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4,227,245 | 13 | 14 | 13. A method in accordance with claim 12 wherein the grouping of files is achieved by linking the files within a group or a set into a continuous chain-linkage of files. | 13. A method in accordance with claim 12 wherein the grouping of files is achieved by linking the files within a group or a set into a continuous chain-linkage of files. 14. A method in accordance with claim 13 wherein the linking of files is carried out one file at a time as files are fed into the system; and wherein each new file is added into an existing linkage, if an appropriate existing linkage does exist, and into an existing converter set within that linkage, if the new file can possibly be included in an existing converter set. | 0.920615 |
9,703,832 | 11 | 12 | 11. The data processing system of claim 10 , wherein the processor is further configured with the computer software to cause the data processing system to execute the data collection sub-process, for each of the plurality of parallel data collection sub-processes by: retrieving, from a rule category sequence, a list of corresponding fare keys; for each fare key, retrieving a fare and an associated fare application entity, and merging matching rule category sequences to form a fare record; matching each fare record with rule category data and first rule provision data corresponding to the attribute set; matching each fare record against the rule category sequence and retaining those fare records previously identified during the first search; retrieving second rule provision data corresponding to the rule category sequence and matching the rule category data against the fare record and the attribute set; and only storing the fare record in a fare list where a match occurs, wherein the plurality of parallel data collection sub-processes operate on disjoint data domains to avoid performing redundant operations. | 11. The data processing system of claim 10 , wherein the processor is further configured with the computer software to cause the data processing system to execute the data collection sub-process, for each of the plurality of parallel data collection sub-processes by: retrieving, from a rule category sequence, a list of corresponding fare keys; for each fare key, retrieving a fare and an associated fare application entity, and merging matching rule category sequences to form a fare record; matching each fare record with rule category data and first rule provision data corresponding to the attribute set; matching each fare record against the rule category sequence and retaining those fare records previously identified during the first search; retrieving second rule provision data corresponding to the rule category sequence and matching the rule category data against the fare record and the attribute set; and only storing the fare record in a fare list where a match occurs, wherein the plurality of parallel data collection sub-processes operate on disjoint data domains to avoid performing redundant operations. 12. The data processing system of claim 11 , wherein the processor is further configured with the computer software to cause the data processing system to: build the list of eligible fares by merging the plurality of fare lists into the list of eligible fares; and merge a fare list output from each of the plurality of parallel data collection sub-processes into the list of eligible fares by sorting and removing duplicate fare records. | 0.86183 |
9,298,452 | 17 | 20 | 17. An apparatus for improving quality of a code listing, comprising: a concept specification information receiving input device to receive concept specification information corresponding to a concept to be identified in a code listing, the concept specification information comprising a code pattern illustrating the concept; a code pattern based rule definition engine, executable by a processor, to parse the code pattern to generate a pre-configured prompt specific to the code pattern, and identify at least one token, receive selection of a code element related to the code pattern in response to the pre-configured prompt, and define a rule based on the received selection of the code element, and the at least one token and according to a token-to-rule mapping; a memory to store the defined rule and the code listing; a concept identification engine to receive the defined rule and the code listing from the memory, and executable by the processor to identify occurrence of the concept in the code listing based on the defined rule; a code quality engine, executable by the processor, to generate information regarding the occurrence of the concept in the code listing as an indication of the quality of the code listing; and a code transformation engine, executable by the processor, to recommend a measure to transform the code listing to satisfy the concept to improve the quality of the code listing. | 17. An apparatus for improving quality of a code listing, comprising: a concept specification information receiving input device to receive concept specification information corresponding to a concept to be identified in a code listing, the concept specification information comprising a code pattern illustrating the concept; a code pattern based rule definition engine, executable by a processor, to parse the code pattern to generate a pre-configured prompt specific to the code pattern, and identify at least one token, receive selection of a code element related to the code pattern in response to the pre-configured prompt, and define a rule based on the received selection of the code element, and the at least one token and according to a token-to-rule mapping; a memory to store the defined rule and the code listing; a concept identification engine to receive the defined rule and the code listing from the memory, and executable by the processor to identify occurrence of the concept in the code listing based on the defined rule; a code quality engine, executable by the processor, to generate information regarding the occurrence of the concept in the code listing as an indication of the quality of the code listing; and a code transformation engine, executable by the processor, to recommend a measure to transform the code listing to satisfy the concept to improve the quality of the code listing. 20. The apparatus according to claim 17 , wherein the concept specification information further comprises an indication of an abstraction level applicable to the concept. | 0.540541 |
