit durations and exemplary acoustic unit durations. | 1. In an automatic speech processing system, a method for assessing pronunciation of a student speech sample using a computerized acoustic segmentation system, the method comprising: accepting said student speech sample which comprises a sequence of words spoken by a student speaker; operating said computerized acoustic segmentation system to define sample acoustic units within said student speech sample based on speech acoustic models within said segmentation system, said speech acoustic models being established using training speech data from at least one speaker, said training speech data not necessarily including said sequence of spoken words; measuring duration of said sample acoustic units; and comparing said durations of sample acoustic units to a model of exemplary acoustic unit duration to compute a duration score indicative of similarity between said sample acoustic unit durations and exemplary acoustic unit durations. 16. The method according to claim 1 wherein: said exemplary acoustic unit duration distribution model is a model of speaker-normalized acoustic unit durations, and the duration measuring step comprises the steps of: analyzing said student speech sample to determine a student speaker normalization factor; and employing said student speaker normalization factor to measure speaker-normalized durations as said measured sample acoustic unit durations, whereby the comparing step compares said speaker-normalized sample acoustic unit durations to said exemplary speaker-normalized acoustic unit duration distribution model. | 0.581536 |
7,805,435 | 15 | 17 | 15. A method of generating an abstract query from a physical query, comprising: receiving, from a requesting entity, a physical query composed in a query language used to query a physical database; generating, from the physical query, an intermediate representation of the physical query that indicates (i) data sources within the physical database containing data queried by the physical query, (ii) conditions specified by the physical query on the data queried and (iii) relationships between the data sources queried by the physical query; and generating, from the intermediate representation, an abstract query composed from a plurality of logical fields, wherein each logical field specifies (i) a name used to identify the logical field, and (ii) an access method that maps the logical field to data in the physical database. | 15. A method of generating an abstract query from a physical query, comprising: receiving, from a requesting entity, a physical query composed in a query language used to query a physical database; generating, from the physical query, an intermediate representation of the physical query that indicates (i) data sources within the physical database containing data queried by the physical query, (ii) conditions specified by the physical query on the data queried and (iii) relationships between the data sources queried by the physical query; and generating, from the intermediate representation, an abstract query composed from a plurality of logical fields, wherein each logical field specifies (i) a name used to identify the logical field, and (ii) an access method that maps the logical field to data in the physical database. 17. The method of claim 15 , wherein the physical query comprises an SQL statement and the physical database comprises a relational database; and wherein the relationships between data sources are indicated by a relational schema of the relational database, and wherein each data source comprises a relational table | 0.900881 |
8,489,625 | 8 | 11 | 8. One or more computer-readable storage media encoded with instructions that, when executed by a processor, perform acts comprising: receiving, from a mobile computing device of a particular user, a query input associated with a current time and with a current location of the mobile computing device; and at least partly in response to the receiving of the query input: accessing a query-user graph constructed from mobile search logs that identifies for, each of multiple queries, a user that sent the query, a time at which the user sent the query, and a location from which the user sent the query; and identifying candidate queries that are related to the query input based at least in part on: calculating a similarity between the users from the query-user graph based at least in part on similarities between the queries, the times at which the users submitted the queries, and the locations from which the users submitted the queries, and calculating a relatedness of each of the candidate queries to the received query input based at least in part on; a difference between the current time associated with the received query input and a time when the candidate query was submitted; a distance between the current location associated with the received query input and the location where the candidate query was sent; computing a weight of a user based at least in part on measuring distances between a time and a location at which the user has previously submitted the received query input and the current time and the current location; and applying the computed weight to one or more candidate queries. | 8. One or more computer-readable storage media encoded with instructions that, when executed by a processor, perform acts comprising: receiving, from a mobile computing device of a particular user, a query input associated with a current time and with a current location of the mobile computing device; and at least partly in response to the receiving of the query input: accessing a query-user graph constructed from mobile search logs that identifies for, each of multiple queries, a user that sent the query, a time at which the user sent the query, and a location from which the user sent the query; and identifying candidate queries that are related to the query input based at least in part on: calculating a similarity between the users from the query-user graph based at least in part on similarities between the queries, the times at which the users submitted the queries, and the locations from which the users submitted the queries, and calculating a relatedness of each of the candidate queries to the received query input based at least in part on; a difference between the current time associated with the received query input and a time when the candidate query was submitted; a distance between the current location associated with the received query input and the location where the candidate query was sent; computing a weight of a user based at least in part on measuring distances between a time and a location at which the user has previously submitted the received query input and the current time and the current location; and applying the computed weight to one or more candidate queries. 11. The computer-readable storage media of claim 8 , wherein the calculating the similarity comprises: representing the times of queries submitted by a first user and a second user using 24-dimension vectors to identify an hour of a day; and applying a cosine similarity function to compute a distance of the 24-dimension vectors representing the times of the queries submitted by the first user and the second user to identify an hour between the 24-dimension vectors. | 0.589317 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.