patent_num
int64 3.93M
10.2M
| claim_num1
int64 1
519
| claim_num2
int64 2
520
| sentence1
stringlengths 40
15.9k
| sentence2
stringlengths 88
20k
| label
float64 0.5
0.99
|
|---|---|---|---|---|---|
8,610,719 | 1 | 9 | 1. A system for reorienting a display of clusters, comprising: a display of clusters, each cluster having a center located at a distance relative to a common origin for the display; an overlap check module to compare a bounding region of each cluster to a bounding region of each other cluster and to determine that two or more of the clusters overlap; a reorient module to reorient at least one of the overlapping clusters until no overlap occurs; and a processor to execute the modules. | 1. A system for reorienting a display of clusters, comprising: a display of clusters, each cluster having a center located at a distance relative to a common origin for the display; an overlap check module to compare a bounding region of each cluster to a bounding region of each other cluster and to determine that two or more of the clusters overlap; a reorient module to reorient at least one of the overlapping clusters until no overlap occurs; and a processor to execute the modules. 9. A system according to claim 1 , further comprising: a sort module to order the clusters relative to the distance from the common origin for each cluster. | 0.694118 |
8,065,527 | 5 | 7 | 5. A method as in claim 1 , further comprising adding a plurality of signing individuals to the signer list to enable the signature tag to be associated with one of the plurality of signing individuals, and verifying that the plurality of signing individuals added to the list includes no duplicate names using a signer list module. | 5. A method as in claim 1 , further comprising adding a plurality of signing individuals to the signer list to enable the signature tag to be associated with one of the plurality of signing individuals, and verifying that the plurality of signing individuals added to the list includes no duplicate names using a signer list module. 7. A method as in claim 5 , further comprising associating each of the plurality of signing individuals as one of a signer and a notary. | 0.75 |
8,650,172 | 2 | 3 | 2. The method of claim 1 , further comprising: obtaining query behavior from client-side query behavior logs by extracting data from the client-side query behavior logs to obtain a plurality of descriptive features for the searchable web sites, where the client-side query behavior logs are obtained by doing one or more of the following: (a) extracting at least some of the data from client-side browsing logs; (b) requesting query logs from searchable web site providers of the searchable web sites; (c) obtaining idealized query sets that describe ideal queries and associated frequencies, the idealized query sets being obtained by authoring the ideal queries, by using click behavior to obtain the ideal queries, or by requesting the ideal queries from the searchable web site providers; or (d) using random walks on a query-click graph of a general search engine. | 2. The method of claim 1 , further comprising: obtaining query behavior from client-side query behavior logs by extracting data from the client-side query behavior logs to obtain a plurality of descriptive features for the searchable web sites, where the client-side query behavior logs are obtained by doing one or more of the following: (a) extracting at least some of the data from client-side browsing logs; (b) requesting query logs from searchable web site providers of the searchable web sites; (c) obtaining idealized query sets that describe ideal queries and associated frequencies, the idealized query sets being obtained by authoring the ideal queries, by using click behavior to obtain the ideal queries, or by requesting the ideal queries from the searchable web site providers; or (d) using random walks on a query-click graph of a general search engine. 3. The method of claim 2 , further comprising: determining static ranks for the searchable web sites using the plurality of descriptive features, and computing the probabilities using the static ranks. | 0.5 |
9,946,706 | 13 | 18 | 13. An electronic device, comprising: one or more processors; memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for: obtaining a document including text; receiving, from an automatic language identifier service, a first language identification for the document; in response to receiving the first language identification, automatically invoking a modifying operation; performing the modifying operation on the document in accordance with the first language identification; determining, based at least in part on results from the modifying operation, whether the first language identification for the document is correct, wherein the results from the modifying operation include at least one of the amount of errors or the nature of the errors associated with the modifying operation; and in accordance with a determination that the first language identification is correct, providing the first language identification to a user application; in accordance with a determination that the first language identification is incorrect, determining a second language identification of the document, and performing a modifying function on the document in accordance with one or more alternate languages different from the first language, wherein the second language identification of the document is determined based at least in part on the results from performing the modifying function on the document in accordance with the one or more alternate languages. | 13. An electronic device, comprising: one or more processors; memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for: obtaining a document including text; receiving, from an automatic language identifier service, a first language identification for the document; in response to receiving the first language identification, automatically invoking a modifying operation; performing the modifying operation on the document in accordance with the first language identification; determining, based at least in part on results from the modifying operation, whether the first language identification for the document is correct, wherein the results from the modifying operation include at least one of the amount of errors or the nature of the errors associated with the modifying operation; and in accordance with a determination that the first language identification is correct, providing the first language identification to a user application; in accordance with a determination that the first language identification is incorrect, determining a second language identification of the document, and performing a modifying function on the document in accordance with one or more alternate languages different from the first language, wherein the second language identification of the document is determined based at least in part on the results from performing the modifying function on the document in accordance with the one or more alternate languages. 18. The electronic device of claim 13 , wherein determining whether the first language identification for the document is correct is further based on length of the text included in the document. | 0.688103 |
7,882,121 | 1 | 5 | 1. A method for testing a component of a database application, the method comprising: populating, as specified by a user, a column in a database with test data that falls within a certain range specified by the user; specifying, by the user, a desired cardinality constraint suitable for testing the component, the component operating on a computing device, wherein the component is a software component; specifying, by the user, a parametric pattern query that includes a parameter, wherein the parametric pattern query is compatible with the database, and wherein the parametric pattern query is configured to restrict cardinality when evaluated against the database; selecting a candidate value; evaluating, by the computing device via the component, the parametric pattern query against the database with the parameter set to the candidate value; calculating, by the computing device, a cardinality error as a difference between a returned cardinality and the desired cardinality constraint, wherein the returned cardinality results from the evaluating; and adjusting the candidate value based on the calculated cardinality error and then repeating the evaluating the parametric pattern query against the database with the parameter set to the adjusted candidate value and the calculating the cardinality error until the calculated cardinality error is within an allowable limit. | 1. A method for testing a component of a database application, the method comprising: populating, as specified by a user, a column in a database with test data that falls within a certain range specified by the user; specifying, by the user, a desired cardinality constraint suitable for testing the component, the component operating on a computing device, wherein the component is a software component; specifying, by the user, a parametric pattern query that includes a parameter, wherein the parametric pattern query is compatible with the database, and wherein the parametric pattern query is configured to restrict cardinality when evaluated against the database; selecting a candidate value; evaluating, by the computing device via the component, the parametric pattern query against the database with the parameter set to the candidate value; calculating, by the computing device, a cardinality error as a difference between a returned cardinality and the desired cardinality constraint, wherein the returned cardinality results from the evaluating; and adjusting the candidate value based on the calculated cardinality error and then repeating the evaluating the parametric pattern query against the database with the parameter set to the adjusted candidate value and the calculating the cardinality error until the calculated cardinality error is within an allowable limit. 5. The method of claim 1 , wherein the parametric pattern query is a SQL query of the form:
SELECT * from T WHERE p # T.c where T represents a table of the database that includes the column, and where p is the parameter, and where # represents an arithmetic expression, and where T.c represents the column. | 0.746711 |
6,029,182 | 7 | 9 | 7. An automated method for retrieving articles from a hypermedia-linked computer network and for formatting the articles into a personalized newspaper, the method comprising the steps of: retrieving a stored personal-news-profile which comprises address data for a site on the hypermedia-linked computer network, command data for accessing data from the site, and newspaper layout commands; contacting the site based on address data stored in the personal-news-profile; traversing selectively links in the site; downloading articles from the site and/or links in the site based on command data stored in the personal-news-profile; flattening the articles into a linear document; and formatting the linear document into the personalized newspaper according to layout commands stored in the personal-news-profile, the personalized newspaper consisting of text and/or images, wherein a number of links traversed in the traversing step can be limited to a predefined numbers of links based on command data in the personal-news-profile. | 7. An automated method for retrieving articles from a hypermedia-linked computer network and for formatting the articles into a personalized newspaper, the method comprising the steps of: retrieving a stored personal-news-profile which comprises address data for a site on the hypermedia-linked computer network, command data for accessing data from the site, and newspaper layout commands; contacting the site based on address data stored in the personal-news-profile; traversing selectively links in the site; downloading articles from the site and/or links in the site based on command data stored in the personal-news-profile; flattening the articles into a linear document; and formatting the linear document into the personalized newspaper according to layout commands stored in the personal-news-profile, the personalized newspaper consisting of text and/or images, wherein a number of links traversed in the traversing step can be limited to a predefined numbers of links based on command data in the personal-news-profile. 9. The method of claim 7, wherein said hypermedia-linked computer network is the World Wide Web. | 0.659574 |
8,190,647 | 8 | 10 | 8. A computer system for constructing a decision tree for classifying computer files based on the computational complexities of attributes of the files, comprising: a computer-readable storage medium storing executable computer program code; and a processor for executing the executable computer program code, wherein said executable computer program code comprising: an attribute complexity determination module for creating a plurality of attribute vectors for a plurality of training files of known classification, each attribute vector comprising values of a predetermined set of attributes for an associated training file, and determining a complexity score for each attribute in the predetermined set of attributes, the complexity score measuring a cost associated with determining a value of an associated attribute for a file; and a decision tree construction module for growing a decision tree based on the plurality of attribute vectors, comprising: (1) setting the plurality of attribute vectors as a current set, (2) determining a weighted impurity reduction score for at least one attribute of the predetermined set of attributes based on the complexity score of the attribute, the weighted impurity reduction score quantifying a cost-benefit tradeoff for an associated attribute in classifying the current set, (3) selecting a splitting attribute from the at least one attribute of the predetermined set of attributes, (4) splitting the current set into subsets using the splitting attribute, and (5) repeating steps (2) through (4) for each of the subsets as the current set. | 8. A computer system for constructing a decision tree for classifying computer files based on the computational complexities of attributes of the files, comprising: a computer-readable storage medium storing executable computer program code; and a processor for executing the executable computer program code, wherein said executable computer program code comprising: an attribute complexity determination module for creating a plurality of attribute vectors for a plurality of training files of known classification, each attribute vector comprising values of a predetermined set of attributes for an associated training file, and determining a complexity score for each attribute in the predetermined set of attributes, the complexity score measuring a cost associated with determining a value of an associated attribute for a file; and a decision tree construction module for growing a decision tree based on the plurality of attribute vectors, comprising: (1) setting the plurality of attribute vectors as a current set, (2) determining a weighted impurity reduction score for at least one attribute of the predetermined set of attributes based on the complexity score of the attribute, the weighted impurity reduction score quantifying a cost-benefit tradeoff for an associated attribute in classifying the current set, (3) selecting a splitting attribute from the at least one attribute of the predetermined set of attributes, (4) splitting the current set into subsets using the splitting attribute, and (5) repeating steps (2) through (4) for each of the subsets as the current set. 10. The computer system of claim 8 , wherein determining the weighted impurity reduction score comprises: determining an impurity reduction score for the at least one of the predetermined set of attributes, wherein the impurity reduction score for an attribute measures how well the attribute separates the current set and the weighted impurity reduction score for the attribute is determined based on the impurity reduction score and the complexity score of the attribute. | 0.581416 |
9,146,957 | 8 | 9 | 8. A web server comprising: a processor; memory; a optimization unit configured to: traverse an abstract syntax tree representing the query, for each node in the abstract syntax tree: setting a conjunct position field in a data structure corresponding to the node as true when the node's parent is a WHERE node; setting a conjunct position field in a data structure corresponding to the node as false when the node's parent is an OR node; setting a conjunct position field in a data structure corresponding to the node as identical to a conjunct position field in a data structure corresponding to the node's parent node when the node's parent is an AND node; and transform an IN node in the abstract syntax tree to an INNER JOIN node when the conjunct position field in the data structure corresponding to the IN node is set as true; and transform an IN node in the abstract syntax tree to a LEFT OUTER JOIN node when the conjunct position field in the data structure corresponding to the IN node is set as false; and an abstract syntax tree to SQL converter configured to convert the abstract syntax tree into a SQL query and to transmit the SQL query to a database for processing. | 8. A web server comprising: a processor; memory; a optimization unit configured to: traverse an abstract syntax tree representing the query, for each node in the abstract syntax tree: setting a conjunct position field in a data structure corresponding to the node as true when the node's parent is a WHERE node; setting a conjunct position field in a data structure corresponding to the node as false when the node's parent is an OR node; setting a conjunct position field in a data structure corresponding to the node as identical to a conjunct position field in a data structure corresponding to the node's parent node when the node's parent is an AND node; and transform an IN node in the abstract syntax tree to an INNER JOIN node when the conjunct position field in the data structure corresponding to the IN node is set as true; and transform an IN node in the abstract syntax tree to a LEFT OUTER JOIN node when the conjunct position field in the data structure corresponding to the IN node is set as false; and an abstract syntax tree to SQL converter configured to convert the abstract syntax tree into a SQL query and to transmit the SQL query to a database for processing. 9. The web server of claim 8 , further comprising: an ActiveRecord Query domain specific language unit configured to transform the query into an ActiveRecord query domain specific language abstract syntax tree. | 0.511628 |
10,073,794 | 12 | 13 | 12. The method of claim 7 , further comprising continuously updating the consumer preference data stored in the consumer preference storage area. | 12. The method of claim 7 , further comprising continuously updating the consumer preference data stored in the consumer preference storage area. 13. The method of claim 12 , wherein the updating is done based on user inputs and current and historic actions of the user. | 0.5 |
9,098,836 | 1 | 3 | 1. A computer-based method, comprising: automatically identifying intention metadata associated with an attachment to an email based upon at least one of a body of the email or a header of the email, where the attachment comprises a text-based file or an image file and where the intention metadata indicates a sender intention for presentation of the attachment with a first portion of the attachment comprising a first portion of the text-based file or a first portion of the image file highlighted and with a second portion of the attachment comprising a second portion of the text-based file or a second portion of the image file highlighted; and applying the intention metadata to the attachment such that a recipient can identify the sender intention for the attachment, applying the intention metadata comprising applying the intention metadata to the attachment such that the sender intention for the attachment is displayed in the body of the email. | 1. A computer-based method, comprising: automatically identifying intention metadata associated with an attachment to an email based upon at least one of a body of the email or a header of the email, where the attachment comprises a text-based file or an image file and where the intention metadata indicates a sender intention for presentation of the attachment with a first portion of the attachment comprising a first portion of the text-based file or a first portion of the image file highlighted and with a second portion of the attachment comprising a second portion of the text-based file or a second portion of the image file highlighted; and applying the intention metadata to the attachment such that a recipient can identify the sender intention for the attachment, applying the intention metadata comprising applying the intention metadata to the attachment such that the sender intention for the attachment is displayed in the body of the email. 3. The method of claim 1 , automatically identifying the intention metadata comprising examining at least a portion of at least one of the body or the header. | 0.724739 |
7,703,028 | 11 | 14 | 11. A computer-implemented method, said computer including a processor and having a graphical display that includes at least two objects, said graphical display showing a relationship between said at least two objects, said method comprising: splitting said graphical display into a left area forming a left rectangle, a center area forming a center rectangle, and a right area forming a right rectangle; displaying a star schema in said graphical display by displaying at least one dimension object including at least one dimension table that includes attribute data in said left area, a facts object including a facts table that includes measurement data in said center area, and at least one additional dimension object including at least one dimension table that includes attribute data in said right area, wherein said at least one object in each area is manipulated independently of said other objects in said each area and said at least one object in each other area; displaying in at least one area said at least two objects in said graphical display; accepting input that enlarges at least one of said at least two objects in one area; and in response to enlarging said at least one object in said one area; enlarging said display of said one area that includes said enlarged at least one object; moving and realigning vertically and horizontally at least one of said at least two objects that is not enlarged relative to said enlarged at least one object in said enlarged area thereby showing said relationship between said at least two objects; moving said display of said each other area to create more space on the graphical display for said enlarged area; realigning said objects in said each other area with the realigned other objects within said enlarged area, wherein positions of said objects in said each other area are adjusted in at least one of a horizontal direction and a vertical direction to accommodate movement and alignment of at least one of said at least two objects that is not enlarged; and displaying said relationship on said graphical display by including at least one connecting line that connects said at least one object in said enlarged area with said at least one object that is not included in said enlarged area. | 11. A computer-implemented method, said computer including a processor and having a graphical display that includes at least two objects, said graphical display showing a relationship between said at least two objects, said method comprising: splitting said graphical display into a left area forming a left rectangle, a center area forming a center rectangle, and a right area forming a right rectangle; displaying a star schema in said graphical display by displaying at least one dimension object including at least one dimension table that includes attribute data in said left area, a facts object including a facts table that includes measurement data in said center area, and at least one additional dimension object including at least one dimension table that includes attribute data in said right area, wherein said at least one object in each area is manipulated independently of said other objects in said each area and said at least one object in each other area; displaying in at least one area said at least two objects in said graphical display; accepting input that enlarges at least one of said at least two objects in one area; and in response to enlarging said at least one object in said one area; enlarging said display of said one area that includes said enlarged at least one object; moving and realigning vertically and horizontally at least one of said at least two objects that is not enlarged relative to said enlarged at least one object in said enlarged area thereby showing said relationship between said at least two objects; moving said display of said each other area to create more space on the graphical display for said enlarged area; realigning said objects in said each other area with the realigned other objects within said enlarged area, wherein positions of said objects in said each other area are adjusted in at least one of a horizontal direction and a vertical direction to accommodate movement and alignment of at least one of said at least two objects that is not enlarged; and displaying said relationship on said graphical display by including at least one connecting line that connects said at least one object in said enlarged area with said at least one object that is not included in said enlarged area. 14. The computer-implemented method of claim 11 , said computer having data and at least two entities that are collections of said data, the method further comprising representing said at least two entities with said at least two objects. | 0.666667 |
9,239,823 | 5 | 7 | 5. 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: obtaining, at one or more computers, a pair of terms in a first language, the pair of terms being commonly co-occurring non-synonyms in a corpus of documents, the corpus of documents being in the first language; determining a set of variations for each term in the pair of terms; generating a set of known related input pairs based on the sets of variations for each term in the pair of terms; for each input pair of terms in the set of known related input pairs, translating, by an automatic translation system, each term in the pair of terms into a plurality of languages to generate a set of translated terms; adding, at the one or more computers, the set of translated terms to a blacklist of known non-synonym pairs for at least one of the plurality of languages; and determining, based on the blacklist of known non-synonym pairs, whether a pair of candidate terms in at least one of the plurality of languages are synonyms. | 5. 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: obtaining, at one or more computers, a pair of terms in a first language, the pair of terms being commonly co-occurring non-synonyms in a corpus of documents, the corpus of documents being in the first language; determining a set of variations for each term in the pair of terms; generating a set of known related input pairs based on the sets of variations for each term in the pair of terms; for each input pair of terms in the set of known related input pairs, translating, by an automatic translation system, each term in the pair of terms into a plurality of languages to generate a set of translated terms; adding, at the one or more computers, the set of translated terms to a blacklist of known non-synonym pairs for at least one of the plurality of languages; and determining, based on the blacklist of known non-synonym pairs, whether a pair of candidate terms in at least one of the plurality of languages are synonyms. 7. The system of claim 5 , wherein generating a set of known related input pairs based on the sets of variations for each term in the pair comprises calculating a cross-product between the sets of variations for each term in the pair. | 0.5 |
10,078,651 | 11 | 17 | 11. A system for providing recommendations that take into account users' casual references to certain media assets during conversational communications by isolating a term in the conversational communications and determining relationships to that term through the use of a knowledge graph organized to store relationships between different pieces of information, comprising: storage circuitry configured to store a knowledge graph having a plurality of nodes representing different pieces of information and a plurality of edges connecting the plurality of nodes representing relationships between the different pieces of information; communications circuitry configured to receive a user communication; and control circuitry configured to: analyze the user communication based on a previously stored template from a database to determine a term of the user communication; transmit a query based on the term to a knowledge graph; identify a first node representing a candidate component that is connected to a second node representing the term based on the knowledge graph; generating for display, via the user interface, a request causing user input directed to confirming whether the term is associated with the candidate component; in response to receiving the user input, modify a strength of association represented by a weight allocated to an edge connecting the second node representing the term and the first node representing the candidate component in the knowledge graph; and generate a content recommendation associated with the candidate component based on the strength of association. | 11. A system for providing recommendations that take into account users' casual references to certain media assets during conversational communications by isolating a term in the conversational communications and determining relationships to that term through the use of a knowledge graph organized to store relationships between different pieces of information, comprising: storage circuitry configured to store a knowledge graph having a plurality of nodes representing different pieces of information and a plurality of edges connecting the plurality of nodes representing relationships between the different pieces of information; communications circuitry configured to receive a user communication; and control circuitry configured to: analyze the user communication based on a previously stored template from a database to determine a term of the user communication; transmit a query based on the term to a knowledge graph; identify a first node representing a candidate component that is connected to a second node representing the term based on the knowledge graph; generating for display, via the user interface, a request causing user input directed to confirming whether the term is associated with the candidate component; in response to receiving the user input, modify a strength of association represented by a weight allocated to an edge connecting the second node representing the term and the first node representing the candidate component in the knowledge graph; and generate a content recommendation associated with the candidate component based on the strength of association. 17. The system of claim 11 , wherein the control circuitry is further configured to provide information associated with an additional component of the knowledge graph that has high strength of association between the term and the additional component. | 0.815169 |