8,868,420 | 14 | 17 | 14. A non-transitory computer-readable medium storing instructions that, when executed by a processor on a computing device, cause the computing device to at least: receive a first transcription comprising first text and second text, wherein the first transcription was created by transcribing first audio data using a speech recognition engine configured to filter the first transcription by automatically replacing one or more words with corresponding numbers or digits formatted as a telephone number; receive a first value associated with the first text and a second confidence value associated with the second text; and cause presentation of the first text with a first graphical element indicating the first value and presentation of the second text with a second graphical element indicating the second value. | 14. A non-transitory computer-readable medium storing instructions that, when executed by a processor on a computing device, cause the computing device to at least: receive a first transcription comprising first text and second text, wherein the first transcription was created by transcribing first audio data using a speech recognition engine configured to filter the first transcription by automatically replacing one or more words with corresponding numbers or digits formatted as a telephone number; receive a first value associated with the first text and a second confidence value associated with the second text; and cause presentation of the first text with a first graphical element indicating the first value and presentation of the second text with a second graphical element indicating the second value. 17. The non-transitory computer-readable medium of claim 14 , wherein the first audio data comprises at least part of a voicemail message. | 0.798834 |
8,180,776 | 8 | 9 | 8. A computer readable storage medium encoded with a computer program, the computer program comprising instructions that, when executed, operate to cause a computer to perform operations comprising: providing, from a web browser, a search query to a server; receiving, at the web browser: a mapping between an indicia of each of first documents responsive to the search query and an identification identifying each of the first documents, and a representation for each of two or more rankings of the first documents, wherein for each representation: a respective ranking for the representation is based on one of two or more distinct degrees of influence of an interest profile that is different from the distinct degrees of influence that other representations are based on, wherein each distinct degree of influence corresponds to a unique value; and the representation includes the identifications associated with the first documents included in the respective ranking; wherein the interest profile comprises data representing a plurality of topics of interest to a user; receiving a selection from the user of one of the distinct degrees of influence of the interest profile; and displaying the representation of the ranking associated with the selected one of the distinct degrees of influence of the interest profile using the mapping and the representation of the ranking associated with the selected one of the distinct degrees of influence, wherein each of the first documents is associated with a respective information retrieval score and a respective boost value, and wherein the web browser selectively displays each ranking without sending a request to the server for a new ranking. | 8. A computer readable storage medium encoded with a computer program, the computer program comprising instructions that, when executed, operate to cause a computer to perform operations comprising: providing, from a web browser, a search query to a server; receiving, at the web browser: a mapping between an indicia of each of first documents responsive to the search query and an identification identifying each of the first documents, and a representation for each of two or more rankings of the first documents, wherein for each representation: a respective ranking for the representation is based on one of two or more distinct degrees of influence of an interest profile that is different from the distinct degrees of influence that other representations are based on, wherein each distinct degree of influence corresponds to a unique value; and the representation includes the identifications associated with the first documents included in the respective ranking; wherein the interest profile comprises data representing a plurality of topics of interest to a user; receiving a selection from the user of one of the distinct degrees of influence of the interest profile; and displaying the representation of the ranking associated with the selected one of the distinct degrees of influence of the interest profile using the mapping and the representation of the ranking associated with the selected one of the distinct degrees of influence, wherein each of the first documents is associated with a respective information retrieval score and a respective boost value, and wherein the web browser selectively displays each ranking without sending a request to the server for a new ranking. 9. The computer readable storage medium of claim 8 , where each boost value represents a degree of relevance of the document to a distinct topic in the plurality of topics. | 0.72524 |
9,734,109 | 2 | 3 | 2. The MCSIT data translator of claim 1 , wherein: the word type comprises one of a command word, a status word and a data word, when the word type comprises one of a command word and a status word, the channel ID tagger is configured to determine to tag the word with the channel ID to generate the processed word, and when the word type comprises a data word, the channel ID tagger is configured to determine not to tag the word with the channel ID and to provide the word unmodified as the processed word. | 2. The MCSIT data translator of claim 1 , wherein: the word type comprises one of a command word, a status word and a data word, when the word type comprises one of a command word and a status word, the channel ID tagger is configured to determine to tag the word with the channel ID to generate the processed word, and when the word type comprises a data word, the channel ID tagger is configured to determine not to tag the word with the channel ID and to provide the word unmodified as the processed word. 3. The MCSIT data translator of claim 2 , wherein the channel ID tagger is configured to tag the word with the channel ID by overwriting the shared address in the word with the channel ID. | 0.912395 |
8,775,176 | 15 | 19 | 15. A computer-readable storage device having instructions stored which, when executed by a computing device, cause the computing device to perform operations comprising: upon verifying an identity of a user: identifying, via a processor, a template for a domain associated with the user; receiving input speech from the user, the input speech comprising a substantive portion and an instructional portion, the instructional portion related to navigation between fields in the template; transcribing the substantive portion of the input speech to text, to yield transcribed text; inserting the transcribed text into the template, to yield a completed template; and storing the completed template in a database; and upon receiving a request to play a dictation for a particular word in the completed template, playing the dictation of the particular word. | 15. A computer-readable storage device having instructions stored which, when executed by a computing device, cause the computing device to perform operations comprising: upon verifying an identity of a user: identifying, via a processor, a template for a domain associated with the user; receiving input speech from the user, the input speech comprising a substantive portion and an instructional portion, the instructional portion related to navigation between fields in the template; transcribing the substantive portion of the input speech to text, to yield transcribed text; inserting the transcribed text into the template, to yield a completed template; and storing the completed template in a database; and upon receiving a request to play a dictation for a particular word in the completed template, playing the dictation of the particular word. 19. The computer-readable storage device of claim 15 , wherein transcribing the substantive portion of the input speech further comprises transcribing the substantive portion into a predefined document template. | 0.689706 |
8,036,889 | 1 | 6 | 1. A method for filtering dictated and non-dictated sections of documents, the method comprising steps of: gathering speech recognition output and a first set of corresponding documents; conforming at least one associated document from the first set of corresponding documents to a selected speech recognition format; comparing the speech recognition output and the at least one associated document; determining, using a processing unit, long homogeneous sequences of misaligned tokens from the speech recognition output and the at least one associated document; detecting boundaries between dictated and non-dictated sections in the at least one associated document; and annotating the at least one associated document with the boundaries. | 1. A method for filtering dictated and non-dictated sections of documents, the method comprising steps of: gathering speech recognition output and a first set of corresponding documents; conforming at least one associated document from the first set of corresponding documents to a selected speech recognition format; comparing the speech recognition output and the at least one associated document; determining, using a processing unit, long homogeneous sequences of misaligned tokens from the speech recognition output and the at least one associated document; detecting boundaries between dictated and non-dictated sections in the at least one associated document; and annotating the at least one associated document with the boundaries. 6. The method according to claim 1 , wherein the determining long homogeneous sequences of misaligned tokens comprises detecting formatting anchors. | 0.914943 |
8,555,269 | 12 | 13 | 12. A non-transitory computer-readable medium containing program instructions which, when executed by at least one processor, perform the steps of: statically generating an abstract interpretation of a software application which approximates runtime states of said software, wherein said abstract interpretation preserves said software application's information flow properties; verifying a correctness of safety states of the abstract interpretation; identifying vulnerabilities in said software application; and securing said vulnerabilities in said software application, wherein said step of verifying the correctness of safety states further comprises the step of: using bounded model checking to verify said correctness of said safety states, and further wherein said step of using bounded model checking to verify said correctness of said safety states further comprises the steps of: generating an assertion based on said abstract interpretation; testing said assertion to identify counterexamples; and negating counterexamples until said assertion becomes unsatisfiable. | 12. A non-transitory computer-readable medium containing program instructions which, when executed by at least one processor, perform the steps of: statically generating an abstract interpretation of a software application which approximates runtime states of said software, wherein said abstract interpretation preserves said software application's information flow properties; verifying a correctness of safety states of the abstract interpretation; identifying vulnerabilities in said software application; and securing said vulnerabilities in said software application, wherein said step of verifying the correctness of safety states further comprises the step of: using bounded model checking to verify said correctness of said safety states, and further wherein said step of using bounded model checking to verify said correctness of said safety states further comprises the steps of: generating an assertion based on said abstract interpretation; testing said assertion to identify counterexamples; and negating counterexamples until said assertion becomes unsatisfiable. 13. The non-transitory computer-readable medium of claim 12 , wherein said step of generating an abstract interpretation further comprises the steps of: providing a type qualifier for each variable associated with at least one function within said software application; and providing a precondition for each of said at least one function within said software application. | 0.501344 |
9,189,507 | 1 | 2 | 1. A system for supporting agile development, the system comprising: a computer comprising at least one microprocessor; an enterprise crawl and search framework (ECSF), executing on the computer, which abstracts an underlying search engine, provides a common set of application programming interfaces (APIs) for developing search functionalities, and provides an integration layer between the search engine and one or more enterprise applications; a plurality of view objects, each view object representing a top level object associated with one or more additional view objects to form relationships among the top level object and the additional view objects, wherein each view object of the plurality of view objects and the additional view objects represent data associated with an enterprise application and residing in an environment associated with the enterprise application; and a plurality of searchable objects derived from the enterprise applications, wherein each searchable object includes a set of searchable properties that make data represented by one or more view objects available for