9,195,639 | 7 | 8 | 7. Non-transitory, tangible, computer readable media containing computer programming instructions that, when loaded in a computer system having a memory, cause the computer system, in response to user commands, to automatically analyze text documents by performing the following steps: comparing text from a subject text document to text of a plurality of given text templates, each text template containing at least one paragraph of text; determining which given text template or text templates has text that matches the text from the subject text document to a given degree of correspondence; generating a report of the differences between the text from the subject text document and the text of the matching text template or text templates; comparing a family of specimen text documents; identifying one paragraph of text within one of the family of specimen text documents that most closely matches a paragraph of text in all of the other specimen text documents, as compared to all of the other paragraphs in the one specimen text document; and generating one of the text templates containing at least the one identified paragraph of text. | 7. Non-transitory, tangible, computer readable media containing computer programming instructions that, when loaded in a computer system having a memory, cause the computer system, in response to user commands, to automatically analyze text documents by performing the following steps: comparing text from a subject text document to text of a plurality of given text templates, each text template containing at least one paragraph of text; determining which given text template or text templates has text that matches the text from the subject text document to a given degree of correspondence; generating a report of the differences between the text from the subject text document and the text of the matching text template or text templates; comparing a family of specimen text documents; identifying one paragraph of text within one of the family of specimen text documents that most closely matches a paragraph of text in all of the other specimen text documents, as compared to all of the other paragraphs in the one specimen text document; and generating one of the text templates containing at least the one identified paragraph of text. 8. The media of claim 7 wherein the identifying the paragraph that most closely matches step uses an edit distance algorithm. | 0.543796 |
8,069,406 | 8 | 9 | 8. The method of claim 1 , wherein the first file includes a plurality of browser links and hyper links, and the step of selecting a predicted link further comprises the steps of: ranking one or more of the hyper links in the first file; and selecting the predicted link by selecting the hyper link with the highest ranking. | 8. The method of claim 1 , wherein the first file includes a plurality of browser links and hyper links, and the step of selecting a predicted link further comprises the steps of: ranking one or more of the hyper links in the first file; and selecting the predicted link by selecting the hyper link with the highest ranking. 9. The method of claim 8 , wherein the step of ranking the one or more hyper links further comprises ranking the one or more hyper links based on a previous hit rate for the one or more hyper links. | 0.5 |
9,542,820 | 4 | 5 | 4. The method of claim 3 , wherein the second haptic component precedes the first haptic component. | 4. The method of claim 3 , wherein the second haptic component precedes the first haptic component. 5. The method of claim 4 , including, in accordance with a determination that the first event is a third type of event of the plurality of types of events that are affected by the alert-salience setting, providing a third alert that includes a haptic output selected based at least in part on the alert-salience setting of the device, wherein providing the third alert that includes the haptic output selected based at least in part on the alert-salience setting of the device includes: determining a value of the alert-salience setting; in accordance with a determination that the alert-salience setting has the first value, the third alert includes a third haptic component and a fourth haptic component; and in accordance with a determination that the alert-salience setting has the second value different from the first value, the third alert includes the third haptic component but does not include the fourth haptic component. | 0.5 |
8,803,713 | 15 | 16 | 15. A handheld electronic device comprising: an input apparatus; an output apparatus; and a processor apparatus comprising a processor and a memory, wherein the processor apparatus is structured to: detect an ambiguous input that comprises one or more selections of one or more input characters; generate a plurality of character permutations of the ambiguous input, at least one of the character permutations being a potential artificial variant that is neither a prefix of a word object nor is identical to a word object; make a determination that at least a portion of the potential artificial variant fails to correspond with an N-gram object; make, as at least a part of said determination, a determination that no N-gram object corresponds with a final set of characters of the potential artificial variant; output at least one of the character permutations; and determine that the potential artificial variant includes a character string for which a spelling substitution exists; and generate an orphan object corresponding to the spelling substitution. | 15. A handheld electronic device comprising: an input apparatus; an output apparatus; and a processor apparatus comprising a processor and a memory, wherein the processor apparatus is structured to: detect an ambiguous input that comprises one or more selections of one or more input characters; generate a plurality of character permutations of the ambiguous input, at least one of the character permutations being a potential artificial variant that is neither a prefix of a word object nor is identical to a word object; make a determination that at least a portion of the potential artificial variant fails to correspond with an N-gram object; make, as at least a part of said determination, a determination that no N-gram object corresponds with a final set of characters of the potential artificial variant; output at least one of the character permutations; and determine that the potential artificial variant includes a character string for which a spelling substitution exists; and generate an orphan object corresponding to the spelling substitution. 16. The device of claim 15 , wherein the processor apparatus is further structured to suppress the potential artificial variant from the output. | 0.875217 |
7,982,637 | 10 | 15 | 10. In a computer system, a computer readable storage medium containing one or more instructions for performing a method for character encoding and decoding: said method comprising translating a source data into a sequence of Unicode code points occupying the Private Use Area of the Unicode Basic Multilingual Plane, said method comprising; using one or more bits of the first code point or code points of each sequence as an identification tag, each tag identifying both the type of data encoded and the length of the data encoded; said encoding method comprising one of the following methods: a. the construction of a Unicode code point as the mathematical “OR” of the constant E000 hexadecimal with a 12-bit data value from 0 to FFF hexadecimal; said code points occupying the Unicode Basic Multilingual Plane in the range from E000 to EFFF hexadecimal; the decoding of the original data value as the Boolean “AND” of the code point with the constant FFF hexadecimal; or b. the construction of a Unicode code point as the mathematical “addition” of a fixed constant in the range E000 to E900 hexadecimal with a 12-bit data value from 0 to FFF hexadecimal; said code points occupying the Unicode Basic Multilingual Plane in the range from E000 to F8FF hexadecimal; the decoding of the original data value as the mathematical “subtraction” of the same fixed constant from the code point. | 10. In a computer system, a computer readable storage medium containing one or more instructions for performing a method for character encoding and decoding: said method comprising translating a source data into a sequence of Unicode code points occupying the Private Use Area of the Unicode Basic Multilingual Plane, said method comprising; using one or more bits of the first code point or code points of each sequence as an identification tag, each tag identifying both the type of data encoded and the length of the data encoded; said encoding method comprising one of the following methods: a. the construction of a Unicode code point as the mathematical “OR” of the constant E000 hexadecimal with a 12-bit data value from 0 to FFF hexadecimal; said code points occupying the Unicode Basic Multilingual Plane in the range from E000 to EFFF hexadecimal; the decoding of the original data value as the Boolean “AND” of the code point with the constant FFF hexadecimal; or b. the construction of a Unicode code point as the mathematical “addition” of a fixed constant in the range E000 to E900 hexadecimal with a 12-bit data value from 0 to FFF hexadecimal; said code points occupying the Unicode Basic Multilingual Plane in the range from E000 to F8FF hexadecimal; the decoding of the original data value as the mathematical “subtraction” of the same fixed constant from the code point. 15. The method of claim 10 , wherein said tag identifies at least one of variable precision signed and unsigned integers, binary and decimal floating point numbers or arrays thereof. | 0.5 |
8,935,672 | 1 | 3 | 1. A computer-based method comprising at least: evaluating input program code that includes at least one rule that is syntactically consistent with a procedural programming language, wherein the at least one rule wraps a first function defined by the procedural programming language; identifying at least one input and at least one output to the first function; building a dependency graph that relates the first function to at least a second function, based on one of the input or the output to the first function; analyzing the dependency graph, and further comprising marking as out of date at least one cached output of at least the second function, in response to detecting that the at least one input to at least the second function has changed value; delaying evaluation of the first function, in response to the rule, until an occurrence of at least one triggering event; and evaluating at least the first function to produce at least one output in response to the triggering event, wherein the first function relates to a geometric design of at least one object. | 1. A computer-based method comprising at least: evaluating input program code that includes at least one rule that is syntactically consistent with a procedural programming language, wherein the at least one rule wraps a first function defined by the procedural programming language; identifying at least one input and at least one output to the first function; building a dependency graph that relates the first function to at least a second function, based on one of the input or the output to the first function; analyzing the dependency graph, and further comprising marking as out of date at least one cached output of at least the second function, in response to detecting that the at least one input to at least the second function has changed value; delaying evaluation of the first function, in response to the rule, until an occurrence of at least one triggering event; and evaluating at least the first function to produce at least one output in response to the triggering event, wherein the first function relates to a geometric design of at least one object. 3. The computer-based method of claim 1 , wherein building the dependency graph includes relating the first function to a least one downstream function that receives as input the output of the first function. | 0.595331 |
8,824,799 | 1 | 4 | 1. A method of encoding an image comprising: analyzing, by a computer having one or more processors, colors and spatial features of pixels of the image to identify a text region of the image that is separate from a picture region of the image; generating, by the computer, for a portion of the text region, a mask dividing the portion into background pixels and a plurality of text pixels, wherein the background pixels are identified as pixels with a constant color, the plurality of text pixels are identified as pixels contrasting the constant color, and the plurality of text pixels comprise a plurality of colors; analyzing chrominance values of the plurality of colors to determine a text chrominance; generating, for each text pixel of the plurality of text pixels, a text pixel value to generate a plurality of text pixel values, wherein each text pixel value is based on a luminance of a text pixel for which it was generated; and transmitting an encoding of the portion comprising an encoding of each of the constant color, the mask, the text chrominance and the plurality of text pixel values. | 1. A method of encoding an image comprising: analyzing, by a computer having one or more processors, colors and spatial features of pixels of the image to identify a text region of the image that is separate from a picture region of the image; generating, by the computer, for a portion of the text region, a mask dividing the portion into background pixels and a plurality of text pixels, wherein the background pixels are identified as pixels with a constant color, the plurality of text pixels are identified as pixels contrasting the constant color, and the plurality of text pixels comprise a plurality of colors; analyzing chrominance values of the plurality of colors to determine a text chrominance; generating, for each text pixel of the plurality of text pixels, a text pixel value to generate a plurality of text pixel values, wherein each text pixel value is based on a luminance of a text pixel for which it was generated; and transmitting an encoding of the portion comprising an encoding of each of the constant color, the mask, the text chrominance and the plurality of text pixel values. 4. The method of claim 1 , further comprising modifying the mask to identify at least one of (i) one of the pixels having the constant color as one of the text pixels of the plurality of text pixels, or (ii) one of the pixels contrasting the constant color as one of the background pixels, wherein modifying the mask reduces a size of the encoding of the mask over an absence of modifying the mask. | 0.533958 |
8,909,703 | 1 | 7 | 1. A computer program product comprising a non-transitory computer useable readable storage medium having computer useable program code for facilitating a real-time virtual interaction between two or more users, said computer program product including: computer useable program code for extracting a dynamically changing context from two or more users, wherein the context comprises at least one of user-provided information and one or more items related to at least one of current activity and past activity of the two or more users; computer useable program code for analyzing the context from each user to create a distinct classification for each user; computer useable program code for comparing the distinct classification for each user with a distinct classification for each additional user, wherein comparing comprises ordering each user in terms of closeness to each additional user; and computer useable program code for using the ordering of each user in terms of closeness to each additional user to facilitate a real-time virtual interaction between two or more users. | 1. A computer program product comprising a non-transitory computer useable readable storage medium having computer useable program code for facilitating a real-time virtual interaction between two or more users, said computer program product including: computer useable program code for extracting a dynamically changing context from two or more users, wherein the context comprises at least one of user-provided information and one or more items related to at least one of current activity and past activity of the two or more users; computer useable program code for analyzing the context from each user to create a distinct classification for each user; computer useable program code for comparing the distinct classification for each user with a distinct classification for each additional user, wherein comparing comprises ordering each user in terms of closeness to each additional user; and computer useable program code for using the ordering of each user in terms of closeness to each additional user to facilitate a real-time virtual interaction between two or more users. 7. The computer program product of claim 1 , wherein the computer useable program code for extracting context comprises: computer useable program code for parsing a document object model (DOM) for the current user activity; computer useable program code for using the parsed DOM to obtain one or more words of text from the activity; computer useable program code for removing each word that conveys no context information; computer useable program code for removing each set of equivalent words; computer useable program code for computing a count of one or more unique words from the activity; and computer useable program code for computing a content vector and normalized term-frequency vector of one or more frequently occurring terms. | 0.5 |
9,614,724 | 1 | 4 | 1. A system comprising: at least one processor; and one or more computer-readable storage media including instructions stored thereon that, responsive to execution by the at least one processor, cause the system perform operations including: receiving a notification that a communication session is initiated in a network, the notification including a session notification application programming interface (API) that includes a value for an attribute of the communication session; ascertaining that a client device is connected to the network based on the attribute of the communication session received as part of the session notification API; applying the attribute of the communication session received as part of the session notification API to a network policy for the network to specify a parameter for the communication session; configuring a session configuration application programming interface (API) with the parameter for the communication session by applying the value for the attribute included in the session notification API to the network policy; generating a configuration event that includes the session configuration API configured with the parameter for the communication session; and communicating the configuration event to the client device. | 1. A system comprising: at least one processor; and one or more computer-readable storage media including instructions stored thereon that, responsive to execution by the at least one processor, cause the system perform operations including: receiving a notification that a communication session is initiated in a network, the notification including a session notification application programming interface (API) that includes a value for an attribute of the communication session; ascertaining that a client device is connected to the network based on the attribute of the communication session received as part of the session notification API; applying the attribute of the communication session received as part of the session notification API to a network policy for the network to specify a parameter for the communication session; configuring a session configuration application programming interface (API) with the parameter for the communication session by applying the value for the attribute included in the session notification API to the network policy; generating a configuration event that includes the session configuration API configured with the parameter for the communication session; and communicating the configuration event to the client device. 4. The system as recited in claim 1 , wherein the attribute includes one or more media types for the communication session. | 0.843112 |
7,617,093 | 16 | 17 | 16. A method of forming a grammar, the method comprising: displaying a user interface that provides a text area for a user to enter sentences representing possible user responses and an area for associating text strings with a semantic class, at least one semantic class being associated with multiple text strings; a processor forming modified sentences from the sentences entered through the user interface by performing steps for each sentence entered through the user interface, the steps comprising: identifying a group of words in the sentence; comparing the group of words to a list of the text strings associated with the semantic class; determining that the group of words matches a text string in the list of text strings associated with the semantic class; replacing the group of words in the sentences with a tag for a semantic class associated with the text string that matches the group of words while leaving other words in the sentence unchanged to form a modified sentence, the tag representing multiple text strings associated with the semantic class; and using the modified sentences to form the grammar. | 16. A method of forming a grammar, the method comprising: displaying a user interface that provides a text area for a user to enter sentences representing possible user responses and an area for associating text strings with a semantic class, at least one semantic class being associated with multiple text strings; a processor forming modified sentences from the sentences entered through the user interface by performing steps for each sentence entered through the user interface, the steps comprising: identifying a group of words in the sentence; comparing the group of words to a list of the text strings associated with the semantic class; determining that the group of words matches a text string in the list of text strings associated with the semantic class; replacing the group of words in the sentences with a tag for a semantic class associated with the text string that matches the group of words while leaving other words in the sentence unchanged to form a modified sentence, the tag representing multiple text strings associated with the semantic class; and using the modified sentences to form the grammar. 17. The method of claim 16 further comprising identifying n-grams of a plurality of orders in the modified sentences. | 0.680328 |
9,026,531 | 10 | 11 | 10. Computer hardware storage media having computer-executable instructions embodied thereon, that when executed, perform a method for presenting a web-accessible topic focused data mart, the method comprising: receiving health data from a data source comprising at least one of receiving internal health data collected by a health care facility and receiving public health data provided by a public agency; populating a topic focused data mart having at least a portion of the health data received from the data source, the topic focused data mart only comprising data relevant to a predetermined topic associated with the one topic focused data mart, the at least the portion of the health data being associated with the predetermined topic; associating the topic focused data mart with a web service; receiving demographic information from an Electronic Health Record (EHR) associated with a patient; querying the topic focused data mart based on the demographic information received from the EHR, the querying the topic focused data mart comprising querying only the at least the portion of the health data included in the topic focused data mart; and presenting context-specific data derived from the topic focused data mart in the EHR, the context-specific data being based on the demographic information, the receiving, querying, and presenting being performed without input from a clinician. | 10. Computer hardware storage media having computer-executable instructions embodied thereon, that when executed, perform a method for presenting a web-accessible topic focused data mart, the method comprising: receiving health data from a data source comprising at least one of receiving internal health data collected by a health care facility and receiving public health data provided by a public agency; populating a topic focused data mart having at least a portion of the health data received from the data source, the topic focused data mart only comprising data relevant to a predetermined topic associated with the one topic focused data mart, the at least the portion of the health data being associated with the predetermined topic; associating the topic focused data mart with a web service; receiving demographic information from an Electronic Health Record (EHR) associated with a patient; querying the topic focused data mart based on the demographic information received from the EHR, the querying the topic focused data mart comprising querying only the at least the portion of the health data included in the topic focused data mart; and presenting context-specific data derived from the topic focused data mart in the EHR, the context-specific data being based on the demographic information, the receiving, querying, and presenting being performed without input from a clinician. 11. The media of claim 10 , wherein the context-specific data is average birth weights for a county, state, and country. | 0.568345 |
7,945,662 | 13 | 20 | 13. A computerized system for optimizing a served domain name page, the system comprising: a processor, said processor being arranged to serve a domain name page in response to a request from a first group of at least one user device controlled by a first group of at least one user, the domain name page including a dynamic set of one or more keywords, and at least one interactive region, each interactive region being identified by a position identifier and being associated with a weight; an optimizing engine, said optimizing engine including at least one data processing device, said optimizing engine being arranged to track a plurality of interactions of each of the first group of users with the domain name page, wherein the tracked interactions include identifying one or more of the interactive regions in which each user interaction occurs for each of the keywords and data sufficient to determine a frequency associated with the user interactions in the corresponding interactive region for each of the keywords; said optimizing engine being arranged to be capable of modifying the set of keywords to be assigned to the domain name page based on the tracked user interactions, said optimizing engine being capable of computing a keyword point for each of the keywords as a function of the frequency of the user interactions for the corresponding keyword for each of the one or more of the interactive regions and the weight assigned to the corresponding interactive region, and capable of updating the content including a modified set of keywords to be served by the processor in response to a request from a second group of at least one user to serve the domain name page; and, interactive addressable memory, said interactive addressable memory being capable of storing data concerning the tracked interactions for each of the first group of at least one user, the particular keyword identified by each the user for a particular one of the user interactions and the associated position identifier identifying the interactive region on the domain name page in which the particular one of the user interactions occurred, said interactive addressable memory being in communication with the optimizing engine. | 13. A computerized system for optimizing a served domain name page, the system comprising: a processor, said processor being arranged to serve a domain name page in response to a request from a first group of at least one user device controlled by a first group of at least one user, the domain name page including a dynamic set of one or more keywords, and at least one interactive region, each interactive region being identified by a position identifier and being associated with a weight; an optimizing engine, said optimizing engine including at least one data processing device, said optimizing engine being arranged to track a plurality of interactions of each of the first group of users with the domain name page, wherein the tracked interactions include identifying one or more of the interactive regions in which each user interaction occurs for each of the keywords and data sufficient to determine a frequency associated with the user interactions in the corresponding interactive region for each of the keywords; said optimizing engine being arranged to be capable of modifying the set of keywords to be assigned to the domain name page based on the tracked user interactions, said optimizing engine being capable of computing a keyword point for each of the keywords as a function of the frequency of the user interactions for the corresponding keyword for each of the one or more of the interactive regions and the weight assigned to the corresponding interactive region, and capable of updating the content including a modified set of keywords to be served by the processor in response to a request from a second group of at least one user to serve the domain name page; and, interactive addressable memory, said interactive addressable memory being capable of storing data concerning the tracked interactions for each of the first group of at least one user, the particular keyword identified by each the user for a particular one of the user interactions and the associated position identifier identifying the interactive region on the domain name page in which the particular one of the user interactions occurred, said interactive addressable memory being in communication with the optimizing engine. 20. The system of claim 13 further comprising: an optimizing engine capable of ranking the keywords in the modified keyword set based on the computed keyword point. | 0.82516 |
8,977,601 | 5 | 6 | 5. An article of manufacture comprising software stored on non-transitory computer readable storage medium, the software comprising: a disappearing index mechanism in a database manager to manage a computer database that processes a first query and a subsequent query to the database; wherein the disappearing index mechanism processes the first query with an initially full index and removes a value from the index with a corresponding pointer while processing the first query where the first query does not select a record, to create a sparse index for the subsequent query to the database; and wherein the disappearing index mechanism determines there are multiple pointers for the value in the index and removes pointers from the value in the full index that point to a record that is not selected and removes the value from the index where all the pointers point to records that are not selected. | 5. An article of manufacture comprising software stored on non-transitory computer readable storage medium, the software comprising: a disappearing index mechanism in a database manager to manage a computer database that processes a first query and a subsequent query to the database; wherein the disappearing index mechanism processes the first query with an initially full index and removes a value from the index with a corresponding pointer while processing the first query where the first query does not select a record, to create a sparse index for the subsequent query to the database; and wherein the disappearing index mechanism determines there are multiple pointers for the value in the index and removes pointers from the value in the full index that point to a record that is not selected and removes the value from the index where all the pointers point to records that are not selected. 6. The article of manufacture of claim 5 further comprising a bit map with a bit corresponding to each value in the initially full index and the disappearing index mechanism effectively removes the value from the initially full index by changing a corresponding bit value in the bit map for query operations that do not select a record. | 0.5 |