full text search; wherein the ECSF is executed to: create search metadata, based on the deriving from the enterprise application, wherein the search metadata binds a searchable object to a particular context at runtime and defines behaviors of the searchable object when used in the particular context, including defining whether the searchable object is accessible to a particular user; determine if a particular user has access to a first search category of a search category list based on whether the particular user has permission to access searchable objects in the first search category in accordance with the search metadata; selectively cause the first search category to appear in the search category list in accordance with determining whether the particular user has the access to the first search category; use an input to index data represented by the view objects, and expose the indexed data for use by the search engine. | 1. A system for supporting agile development, the system comprising: a computer comprising at least one microprocessor; an enterprise crawl and search framework (ECSF), executing on the computer, which abstracts an underlying search engine, provides a common set of application programming interfaces (APIs) for developing search functionalities, and provides an integration layer between the search engine and one or more enterprise applications; a plurality of view objects, each view object representing a top level object associated with one or more additional view objects to form relationships among the top level object and the additional view objects, wherein each view object of the plurality of view objects and the additional view objects represent data associated with an enterprise application and residing in an environment associated with the enterprise application; and a plurality of searchable objects derived from the enterprise applications, wherein each searchable object includes a set of searchable properties that make data represented by one or more view objects available for full text search; wherein the ECSF is executed to: create search metadata, based on the deriving from the enterprise application, wherein the search metadata binds a searchable object to a particular context at runtime and defines behaviors of the searchable object when used in the particular context, including defining whether the searchable object is accessible to a particular user; determine if a particular user has access to a first search category of a search category list based on whether the particular user has permission to access searchable objects in the first search category in accordance with the search metadata; selectively cause the first search category to appear in the search category list in accordance with determining whether the particular user has the access to the first search category; use an input to index data represented by the view objects, and expose the indexed data for use by the search engine. 2. The system of claim 1 , wherein the searchable object is bound to the particular context and to be described and used within that context. | 0.715726 |
7,848,915 | 1 | 2 | 1. A concept-based back translation system, comprising: a target language semantic parser module, which uses statistical techniques including decision trees to train a parser based on annotated corpus in the target language, parses a translated target sentence with the parser, once it has been trained, to extract semantic concepts, and based on results of parsing the translated target sentence, obtains a backward translation of the translated sentence into a source language; a source language semantic parser module, which parses a source language sentence to extract semantic concepts and uses results of parsing the source language sentence to determine a significance and relevance of the extract concepts generated in the target language sentence; a bi-directional machine translation module, which receives a translated target language sentence and converts the translated target language sentence back into the source language; a relevancy judging module, which measures a significance and a relevance of the back translation by semantically comparing the back translation with an original source language sentence; and a back translation display module, which displays the back translation dynamically based on the relevance and the significance of the back translation wherein the relevancy judging module measures the relevancy using a semantically weighted score based on modified n-gram precision where the original source language sentence is treated as a reference, and the back translation is treated as a translation hypothesis, wherein the relevancy between the hypothesis and the reference are compared based on a modified n-gram precision, which automatically evaluates translation performance, and wherein a higher weight is assigned to n-grams that contain significant semantic annotations, and a lower weight is assigned to n-grams that contain no significant semantic role, the significance of the n-grams is judged based on semantic parsing results, and on a pre-defined list of key concepts for an application domain. | 1. A concept-based back translation system, comprising: a target language semantic parser module, which uses statistical techniques including decision trees to train a parser based on annotated corpus in the target language, parses a translated target sentence with the parser, once it has been trained, to extract semantic concepts, and based on results of parsing the translated target sentence, obtains a backward translation of the translated sentence into a source language; a source language semantic parser module, which parses a source language sentence to extract semantic concepts and uses results of parsing the source language sentence to determine a significance and relevance of the extract concepts generated in the target language sentence; a bi-directional machine translation module, which receives a translated target language sentence and converts the translated target language sentence back into the source language; a relevancy judging module, which measures a significance and a relevance of the back translation by semantically comparing the back translation with an original source language sentence; and a back translation display module, which displays the back translation dynamically based on the relevance and the significance of the back translation wherein the relevancy judging module measures the relevancy using a semantically weighted score based on modified n-gram precision where the original source language sentence is treated as a reference, and the back translation is treated as a translation hypothesis, wherein the relevancy between the hypothesis and the reference are compared based on a modified n-gram precision, which automatically evaluates translation performance, and wherein a higher weight is assigned to n-grams that contain significant semantic annotations, and a lower weight is assigned to n-grams that contain no significant semantic role, the significance of the n-grams is judged based on semantic parsing results, and on a pre-defined list of key concepts for an application domain. 2. The concept-based translation system according to claim 1 , wherein the back translation display module, assigns appropriate confidence scores to the concepts in the back translation, wherein the confidence scores are obtained as a combination of parsing confidence of source language, target language, a significance score of a corresponding concept in the application domain, and relevance judge score obtained from the relevancy judging module. | 0.501109 |
9,477,937 | 14 | 17 | 14. A computer system, comprising one or more processors and a memory, operatively interconnected to one another and configured to implement steps for analyzing a control-flow in a business process, comprising: invoking a representation of the business process as an acyclic workflow graph containing AND-, XOR- and IOR-types of nodes and edges linking nodes of the graph; labeling edges of the graph such that a label assigned to a labeled edge comprises a plurality of edge identifiers identifying respective edges, each of the edges identified being an outgoing edge of an XOR-split or an IOR-split node in the graph, whereby executing any one of the identified edges ensures that the first edge will be executed; and automatically checking the labels for a deadlock using a processor, while labeling the edges of the graph, wherein a deadlock is found if a condition for relaxed soundness is true. | 14. A computer system, comprising one or more processors and a memory, operatively interconnected to one another and configured to implement steps for analyzing a control-flow in a business process, comprising: invoking a representation of the business process as an acyclic workflow graph containing AND-, XOR- and IOR-types of nodes and edges linking nodes of the graph; labeling edges of the graph such that a label assigned to a labeled edge comprises a plurality of edge identifiers identifying respective edges, each of the edges identified being an outgoing edge of an XOR-split or an IOR-split node in the graph, whereby executing any one of the identified edges ensures that the first edge will be executed; and automatically checking the labels for a deadlock using a processor, while labeling the edges of the graph, wherein a deadlock is found if a condition for relaxed soundness is true. 17. The computer system of claim 14 , wherein, at the step of labeling, a label of an outgoing edge of an XOR-split node or an IOR-split node of the graph contains only one edge identifier of the said outgoing edge. | 0.785857 |
6,052,656 | 7 | 10 | 7. The natural language processing system according to claim 1, wherein said input means inputs a set of sentences, wherein said analysis means processes a set of sentences as sentence by sentence and outputs concepts as a result of an analysis. | 7. The natural language processing system according to claim 1, wherein said input means inputs a set of sentences, wherein said analysis means processes a set of sentences as sentence by sentence and outputs concepts as a result of an analysis. 10. The natural language processing system according to claim 7, which further comprises separation means for separating concepts, which are unsuitably connected with one another, from among concepts outputted by the analysis means. | 0.910078 |
8,442,974 | 8 | 13 | 8. A system for ranking Web pages in a Web search engine, the system comprising: at least one processor; a communication interface; and a memory containing a plurality of program instructions configured to cause the at least one processor to: receive, via the communication interface over a network, a Web search query from a particular user, the Web search query including at least one keyword; identify one or more Web pages that contain the at least one keyword; determine, for each of the one or more Web pages, a raw page ranking; adjust the raw page ranking of each of at least one Web page among the one or more Web pages based on direct evidence of how interesting the respective Web page is to users to produce an adjusted page ranking, the direct evidence being derived from clickstream data collected from the users; and present, as search results, the at least one Web page to the particular user in accordance with the adjusted page rankings, wherein the direct evidence of how interesting a Web page is to users includes a measure of how often the users traverse to or from the Web page in browsing the Web and a measure of how many users have recently visited the Web page compared with how many users normally visit the Web page, and wherein the measure of how many users have recently visited the Web page compared with how many users normally visit the Web page is given greater weight than the measure of how often the users traverse to or from the Web page in browsing the Web. | 8. A system for ranking Web pages in a Web search engine, the system comprising: at least one processor; a communication interface; and a memory containing a plurality of program instructions configured to cause the at least one processor to: receive, via the communication interface over a network, a Web search query from a particular user, the Web search query including at least one keyword; identify one or more Web pages that contain the at least one keyword; determine, for each of the one or more Web pages, a raw page ranking; adjust the raw page ranking of each of at least one Web page among the one or more Web pages based on direct evidence of how interesting the respective Web page is to users to produce an adjusted page ranking, the direct evidence being derived from clickstream data collected from the users; and present, as search results, the at least one Web page to the particular user in accordance with the adjusted page rankings, wherein the direct evidence of how interesting a Web page is to users includes a measure of how often the users traverse to or from the Web page in browsing the Web and a measure of how many users have recently visited the Web page compared with how many users normally visit the Web page, and wherein the measure of how many users have recently visited the Web page compared with how many users normally visit the Web page is given greater weight than the measure of how often the users traverse to or from the Web page in browsing the Web. 13. The system of claim 8 , wherein the raw page ranking of each of the at least one Web page is based, at least in part, on how frequently the at least one keyword occurs in the respective Web page. | 0.800601 |