7,706,616 | 1 | 4 | 1. A method of recognizing words, comprising: accepting a stroke as an input on a virtual keyboard coupled to a computer, the computer programmed to perform the steps of: defining word patterns of a plurality of known words by a plurality of paths, wherein each path connects elements in the known word on the virtual keyboard, wherein the virtual keyboard comprises virtual keys, each virtual key representing a letter in a word without a temporary target letter being placed adjacent to a location of a stroke; processing the stroke using a combination of a plurality of channels, each channel selectively measuring a different aspect of a similarity of the stroke to a plurality of possible paths on the virtual keyboard; converting each different aspect of the stroke's similarity to probability estimates; a shape channel of the plurality of channels measuring a shape aspect of the stroke, and outputting a probability estimate; a location channel of the plurality of channels measuring location aspect of the stroke, and outputting a probability estimate, wherein the location channel measures the location aspect of the stroke concurrently with the shape channel measuring the shape aspect of the stroke; mathematically integrating, using Bayes' theorem, the probability estimates of the plurality of channels to produce integrated probability estimates of candidate words corresponding to the stroke; and based on the integrated probability estimates of the candidate words, recognizing the stroke as a known word. | 1. A method of recognizing words, comprising: accepting a stroke as an input on a virtual keyboard coupled to a computer, the computer programmed to perform the steps of: defining word patterns of a plurality of known words by a plurality of paths, wherein each path connects elements in the known word on the virtual keyboard, wherein the virtual keyboard comprises virtual keys, each virtual key representing a letter in a word without a temporary target letter being placed adjacent to a location of a stroke; processing the stroke using a combination of a plurality of channels, each channel selectively measuring a different aspect of a similarity of the stroke to a plurality of possible paths on the virtual keyboard; converting each different aspect of the stroke's similarity to probability estimates; a shape channel of the plurality of channels measuring a shape aspect of the stroke, and outputting a probability estimate; a location channel of the plurality of channels measuring location aspect of the stroke, and outputting a probability estimate, wherein the location channel measures the location aspect of the stroke concurrently with the shape channel measuring the shape aspect of the stroke; mathematically integrating, using Bayes' theorem, the probability estimates of the plurality of channels to produce integrated probability estimates of candidate words corresponding to the stroke; and based on the integrated probability estimates of the candidate words, recognizing the stroke as a known word. 4. The method of claim 1 , wherein the plurality of channels comprises a language context channel that stores recognized known words, and wherein the language context channel provides clues for recognizing a word based on a stored previously recognized known word. | 0.781457 |
8,527,540 | 2 | 4 | 2. A method as in claim 1 further comprising: augmenting the report document with metadata including a report identifier and parameter information. | 2. A method as in claim 1 further comprising: augmenting the report document with metadata including a report identifier and parameter information. 4. A method as in claim 2 , wherein the metadata to augment the report document is selected from a group consisting of: a report server name, drill down path information, export dynamic link library (dll) information, export format and options, report parameters, report prompts, login information, and report language. | 0.5 |
7,805,398 | 26 | 31 | 26. A system comprising: a processor for: registering one or more rules related to an entity associated with a model; receiving input specifying a value of a first attribute associated with the entity; and applying the one or more registered rules to the value of the first attribute to at least one of: infer a value of a second attribute associated with the entity or another entity associated with the model, or validate the value of the first attribute or the value of the second attribute. | 26. A system comprising: a processor for: registering one or more rules related to an entity associated with a model; receiving input specifying a value of a first attribute associated with the entity; and applying the one or more registered rules to the value of the first attribute to at least one of: infer a value of a second attribute associated with the entity or another entity associated with the model, or validate the value of the first attribute or the value of the second attribute. 31. The system of claim 26 , wherein the processor further resolves a conflict in the one or more registered rules. | 0.735023 |
6,124,864 | 32 | 33 | 32. A method for developing a computerized scene model from a digital image, the method comprising the steps of: (a) running an automated image analysis algorithm according to input parameters to provide image analysis results data; (b) storing portion of the image analysis results data as an image-based data object in the scene model; (c) storing other portions of the image analysis results data as an abstraction-based data object in the scene model; (d) annotating a correlation mesh data object in the scene model with data indicating a link between the image-based data object and the abstraction-based data object; (e) refining the scene model by accepting input parameters concerning at least one new abstract representation of the digital image, the new abstract object containing an abstract representation differing from the abstraction-based object stored in step (c); (f) annotating a correlation mesh data object in the scene model with data indicating a link between the new abstraction-based data object and other data objects in the scene model; and (g) iterating selected ones of steps (a) through (f) in response to user input until a desired level of refinement is obtained in the scene model such that selected link objects in the correlation mesh data structure added in iterations of the refining step (e) are used to provide additional input parameters to subsequent iterations, thereby allowing the scene model to converge. | 32. A method for developing a computerized scene model from a digital image, the method comprising the steps of: (a) running an automated image analysis algorithm according to input parameters to provide image analysis results data; (b) storing portion of the image analysis results data as an image-based data object in the scene model; (c) storing other portions of the image analysis results data as an abstraction-based data object in the scene model; (d) annotating a correlation mesh data object in the scene model with data indicating a link between the image-based data object and the abstraction-based data object; (e) refining the scene model by accepting input parameters concerning at least one new abstract representation of the digital image, the new abstract object containing an abstract representation differing from the abstraction-based object stored in step (c); (f) annotating a correlation mesh data object in the scene model with data indicating a link between the new abstraction-based data object and other data objects in the scene model; and (g) iterating selected ones of steps (a) through (f) in response to user input until a desired level of refinement is obtained in the scene model such that selected link objects in the correlation mesh data structure added in iterations of the refining step (e) are used to provide additional input parameters to subsequent iterations, thereby allowing the scene model to converge. 33. A method as in claim 32 wherein the image processing algorithm is selected from the group consisting of feature tracking, image segmentation, and optical flow. | 0.531609 |
7,512,633 | 1 | 12 | 1. A method for storing communication messages in a relational database, comprising: accepting an object model comprising data elements having respective data type definitions and further comprising associations between the data elements, wherein the data elements, the data type definitions and the associations are derived from a hierarchically-structured HL7 specification and comprise at least one data element whose data type definition corresponds to multiple possible data types; defining a relational database that represents the object model based on the data elements and the associations; receiving a communication message that conforms to the HL7 specification and comprises data items corresponding to one or more of the data elements, including at least one data item having the data type definition that corresponds to the multiple possible data types; processing the received communication message so as to identify an actual data type, selected from among the possible data types, to which the at least one data item belongs; and storing the data items, including the at least one data item, in the relational database so as to preserve the data type definitions of the data items, including the identified actual data type, and the associations between the data items, as defined in the object model, wherein the data elements comprise classes, each having one or more class attributes, wherein defining the relational database comprises creating for each class a respective class table comprising at least one unique identifier in the relational database, and mapping at least one of the class attributes to columns of the class table, and wherein the object model comprises at least one of an association of cardinality 1:n representing a relationship between a parent class and a child class, an association of cardinality n:m representing a relationship between one or more parent classes and one or more child classes, a recursive association representing a relationship between a parent class and a child class wherein the parent class is equal to the child class, and a group association representing a relationship between a group comprising two or more classes and a child class. | 1. A method for storing communication messages in a relational database, comprising: accepting an object model comprising data elements having respective data type definitions and further comprising associations between the data elements, wherein the data elements, the data type definitions and the associations are derived from a hierarchically-structured HL7 specification and comprise at least one data element whose data type definition corresponds to multiple possible data types; defining a relational database that represents the object model based on the data elements and the associations; receiving a communication message that conforms to the HL7 specification and comprises data items corresponding to one or more of the data elements, including at least one data item having the data type definition that corresponds to the multiple possible data types; processing the received communication message so as to identify an actual data type, selected from among the possible data types, to which the at least one data item belongs; and storing the data items, including the at least one data item, in the relational database so as to preserve the data type definitions of the data items, including the identified actual data type, and the associations between the data items, as defined in the object model, wherein the data elements comprise classes, each having one or more class attributes, wherein defining the relational database comprises creating for each class a respective class table comprising at least one unique identifier in the relational database, and mapping at least one of the class attributes to columns of the class table, and wherein the object model comprises at least one of an association of cardinality 1:n representing a relationship between a parent class and a child class, an association of cardinality n:m representing a relationship between one or more parent classes and one or more child classes, a recursive association representing a relationship between a parent class and a child class wherein the parent class is equal to the child class, and a group association representing a relationship between a group comprising two or more classes and a child class. 12. The method according to claim 1 , wherein the object model comprises the group association, wherein the association has one or more group association attributes, and wherein defining the relational database comprises adding columns to the class table corresponding to the child class, the columns comprising an element name of an actual parent class in the group and at least one of the one or more group association attributes. | 0.581395 |
9,875,289 | 13 | 15 | 13. A method for processing an ontological query for data from any of a plurality of different databases on a network coupled to a computer, comprising: loading a ontological data model that comprises a plurality of logical models based on data from the plurality of different databases; compiling the ontological query and optimizing the compiled ontological query according to join and combination rules based on the logical models and describing meta-properties of the data and meta-relationships based on the meta-properties between the data from the plurality of different databases; and processing logical operations on the compiled ontological query, wherein the data from the plurality of different databases defines a hierarchy of ontologies in parent-child relationship, wherein the ontology management system comprises: an ontological data federation operating mode; and an ontological data warehouse operating mode. | 13. A method for processing an ontological query for data from any of a plurality of different databases on a network coupled to a computer, comprising: loading a ontological data model that comprises a plurality of logical models based on data from the plurality of different databases; compiling the ontological query and optimizing the compiled ontological query according to join and combination rules based on the logical models and describing meta-properties of the data and meta-relationships based on the meta-properties between the data from the plurality of different databases; and processing logical operations on the compiled ontological query, wherein the data from the plurality of different databases defines a hierarchy of ontologies in parent-child relationship, wherein the ontology management system comprises: an ontological data federation operating mode; and an ontological data warehouse operating mode. 15. The method of claim 13 , wherein the ontological data warehouse operating mode is invoked by a request that a particular predicate in the ontology be materialized. | 0.571795 |
9,892,189 | 15 | 18 | 15. A system, comprising at least one server computer in communication with a network, the at least one server computer including a processor configured to: receive one or more tokens together forming all or part of a string comprising an input related to a website; compare each of the one or more tokens to each of a plurality of categories in a category structure to determine, for each pairing of one of the tokens with one of the categories, a token probability that the token belongs to the category; for a first token probability of the plurality of token probabilities, the first token probability being associated with a first token of the one or more tokens and with a first category of the plurality of categories, modify the first token probability according to a first frequency at which the first category is selected as a correct category for the first token, the first frequency identified from a plurality of domain name searches previously processed by a first of the at least one server; calculate, from the token probabilities for each of the plurality of categories, a final probability of the string belonging to the corresponding category; and categorize one or both of the string and the website in the category having the highest final probability. | 15. A system, comprising at least one server computer in communication with a network, the at least one server computer including a processor configured to: receive one or more tokens together forming all or part of a string comprising an input related to a website; compare each of the one or more tokens to each of a plurality of categories in a category structure to determine, for each pairing of one of the tokens with one of the categories, a token probability that the token belongs to the category; for a first token probability of the plurality of token probabilities, the first token probability being associated with a first token of the one or more tokens and with a first category of the plurality of categories, modify the first token probability according to a first frequency at which the first category is selected as a correct category for the first token, the first frequency identified from a plurality of domain name searches previously processed by a first of the at least one server; calculate, from the token probabilities for each of the plurality of categories, a final probability of the string belonging to the corresponding category; and categorize one or both of the string and the website in the category having the highest final probability. 18. The system of claim 15 , wherein the input is one or more keywords obtained from the website. | 0.859012 |
9,472,189 | 1 | 4 | 1. A language processing method, comprising: receiving an user utterance as an input sequence of token elements; parsing, using a parsing processor, the input sequence of the token elements using a parsing algorithm in a first mode applying regular production rules on the token elements and on multi-token classifiers for phrases obtained from the token elements, wherein each token element contains a token of an input string and/or at least one corresponding token classifier; controlling, when the first mode parsing does not result in a multi-token phrase encompassing all tokens of the input string, the parsing processor to parse the input sequence using the parsing algorithm in a second mode applying both the regular and artificial production rules, wherein the second mode comprises generating the artificial production rules on the basis of the input sequence and intermediate results of the parsing using the parsing algorithm in the first mode, the intermediate results being based on lexical category of the token of the input string; and matching the parsed input sequence to one of a plurality of predefined machine commands to generate a command based on the input sequence, wherein the parsing algorithm comprises calculating probabilities for all possible subsequences and selecting, for each sequence of token classifiers within the input sequence, the subsequences with a highest probability, and each of the artificial production rules has a probability value lower than any of the regular production rules, the probability value being set such that at most one artificial production rule of the artificial production rules is used when parsing the input sequence using the parsing algorithm in the second mode, and the probability value being increased when the artificial production rule is successfully applied. | 1. A language processing method, comprising: receiving an user utterance as an input sequence of token elements; parsing, using a parsing processor, the input sequence of the token elements using a parsing algorithm in a first mode applying regular production rules on the token elements and on multi-token classifiers for phrases obtained from the token elements, wherein each token element contains a token of an input string and/or at least one corresponding token classifier; controlling, when the first mode parsing does not result in a multi-token phrase encompassing all tokens of the input string, the parsing processor to parse the input sequence using the parsing algorithm in a second mode applying both the regular and artificial production rules, wherein the second mode comprises generating the artificial production rules on the basis of the input sequence and intermediate results of the parsing using the parsing algorithm in the first mode, the intermediate results being based on lexical category of the token of the input string; and matching the parsed input sequence to one of a plurality of predefined machine commands to generate a command based on the input sequence, wherein the parsing algorithm comprises calculating probabilities for all possible subsequences and selecting, for each sequence of token classifiers within the input sequence, the subsequences with a highest probability, and each of the artificial production rules has a probability value lower than any of the regular production rules, the probability value being set such that at most one artificial production rule of the artificial production rules is used when parsing the input sequence using the parsing algorithm in the second mode, and the probability value being increased when the artificial production rule is successfully applied. 4. The method according to claim 1 , wherein the parsing algorithm generates parse information representing a parse table with cells, wherein each cell is assigned to one of the possible sequences of token classifiers within the input sequence and contains (i) constituent information descriptive for one or more grammatical functions assigned by the production rules to a sequence assigned to the cell and (ii) a parse tree information descriptive for a derivation of the one or more grammatical functions of the sequence from the token classifiers. | 0.5 |
9,116,976 | 7 | 9 | 7. A computer program product, encoded on one or more non-transitory computer storage media, comprising instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: receiving a search query from a user; obtaining a plurality of search results that satisfy the search query, wherein each search result identifies respective document of a plurality of documents; identifying a respective condition for each document of the plurality of documents, wherein each condition comprises one or more features of the user, the search query, and the document; obtaining a ranking model that produces a score for a particular document given a particular condition for the particular document, the score representing a likelihood that the user will select the particular document when identified by a search result provided in response to the search query, the ranking model being trained on training instances that each identify a first document selected by a particular user when the first document was identified in search results provided to the particular user in response to a particular search query; using the ranking model to compute a respective score for each document of the plurality of documents; and ranking the plurality of search results according to the respective computed score for each document of the plurality of documents. | 7. A computer program product, encoded on one or more non-transitory computer storage media, comprising instructions that when executed by one or more computers cause the one or more computers to perform operations comprising: receiving a search query from a user; obtaining a plurality of search results that satisfy the search query, wherein each search result identifies respective document of a plurality of documents; identifying a respective condition for each document of the plurality of documents, wherein each condition comprises one or more features of the user, the search query, and the document; obtaining a ranking model that produces a score for a particular document given a particular condition for the particular document, the score representing a likelihood that the user will select the particular document when identified by a search result provided in response to the search query, the ranking model being trained on training instances that each identify a first document selected by a particular user when the first document was identified in search results provided to the particular user in response to a particular search query; using the ranking model to compute a respective score for each document of the plurality of documents; and ranking the plurality of search results according to the respective computed score for each document of the plurality of documents. 9. The computer program product of claim 7 , wherein each training instance identifies one or more second documents that the particular user did not select when the one or more second documents were identified by the search results provided to the particular user in response to the particular search query. | 0.5 |
8,712,850 | 34 | 36 | 34. The method of claim 33 wherein the title constitutes a first line of text of the advertisement, and the at least one line of additional text includes second and third lines of text of the advertisement, and wherein determining a portion of text from the at least one line of additional text to promote into the title includes evaluating the one or both of the second and third lines of text to identify the portion of text. | 34. The method of claim 33 wherein the title constitutes a first line of text of the advertisement, and the at least one line of additional text includes second and third lines of text of the advertisement, and wherein determining a portion of text from the at least one line of additional text to promote into the title includes evaluating the one or both of the second and third lines of text to identify the portion of text. 36. The method of claim 34 wherein determining a portion of text from the at least one line of additional text to promote into the title includes evaluating text in the third line to determine when the second line constitutes a sentence and promoting the second line into the title when the second line constitutes a sentence. | 0.5 |
9,195,649 | 14 | 15 | 14. An apparatus to form an audio template for determining semantic audio information, comprising: a processor to: extract a first audio feature from audio, the first audio feature including at least one of a temporal feature, a spectral feature, a harmonic feature, or a rhythmic feature; extract a second audio feature from the audio, the second audio feature including at least one of a temporal feature, a spectral feature, a harmonic feature, or a rhythmic feature, and the second audio feature is different from the first audio feature; and determine a first range for the first audio feature and a second range for the second audio feature; and a storage to store the first and second ranges to compare against other audio features from subsequent audio to generate semantic audio information for the subsequent audio. | 14. An apparatus to form an audio template for determining semantic audio information, comprising: a processor to: extract a first audio feature from audio, the first audio feature including at least one of a temporal feature, a spectral feature, a harmonic feature, or a rhythmic feature; extract a second audio feature from the audio, the second audio feature including at least one of a temporal feature, a spectral feature, a harmonic feature, or a rhythmic feature, and the second audio feature is different from the first audio feature; and determine a first range for the first audio feature and a second range for the second audio feature; and a storage to store the first and second ranges to compare against other audio features from subsequent audio to generate semantic audio information for the subsequent audio. 15. The apparatus of claim 14 , wherein the temporal features include at least one of amplitude, power, or zero crossing of at least some of the audio. | 0.798128 |
8,380,490 | 10 | 11 | 10. The reusable automatic text translation control of claim 9 , wherein said means for initializing further comprises means for initializing transactional needs. | 10. The reusable automatic text translation control of claim 9 , wherein said means for initializing further comprises means for initializing transactional needs. 11. The reusable automatic text translation control of claim 10 , wherein said means for initializing further comprises means for initializing input and output locations. | 0.5 |
4,516,260 | 44 | 45 | 44. A talking electronic apparatus as set forth in claim 32, wherein said means responsive to said digital control data and said operator response to said selected audible request is effective to cause said speech synthesizer means to repeat said selected audible request if said operator response is inappropriate. | 44. A talking electronic apparatus as set forth in claim 32, wherein said means responsive to said digital control data and said operator response to said selected audible request is effective to cause said speech synthesizer means to repeat said selected audible request if said operator response is inappropriate. 45. A talking electronic apparatus according to claim 44, wherein said plurality of requests includes at least one request for an operator to spell a word in a human language and wherein said appropriate operator response comprises the correct spelling of said word. | 0.518116 |
8,799,271 | 10 | 11 | 10. A method comprising: generating a materialized view associated with a portion of a set of data represented by a base table; translating a range predicate of the materialized view into a canonical range representation (CRR) format in materialized view metadata; receiving a query programmed to search the base table; selecting from among a plurality of possible search plans that includes the materialized view via a query optimizer for searching the base table for data associated with the range predicate of the query; translating a plurality of range predicates of the query that are connected by a Boolean operator into a single range-oriented predicate in a CRR format in query metadata, wherein translating at least one of the range predicate of the materialized view and the plurality of range predicates of the query comprises translating a data type associated with the materialized view into CRR format; merging a first range associated with a first of the plurality of range predicates and a second range associated with a second of the plurality of range predicates into a third range corresponding to a single range predicate in the CRR format and comprising both the first range and the second range, the first and second of the plurality of range predicates being associated with at least one of the query and the materialized view and the first and second ranges comprising one of overlapping and adjacent values with respect to each other; comparing the materialized view metadata and the query metadata; enabling a search of the materialized view by the query if the query metadata is subsumed by the materialized view metadata; and searching the materialized view via the query in response to the materialized view being selected as a most efficient one of the plurality of possible search plans by the query optimizer and in response to enabling the search. | 10. A method comprising: generating a materialized view associated with a portion of a set of data represented by a base table; translating a range predicate of the materialized view into a canonical range representation (CRR) format in materialized view metadata; receiving a query programmed to search the base table; selecting from among a plurality of possible search plans that includes the materialized view via a query optimizer for searching the base table for data associated with the range predicate of the query; translating a plurality of range predicates of the query that are connected by a Boolean operator into a single range-oriented predicate in a CRR format in query metadata, wherein translating at least one of the range predicate of the materialized view and the plurality of range predicates of the query comprises translating a data type associated with the materialized view into CRR format; merging a first range associated with a first of the plurality of range predicates and a second range associated with a second of the plurality of range predicates into a third range corresponding to a single range predicate in the CRR format and comprising both the first range and the second range, the first and second of the plurality of range predicates being associated with at least one of the query and the materialized view and the first and second ranges comprising one of overlapping and adjacent values with respect to each other; comparing the materialized view metadata and the query metadata; enabling a search of the materialized view by the query if the query metadata is subsumed by the materialized view metadata; and searching the materialized view via the query in response to the materialized view being selected as a most efficient one of the plurality of possible search plans by the query optimizer and in response to enabling the search. 11. The method of claim 10 , wherein translating the plurality of range predicates of the query comprises translating a first range and a second range comprising one of overlapping and adjacent values with respect to each other into a third range in CRR format, the third range comprising both the first range and the second range. | 0.5 |