8,682,683 | 1 | 7 | 1. A pre-screening system, comprising: a pre-screening workflow manager executed on one or more servers comprising memory executable via a processor to: execute an automated pre-screener simulator module to grade a plurality of a pre-screeners; approve or deny each of the plurality of pre-screeners based on a respective grade determined via the pre-screener simulator module; receive a pre-screening candidate portfolio; execute an automated pre-screening interview in response to receiving the pre-screening candidate portfolio; and if the automated pre-screening interview has been completed, send the corresponding pre-screening candidate portfolio including a pre-screening interview file to a candidate queue stored in a prescreening database, the candidate queue storing a plurality of pre-screening candidate portfolios; and a pre-screening candidate manager executed on the one or more servers including memory executable by a processor to: request and receive the pre-screening candidate portfolio from the candidate queue; send a portion of the pre-screening candidate portfolio including the pre-screening interview file and at least one pre-screening rubric to a remotely executed pre-screening program, the pre-screening rubric providing a grading criteria to a pre-screener to enable pre-screeners to grade the pre-screening interview file; and receive a portfolio evaluation from the pre-screening program, the evaluation assessing the pre-screening candidate portfolio. | 1. A pre-screening system, comprising: a pre-screening workflow manager executed on one or more servers comprising memory executable via a processor to: execute an automated pre-screener simulator module to grade a plurality of a pre-screeners; approve or deny each of the plurality of pre-screeners based on a respective grade determined via the pre-screener simulator module; receive a pre-screening candidate portfolio; execute an automated pre-screening interview in response to receiving the pre-screening candidate portfolio; and if the automated pre-screening interview has been completed, send the corresponding pre-screening candidate portfolio including a pre-screening interview file to a candidate queue stored in a prescreening database, the candidate queue storing a plurality of pre-screening candidate portfolios; and a pre-screening candidate manager executed on the one or more servers including memory executable by a processor to: request and receive the pre-screening candidate portfolio from the candidate queue; send a portion of the pre-screening candidate portfolio including the pre-screening interview file and at least one pre-screening rubric to a remotely executed pre-screening program, the pre-screening rubric providing a grading criteria to a pre-screener to enable pre-screeners to grade the pre-screening interview file; and receive a portfolio evaluation from the pre-screening program, the evaluation assessing the pre-screening candidate portfolio. 7. The pre-screening system of claim 1 , where the pre-screening rubric includes an interview rubric section configured to present scoring guidelines for the pre-screening interview on a GUI and a resume rubric section configured to present scoring guidelines for a candidate's resume included in the pre-screening candidate portfolio on the GUI. | 0.733025 |
7,877,397 | 1 | 7 | 1. A computer-implemented system comprising at least one processor coupled to at least one machine-readable storage medium storing instructions executable by the at least one processor to implement: a transformation component configured to receive a canonical representation of an object-oriented query in a form of a command tree structure with nodes representing at least one of entity constructs or expressions associated with computational operations and data types; a plan compilation component configured to transform the canonical representation into a flattened command tree; a data model platform configured to provide services common to a plurality of disparate application frameworks, the services including support of the object-oriented query via object-relational mappings; a data reader object configured to return results from execution of the flattened command tree against a relational database; a column mapper configured to assemble the results into a nested instruction-type command tree; a query processor component; a sessions and transactions component; an object cache including a session cache and an explicit cache; a services component configured to perform change tracking and conflict detection; a cursors and rules component; a business logic hosting component; and a persistence and query engine configured to provide core persistence and query services. | 1. A computer-implemented system comprising at least one processor coupled to at least one machine-readable storage medium storing instructions executable by the at least one processor to implement: a transformation component configured to receive a canonical representation of an object-oriented query in a form of a command tree structure with nodes representing at least one of entity constructs or expressions associated with computational operations and data types; a plan compilation component configured to transform the canonical representation into a flattened command tree; a data model platform configured to provide services common to a plurality of disparate application frameworks, the services including support of the object-oriented query via object-relational mappings; a data reader object configured to return results from execution of the flattened command tree against a relational database; a column mapper configured to assemble the results into a nested instruction-type command tree; a query processor component; a sessions and transactions component; an object cache including a session cache and an explicit cache; a services component configured to perform change tracking and conflict detection; a cursors and rules component; a business logic hosting component; and a persistence and query engine configured to provide core persistence and query services. 7. The computer-implemented system of claim 1 , wherein the application frameworks include at least one of a Personal Information Manager (PIM) framework or a Line-of-Business (LOB) framework. | 0.502591 |
5,384,703 | 7 | 8 | 7. The method of claim 1, wherein each expression is a word, so that in step (a) said computer determines a frequency of occurrence of words in the document. | 7. The method of claim 1, wherein each expression is a word, so that in step (a) said computer determines a frequency of occurrence of words in the document. 8. The method of claim 7, wherein the complexity of each word is determined by said computer based on a length of said word. | 0.969563 |