8,655,659 | 26 | 29 | 26. A personalized speech feature extraction device, comprising: a processor; a memory; a keyword setting unit, configured to set one or more keywords suitable for reflecting the pronunciation characteristics of a specific speaker with respect to a specific language, and store the keywords in association with the specific speaker; a speech feature recognition unit, configured to recognize whether any keyword associated with the specific speaker occurs in a random speech fragment of the specific speaker that includes multiple words including the keyword and speech in addition to the keyword, the random speech fragment obtained from a multiple speaker conversation including the speaker, and when a keyword associated with the specific speaker is found in the speech fragment of the specific speaker, recognize speech features of the specific speaker according to a standard pronunciation of the recognized keyword and the pronunciation of the speaker; a speech feature filtration unit, configured to filter out abnormal speech features from the keyword as found in the speech fragment through statistical analysis while retaining speech features reflecting the normal pronunciation characteristics of the specific speaker, when the speech features of the specific speaker recognized by the speech feature recognition unit reach a predetermined number, thereby to create a personalized speech feature library associated with the specific speaker, and store the personalized speech feature library in association with the specific speaker; and a text-to-speech synthesizer, configured to perform a speech synthesis of a text message from the specific speaker, based on the stored personalized speech feature library associated with the specific speaker. | 26. A personalized speech feature extraction device, comprising: a processor; a memory; a keyword setting unit, configured to set one or more keywords suitable for reflecting the pronunciation characteristics of a specific speaker with respect to a specific language, and store the keywords in association with the specific speaker; a speech feature recognition unit, configured to recognize whether any keyword associated with the specific speaker occurs in a random speech fragment of the specific speaker that includes multiple words including the keyword and speech in addition to the keyword, the random speech fragment obtained from a multiple speaker conversation including the speaker, and when a keyword associated with the specific speaker is found in the speech fragment of the specific speaker, recognize speech features of the specific speaker according to a standard pronunciation of the recognized keyword and the pronunciation of the speaker; a speech feature filtration unit, configured to filter out abnormal speech features from the keyword as found in the speech fragment through statistical analysis while retaining speech features reflecting the normal pronunciation characteristics of the specific speaker, when the speech features of the specific speaker recognized by the speech feature recognition unit reach a predetermined number, thereby to create a personalized speech feature library associated with the specific speaker, and store the personalized speech feature library in association with the specific speaker; and a text-to-speech synthesizer, configured to perform a speech synthesis of a text message from the specific speaker, based on the stored personalized speech feature library associated with the specific speaker. 29. The personalized speech feature extraction device according to claim 26 , wherein parameters representing the speech features include frequency, volume, rhythm and end sound. | 0.823062 |
9,130,651 | 1 | 81 | 1. An energy harvesting communication device configured with on chip signal booster apparatus; comprising: at least a communication apparatus; at least an antenna apparatus in association with an input and/or output device; at least a processor in communication with at least one of: said at least a communication apparatus; said at least an antenna apparatus; a charge platform in association with at least charging circuit; at least an interactive media in association with at least an interactive interface; an output device; and at least a sensor apparatus embedded in silicon substrate and etched and/or fused in nano-fiber and/or microfiber material, said at least a sensor apparatus in association with said at least a communication apparatus to provide at least one of an effective energy harvesting medium, communication clarity, communication medium, detection selectivity medium, and detection platform. | 1. An energy harvesting communication device configured with on chip signal booster apparatus; comprising: at least a communication apparatus; at least an antenna apparatus in association with an input and/or output device; at least a processor in communication with at least one of: said at least a communication apparatus; said at least an antenna apparatus; a charge platform in association with at least charging circuit; at least an interactive media in association with at least an interactive interface; an output device; and at least a sensor apparatus embedded in silicon substrate and etched and/or fused in nano-fiber and/or microfiber material, said at least a sensor apparatus in association with said at least a communication apparatus to provide at least one of an effective energy harvesting medium, communication clarity, communication medium, detection selectivity medium, and detection platform. 81. The energy harvesting communication device of claim 1 , wherein said antenna apparatus further comprising at least one of: means for associating radiating RF fields with said means for feeding electromagnetic signals; at least an antenna having at least a grounding portion operable for radiating RF fields between the silicon substrate and the shorted end portion; providing the shorted end portion with at least a common node; shielding the means for radiating RF fields to increase conductive compensation effect; shielding the means for radiating RF fields to increase capacitive compensation effects; apparatus for radiating electromagnetic signals; providing at least a feeding portion for feeding electromagnetic signals; at least an integrated rectifier, at least means for converting infrared and/or THz electromagnetic radiation into DC power; means for rectifying at least an induced voltage to at least terahertz frequency; at least a receiving nano-antenna in association with a rectifying circuit; at least a broadband rectifying antennas; at least a CMOS active circulator; at least on-chip-inductance comprising a frequency dependent apparatus; at least a waveguide reflector comprising at least a meta-material resonant cavity; at least a silicon CMOS comprising at least one of: an FPGA layer, a chip, operable for data transmission. | 0.619809 |
9,158,811 | 1 | 18 | 1. A method comprising: automatically performing a correlation search in accordance with a defined frequency, the correlation search associated with a service provided by one or more entities that each have corresponding machine data, the service having one or more key performance indicators (KPIs), each KPI defined by a search query that derives a value from the corresponding machine data to indicate a state of the service at a point in time or during a period of time; wherein the correlation search associated with the service comprises search criteria pertaining to the one or more KPIs, and a triggering condition to be applied to data identified by a search query using the search criteria; storing a notable event in response to the data identified by the search query satisfying the triggering condition; and causing display of a graphical user interface presenting information pertaining to the stored notable event, the information comprising an identification of the correlation search that triggered the storing of the notable event and an identification of the service associated with the correlation search; wherein each of the entities corresponds to a stored entity definition having an identification of the corresponding machine data, and the service corresponds to a stored service definition referencing the stored entity definitions; wherein the method is performed by a computer system comprising one or more processing devices coupled to a memory for storing the notable event, the service definition, the entity definitions, and the KPIs. | 1. A method comprising: automatically performing a correlation search in accordance with a defined frequency, the correlation search associated with a service provided by one or more entities that each have corresponding machine data, the service having one or more key performance indicators (KPIs), each KPI defined by a search query that derives a value from the corresponding machine data to indicate a state of the service at a point in time or during a period of time; wherein the correlation search associated with the service comprises search criteria pertaining to the one or more KPIs, and a triggering condition to be applied to data identified by a search query using the search criteria; storing a notable event in response to the data identified by the search query satisfying the triggering condition; and causing display of a graphical user interface presenting information pertaining to the stored notable event, the information comprising an identification of the correlation search that triggered the storing of the notable event and an identification of the service associated with the correlation search; wherein each of the entities corresponds to a stored entity definition having an identification of the corresponding machine data, and the service corresponds to a stored service definition referencing the stored entity definitions; wherein the method is performed by a computer system comprising one or more processing devices coupled to a memory for storing the notable event, the service definition, the entity definitions, and the KPIs. 18. The method of claim 1 , further comprising: generating a time-based graphical visualization of values pertaining to the one or more KPIs of the service associated with the correlation search that caused the storing of the notable event. | 0.702233 |
6,134,541 | 23 | 24 | 23. The program storage device of claim 21, wherein said reducing step comprises a singular value decomposition, said searching further comprising the step of: searching an index for a matching reduced dimensionality cluster, based on decomposed specified data. | 23. The program storage device of claim 21, wherein said reducing step comprises a singular value decomposition, said searching further comprising the step of: searching an index for a matching reduced dimensionality cluster, based on decomposed specified data. 24. The program storage device of claim 23, wherein the specified data includes a search template, further comprising the steps of: said associating comprising identifying the search template with the cluster, based on the clustering information; said singular value decomposition comprising projecting the search template onto a subspace for an identified cluster, based on the dimensionality reduction information; and said searching comprising performing an intra-cluster search for a projected template. | 0.5 |
8,775,418 | 1 | 7 | 1. A computer-implemented method performed by one or more computing devices and comprising: (A) obtaining a social graph associated with a particular user, the social graph comprising a plurality of nodes each representing a different user and being associated with voting values associated with a plurality of items of interest, a particular one of the plurality of nodes representing the particular user, ones of the plurality of nodes connected directly to the particular node without any intervening nodes being connected therebetween having first degree connections to the particular node; (B) obtaining, from the particular user, an importance rating for each of the plurality of nodes other than the particular node, a first importance rating being obtained for a first node having a first degree connection to the particular node, a second importance rating being obtained for a second node having a first degree connection to the particular node, the first importance rating being different from the second importance rating; (C) receiving search criteria from the particular user; (D) conducting a search using the search criteria to obtain search results; (E) identifying at least one of the plurality of items of interest in the search results; (F) determining a sort value for each of the search results in which at least one of the plurality of items of interest was identified, the sort value being determined for each of the search results in which at least one of the plurality of items of interest was identified by: (i) identifying one or more nodes of the social graph associated with a voting value associated with any of the plurality of items of interest identified in the search result; (ii) determining a node value for each node identified based at least in part on the importance rating obtained for the node and the voting value associated with both the node and any of the plurality of items of interest identified in the search result; and (iii) determining the sort value for the search result based at least in part on the node value determined for each node identified; (G) ordering the search results based at least in part on the sort value determined for each of the search results in which at least one of the plurality of items of interest was identified; and (H) generating a user interface including the ordered search results that is displayable to the particular user via a display device. | 1. A computer-implemented method performed by one or more computing devices and comprising: (A) obtaining a social graph associated with a particular user, the social graph comprising a plurality of nodes each representing a different user and being associated with voting values associated with a plurality of items of interest, a particular one of the plurality of nodes representing the particular user, ones of the plurality of nodes connected directly to the particular node without any intervening nodes being connected therebetween having first degree connections to the particular node; (B) obtaining, from the particular user, an importance rating for each of the plurality of nodes other than the particular node, a first importance rating being obtained for a first node having a first degree connection to the particular node, a second importance rating being obtained for a second node having a first degree connection to the particular node, the first importance rating being different from the second importance rating; (C) receiving search criteria from the particular user; (D) conducting a search using the search criteria to obtain search results; (E) identifying at least one of the plurality of items of interest in the search results; (F) determining a sort value for each of the search results in which at least one of the plurality of items of interest was identified, the sort value being determined for each of the search results in which at least one of the plurality of items of interest was identified by: (i) identifying one or more nodes of the social graph associated with a voting value associated with any of the plurality of items of interest identified in the search result; (ii) determining a node value for each node identified based at least in part on the importance rating obtained for the node and the voting value associated with both the node and any of the plurality of items of interest identified in the search result; and (iii) determining the sort value for the search result based at least in part on the node value determined for each node identified; (G) ordering the search results based at least in part on the sort value determined for each of the search results in which at least one of the plurality of items of interest was identified; and (H) generating a user interface including the ordered search results that is displayable to the particular user via a display device. 7. The method of claim 1 , further comprising: obtaining a proximity rating for each of the plurality of nodes other than the particular node, the proximity rating indicating at least one of (1) a distance between the node and the particular node on the social graph, or (2) a physical distance between an entity represented by the node and the particular user, wherein for each of the search results in which at least one of the plurality of items of interest was identified, the node value for each node identified is based at least in part on the proximity rating associated with the node. | 0.872304 |
8,688,456 | 3 | 4 | 3. The method of claim 2 , further comprising: generating a website specific language model using the linguistic item and the weighted anchor text. | 3. The method of claim 2 , further comprising: generating a website specific language model using the linguistic item and the weighted anchor text. 4. The method of claim 3 , further comprising: integrating the website specific language model into the live spoken dialog. | 0.5 |
8,103,510 | 1 | 10 | 1. A device control device comprising: speech recognition means which acquires speech data representing a speech and specifies words candidates included in the speech by performing speech recognition on the speech data and calculates a likelihood of each of the specified words candidates; specifying means which specifies words included in the speech based on the likelihoods calculated by the speech recognition means and specifies a content of the speech uttered by an utterer based on the words specified; a database which stores preceding controls, subsequent controls, and weighting factors, each of which is associated with one another; and process execution means which specifies content of a subsequent control to be performed on an external device to be a control target based on a currently executed control, a weighting factor stored in association with the currently executed control and the content of the uttered speech specified by the specifying means, and performs the subsequent control, wherein the process execution means obtains the weighting factor by calculating a product of transition constants defined on routes from the currently executed control to the subsequent control associated with the currently executed control, writes the obtained weighting factor into the database, and, among the subsequent controls stored in the database associated with the currently executed control, identifies a control in which a product is a largest product of the weighting factor and the calculated likelihood. | 1. A device control device comprising: speech recognition means which acquires speech data representing a speech and specifies words candidates included in the speech by performing speech recognition on the speech data and calculates a likelihood of each of the specified words candidates; specifying means which specifies words included in the speech based on the likelihoods calculated by the speech recognition means and specifies a content of the speech uttered by an utterer based on the words specified; a database which stores preceding controls, subsequent controls, and weighting factors, each of which is associated with one another; and process execution means which specifies content of a subsequent control to be performed on an external device to be a control target based on a currently executed control, a weighting factor stored in association with the currently executed control and the content of the uttered speech specified by the specifying means, and performs the subsequent control, wherein the process execution means obtains the weighting factor by calculating a product of transition constants defined on routes from the currently executed control to the subsequent control associated with the currently executed control, writes the obtained weighting factor into the database, and, among the subsequent controls stored in the database associated with the currently executed control, identifies a control in which a product is a largest product of the weighting factor and the calculated likelihood. 10. A device control device according to claim 1 further comprising: information acquisition means which acquires information via predetermined communication means; and speech output means which outputs a speech based on the information acquired by the information acquisition means, whereby when the control specified by the process execution means is to output the information acquired by the information acquisition means, the speech output means outputs a speech based on the information. | 0.5 |
8,306,752 | 1 | 27 | 1. A computer-implemented method for identifying a regulatory interaction between a transcription factor and a gene target of said transcription factor, the method comprising: a) providing a compendium of biochemical expression measurements reflecting gene expression for a set of biochemical species in an organism wherein at least a subset of said species are transcription factors and a second subset of said species are gene targets of transcription factors; b) in a specifically programmed computer, computing mutual information between members of said set of biochemical species; c) in a specifically programmed computer, applying a background correction to each said mutual information value so as to identify a set of those mutual information values that are significantly higher than background mutual information values, wherein the step of applying a background correction comprises the step of estimating a likelihood of the mutual information score, MI, for each possible pair of genes, by comparing the mutual information score for that pair to a background distribution of mutual information values, and wherein said set of mutual information values identified in step (c) identifies a regulatory interaction between a transcription factor and a gene target of said transcription factor; and d) outputting the identified regulatory interaction to a user interface. | 1. A computer-implemented method for identifying a regulatory interaction between a transcription factor and a gene target of said transcription factor, the method comprising: a) providing a compendium of biochemical expression measurements reflecting gene expression for a set of biochemical species in an organism wherein at least a subset of said species are transcription factors and a second subset of said species are gene targets of transcription factors; b) in a specifically programmed computer, computing mutual information between members of said set of biochemical species; c) in a specifically programmed computer, applying a background correction to each said mutual information value so as to identify a set of those mutual information values that are significantly higher than background mutual information values, wherein the step of applying a background correction comprises the step of estimating a likelihood of the mutual information score, MI, for each possible pair of genes, by comparing the mutual information score for that pair to a background distribution of mutual information values, and wherein said set of mutual information values identified in step (c) identifies a regulatory interaction between a transcription factor and a gene target of said transcription factor; and d) outputting the identified regulatory interaction to a user interface. 27. The method of claim 1 further comprising the step, after step (c), of confirming a physical interaction of a said transcription factor with a said gene target. | 0.81969 |
9,576,473 | 17 | 18 | 17. A method of a networked device comprising: applying an automatic content recognition algorithm to determine a content identifier of an audio-visual data; and associating the content identifier with an advertisement data based on a semantic correlation between a meta-data of the advertisement provided by a content provider and the content identifier, wherein a capture infrastructure annotates the audio-visual data with at least one of a brand name and a product name by comparing entries in the master database with at least one of a closed captioning data of the audio-visual data and through an application of an optical character recognition algorithm in the audio-visual data; wherein the content identifier is at least one of a music identification, an object identification, a facial identification, and a voice identification, wherein a minimal functionality comprising accessing at least one of a tuner and a stream decoder that identifies at least one of a channel and a content is found in the networked device, wherein the networked device produces at least one of an audio fingerprint and a video fingerprint that are communicated with the capture infrastructure, wherein the capture infrastructure compares at least one of the audio fingerprint and the video fingerprint with a master database, wherein the capture infrastructure annotates the audio-visual data with a logo name by comparing entries in the master database with a logo data of the audio-visual data identified using a logo detection algorithm, wherein the capture infrastructure automatically divides the audio-visual data into a series of scenes based on a sematic grouping of actions m the audio-visual data, wherein the audio-visual data is analyzed in advance of a broadcast to determine content identifiers associated with each commercial in the audio-visual data such that advertisements are pre-inserted into the audio-visual data prior to broadcast, wherein the capture infrastructure applies a time-order algorithm to automatically match advertisements to the audio-visual data when a correlation pattern is identified by the capture infrastructure with other audio-visual content previously analyzed, wherein the capture infrastructure includes a buffer that is saved to a persistent storage and for which a label is generated to facilitate identification of reoccurring sequences, wherein a post processing operation is at least one of automated through a post-processing algorithm and a crowd-sourced operation using a plurality of users in which a turing test is applied to determine a veracity of an input, wherein a device pairing algorithm is used in which a cookie data associated with a web page visited by the user stored on a browser on a client device is paired with the networked device when the client device is communicatively coupled with the networked device, wherein a transitive public IP matching algorithm is utilized in which at least one of the client device and the networked device communicates each public IP address with any paired entity to the capture infrastructure, and wherein a tag that is unconstrained from a same-origin policy is used to automatically load the advertisement in the browser, wherein the tag is at least one of an image tag, a frame, a iframe, and a script tag. | 17. A method of a networked device comprising: applying an automatic content recognition algorithm to determine a content identifier of an audio-visual data; and associating the content identifier with an advertisement data based on a semantic correlation between a meta-data of the advertisement provided by a content provider and the content identifier, wherein a capture infrastructure annotates the audio-visual data with at least one of a brand name and a product name by comparing entries in the master database with at least one of a closed captioning data of the audio-visual data and through an application of an optical character recognition algorithm in the audio-visual data; wherein the content identifier is at least one of a music identification, an object identification, a facial identification, and a voice identification, wherein a minimal functionality comprising accessing at least one of a tuner and a stream decoder that identifies at least one of a channel and a content is found in the networked device, wherein the networked device produces at least one of an audio fingerprint and a video fingerprint that are communicated with the capture infrastructure, wherein the capture infrastructure compares at least one of the audio fingerprint and the video fingerprint with a master database, wherein the capture infrastructure annotates the audio-visual data with a logo name by comparing entries in the master database with a logo data of the audio-visual data identified using a logo detection algorithm, wherein the capture infrastructure automatically divides the audio-visual data into a series of scenes based on a sematic grouping of actions m the audio-visual data, wherein the audio-visual data is analyzed in advance of a broadcast to determine content identifiers associated with each commercial in the audio-visual data such that advertisements are pre-inserted into the audio-visual data prior to broadcast, wherein the capture infrastructure applies a time-order algorithm to automatically match advertisements to the audio-visual data when a correlation pattern is identified by the capture infrastructure with other audio-visual content previously analyzed, wherein the capture infrastructure includes a buffer that is saved to a persistent storage and for which a label is generated to facilitate identification of reoccurring sequences, wherein a post processing operation is at least one of automated through a post-processing algorithm and a crowd-sourced operation using a plurality of users in which a turing test is applied to determine a veracity of an input, wherein a device pairing algorithm is used in which a cookie data associated with a web page visited by the user stored on a browser on a client device is paired with the networked device when the client device is communicatively coupled with the networked device, wherein a transitive public IP matching algorithm is utilized in which at least one of the client device and the networked device communicates each public IP address with any paired entity to the capture infrastructure, and wherein a tag that is unconstrained from a same-origin policy is used to automatically load the advertisement in the browser, wherein the tag is at least one of an image tag, a frame, a iframe, and a script tag. 18. The method of claim 17 further comprising: accessing a pairing server when processing an identification data associated with a sandbox reachable service of the networked device that shares a public address with a client device, wherein the pairing server performs a discovery lookup of any device that has announced that it shares the public address associated with the client device, and wherein the sandbox reachable service announces itself to the pairing server prior to the establishment of the communication session between the sandboxed application and the sandbox reachable service, appending a header of a hypertext transfer protocol to permit the networked device to communicate with the sandboxed application as a permitted origin domain through a Cross-origin resource sharing (CORS) algorithm, wherein the header is either one of a origin header when the CORS algorithm is applied and a referrer header in an alternate algorithm, and wherein the client device to operate in at least one manner such that the client device: to process an identification data associated with the sandbox reachable service sharing a public address with the client device; to determine a private address pair of the sandbox reachable service based on the identification data; and to establish a communication session between the sandboxed application and the sandbox reachable service using a cross-site scripting technique of a security sandbox. | 0.5 |