8,442,823 | 6 | 8 | 6. The method of claim 1 , wherein performing the KLSH comprises: computing, using the kernel function, query hash key bits from the query utterance statistics; performing a search function, using as input the query hash key bits and hash key bits computed from the utterance statistics for the plurality of utterances using the kernel function, in order to select the subset of the plurality of utterances. | 6. The method of claim 1 , wherein performing the KLSH comprises: computing, using the kernel function, query hash key bits from the query utterance statistics; performing a search function, using as input the query hash key bits and hash key bits computed from the utterance statistics for the plurality of utterances using the kernel function, in order to select the subset of the plurality of utterances. 8. The method of claim 6 , wherein the search function is an approximate nearest neighbor search. | 0.96899 |
8,423,383 | 1 | 27 | 1. A method of providing a consultation of medical professionals utilizing a computer-implemented system connected to the internet, integrated with human resources provided by professionals affiliated with the system, to form a consensus of opinions by a panel of medical professionals selected by the system but not provided with financial incentives which could bias their decision making, the method comprising the steps of: (a) creating a database of potential participating medical professionals, along with information about each the potential participating medical professionals, including the qualifications of the potential participating medical professionals; (b) user seeking a medical consultation accesses the system; (c) user submits information to the system regarding the desired medical consultation; (d) at least one professional affiliated with the system reviews information submitted by the user and structures one or more questions, based upon the user submitted information, for consideration by a panel of potential participating medical professionals selected from the database; (e) medical professionals qualified to provide response(s) to the structured question(s) are selected by the system to form a panel to provide responses to the structured question(s) based upon information in the database; (f) the structured question(s) are provided electronically to the medical professionals in the selected panel; (g) the medical professionals in the selected panel review the structured question(s) and provide responses thereto; (h) responses from the medical professionals in the selected panel are forwarded electronically to the system; and (i) system compiles responses, calculates the degree of consensus of the responses, and displays the calculated degree of consensus on a system website accessible to the user. | 1. A method of providing a consultation of medical professionals utilizing a computer-implemented system connected to the internet, integrated with human resources provided by professionals affiliated with the system, to form a consensus of opinions by a panel of medical professionals selected by the system but not provided with financial incentives which could bias their decision making, the method comprising the steps of: (a) creating a database of potential participating medical professionals, along with information about each the potential participating medical professionals, including the qualifications of the potential participating medical professionals; (b) user seeking a medical consultation accesses the system; (c) user submits information to the system regarding the desired medical consultation; (d) at least one professional affiliated with the system reviews information submitted by the user and structures one or more questions, based upon the user submitted information, for consideration by a panel of potential participating medical professionals selected from the database; (e) medical professionals qualified to provide response(s) to the structured question(s) are selected by the system to form a panel to provide responses to the structured question(s) based upon information in the database; (f) the structured question(s) are provided electronically to the medical professionals in the selected panel; (g) the medical professionals in the selected panel review the structured question(s) and provide responses thereto; (h) responses from the medical professionals in the selected panel are forwarded electronically to the system; and (i) system compiles responses, calculates the degree of consensus of the responses, and displays the calculated degree of consensus on a system website accessible to the user. 27. The method of claim 1 further comprising the step of selecting the panel of medical professionals from a group of physicians, nurses, chiropractors and/or pharmacists. | 0.820755 |
8,695,021 | 11 | 12 | 11. A computing machine comprising: one or more processors; one or more computer storage media; computer program instructions stored on the computer storage medium which, when processed by a processing device, implement a language projection between application programming interfaces of an operating system and applications in a target programming language; wherein the application programming interfaces of the operating system are described by metadata stored in memory; and wherein the language projection receives an indication of a named element in a program in a target programming language referring to an element of one of the application programming interfaces of the operating system, and, using the metadata, creates a projection element in the program in the target programming language, the projection element being a projection of the element of the application programming interface of the operating system to the named element in the program, such that at runtime, the projection element in the program allows the program to access the application program interface of the operating system; wherein the projection element marshals marshaling data between an operating system representation and an application representation according to type; wherein the metadata is stored in a metadata file, wherein the metadata file describes elements of the application programming interface in a machine readable programming language independent format and is stored in a standard file format in a known location as part of the operating system. | 11. A computing machine comprising: one or more processors; one or more computer storage media; computer program instructions stored on the computer storage medium which, when processed by a processing device, implement a language projection between application programming interfaces of an operating system and applications in a target programming