7,765,271 | 18 | 19 | 18. A method comprising the steps of: a) generating at a client device a start scan signal using a control element defined by a hypertext mark-up language (HTML) document stored in the client device and displayed by a web browser of a user interface of the client device in response to a user's operation of an input device of the client device; b) at the client device, converting the start scan signal into a form compatible with a scanner; c) at the client device, transmitting the converted start scan signal from the client device to the scanner; d) receiving the converted start scan signal at the scanner; and e) scanning a document with the scanner to generate document data, in response to the converted start scan signal received in said step (d). | 18. A method comprising the steps of: a) generating at a client device a start scan signal using a control element defined by a hypertext mark-up language (HTML) document stored in the client device and displayed by a web browser of a user interface of the client device in response to a user's operation of an input device of the client device; b) at the client device, converting the start scan signal into a form compatible with a scanner; c) at the client device, transmitting the converted start scan signal from the client device to the scanner; d) receiving the converted start scan signal at the scanner; and e) scanning a document with the scanner to generate document data, in response to the converted start scan signal received in said step (d). 19. A method as claimed in claim 18 , wherein said step (a) is performed by depressing and releasing a control element of the user interface of the client device using a mouse constituting at least part of the input device. | 0.767223 |
8,458,192 | 7 | 8 | 7. The method of claim 1 , further comprising: storing a plurality of interest signature values in a database; and retrieving and displaying one or more users from the database having a predetermined rank or interest signature value for a query topic in response to a request regarding the query topic. | 7. The method of claim 1 , further comprising: storing a plurality of interest signature values in a database; and retrieving and displaying one or more users from the database having a predetermined rank or interest signature value for a query topic in response to a request regarding the query topic. 8. The method of claim 7 , wherein the retrieving and displaying step comprises: outputting one or more users with highest ranked interest signature values corresponding to the query topic. | 0.502632 |
8,452,774 | 1 | 7 | 1. A method for establishing term co-relationships using sentence boundaries, said method comprising: providing a document corpus containing text data to be analyzed; analyzing the document corpus, using a microprocessor, to identify sentence boundaries and produce a list of sentences, including applying rules from a rule base to each text record in the document corpus, where the rules are used to identify periods which designate valid sentence endings, and the rule base includes establishing a sentence ending where a word ends with a period, followed by one or more spaces, followed by a word beginning with a capital letter, establishing a sentence ending where an abbreviation ends with a period, followed by one or more spaces, followed by a word beginning with a capital letter, not establishing a sentence ending where an abbreviation ends with a period, followed by one or more spaces, followed by a word beginning with a lower case letter, not establishing a sentence ending where a numeral is followed by a period, followed by one or more numerals, followed by a space, followed by one or more words, establishing a sentence ending where a numeral is followed by a first period, followed by one or more numerals, followed by a second period, where the sentence ending is at the second period, not establishing a sentence ending where a letter is followed by a period, followed by another letter, followed by another period, followed by one or more words, not establishing a sentence ending where a letter is followed by a period, followed by another letter, followed by one or more words, establishing a sentence ending where a letter is followed by a first period, followed by another letter, followed by a second period, followed by nothing else in the text record, where the sentence ending is at the second period, not establishing a sentence ending where a period is preceded by no space and followed by a space, followed by a numeral, followed by a space, followed by one or more words, and not establishing a sentence ending where an ellipsis appears in a sentence; extracting terms and establishing term correlations from the list of sentences; and validating the term correlations to produce a validated term correlation database. | 1. A method for establishing term co-relationships using sentence boundaries, said method comprising: providing a document corpus containing text data to be analyzed; analyzing the document corpus, using a microprocessor, to identify sentence boundaries and produce a list of sentences, including applying rules from a rule base to each text record in the document corpus, where the rules are used to identify periods which designate valid sentence endings, and the rule base includes establishing a sentence ending where a word ends with a period, followed by one or more spaces, followed by a word beginning with a capital letter, establishing a sentence ending where an abbreviation ends with a period, followed by one or more spaces, followed by a word beginning with a capital letter, not establishing a sentence ending where an abbreviation ends with a period, followed by one or more spaces, followed by a word beginning with a lower case letter, not establishing a sentence ending where a numeral is followed by a period, followed by one or more numerals, followed by a space, followed by one or more words, establishing a sentence ending where a numeral is followed by a first period, followed by one or more numerals, followed by a second period, where the sentence ending is at the second period, not establishing a sentence ending where a letter is followed by a period, followed by another letter, followed by another period, followed by one or more words, not establishing a sentence ending where a letter is followed by a period, followed by another letter, followed by one or more words, establishing a sentence ending where a letter is followed by a first period, followed by another letter, followed by a second period, followed by nothing else in the text record, where the sentence ending is at the second period, not establishing a sentence ending where a period is preceded by no space and followed by a space, followed by a numeral, followed by a space, followed by one or more words, and not establishing a sentence ending where an ellipsis appears in a sentence; extracting terms and establishing term correlations from the list of sentences; and validating the term correlations to produce a validated term correlation database. 7. The method of claim 1 wherein the text data to be analyzed includes service technician text verbatim records. | 0.705263 |
9,330,191 | 12 | 13 | 12. The system of claim 11 , wherein the comparing further comprises: performing a first hash function on the first DOM element tree to generate a first hash function output; performing a second hash function on the second DOM element tree to generate a second hash function output; and comparing the first hash function output to the second hash function output. | 12. The system of claim 11 , wherein the comparing further comprises: performing a first hash function on the first DOM element tree to generate a first hash function output; performing a second hash function on the second DOM element tree to generate a second hash function output; and comparing the first hash function output to the second hash function output. 13. The system of claim 12 , the comparing the first hash function output to the second hash function output comprising: identifying a mismatch between the first hash function output and the second hash function output; and responsive to the identifying a mismatch, traversing a branch of the first DOM element tree and a corresponding branch of the second DOM element tree to detect a difference between an element associated with the first DOM element tree and a corresponding element associated with the second DOM element tree. | 0.5 |
9,256,650 | 10 | 11 | 10. The computer program product of claim 9 , wherein the program code instructions for determining the one or more metaphors comprise program code instructions for searching one or more repositories for a relation between the topic information and the knowledge information. | 10. The computer program product of claim 9 , wherein the program code instructions for determining the one or more metaphors comprise program code instructions for searching one or more repositories for a relation between the topic information and the knowledge information. 11. The computer program product of claim 10 , wherein the program code instructions for searching the repository comprise program code instructions for searching for the relation between the topic information and one or more types of terminologies related to the knowledge information, and wherein the program code instructions for determining the one or more metaphors comprise program code instructions for determining the one or more metaphors based at least in part on the one or more types of terminologies. | 0.5 |
5,550,928 | 10 | 12 | 10. An image recognition system for identifying an individual in a monitored area comprising: means for storing a plurality of reference facial image signatures and a plurality of reference body shape signatures, each stored reference facial image signature and each reference body shape signature corresponding to a predetermined individual; video camera apparatus adapted to capture a current image of an individual in the monitored area; means responsive to the video camera apparatus for extracting a current facial image signature from the current image, for extracting a current body shape signature from the current image, for comparing the current facial image signature with the stored reference facial image signatures to thereby generate a first set of scores wherein each score of the first set of scores represents a degree of agreement between the current facial image signature and a corresponding stored reference facial signature, for comparing the current body shape signature with the stored reference body shape signatures to thereby generate a second set of scores wherein each score of the second set of scores represents a degree of agreement between the current body shape signature and a corresponding stored reference body shape signature, for forming a composite set of scores from the first and second sets of scores, and for selecting a maximum score from the composite set of scores. | 10. An image recognition system for identifying an individual in a monitored area comprising: means for storing a plurality of reference facial image signatures and a plurality of reference body shape signatures, each stored reference facial image signature and each reference body shape signature corresponding to a predetermined individual; video camera apparatus adapted to capture a current image of an individual in the monitored area; means responsive to the video camera apparatus for extracting a current facial image signature from the current image, for extracting a current body shape signature from the current image, for comparing the current facial image signature with the stored reference facial image signatures to thereby generate a first set of scores wherein each score of the first set of scores represents a degree of agreement between the current facial image signature and a corresponding stored reference facial signature, for comparing the current body shape signature with the stored reference body shape signatures to thereby generate a second set of scores wherein each score of the second set of scores represents a degree of agreement between the current body shape signature and a corresponding stored reference body shape signature, for forming a composite set of scores from the first and second sets of scores, and for selecting a maximum score from the composite set of scores. 12. The image recognition system of claim 10 further comprising: sensing means for sensing the presence of an individual in the monitored area; and, means responsive to the sensing means for estimating the number of individuals present in the monitored area; wherein the means responsive to the video camera apparatus for extracting a current facial image signature from the current image determines the identity of the predetermined individual from the first and second sets of scores and from the estimated number of individuals present in the monitored area. | 0.537891 |
7,801,721 | 11 | 12 | 11. A system comprising: one or more processors configured to interact with a computer-readable storage medium in order to perform operations including presenting a user interface on a display of a computing device, the user interface comprising: an input area for receiving a location of a web document to be translated from a first language text to a second language text; and a presentation area for displaying a translated web document in the second language text, wherein the first language text that corresponds to a portion of the second language text is displayed in a graphical element within the presentation area in response to a user pointing to the portion of the second language text. | 11. A system comprising: one or more processors configured to interact with a computer-readable storage medium in order to perform operations including presenting a user interface on a display of a computing device, the user interface comprising: an input area for receiving a location of a web document to be translated from a first language text to a second language text; and a presentation area for displaying a translated web document in the second language text, wherein the first language text that corresponds to a portion of the second language text is displayed in a graphical element within the presentation area in response to a user pointing to the portion of the second language text. 12. The system of claim 11 , wherein the first language text is displayed after a predetermined period of time has elapsed after the user points to the portion of the second language text, and wherein the first language text display is removed after a second predetermined period of time has elapsed after the user ceases to point at the portion of the second language text. | 0.5 |
7,782,203 | 1 | 9 | 1. A system that facilitates verifying data within a radio frequency identification (RFID) business process, comprising: a radio frequency identification (RFID) business process includes at least one component configured to receive an event from a logical source; and a strong typing module configured to employ strong typing of the at least one component that defines at least one of an event type for the at least one component, an input event type for the at least one component, or an output event type for the at least one component. | 1. A system that facilitates verifying data within a radio frequency identification (RFID) business process, comprising: a radio frequency identification (RFID) business process includes at least one component configured to receive an event from a logical source; and a strong typing module configured to employ strong typing of the at least one component that defines at least one of an event type for the at least one component, an input event type for the at least one component, or an output event type for the at least one component. 9. The system of claim 1 , the strong typing module is configured to employ the strong typing at design time, the design time includes a process of conceptualizing the RFID process by specifying at least one of the following: a logical device element; a logical source as a container for the logical device element; or a processing pipeline with a pipeline component configured to receive the event from the logical source. | 0.632813 |
7,774,341 | 1 | 9 | 1. A user-interface method of selecting and presenting a collection of content items in which the presentation is ordered at least in part based on learning the preferred microgenres of content of the user as contained in content items selected by the user, the method comprising: providing access to a content system including a set of content items organized by genre information that characterizes the content items, wherein the genre information is specified by the content system, and wherein the set of content items contains microgenre metadata further characterizing the content items; receiving incremental input entered by the user for incrementally identifying desired content items; in response to the incremental input entered by the user, presenting a subset of content items to the user; receiving actions from the user selecting content items from the subset; analyzing the microgenre metadata within the selected content items to learn the preferred microgenres of the user; analyzing the date, day, and time of the user selection actions and analyzing at least one of the genre information and microgenre metadata of the selected content items to learn a periodicity of user selections of similar content items, wherein similarity is determined by comparing the at least one of the genre information and microgenre metadata of the selected content item with a previously selected content item, and wherein the periodicity indicates the amount of time between user selections of similar content items relative to a reference point; and associating the learned periodicity with the at least one of the genre information and microgenre metadata of the similar content items; in response to receiving subsequent incremental input entered by the user, selecting and ranking a collection of content items, wherein content items containing microgenre metadata matching more learned microgenre preferences of the user relative to other microgenre preferences are ranked more highly than other content items of the collection containing microgenre metadata matching less learned microgenre preferences of the user relative to other microgenre preferences, and wherein the selecting and presenting the collection of content items is further based on promoting the relevance of those content items characterized by genre information or containing microgenre metadata associated with periodicities matching the date, day, and time of the subsequent incremental input; and presenting the ranked collection of content items on a display device in an order reflecting the ranking of the content items. | 1. A user-interface method of selecting and presenting a collection of content items in which the presentation is ordered at least in part based on learning the preferred microgenres of content of the user as contained in content items selected by the user, the method comprising: providing access to a content system including a set of content items organized by genre information that characterizes the content items, wherein the genre information is specified by the content system, and wherein the set of content items contains microgenre metadata further characterizing the content items; receiving incremental input entered by the user for incrementally identifying desired content items; in response to the incremental input entered by the user, presenting a subset of content items to the user; receiving actions from the user selecting content items from the subset; analyzing the microgenre metadata within the selected content items to learn the preferred microgenres of the user; analyzing the date, day, and time of the user selection actions and analyzing at least one of the genre information and microgenre metadata of the selected content items to learn a periodicity of user selections of similar content items, wherein similarity is determined by comparing the at least one of the genre information and microgenre metadata of the selected content item with a previously selected content item, and wherein the periodicity indicates the amount of time between user selections of similar content items relative to a reference point; and associating the learned periodicity with the at least one of the genre information and microgenre metadata of the similar content items; in response to receiving subsequent incremental input entered by the user, selecting and ranking a collection of content items, wherein content items containing microgenre metadata matching more learned microgenre preferences of the user relative to other microgenre preferences are ranked more highly than other content items of the collection containing microgenre metadata matching less learned microgenre preferences of the user relative to other microgenre preferences, and wherein the selecting and presenting the collection of content items is further based on promoting the relevance of those content items characterized by genre information or containing microgenre metadata associated with periodicities matching the date, day, and time of the subsequent incremental input; and presenting the ranked collection of content items on a display device in an order reflecting the ranking of the content items. 9. The method of claim 1 , further comprising presenting the ordered collection of content items on at least part of a television screen. | 0.908788 |
8,335,791 | 42 | 47 | 42. One or more computer-readable storage media encoded with instructions that, when executed, cause a processor to perform acts comprising: receiving, via a network connection, a threshold parameter indicating a predetermined threshold similarity of subject matter and an input document associated with a new item available for purchase, the input document to be indexed into a search index file; comparing a subject matter of the input document to a subject matter of an existing document that relates to an item available for purchase and is already indexed into the search index file, wherein the structure of the input document includes at least two fields, the at least two fields each including content, and the predetermined threshold similarity of subject matter exists at least when a percentage of fields in a structure of the input document correspond to fields in a structure of the existing document; determining, based at least on the comparison, that the input document and the existing document meet the predetermined threshold similarity of subject matter; responsive to determining that the input document and the existing document meet the predetermined threshold similarity of subject matter, identifying text in the input document that is dissimilar to text in the existing document; designating any dissimilar text between the input document and the existing document as candidate synonyms; merging the candidate synonyms into the search index file, wherein the merging associates the dissimilar text as synonyms in the search index file; and indexing the input document into the search index file. | 42. One or more computer-readable storage media encoded with instructions that, when executed, cause a processor to perform acts comprising: receiving, via a network connection, a threshold parameter indicating a predetermined threshold similarity of subject matter and an input document associated with a new item available for purchase, the input document to be indexed into a search index file; comparing a subject matter of the input document to a subject matter of an existing document that relates to an item available for purchase and is already indexed into the search index file, wherein the structure of the input document includes at least two fields, the at least two fields each including content, and the predetermined threshold similarity of subject matter exists at least when a percentage of fields in a structure of the input document correspond to fields in a structure of the existing document; determining, based at least on the comparison, that the input document and the existing document meet the predetermined threshold similarity of subject matter; responsive to determining that the input document and the existing document meet the predetermined threshold similarity of subject matter, identifying text in the input document that is dissimilar to text in the existing document; designating any dissimilar text between the input document and the existing document as candidate synonyms; merging the candidate synonyms into the search index file, wherein the merging associates the dissimilar text as synonyms in the search index file; and indexing the input document into the search index file. 47. The one or more computer-readable storage media of claim 42 , wherein identifying text comprises identifying words or phrases. | 0.803625 |
9,892,156 | 2 | 3 | 2. The method of claim 1 , wherein: the tracking the activity includes determining that a category constraint for the first query is the same as a category constraint for the second query; and the counting the number of search events is based on the determining that the category constraint for the first query is the same as the category constraint for the second query. | 2. The method of claim 1 , wherein: the tracking the activity includes determining that a category constraint for the first query is the same as a category constraint for the second query; and the counting the number of search events is based on the determining that the category constraint for the first query is the same as the category constraint for the second query. 3. The method of claim 2 , further comprising causing display of an indication that the category constraint for the first query is the same as the category constraint for the second query via a search user interface on the client device. | 0.5 |
7,814,127 | 5 | 9 | 5. A method of providing natural language support for users running queries against a database, comprising: providing a data abstraction model comprising a plurality of logical fields abstractly describing physical data residing in the database; providing translation information for the data abstraction model describing translations of each of the plurality of logical fields from a first natural language expression to two or more second natural language expressions; and displaying one of the second natural language expressions to a user, wherein which of the two or more second natural language expressions is displayed depends upon which natural language expression files are loaded to define a language resource component associated with the data abstraction model. | 5. A method of providing natural language support for users running queries against a database, comprising: providing a data abstraction model comprising a plurality of logical fields abstractly describing physical data residing in the database; providing translation information for the data abstraction model describing translations of each of the plurality of logical fields from a first natural language expression to two or more second natural language expressions; and displaying one of the second natural language expressions to a user, wherein which of the two or more second natural language expressions is displayed depends upon which natural language expression files are loaded to define a language resource component associated with the data abstraction model. 9. The method of claim 5 , wherein the data abstraction model further comprises a reference to at least a portion of the translation information. | 0.847046 |
7,729,916 | 1 | 35 | 1. A method for providing conversational computing between a user and at least one application, the method comprising the steps of: engaging in dialog with the user; processing the dialog, by a processor executing the at least one application, to one of complete a query, disambiguate a query, summarize a query, correct a query, correct a result of an executed task, communicate the result of such execution, determine a target application of an input/output event, and a combination thereof based on one of past dialog history, context, user preferences, meta information, and a combination thereof; and presenting a unified and coordinated user interface across the plurality of applications; wherein presenting a unified and coordinated user interface across a plurality of applications comprises the steps of: registering, by each application, information comprising application state, application modes, arguments, context, modalities, engine resources, and a combination thereof; and managing the dialog across the plurality of applications based on their registered information. | 1. A method for providing conversational computing between a user and at least one application, the method comprising the steps of: engaging in dialog with the user; processing the dialog, by a processor executing the at least one application, to one of complete a query, disambiguate a query, summarize a query, correct a query, correct a result of an executed task, communicate the result of such execution, determine a target application of an input/output event, and a combination thereof based on one of past dialog history, context, user preferences, meta information, and a combination thereof; and presenting a unified and coordinated user interface across the plurality of applications; wherein presenting a unified and coordinated user interface across a plurality of applications comprises the steps of: registering, by each application, information comprising application state, application modes, arguments, context, modalities, engine resources, and a combination thereof; and managing the dialog across the plurality of applications based on their registered information. 35. The method of claim 1 , further comprising the step of customizing the dialog based on one of user identity, usage history of the user, user preferences, active applications, context, and a combination. | 0.815081 |
9,189,539 | 11 | 20 | 11. A computer program product comprising a non-transitory computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to: analyze a first electronic document to identify a reference to a second electronic document; analyze the second electronic document to identify document dependencies with zero or more other electronic documents; generate a dependency information data structure based on the analysis of the first electronic document and the analysis of the second electronic document, wherein the dependent information data structure comprises a dependency graph data structure of the electronic document collection, the dependency graph data structure comprising first nodes representing electronic documents in the electronic document collection, second nodes representing authors of electronic documents in the electronic document collection, and edges between nodes representing relationships between nodes, wherein each of the first nodes and the second nodes have an associated node strength attribute, and wherein the associated node strength attribute is a measure of a relative importance of the associated first node or the associated second node to the dependency graph data structure of the electronic document collection and a fragility of the dependency graph data structure with regard to the associated first node or the associated second node; analyze the dependency information data structure to identify a loaded document subset of the electronic document collection that is a subset of electronic documents to be loaded into memory when performing an information analysis operation; generate an electronic document curation action recommendation based on the identified subset of the electronic document collection; and output the electronic document curation action recommendation. | 11. A computer program product comprising a non-transitory computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to: analyze a first electronic document to identify a reference to a second electronic document; analyze the second electronic document to identify document dependencies with zero or more other electronic documents; generate a dependency information data structure based on the analysis of the first electronic document and the analysis of the second electronic document, wherein the dependent information data structure comprises a dependency graph data structure of the electronic document collection, the dependency graph data structure comprising first nodes representing electronic documents in the electronic document collection, second nodes representing authors of electronic documents in the electronic document collection, and edges between nodes representing relationships between nodes, wherein each of the first nodes and the second nodes have an associated node strength attribute, and wherein the associated node strength attribute is a measure of a relative importance of the associated first node or the associated second node to the dependency graph data structure of the electronic document collection and a fragility of the dependency graph data structure with regard to the associated first node or the associated second node; analyze the dependency information data structure to identify a loaded document subset of the electronic document collection that is a subset of electronic documents to be loaded into memory when performing an information analysis operation; generate an electronic document curation action recommendation based on the identified subset of the electronic document collection; and output the electronic document curation action recommendation. 20. The computer program product of claim 11 , wherein the information analysis operation is a question and answer determination operation of a question and answer system in response to the submission of a question by a client computing device. | 0.789292 |
8,935,323 | 1 | 2 | 1. A method for integrating a blogging application in a collaborative environment, the method comprising the steps of: processing a single sign-on for a member of the collaborative environment; associating a role with the member so as to place the member into a collaborative space in the collaborative environment and to regulate the member in interacting with the collaborative space; retrieving a list of collaborative applications for the role assigned to the member; and, loading the collaborative applications in the list into the collaborative space for use by the member from within the collaborative space, said collaborative applications including at least a blogging application. | 1. A method for integrating a blogging application in a collaborative environment, the method comprising the steps of: processing a single sign-on for a member of the collaborative environment; associating a role with the member so as to place the member into a collaborative space in the collaborative environment and to regulate the member in interacting with the collaborative space; retrieving a list of collaborative applications for the role assigned to the member; and, loading the collaborative applications in the list into the collaborative space for use by the member from within the collaborative space, said collaborative applications including at least a blogging application. 2. The method of claim 1 , further comprising the step of importing at least one blog feed reader into said blogging application. | 0.601852 |
7,596,568 | 1 | 2 | 1. A computer implemented system for resolving ambiguity comprising: a processor; the processor operatively coupled to a computer readable storage medium including program modules that include executable instructions, the computer readable storage medium including: at least one program module that receives inputs; at least one program module configured to parse a grammatical structure of the received inputs to identify a token not present in the received inputs, wherein the token includes a word that is statistically associated with documents that have grammatical structures similar to the received inputs; at least one program module configured to add the token to the received inputs thereby generating a modified inputs; and at least one program module configured to generate from the modified inputs, a collection of ranked interpretations representing a list of probable intent comprising a set of fragments of data types structurally compatible to other fragments in the set, wherein a fragment of the set of compatible fragments is generated by analyzing a grammatical structure of one or more of the modified inputs at a linguistic level, wherein the collection of ranked interpretation is determined based on a number of matching data types; connecting to a plurality of search providers and receives a collection of search results from at least one of the plurality of search providers and displaying results according to a specified expansion policy, in relevance order, as blocks of results from search providers, merged results from multiple search providers in relevance order and eliminate duplicate results. | 1. A computer implemented system for resolving ambiguity comprising: a processor; the processor operatively coupled to a computer readable storage medium including program modules that include executable instructions, the computer readable storage medium including: at least one program module that receives inputs; at least one program module configured to parse a grammatical structure of the received inputs to identify a token not present in the received inputs, wherein the token includes a word that is statistically associated with documents that have grammatical structures similar to the received inputs; at least one program module configured to add the token to the received inputs thereby generating a modified inputs; and at least one program module configured to generate from the modified inputs, a collection of ranked interpretations representing a list of probable intent comprising a set of fragments of data types structurally compatible to other fragments in the set, wherein a fragment of the set of compatible fragments is generated by analyzing a grammatical structure of one or more of the modified inputs at a linguistic level, wherein the collection of ranked interpretation is determined based on a number of matching data types; connecting to a plurality of search providers and receives a collection of search results from at least one of the plurality of search providers and displaying results according to a specified expansion policy, in relevance order, as blocks of results from search providers, merged results from multiple search providers in relevance order and eliminate duplicate results. 2. The system of claim 1 , wherein the inputs comprise a natural language request. | 0.55914 |
9,218,546 | 11 | 18 | 11. A system, comprising: a data processing apparatus; and a memory coupled to the data processing apparatus having instructions stored thereon which, when executed by the data processing apparatus cause the data processing apparatus to perform operations comprising: receiving data specifying a first image; receiving text labels for the first image; for each of one or more of the text labels: receiving search results in response to a web search performed using the text label as a query, each search result referencing a web resource; assigning a text label ranking score to the text label based on a number of the resources referenced by the received search results that include an image matching the first image; ranking the text labels, at least in part, based on the text label ranking scores assigned to text labels; and selecting an image label for the first image from the ranked text labels, the image label being selected based on the ranking. | 11. A system, comprising: a data processing apparatus; and a memory coupled to the data processing apparatus having instructions stored thereon which, when executed by the data processing apparatus cause the data processing apparatus to perform operations comprising: receiving data specifying a first image; receiving text labels for the first image; for each of one or more of the text labels: receiving search results in response to a web search performed using the text label as a query, each search result referencing a web resource; assigning a text label ranking score to the text label based on a number of the resources referenced by the received search results that include an image matching the first image; ranking the text labels, at least in part, based on the text label ranking scores assigned to text labels; and selecting an image label for the first image from the ranked text labels, the image label being selected based on the ranking. 18. The system of claim 11 wherein ranking the text labels includes arranging the text labels according to the corresponding text label ranking scores. | 0.763323 |
9,825,890 | 1 | 3 | 1. A method comprising: determining one or more events performed on a first data content object of a file, the one or more events being performed by a first user on a first client device using a data-content-object processing application, the file being stored in a data store, the first data content object being a defined portion less than all of the file; storing one or more event indications about the one or more events in a history buffer, each of the one or more event indications including event metadata indicating the one or more events performed by the first user on the first data content object; retrieving a notification threshold condition associated with a second user, wherein the notification threshold condition is based on a notification frequency determined by the second user, the notification threshold condition defining first criteria for determining which of the one or more event indications are relevant to the second user and second criteria for determining based on at least one of the one or more event indications being relevant to the second user whether to generate and send a notification to the second user, the first criteria defining that a particular event indication is relevant to the second user based on, at least in part, whether the second user has collaboratively worked with another user that has commented on the first data content object of the file, the second criteria defining whether to generate and send the notification to the second user based on, at least in part, second user preferences configurable by the second user and defining threshold quality of change to the first data content object before the generating and sending of the notification; using the first criteria to determine which of the one or more event indications are relevant to the second user; using the second criteria to determine whether the at least one of the one or more event indications being relevant to the second user satisfies the threshold quality of change to the first data content object; and if the second criteria is satisfied, generating and sending the notification for the second user. | 1. A method comprising: determining one or more events performed on a first data content object of a file, the one or more events being performed by a first user on a first client device using a data-content-object processing application, the file being stored in a data store, the first data content object being a defined portion less than all of the file; storing one or more event indications about the one or more events in a history buffer, each of the one or more event indications including event metadata indicating the one or more events performed by the first user on the first data content object; retrieving a notification threshold condition associated with a second user, wherein the notification threshold condition is based on a notification frequency determined by the second user, the notification threshold condition defining first criteria for determining which of the one or more event indications are relevant to the second user and second criteria for determining based on at least one of the one or more event indications being relevant to the second user whether to generate and send a notification to the second user, the first criteria defining that a particular event indication is relevant to the second user based on, at least in part, whether the second user has collaboratively worked with another user that has commented on the first data content object of the file, the second criteria defining whether to generate and send the notification to the second user based on, at least in part, second user preferences configurable by the second user and defining threshold quality of change to the first data content object before the generating and sending of the notification; using the first criteria to determine which of the one or more event indications are relevant to the second user; using the second criteria to determine whether the at least one of the one or more event indications being relevant to the second user satisfies the threshold quality of change to the first data content object; and if the second criteria is satisfied, generating and sending the notification for the second user. 3. The method of claim 1 , wherein the first data content object and a second data content object belong to a workspace object, and the history buffer is dedicated to the workspace object. | 0.5 |
8,228,215 | 1 | 5 | 1. A method comprising: obtaining, by a computer system including one or more computers, a first set of test values, a second set of test values, and a third set of test values; receiving, by the computer system, text comprising characters represented as code point values, the characters identified as being in a first encoding format, each code point value representing one character in the text; and making a determination by the computer system that the text likely includes characters incorrectly converted from a second encoding format to the first encoding format, wherein making the determination includes (i) determining for a sequence of code point values consisting of a first code point value followed by a second code point value that the first code point value is in the first set of test values and that the second code point value is in the second set of test values, or (ii) determining for a sequence of code point values consisting of a first code point value followed by a second code point value followed by a third code point value that the first code point value is in the third set of test values, that the second code point value is in the second set of test values, and that the third code point value is in the second set of test values. | 1. A method comprising: obtaining, by a computer system including one or more computers, a first set of test values, a second set of test values, and a third set of test values; receiving, by the computer system, text comprising characters represented as code point values, the characters identified as being in a first encoding format, each code point value representing one character in the text; and making a determination by the computer system that the text likely includes characters incorrectly converted from a second encoding format to the first encoding format, wherein making the determination includes (i) determining for a sequence of code point values consisting of a first code point value followed by a second code point value that the first code point value is in the first set of test values and that the second code point value is in the second set of test values, or (ii) determining for a sequence of code point values consisting of a first code point value followed by a second code point value followed by a third code point value that the first code point value is in the third set of test values, that the second code point value is in the second set of test values, and that the third code point value is in the second set of test values. 5. The method of claim 1 , wherein the first set of test values matches a bit mask having a format 110x xxxx, and includes hexadecimal values in Win-1252 encoding format, wherein hexadecimal values C or D matches a first half-byte of the bit mask and one of hexadecimal values 0-F matches a second half-byte of the bit mask. | 0.512048 |
9,183,039 | 9 | 10 | 9. The method of claim 2 , further comprising: identifying a second associated task of the tasks of the task group, wherein the second associated task is associated with a second task completion step, wherein the second associated task is unique from the associated task; and determining a task completion step similarity measurement, wherein the task completion step similarity measurement is indicative of similarity between the associated task completion step and the second associated task completion step, and wherein the similarity of the second associated task with the tasks of the task group is based on the task completion step similarity measurement. | 9. The method of claim 2 , further comprising: identifying a second associated task of the tasks of the task group, wherein the second associated task is associated with a second task completion step, wherein the second associated task is unique from the associated task; and determining a task completion step similarity measurement, wherein the task completion step similarity measurement is indicative of similarity between the associated task completion step and the second associated task completion step, and wherein the similarity of the second associated task with the tasks of the task group is based on the task completion step similarity measurement. 10. The method of claim 9 , wherein the task completion step includes a first completion action and a first completion object, wherein the second task completion step includes a second completion action and a second completion object. | 0.5 |
9,747,516 | 12 | 22 | 12. A Mobile Station (MS) comprising: a camera to capture a plurality of images comprising a first image; a memory to store the plurality of images; and a processor coupled to the camera and the memory, wherein the processor is configured to determine a set of candidate keypoints based on the first image captured with a camera; determine, for each candidate keypoint in the set of candidate keypoints, a corresponding first similarity score, wherein the first similarity score corresponding to each candidate keypoint is determined, in part, by comparing an image patch associated with the corresponding candidate keypoint to a plurality of corresponding image sections in the first image in a region around the image patch; and select a first subset of the candidate keypoints, wherein the first subset comprises a predefined number of candidate keypoints with lowest similarity scores. | 12. A Mobile Station (MS) comprising: a camera to capture a plurality of images comprising a first image; a memory to store the plurality of images; and a processor coupled to the camera and the memory, wherein the processor is configured to determine a set of candidate keypoints based on the first image captured with a camera; determine, for each candidate keypoint in the set of candidate keypoints, a corresponding first similarity score, wherein the first similarity score corresponding to each candidate keypoint is determined, in part, by comparing an image patch associated with the corresponding candidate keypoint to a plurality of corresponding image sections in the first image in a region around the image patch; and select a first subset of the candidate keypoints, wherein the first subset comprises a predefined number of candidate keypoints with lowest similarity scores. 22. The MS of claim 12 , wherein the processor is further configured to: track an object in at least one second image captured by the camera, based, in part, on the first subset of candidate keypoints. | 0.870155 |
7,793,228 | 10 | 12 | 10. A graphical user interface on a portable electronic device with memory and one or more processors to execute one or more programs stored in the memory, the graphical user interface comprising: an input sequence of one or more alphabet characters; one or more partial word suggestions that satisfy predefined usage frequency criteria with respect to the input sequence, each partial word suggestion comprising a concatenation of the input sequence and two or more additional alphabet characters and the partial word by itself is not a complete word; wherein: the input sequence of one or more alphabet characters are received from a user; a character sequence data tree structure is identified, each node of the character sequence tree corresponding to a distinct character sequence, the input sequence corresponding to a first node in the character sequence tree; and one or more partial word suggestions corresponding to one or more descendent nodes of the first node that have values above a predefined threshold are selected, wherein a respective value for a respective descendent node of the first node is the usage frequency weight of the respective descendent node divided by the usage frequency weight of the first node. | 10. A graphical user interface on a portable electronic device with memory and one or more processors to execute one or more programs stored in the memory, the graphical user interface comprising: an input sequence of one or more alphabet characters; one or more partial word suggestions that satisfy predefined usage frequency criteria with respect to the input sequence, each partial word suggestion comprising a concatenation of the input sequence and two or more additional alphabet characters and the partial word by itself is not a complete word; wherein: the input sequence of one or more alphabet characters are received from a user; a character sequence data tree structure is identified, each node of the character sequence tree corresponding to a distinct character sequence, the input sequence corresponding to a first node in the character sequence tree; and one or more partial word suggestions corresponding to one or more descendent nodes of the first node that have values above a predefined threshold are selected, wherein a respective value for a respective descendent node of the first node is the usage frequency weight of the respective descendent node divided by the usage frequency weight of the first node. 12. The graphical user interface of claim 10 , further comprising a plurality of sequences, each of the plurality of sequences comprising a concatenation of the input sequence and a distinct alphabet character from a predefined set of alphabet characters. | 0.5 |
9,207,913 | 10 | 12 | 10. A system comprising: a first apparatus comprising a first processor and a second apparatus comprising a second processor; and a computer readable storage medium to store a first application having computer readable program code and a second application having computer readable program code, wherein the first processor executes the computer readable program code of the first application to: create on a server computing device a plurality of executable modules, each executable module comprising a first variable and a flag comprising a first predetermined value if the executable module may be published to a client computing device, and a second predetermined value if the executable module may not be published to the client computing device; create on the server computing device a first template comprising a selection of one of the executable modules comprising the flag comprising the first predetermined value and a specification of the first variable; link the one executable module to the first template; and communicate information about the first template from the server computing device to the client computing device; wherein the second processor executes the computer readable program code of the second application to: receive on the client computing device a selection of the first template; receive on the client computing device a configuration of the specified first variable; and save the selected template and the configured first variable as an application program interface (API) on the server computing device. | 10. A system comprising: a first apparatus comprising a first processor and a second apparatus comprising a second processor; and a computer readable storage medium to store a first application having computer readable program code and a second application having computer readable program code, wherein the first processor executes the computer readable program code of the first application to: create on a server computing device a plurality of executable modules, each executable module comprising a first variable and a flag comprising a first predetermined value if the executable module may be published to a client computing device, and a second predetermined value if the executable module may not be published to the client computing device; create on the server computing device a first template comprising a selection of one of the executable modules comprising the flag comprising the first predetermined value and a specification of the first variable; link the one executable module to the first template; and communicate information about the first template from the server computing device to the client computing device; wherein the second processor executes the computer readable program code of the second application to: receive on the client computing device a selection of the first template; receive on the client computing device a configuration of the specified first variable; and save the selected template and the configured first variable as an application program interface (API) on the server computing device. 12. The system of claim 10 , wherein the one executable module includes branching logic describing runtime behavior of the API. | 0.518939 |
9,977,775 | 1 | 28 | 1. A computer-readable storage medium that is not a signal, the computer-readable storage medium for storing data for access by a program being executed on a data processing system, comprising: a dictionary data structure stored in the computer-readable storage medium, the dictionary data structure including information used by the program and comprising: a first table comprised of entries each representing a natural language term, each entry of the first table containing a term ID identifying its term; a second table comprised of entries each representing a definition, each entry of the second table containing a definition ID identifying its definition; and a third table comprised of entries each representing correspondence between a term and a definition defining the term, each entry of the third table containing a term ID identifying the defined term and a definition ID identifying the defining definition, such that the contents of the data structure are usable to identify any definitions corresponding to a term. | 1. A computer-readable storage medium that is not a signal, the computer-readable storage medium for storing data for access by a program being executed on a data processing system, comprising: a dictionary data structure stored in the computer-readable storage medium, the dictionary data structure including information used by the program and comprising: a first table comprised of entries each representing a natural language term, each entry of the first table containing a term ID identifying its term; a second table comprised of entries each representing a definition, each entry of the second table containing a definition ID identifying its definition; and a third table comprised of entries each representing correspondence between a term and a definition defining the term, each entry of the third table containing a term ID identifying the defined term and a definition ID identifying the defining definition, such that the contents of the data structure are usable to identify any definitions corresponding to a term. 28. The computer-readable storage medium of claim 1 wherein the first table comprises a first entry representing a first natural language term and a second entry representing a second natural language term, the second natural language term being a non-standard form of the first natural language term, the first natural language term being preferred for usage over the second natural language term, the second entry including a harmonized-to field specifying the term ID identifying the first natural language term. | 0.5 |
9,588,958 | 8 | 12 | 8. A non-transitory computer readable storage medium comprising executable instructions for causing a computing system to perform operations comprising: performing a first syntactic and semantic analysis of a training natural language text to produce a first plurality of language-independent semantic structures representing a plurality of sentences of the training natural language text; producing, based on the first plurality of language-independent semantic structures, a text classifier model; performing a second syntactic and semantic analysis of an input natural language text to produce a second plurality of language-independent semantic structures representing a plurality of sentences of the input natural language text; extracting, using the second plurality of language-independent semantic structures, a set of features, wherein at least one feature references a semantic class of a language-independent semantic hierarchy comprising a plurality of semantic classes, in which the semantic class exhibits one or more properties inherited from its parent semantic class; applying the text classifier model to the set of features to produce a classification spectrum comprising a plurality of weight values, wherein each weight value references a degree of association of the input natural language text with a particular category of natural language texts; and associating the input natural language text with one or more categories using the classification spectrum. | 8. A non-transitory computer readable storage medium comprising executable instructions for causing a computing system to perform operations comprising: performing a first syntactic and semantic analysis of a training natural language text to produce a first plurality of language-independent semantic structures representing a plurality of sentences of the training natural language text; producing, based on the first plurality of language-independent semantic structures, a text classifier model; performing a second syntactic and semantic analysis of an input natural language text to produce a second plurality of language-independent semantic structures representing a plurality of sentences of the input natural language text; extracting, using the second plurality of language-independent semantic structures, a set of features, wherein at least one feature references a semantic class of a language-independent semantic hierarchy comprising a plurality of semantic classes, in which the semantic class exhibits one or more properties inherited from its parent semantic class; applying the text classifier model to the set of features to produce a classification spectrum comprising a plurality of weight values, wherein each weight value references a degree of association of the input natural language text with a particular category of natural language texts; and associating the input natural language text with one or more categories using the classification spectrum. 12. The non-transitory computer readable medium of claim 8 , wherein the second syntactic and semantic analysis further includes determining a semantic feature of the input natural language text. | 0.537915 |
7,953,674 | 13 | 14 | 13. The computer implemented method of claim 9 further comprising determining whether combinations associated with a query have been parsed prior to receiving the query. | 13. The computer implemented method of claim 9 further comprising determining whether combinations associated with a query have been parsed prior to receiving the query. 14. The computer implemented method of claim 13 further comprising executing the query. | 0.5 |
6,112,177 | 1 | 3 | 1. A method for generating a photo-realistic talking head for a text-to-speech synthesis application, comprising the steps of: sampling images of a subject; extracting a plurality of parameters from each image sample; storing the image sample parameters into an animation library; sampling multiphone images of the subject; sampling sounds associated with the multiphone images; extracting a plurality of parameters from each multiphone image sample; storing the multiphone image parameters and associated sound samples into a coarticulation library; reading, based on an input stimulus comprising one or more phoneme sequences, parameters from the coarticulation library corresponding to each phoneme sequence; generating, using parameters from the animation library corresponding to the read parameters, a sequence of animated frames, the sequence tracking the input stimulus. | 1. A method for generating a photo-realistic talking head for a text-to-speech synthesis application, comprising the steps of: sampling images of a subject; extracting a plurality of parameters from each image sample; storing the image sample parameters into an animation library; sampling multiphone images of the subject; sampling sounds associated with the multiphone images; extracting a plurality of parameters from each multiphone image sample; storing the multiphone image parameters and associated sound samples into a coarticulation library; reading, based on an input stimulus comprising one or more phoneme sequences, parameters from the coarticulation library corresponding to each phoneme sequence; generating, using parameters from the animation library corresponding to the read parameters, a sequence of animated frames, the sequence tracking the input stimulus. 3. The method of claim 1, wherein the plurality of parameters extracted from each multiphone image samples comprises one or more rules characterizing mouth shapes. | 0.5 |
9,070,047 | 1 | 4 | 1. A method comprising: defining in processor-readable memory a factor graph including a factor graph model replicated for each variable node of a set of variable nodes, each variable node being informed by one or more factor types in the factor graph model, each factor type being implemented as a single decision tree; training structure and parameterization of each decision tree using training data having a plurality of datasets, each dataset having elements of at least one labeled property, the training executing an objective function that determines the parameters of each decision tree; and storing a trained factor graph. | 1. A method comprising: defining in processor-readable memory a factor graph including a factor graph model replicated for each variable node of a set of variable nodes, each variable node being informed by one or more factor types in the factor graph model, each factor type being implemented as a single decision tree; training structure and parameterization of each decision tree using training data having a plurality of datasets, each dataset having elements of at least one labeled property, the training executing an objective function that determines the parameters of each decision tree; and storing a trained factor graph. 4. The method of claim 1 wherein at least one factor type defines the relationship between at least two variable nodes. | 0.725806 |
8,230,328 | 1 | 7 | 1. A method for imaging device display element localization, said method comprising: registering a remote computing device (RCD) application with an imaging device (IDev) wherein said RCD application is accessed when a trigger input is received at an IDev user interface (UI); receiving a notification at said RCD application, said notification indicating that said trigger input was received at said IDev UI; sending menu data to said IDev in response to said receiving a notification, wherein said menu data is in the form of an XML message and wherein said menu content data comprises at least one prompt to solicit user input at said IDev UI; receiving locale data from said IDev UI, wherein said locale data is embedded in a header of an HTTP request and said locale data defines the geographical locale of a user; receiving a content request from said IDev, wherein said content request is a user interface page in said HTTP request and said content request identifies locale-independent content; identifying a locale from said locale data; retrieving locale-specific data related to said locale using a dynamic link library (DLL), wherein said locale-specific data consists of a language, a time format, a date format, an address format, a calendar format and a currency type; retrieving static content identified in said content request; merging said locale-specific data with said static content to form a localized document; and sending said localized document to said IDev. | 1. A method for imaging device display element localization, said method comprising: registering a remote computing device (RCD) application with an imaging device (IDev) wherein said RCD application is accessed when a trigger input is received at an IDev user interface (UI); receiving a notification at said RCD application, said notification indicating that said trigger input was received at said IDev UI; sending menu data to said IDev in response to said receiving a notification, wherein said menu data is in the form of an XML message and wherein said menu content data comprises at least one prompt to solicit user input at said IDev UI; receiving locale data from said IDev UI, wherein said locale data is embedded in a header of an HTTP request and said locale data defines the geographical locale of a user; receiving a content request from said IDev, wherein said content request is a user interface page in said HTTP request and said content request identifies locale-independent content; identifying a locale from said locale data; retrieving locale-specific data related to said locale using a dynamic link library (DLL), wherein said locale-specific data consists of a language, a time format, a date format, an address format, a calendar format and a currency type; retrieving static content identified in said content request; merging said locale-specific data with said static content to form a localized document; and sending said localized document to said IDev. 7. A method as described in claim 1 wherein said retrieving is at least partially accomplished with a Web Service method. | 0.741453 |
8,006,180 | 6 | 9 | 6. One or more processor readable storage devices having processor readable code embodied on said processor readable storage devices, said processor readable code for programming one or more processors to perform a method comprising: displaying text including text which has been entered by a first user and original email content generated by a second user in a content page provided by a browser; identifying the original email content as text not available for spell checking; associating sections of the displayed text which have been entered by the first user with a plurality of respective nodes; determining that one or more portions of the displayed text are being edited by the first user, wherein the portions of the displayed text being edited have previously been processed for spelling errors; identifying one or more of the nodes that are associated with the text portions being edited; transmitting the one or more nodes that are associated with the text portions being edited to a spell check service, wherein the spell check service generates correction information associated with the text portions being edited; displaying the correction information generated by the spell check service; and processing the text portions being edited with the correction information generated by the spell check service. | 6. One or more processor readable storage devices having processor readable code embodied on said processor readable storage devices, said processor readable code for programming one or more processors to perform a method comprising: displaying text including text which has been entered by a first user and original email content generated by a second user in a content page provided by a browser; identifying the original email content as text not available for spell checking; associating sections of the displayed text which have been entered by the first user with a plurality of respective nodes; determining that one or more portions of the displayed text are being edited by the first user, wherein the portions of the displayed text being edited have previously been processed for spelling errors; identifying one or more of the nodes that are associated with the text portions being edited; transmitting the one or more nodes that are associated with the text portions being edited to a spell check service, wherein the spell check service generates correction information associated with the text portions being edited; displaying the correction information generated by the spell check service; and processing the text portions being edited with the correction information generated by the spell check service. 9. The one or more processor readable storage devices of claim 6 , wherein said step of processing includes: selecting a portion of the text in the content page that corresponds to the correction information. | 0.5 |
9,514,195 | 4 | 5 | 4. The computer-implemented method of claim 1 , wherein inserting the search result identifying the second resource in the set of search results identifying the first resources comprises: determining an insertion score based, in part, on the search probability ratio, wherein the insertion score defines an ordinal insertion position at which a second resource search result referencing the second resource is to be inserted into a ranking of first resource search results referencing the first resources; and generating a search results resource for displaying the first resource search results according to their respective ordinal positions in the ranking and the second resource search results at the ordinal insertion position. | 4. The computer-implemented method of claim 1 , wherein inserting the search result identifying the second resource in the set of search results identifying the first resources comprises: determining an insertion score based, in part, on the search probability ratio, wherein the insertion score defines an ordinal insertion position at which a second resource search result referencing the second resource is to be inserted into a ranking of first resource search results referencing the first resources; and generating a search results resource for displaying the first resource search results according to their respective ordinal positions in the ranking and the second resource search results at the ordinal insertion position. 5. The computer-implemented method of claim 4 , wherein determining an insertion score based, in part, on the search probability ratio comprises: determining an insertion score corresponding to a first ordinal position when the search probability ratio meets a first insertion threshold; determining an insertion score corresponding to a second ordinal position when the search probability ratio meets a second insertion threshold but does not meet the first insertion threshold; and determining an insertion score corresponding to a third ordinal position when the search probability ratio meets a third insertion threshold but does not meet the second insertion threshold. | 0.5 |
9,734,141 | 11 | 12 | 11. The method of claim 10 further comprising the step of generating, using the computer, a numerical string that corresponds to the base word and the second base word, the numerical string comprising the identified number of links to and from the base word, and the second base word. | 11. The method of claim 10 further comprising the step of generating, using the computer, a numerical string that corresponds to the base word and the second base word, the numerical string comprising the identified number of links to and from the base word, and the second base word. 12. The method of claim 11 wherein the numerical string further comprising a reciprocal of the sum of the identified shortest distances between the base word and all other identified words, and a reciprocal of the sum of the identified shortest distances between the second base word and all other identified words. | 0.5 |
9,965,478 | 17 | 24 | 17. A non-transitory computer-readable storage medium comprising: instructions stored on the non-transitory computer-readable storage medium, wherein the instructions, when executed by one or more processors, cause the one or more processors to perform operations comprising: determining a set of categories associated with a plurality of media items based on one or more characteristics of the plurality of media items, wherein at least one of the set of categories comprises subcategories; based on media preferences associated with a user, generating media preference clusters, the media preference clusters being clustered in n-dimensional space, wherein each dimension in the n-dimensional space is a subcategory and each category within the set of categories comprises one or more dimensions in the n-dimensional space; ranking the media preference clusters based on a set of predetermined ranking rules applied to the n-dimensional space, to yield a ranking of the generated media preference clusters, the set of predetermined ranking rules defining ranking of the media preference clusters at least partly based on feedback from a user for one or more media items in the media preference clusters; selecting a number of top ranking media preference clusters from the ranking of media preference clusters; selecting one or more media station seeds from each media preference cluster selected; generating one or more algorithmic media stations customized to the user from the one or more media station seeds from each media preference cluster selected; and providing, to an electronic device associated with the user, the one or more algorithmic media stations customized to the user. | 17. A non-transitory computer-readable storage medium comprising: instructions stored on the non-transitory computer-readable storage medium, wherein the instructions, when executed by one or more processors, cause the one or more processors to perform operations comprising: determining a set of categories associated with a plurality of media items based on one or more characteristics of the plurality of media items, wherein at least one of the set of categories comprises subcategories; based on media preferences associated with a user, generating media preference clusters, the media preference clusters being clustered in n-dimensional space, wherein each dimension in the n-dimensional space is a subcategory and each category within the set of categories comprises one or more dimensions in the n-dimensional space; ranking the media preference clusters based on a set of predetermined ranking rules applied to the n-dimensional space, to yield a ranking of the generated media preference clusters, the set of predetermined ranking rules defining ranking of the media preference clusters at least partly based on feedback from a user for one or more media items in the media preference clusters; selecting a number of top ranking media preference clusters from the ranking of media preference clusters; selecting one or more media station seeds from each media preference cluster selected; generating one or more algorithmic media stations customized to the user from the one or more media station seeds from each media preference cluster selected; and providing, to an electronic device associated with the user, the one or more algorithmic media stations customized to the user. 24. The non-transitory computer-readable storage medium of claim 17 , storing additional instructions which, when executed by the one or more processors, cause the one or more processors to: analyze respective audio content of one or more media items from the plurality of media items; and based on the respective audio content, identify respective subcategories associated with at least a portion of the respective audio content. | 0.502315 |
7,707,566 | 7 | 11 | 7. The one or more computer-readable media of claim 1 wherein the architecture is combinable with the one or more software development components. | 7. The one or more computer-readable media of claim 1 wherein the architecture is combinable with the one or more software development components. 11. The one or more computer-readable media of claim 7 wherein one or more software development components provide a set of class extension declarations to the architecture. | 0.508523 |
10,157,178 | 1 | 9 | 1. A computer-implemented method, comprising: identifying a plurality of documents associated with a predetermined subject, where: each of the plurality of documents contains textual data, and the predetermined subject includes one or more terms identifying common subject matter shared by each of the plurality of documents; analyzing the textual data of each of the plurality of documents to identify one or more categories within the plurality of the documents, the analyzing including: refining the textual data by removing one or more words from the textual data that have a predetermined frequency and a predetermined significance, to create refined textual data, transforming the refined textual data into an array, and determining the one or more categories from the array, where each of the one or more categories includes a plurality of topic vectors that each include one or more identified keywords and a frequency of the one or more keywords within the refined textual data; linking each of the one or more categories to the predetermined subject; returning the one or more categories identified within the plurality of the documents as categories indicative of the predetermined subject; and classifying additional textual data, utilizing the one or more categories, including comparing the additional textual data to the one or more categories to determine a probability that the additional textual data is associated with the predetermined subject linked to the one or more categories. | 1. A computer-implemented method, comprising: identifying a plurality of documents associated with a predetermined subject, where: each of the plurality of documents contains textual data, and the predetermined subject includes one or more terms identifying common subject matter shared by each of the plurality of documents; analyzing the textual data of each of the plurality of documents to identify one or more categories within the plurality of the documents, the analyzing including: refining the textual data by removing one or more words from the textual data that have a predetermined frequency and a predetermined significance, to create refined textual data, transforming the refined textual data into an array, and determining the one or more categories from the array, where each of the one or more categories includes a plurality of topic vectors that each include one or more identified keywords and a frequency of the one or more keywords within the refined textual data; linking each of the one or more categories to the predetermined subject; returning the one or more categories identified within the plurality of the documents as categories indicative of the predetermined subject; and classifying additional textual data, utilizing the one or more categories, including comparing the additional textual data to the one or more categories to determine a probability that the additional textual data is associated with the predetermined subject linked to the one or more categories. 9. The computer-implemented method of claim 1 , wherein analyzing the textual data for each of the plurality of documents includes performing a latent dirichlet allocation (LDA) analysis on the refined textual data to identify the one or more categories. | 0.777193 |
9,633,077 | 9 | 11 | 9. A computer-implemented method comprising: identifying from a plurality of information models (i) a first information model of a database schema comprising a first dimension and a second dimension and (ii) a second information model of the database schema comprising a third dimension and a fourth dimension, where the first information model and the second information model are not joined to remaining information models; generating an auto-join query language statement on the first dimension of the first information model and the third dimension of the second information model, based on the first dimension and the third dimension being identical, where each auto-join is defined on an identical dimension; generating, for each dimension, a definition indicating each of the information models that includes the dimension, and a column defined by each of the information models which corresponds to the dimension; receiving the auto-join query language statement including the first dimension of the first information model and the third dimension of the second information model; generating a structured language query for each of the information models based on the generated definitions indicating the first information model wherein in a case that the first informational model does not include an identical dimension with the remaining information models, include a NULL in place of a missing dimension in a SELECT statement of the structured language query generated for the first information model so that a returned result set includes a same number of columns; obtaining a plurality of result sets, one for each of the structured language queries; determining at least one or more rows from each of result sets having identical dimension values; aggregating the rows from each of the result sets having identical dimension values into a single row in an aggregated result set; and presenting the single rows. | 9. A computer-implemented method comprising: identifying from a plurality of information models (i) a first information model of a database schema comprising a first dimension and a second dimension and (ii) a second information model of the database schema comprising a third dimension and a fourth dimension, where the first information model and the second information model are not joined to remaining information models; generating an auto-join query language statement on the first dimension of the first information model and the third dimension of the second information model, based on the first dimension and the third dimension being identical, where each auto-join is defined on an identical dimension; generating, for each dimension, a definition indicating each of the information models that includes the dimension, and a column defined by each of the information models which corresponds to the dimension; receiving the auto-join query language statement including the first dimension of the first information model and the third dimension of the second information model; generating a structured language query for each of the information models based on the generated definitions indicating the first information model wherein in a case that the first informational model does not include an identical dimension with the remaining information models, include a NULL in place of a missing dimension in a SELECT statement of the structured language query generated for the first information model so that a returned result set includes a same number of columns; obtaining a plurality of result sets, one for each of the structured language queries; determining at least one or more rows from each of result sets having identical dimension values; aggregating the rows from each of the result sets having identical dimension values into a single row in an aggregated result set; and presenting the single rows. 11. A method according to claim 9 , wherein the object-based query includes a filter on a dimension, and wherein generating one structured language query for each of the information models comprises: determining whether one of the information models includes the dimension; and in a case that one of the information models includes the dimension, inclusion of the filter in the structured language query for the one of the two or more information models. | 0.5 |
7,634,546 | 34 | 41 | 34. A method for enhancing communication within a community, the method comprising the steps of: (a) receiving in an application in an application platform a communication sent by a user from a first communication device, wherein said communication is associated with a user selected topic of a plurality of topics such that said user selected topic is selected by said user, and receiving a link to a resource associated with said communication; (b) determining an access right said user has to information stored in a database of said application in said application platform based upon an access status and wherein said access status is selected from the group consisting of an inclusive access in which access to each of said stored communications in said hierarchical structure is allowed except where excluded by an inherited parameter and an exclusive access in which access to each of said stored communications in said hierarchical structure is allowed only where explicitly assigned; (c) accessing a current database hierarchy, authorization parameters, and interaction control parameters for said application; (d) granting access to said user, according to said access right of said user, to a portion of said information stored in said database, wherein said portion of said information is stored in association with said user selected topic; (e) determining a dynamic interaction capability for said user with said portion of said information based on said database hierarchy, said authorization parameters, and said interaction control parameters; (f) prioritizing an order of said portion of said information, wherein an initial thread of said information is assigned a higher priority than a response to a thread of said information; (g) presenting said portion of said information that is ordered to said user for review, wherein said presentation is based on said prioritization; (h) accepting an initial input from said user according to said dynamic interaction capability from said first communication device for storage in said database, wherein said initial input comprises said communication and said link; and (i) outputting said initial input from said user to at least a second communication device. | 34. A method for enhancing communication within a community, the method comprising the steps of: (a) receiving in an application in an application platform a communication sent by a user from a first communication device, wherein said communication is associated with a user selected topic of a plurality of topics such that said user selected topic is selected by said user, and receiving a link to a resource associated with said communication; (b) determining an access right said user has to information stored in a database of said application in said application platform based upon an access status and wherein said access status is selected from the group consisting of an inclusive access in which access to each of said stored communications in said hierarchical structure is allowed except where excluded by an inherited parameter and an exclusive access in which access to each of said stored communications in said hierarchical structure is allowed only where explicitly assigned; (c) accessing a current database hierarchy, authorization parameters, and interaction control parameters for said application; (d) granting access to said user, according to said access right of said user, to a portion of said information stored in said database, wherein said portion of said information is stored in association with said user selected topic; (e) determining a dynamic interaction capability for said user with said portion of said information based on said database hierarchy, said authorization parameters, and said interaction control parameters; (f) prioritizing an order of said portion of said information, wherein an initial thread of said information is assigned a higher priority than a response to a thread of said information; (g) presenting said portion of said information that is ordered to said user for review, wherein said presentation is based on said prioritization; (h) accepting an initial input from said user according to said dynamic interaction capability from said first communication device for storage in said database, wherein said initial input comprises said communication and said link; and (i) outputting said initial input from said user to at least a second communication device. 41. A method according to claim 34 wherein each of said authorization parameters has at least one access level, wherein a higher one of each of said at least one access level provides more management control than a lower one of each of said at least one access level. | 0.762877 |
7,970,824 | 2 | 3 | 2. The Capacity Planning Tool as described in claim 1 , further comprising having said system formalizes the representation of a multimedia document into hierarchy structure. | 2. The Capacity Planning Tool as described in claim 1 , further comprising having said system formalizes the representation of a multimedia document into hierarchy structure. 3. The Capacity Planning Tool as described in claim 2 , further comprising having said hierarchy structure being comprised of a four level hierarchy comprising of object, operation, time and precedence, where each successive level offers a fine-grain representation. | 0.5 |
8,788,460 | 1 | 7 | 1. A computer-implemented method for searching for data from a content database unattached to a content management application, the method executable on a client computing system communicatively connected to a front-end computing system executing the content management application and comprising the steps of: connecting the content management application and the unattached content database; receiving at the client computing system an indication to perform a search for a site collection; receiving at the client computing system a site collection selector user interface (UI) to perform a search for a site collection; entering at the client computing system one or more search requirements for a site collection to search for; receiving from the content management application a list of one or more site collections matching the one or more search requirements, wherein the content management application deletes orphaned databases; selecting a first site collection from the list of one or more site collections matching the one or more search requirements; and receiving at the client computing system information about the first site collection. | 1. A computer-implemented method for searching for data from a content database unattached to a content management application, the method executable on a client computing system communicatively connected to a front-end computing system executing the content management application and comprising the steps of: connecting the content management application and the unattached content database; receiving at the client computing system an indication to perform a search for a site collection; receiving at the client computing system a site collection selector user interface (UI) to perform a search for a site collection; entering at the client computing system one or more search requirements for a site collection to search for; receiving from the content management application a list of one or more site collections matching the one or more search requirements, wherein the content management application deletes orphaned databases; selecting a first site collection from the list of one or more site collections matching the one or more search requirements; and receiving at the client computing system information about the first site collection. 7. The computer-implemented method of claim 1 , wherein the list of one or more site collections matching the one or more search requirements is displayed in an order comprising one from the group consisting of: ascending order and descending order. | 0.722098 |
10,001,978 | 10 | 11 | 10. A method comprising: identifying a set of methods comprising a nested method and an outer method, wherein the nested method is nested inside the outer method, the nested method is associated with a nested method invocation context, and the outer method is associated with an outer method invocation context; identifying a set of bounds for each of a plurality of inference variables associated with the nested method invocation context; based on the set of bounds for each of the plurality of inference variables, determining that a first inference variable, of the plurality of inference variables, is resolvable based on a resolution of a second inference variable of the plurality of inference variables; responsive at least to determining that the first inference variable is resolvable based on the resolution of the second inference variable: propagating the bounds for the second inference variable from the nested method invocation context to the outer method invocation context without propagating the bounds for the first inference variable from the nested method invocation context to the outer method invocation context; determining a set of constraints associated with the outer method invocation context, the set of constraints (a) including the bounds for the second inference variable associated with the nested method invocation context, and (b) not including the bounds for the first inference variable associated with the nested method invocation context; determining a resolution for the second inference variable based on the set of constraints associated with the outer method invocation context; and determining a resolution for the first inference variable based on the resolution for the second inference variable; wherein the resolution for the first inference variable and the resolution for the second inference variable are usable for generating a representation of the set of methods that is executable in a machine environment; wherein the method is performed by at least one device including a hardware processor. | 10. A method comprising: identifying a set of methods comprising a nested method and an outer method, wherein the nested method is nested inside the outer method, the nested method is associated with a nested method invocation context, and the outer method is associated with an outer method invocation context; identifying a set of bounds for each of a plurality of inference variables associated with the nested method invocation context; based on the set of bounds for each of the plurality of inference variables, determining that a first inference variable, of the plurality of inference variables, is resolvable based on a resolution of a second inference variable of the plurality of inference variables; responsive at least to determining that the first inference variable is resolvable based on the resolution of the second inference variable: propagating the bounds for the second inference variable from the nested method invocation context to the outer method invocation context without propagating the bounds for the first inference variable from the nested method invocation context to the outer method invocation context; determining a set of constraints associated with the outer method invocation context, the set of constraints (a) including the bounds for the second inference variable associated with the nested method invocation context, and (b) not including the bounds for the first inference variable associated with the nested method invocation context; determining a resolution for the second inference variable based on the set of constraints associated with the outer method invocation context; and determining a resolution for the first inference variable based on the resolution for the second inference variable; wherein the resolution for the first inference variable and the resolution for the second inference variable are usable for generating a representation of the set of methods that is executable in a machine environment; wherein the method is performed by at least one device including a hardware processor. 11. The method of claim 10 , wherein determining that the first inference variable is resolvable based on the resolution of the second inference variable comprises comparing the bounds for the first inference variable with the bounds for the second inference variable. | 0.753676 |
7,640,497 | 1 | 37 | 1. A method of transforming an input hierarchical data structure to an output hierarchical data structure by using a transformation template that comprises a plurality of transformation entries, wherein the plurality of transformation entries each comprise at least one matching condition and at least one transformation action, the method comprising: receiving the input hierarchical data structure comprising a plurality of input entries, each input entry comprising input data that comprises at least one key value pair, wherein a key value pair includes a key and an associated value for the key; creating a temporary data structure comprising a plurality of default key value pairs; for each particular input entry in the received input hierarchical data structure that matches a particular transformation entry in the transformation template: based on the input data of the particular input entry, determining whether a matching condition of the particular transformation entry is met; and when the matching condition is met, using at least one particular key value pair of the particular input entry to define an associated key value pair in the temporary data structure based on the transformation action of the particular transformation entry, wherein the associated key value pair overwrites a default value for a default key when the associated key value pair's key matches a default key in the temporary data structure; and generating the output hierarchical data structure by extracting key value pairs from the temporary data structure, wherein extracting comprises defining in the output hierarchical data structure a key value pair for each default key value pair in the temporary storage structure that does not get overwritten by key value pairs of any of the particular input entries that matches a particular transformation entry, said output hierarchical data structure being stored in a computer readable medium. | 1. A method of transforming an input hierarchical data structure to an output hierarchical data structure by using a transformation template that comprises a plurality of transformation entries, wherein the plurality of transformation entries each comprise at least one matching condition and at least one transformation action, the method comprising: receiving the input hierarchical data structure comprising a plurality of input entries, each input entry comprising input data that comprises at least one key value pair, wherein a key value pair includes a key and an associated value for the key; creating a temporary data structure comprising a plurality of default key value pairs; for each particular input entry in the received input hierarchical data structure that matches a particular transformation entry in the transformation template: based on the input data of the particular input entry, determining whether a matching condition of the particular transformation entry is met; and when the matching condition is met, using at least one particular key value pair of the particular input entry to define an associated key value pair in the temporary data structure based on the transformation action of the particular transformation entry, wherein the associated key value pair overwrites a default value for a default key when the associated key value pair's key matches a default key in the temporary data structure; and generating the output hierarchical data structure by extracting key value pairs from the temporary data structure, wherein extracting comprises defining in the output hierarchical data structure a key value pair for each default key value pair in the temporary storage structure that does not get overwritten by key value pairs of any of the particular input entries that matches a particular transformation entry, said output hierarchical data structure being stored in a computer readable medium. 37. The method of claim 1 , wherein the input hierarchical data structure is formatted in a FCPDS file format and the output hierarchical data structure is formatted in a XML file format. | 0.780516 |
8,670,997 | 22 | 24 | 22. A method for editing medical related quality metric information, the method comprising: extracting, by a processor, facts about a patient from a patient record, the facts representing characteristics of the patient at a given time and reflected in the patient record; displaying, on a display device, the facts; receiving, by the processor, an edit to a first one of the facts, the edit being a change of the first one of the facts from a first value to be a different value, the first value and the different value representing one of the characteristics of the patient at the same given time; changing, by the processor, the first one of the facts in the patient record based on the edit; and generating, by the processor, a report for at least one quality value calculated from the facts, the generating being as a function of the facts, including the edited first fact. | 22. A method for editing medical related quality metric information, the method comprising: extracting, by a processor, facts about a patient from a patient record, the facts representing characteristics of the patient at a given time and reflected in the patient record; displaying, on a display device, the facts; receiving, by the processor, an edit to a first one of the facts, the edit being a change of the first one of the facts from a first value to be a different value, the first value and the different value representing one of the characteristics of the patient at the same given time; changing, by the processor, the first one of the facts in the patient record based on the edit; and generating, by the processor, a report for at least one quality value calculated from the facts, the generating being as a function of the facts, including the edited first fact. 24. The method of claim 22 wherein extracting comprises extracting from structured and unstructured data of the patient record. | 0.649171 |
8,650,188 | 19 | 23 | 19. A system configured to respond to a request to create a retargeting set by a content item provider, comprising: a data store storing a retargeting set for a retargeted content item, the retargeting set including retargeting identifiers that each specify a user identifier that was received with interaction data indicating that a pre-specified user interaction which facilitates targeting retargeted content items previously occurred; and a data processing apparatus configured to interact with the data store, the data processing apparatus being further configured to perform operations including: providing a code segment that upon execution by a browser causes the browser to submit interaction data indicating that the code segment was executed, the interaction data including a set identifier for the retargeting set and a user identifier for the user device that caused execution of the code segment; storing, in relation to the set identifier for the retargeting set, any interaction data submitted in response to an execution of the code segment, wherein the user identifier for the user device that caused execution of the code segment is stored as a retargeted identifier corresponding to that user identifier; receiving a request for a content item to be provided with a search results page, the request including data indicative of a search query that was submitted by a user device and a particular user identifier for the user device; identifying a plurality of keyword targeted content items that are eligible for presentation with the search results page, each of the eligible keyword targeted content items being a content item that is eligible for presentation based on the search query matching a targeting keyword for the keyword targeted content item; determining that one or more retargeted content items are eligible for presentation with the search results page, each of the retargeted content items being a content item that is eligible for presentation with the search results page based on: the search query matching a targeting keyword for the retargeted content item; and the particular user identifier matching the retargeted identifier that is included in the stored retargeting set for the retargeted content item; selecting, based at least in part on bids that are associated with each of the keyword targeted content items that are eligible for presentation and each of the one or more retargeted content items that are eligible for presentation, a responsive content item to be presented with the search results page; and providing data specifying the responsive content item. | 19. A system configured to respond to a request to create a retargeting set by a content item provider, comprising: a data store storing a retargeting set for a retargeted content item, the retargeting set including retargeting identifiers that each specify a user identifier that was received with interaction data indicating that a pre-specified user interaction which facilitates targeting retargeted content items previously occurred; and a data processing apparatus configured to interact with the data store, the data processing apparatus being further configured to perform operations including: providing a code segment that upon execution by a browser causes the browser to submit interaction data indicating that the code segment was executed, the interaction data including a set identifier for the retargeting set and a user identifier for the user device that caused execution of the code segment; storing, in relation to the set identifier for the retargeting set, any interaction data submitted in response to an execution of the code segment, wherein the user identifier for the user device that caused execution of the code segment is stored as a retargeted identifier corresponding to that user identifier; receiving a request for a content item to be provided with a search results page, the request including data indicative of a search query that was submitted by a user device and a particular user identifier for the user device; identifying a plurality of keyword targeted content items that are eligible for presentation with the search results page, each of the eligible keyword targeted content items being a content item that is eligible for presentation based on the search query matching a targeting keyword for the keyword targeted content item; determining that one or more retargeted content items are eligible for presentation with the search results page, each of the retargeted content items being a content item that is eligible for presentation with the search results page based on: the search query matching a targeting keyword for the retargeted content item; and the particular user identifier matching the retargeted identifier that is included in the stored retargeting set for the retargeted content item; selecting, based at least in part on bids that are associated with each of the keyword targeted content items that are eligible for presentation and each of the one or more retargeted content items that are eligible for presentation, a responsive content item to be presented with the search results page; and providing data specifying the responsive content item. 23. The system of claim 19 , wherein the data processing apparatus is further configured to perform operations including: receiving, from a content item provider, a request to create a retargeting set; and providing, in response to the request, a code segment that upon execution by a browser causes the browser to submit interaction data indicating that the code segment was executed, the interaction data including a set identifier for the retargeting set and a user identifier for the user device that caused execution of the code segment. | 0.732213 |
9,910,852 | 9 | 10 | 9. The method of claim 8 , wherein displaying the logogram sequence comprises displaying the given sub-logogram. | 9. The method of claim 8 , wherein displaying the logogram sequence comprises displaying the given sub-logogram. 10. The method of claim 9 , wherein displaying the given sub-logogram comprises displaying strokes of the given sub-logogram. | 0.632353 |
10,042,923 | 11 | 12 | 11. An apparatus, comprising: one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the apparatus to: obtain a set of clauses in a first set of content items comprising unstructured data; obtain a set of stop words comprising high-frequency words that occur in a second set of content items; automatically extract a set of topics from the set of clauses by: generating a set of n-grams from the set of clauses; excluding a first n-gram from the set of n-grams when the first n-gram contains a word in the set of stop words in a pre-specified position of the first n-gram; and applying a morphological filter to the set of n-grams to yield a subset of the set of n-grams, wherein: the set of topics comprises the subset of the set of n-grams; and the morphological filter is independent of words in the set of stop words; and display the set of topics to a user to improve understanding of the first set of content items by the user without requiring the user to manually analyze the first set of content items; wherein extracting the set of topics from the set of clauses further comprises: excluding a second n-gram from the set of n-grams when the second n-gram contains a proportion of words in the set of stop words that exceeds a threshold. | 11. An apparatus, comprising: one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the apparatus to: obtain a set of clauses in a first set of content items comprising unstructured data; obtain a set of stop words comprising high-frequency words that occur in a second set of content items; automatically extract a set of topics from the set of clauses by: generating a set of n-grams from the set of clauses; excluding a first n-gram from the set of n-grams when the first n-gram contains a word in the set of stop words in a pre-specified position of the first n-gram; and applying a morphological filter to the set of n-grams to yield a subset of the set of n-grams, wherein: the set of topics comprises the subset of the set of n-grams; and the morphological filter is independent of words in the set of stop words; and display the set of topics to a user to improve understanding of the first set of content items by the user without requiring the user to manually analyze the first set of content items; wherein extracting the set of topics from the set of clauses further comprises: excluding a second n-gram from the set of n-grams when the second n-gram contains a proportion of words in the set of stop words that exceeds a threshold. 12. The apparatus of claim 11 , wherein the memory further stores instructions that, when executed by the one or more processors, cause the apparatus to: generate the set of clauses from the first set of content items by separating a content item into two or more clauses based on a presence of a connective punctuation mark between the two or more clauses. | 0.5 |
8,224,826 | 13 | 17 | 13. A computing system comprising: one or more computers; and a non-transitory computer-readable storage medium coupled to the one or more computers having instructions stored thereon which, when executed by the one or more computers, cause the one or more computers to perform operations comprising: receiving multiple digital content items, each of the received digital content items being associated with a digital signature that associates the digital content item with a respective agent, the respective agent being one of multiple agents, each of the multiple agents being associated with at least one of the received digital content items; identifying a first agent of the identified agents and a first group of the received digital content items, each of the digital content items in the first group being associated by digital signature with the first agent, wherein at least one of the digital content items in the first group is a first portion of a web page, the web page including at least a second portion that is not associated with the first agent; identifying one or more second agents of the identified agents, each of the second agents being different from the first agent, each of the second agents having a reputation score, each of the second agents being associated by digital signature with a received digital content item that includes a link to at least one of the digital content items in the first group, wherein at least one of the second agents is associated by digital signature with a received digital content item that includes a link to the digital content item of the first group that is the first portion of the web page; and calculating a reputation score for the first agent that is a function of the respective reputation scores of the second agents, wherein each of the digital signatures associated with the digital content items of the first group further includes a context that specifies a subject of the associated digital content item, and wherein the reputation score for the first agent is further a function of the contexts. | 13. A computing system comprising: one or more computers; and a non-transitory computer-readable storage medium coupled to the one or more computers having instructions stored thereon which, when executed by the one or more computers, cause the one or more computers to perform operations comprising: receiving multiple digital content items, each of the received digital content items being associated with a digital signature that associates the digital content item with a respective agent, the respective agent being one of multiple agents, each of the multiple agents being associated with at least one of the received digital content items; identifying a first agent of the identified agents and a first group of the received digital content items, each of the digital content items in the first group being associated by digital signature with the first agent, wherein at least one of the digital content items in the first group is a first portion of a web page, the web page including at least a second portion that is not associated with the first agent; identifying one or more second agents of the identified agents, each of the second agents being different from the first agent, each of the second agents having a reputation score, each of the second agents being associated by digital signature with a received digital content item that includes a link to at least one of the digital content items in the first group, wherein at least one of the second agents is associated by digital signature with a received digital content item that includes a link to the digital content item of the first group that is the first portion of the web page; and calculating a reputation score for the first agent that is a function of the respective reputation scores of the second agents, wherein each of the digital signatures associated with the digital content items of the first group further includes a context that specifies a subject of the associated digital content item, and wherein the reputation score for the first agent is further a function of the contexts. 17. The system of claim 13 , wherein the digital signatures include a digital signature that is appended to an associated digital content item. | 0.820802 |
7,533,020 | 22 | 23 | 22. An apparatus for performing speech recognition, the apparatus comprising: at least one computer readable storage medium encoded with a plurality of instructions; and at least one processer programmed by at least some of the plurality of instructions to; acquire a speech signal from a user; perform a first recognition pass by applying a first language model to the speech signal, the first language model being constrained in accordance with a structured data source; and generate a subsequent language model based at least in part on results from the first recognition pass. | 22. An apparatus for performing speech recognition, the apparatus comprising: at least one computer readable storage medium encoded with a plurality of instructions; and at least one processer programmed by at least some of the plurality of instructions to; acquire a speech signal from a user; perform a first recognition pass by applying a first language model to the speech signal, the first language model being constrained in accordance with a structured data source; and generate a subsequent language model based at least in part on results from the first recognition pass. 23. The apparatus of claim 22 , wherein the at least one processer is further programmed to: perform a subsequent recognition pass by applying the subsequent language model; and recognize the speech signal. | 0.5 |
9,310,879 | 10 | 11 | 10. The computer-readable non-transitory storage media of claim 9 wherein said highlight specific words within said web page based on said topic model, further comprises: extracting content of said newly selected web page; determining a probability score for each of said plurality of topics in said topic model for how well said each of said plurality of topics describes said content; selecting a set of said plurality of topics that meet a predefined criteria analyzing said content utilizing said set of topics; determining a weight for each of a plurality of keywords associated with said set of topics, wherein different weights are determined for different keywords among said plurality of keywords depending how significant said each of said plurality of keywords are determined to be; and differentially highlighting said plurality of keywords associated with said set of topics within said content of said newly selected web page according to said weight. | 10. The computer-readable non-transitory storage media of claim 9 wherein said highlight specific words within said web page based on said topic model, further comprises: extracting content of said newly selected web page; determining a probability score for each of said plurality of topics in said topic model for how well said each of said plurality of topics describes said content; selecting a set of said plurality of topics that meet a predefined criteria analyzing said content utilizing said set of topics; determining a weight for each of a plurality of keywords associated with said set of topics, wherein different weights are determined for different keywords among said plurality of keywords depending how significant said each of said plurality of keywords are determined to be; and differentially highlighting said plurality of keywords associated with said set of topics within said content of said newly selected web page according to said weight. 11. The computer-readable non-transitory storage media of claim 10 wherein said browsing history is limited to history from a particular browsing session and from a specific tab within said particular browsing session. | 0.735437 |
8,370,275 | 1 | 2 | 1. A method for identifying one or more inconsistencies between an unstructured document and a back-end fact-base, wherein the method comprises: automatically parsing a query document and comparing the document with a back-end fact-base comprising facts relevant to the document; deriving one or more relevant facts from the query document by identifying one or more fact triples of three categorical elements in the back-end fact-base; identifying one or more inconsistencies between the one or more relevant facts from the document and the facts stored in the back-end fact-base; and providing a response to the query document, wherein the response additionally includes the one or more identified inconsistencies. | 1. A method for identifying one or more inconsistencies between an unstructured document and a back-end fact-base, wherein the method comprises: automatically parsing a query document and comparing the document with a back-end fact-base comprising facts relevant to the document; deriving one or more relevant facts from the query document by identifying one or more fact triples of three categorical elements in the back-end fact-base; identifying one or more inconsistencies between the one or more relevant facts from the document and the facts stored in the back-end fact-base; and providing a response to the query document, wherein the response additionally includes the one or more identified inconsistencies. 2. The method of claim 1 , further comprising presenting the response to a user. | 0.918367 |
7,594,163 | 1 | 3 | 1. A method of editing an electronic document created by a first one of a plurality of collaborators connected to a network, said method comprising the steps of: transmitting a copy of said electronic document over said network to at least each of a second and a third one of said plurality of collaborators; receiving from said second and third collaborators, via said network, a first patch from the second collaborator and a second patch from the third collaborator, the first and second patches representing editorial modifications that said second and third collaborators have made to the copies of said electronic document transmitted to said second and third collaborators, wherein the patches are transmitted from said second and third collaborators to said first collaborator separately from each other and from any copy of said electronic document; transmitting, from the first to the second collaborator, an acknowledge receipt for said first patch, and from the first to the third collaborator, an acknowledge receipt for said second patch, the copies of the electronic document transmitted to said second and third collaborators being updated upon receipt of the acknowledge receipts; merging the editorial modification represented by said first and second patches with any local modifications, using said patches, to produce a merged representation of modifications to be made to said electronic document, wherein said merged representation is dependant on whether one or more of said plurality of collaborators was offline at a time when said editorial modifications of said local modification were made; and applying the modifications represented by said merged representation to said electronic document to produce an edited version of said electronic document, the edited version containing data incorporated therein indicating acknowledgment of said modifications made by said second and third collaborators. | 1. A method of editing an electronic document created by a first one of a plurality of collaborators connected to a network, said method comprising the steps of: transmitting a copy of said electronic document over said network to at least each of a second and a third one of said plurality of collaborators; receiving from said second and third collaborators, via said network, a first patch from the second collaborator and a second patch from the third collaborator, the first and second patches representing editorial modifications that said second and third collaborators have made to the copies of said electronic document transmitted to said second and third collaborators, wherein the patches are transmitted from said second and third collaborators to said first collaborator separately from each other and from any copy of said electronic document; transmitting, from the first to the second collaborator, an acknowledge receipt for said first patch, and from the first to the third collaborator, an acknowledge receipt for said second patch, the copies of the electronic document transmitted to said second and third collaborators being updated upon receipt of the acknowledge receipts; merging the editorial modification represented by said first and second patches with any local modifications, using said patches, to produce a merged representation of modifications to be made to said electronic document, wherein said merged representation is dependant on whether one or more of said plurality of collaborators was offline at a time when said editorial modifications of said local modification were made; and applying the modifications represented by said merged representation to said electronic document to produce an edited version of said electronic document, the edited version containing data incorporated therein indicating acknowledgment of said modifications made by said second and third collaborators. 3. A method according to claim 1 , wherein the data identifies the version of the document. | 0.94021 |
7,975,210 | 8 | 10 | 8. The device of claim 1 , wherein the set of words includes a plurality of words. | 8. The device of claim 1 , wherein the set of words includes a plurality of words. 10. The device of claim 8 , wherein the expansion means is adapted to act on each word individually, the conversion means further including means for writing each further pair of consecutive words, including a further word to be expanded, into the second memory area separated by a number of locations corresponding to the additional symbols to be added to the further word. | 0.5 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.