language; wherein the application programming interfaces of the operating system are described by metadata stored in memory; and wherein the language projection receives an indication of a named element in a program in a target programming language referring to an element of one of the application programming interfaces of the operating system, and, using the metadata, creates a projection element in the program in the target programming language, the projection element being a projection of the element of the application programming interface of the operating system to the named element in the program, such that at runtime, the projection element in the program allows the program to access the application program interface of the operating system; wherein the projection element marshals marshaling data between an operating system representation and an application representation according to type; wherein the metadata is stored in a metadata file, wherein the metadata file describes elements of the application programming interface in a machine readable programming language independent format and is stored in a standard file format in a known location as part of the operating system. 12. The computing machine of claim 11 , wherein creating the projection element comprises, at compile time, creating code that defines one or more projection elements. | 0.546196 |
8,145,488 | 13 | 14 | 13. A method for recognizing speech comprising: receiving a speech signal comprising informational content and noise; extracting features associated with the informational content of the speech signal; determining at least one environmental conditioning parameter that impacts speech recognition; and determining, by a processor, the informational content using a plurality of Gaussian mixture components L in modeling state observations and a particular mixture component l of the plurality of mixture components L having a mean parameter μ and a variance parameter Σ represented by: μ i , l , d ( ζ r , t , d ) = μ i , l , d ( 0 ) + ξ ( ζ r , t , d ❘ μ i , l , d ( 1 ) , … , μ i , l , d ( K ) ) , and ∑ i . l . d ( ζ r , t , d ) = ∑ i , l , d ( 0 ) ξ - 2 ( ζ r , t , d ❘ ∑ i , l , d ) ( 1 ) , … , ∑ i . l . d ( K ) ) , where i identifies a state, d identifies a dimension, ζ refers to said at least one environmental conditioning parameter, r refers to utterance, t refers to frame, ξ refers to a spline function which serves as a piecewise function for the particular mixture component l, μ i,l,d (0) and Σ i,l,d (0) are Gaussian-component-specific mean and variance parts of the particular mixture component l, and μ i,l,d (1) . . . , μ i,l,d (K) and Σ i,l,d (1) . . . , Σ i,l,d (K) are spline knots for particular mixture component l that are shareable across different mixture components. | 13. A method for recognizing speech comprising: receiving a speech signal comprising informational content and noise; extracting features associated with the informational content of the speech signal; determining at least one environmental conditioning parameter that impacts speech recognition; and determining, by a processor, the informational content using a plurality of Gaussian mixture components L in modeling state observations and a particular mixture component l of the plurality of mixture components L having a mean parameter μ and a variance parameter Σ represented by: μ i , l , d ( ζ r , t , d ) = μ i , l , d ( 0 ) + ξ ( ζ r , t , d ❘ μ i , l , d ( 1 ) , … , μ i , l , d ( K ) ) , and ∑ i . l . d ( ζ r , t , d ) = ∑ i , l , d ( 0 ) ξ - 2 ( ζ r , t , d ❘ ∑ i , l , d ) ( 1 ) , … , ∑ i . l . d ( K ) ) , where i identifies a state, d identifies a dimension, ζ refers to said at least one environmental conditioning parameter, r refers to utterance, t refers to frame, ξ refers to a spline function which serves as a piecewise function for the particular mixture component l, μ i,l,d (0) and Σ i,l,d (0) are Gaussian-component-specific mean and variance parts of the particular mixture component l, and μ i,l,d (1) . . . , μ i,l,d (K) and Σ i,l,d (1) . . . , Σ i,l,d (K) are spline knots for particular mixture component l that are shareable across different mixture components. 14. The method of claim 13 , wherein the environmental conditioning parameter is based on one or more of: a signal-to-noise-ratio (SNR); a power of noise in the environment; a level of noise in the environment; an accent of a speaker; a specific style of speaking by the speaker, or a rate of speech. | 0.817518 |
7,962,469 | 1 | 13 | 1. A method performed by one or more devices, comprising: obtaining, by one or more processors of the one or more devices, a set of one or more addresses, each of the one or more addresses in the set of one or more addresses corresponding to a respective, particular document; obtaining, by one or more processors of the one or more devices, a search query; obtaining, by one or more processors of the one or more devices, a set of references to documents in response to the search query, where each of the one or more addresses corresponding to the respective, particular document is obtained independent of the set of references to documents obtained in response to the search query; filtering, using one or more processors of the one or more devices, the set of references, based on the set of one or more addresses, to identify one or more of the references that are related to the set of one or more addresses by: determining whether a document, corresponding to one of the references in the set of references, is similar to the respective, particular document, corresponding to an address in the set of one or more addresses, based on whether text from a body of the document is similar to text from a body of the respective, particular document, and including the one of the references in a filtered set of references when the document is similar to the respective, particular document; and presenting, by one or more processors of the one or more devices, the filtered set of references. | 1. A method performed by one or more devices, comprising: obtaining, by one or more processors of the one or more devices, a set of one or more addresses, each of the one or more addresses in the set of one or more addresses corresponding to a respective, particular document; obtaining, by one or more processors of the one or more devices, a search query; obtaining, by one or more processors of the one or more devices, a set of references to documents in response to the search query, where each of the one or more addresses corresponding to the respective, particular document is obtained independent of the set of references to documents obtained in response to the search query; filtering, using one or more processors of the one or more devices, the set of references, based on the set of one or more addresses, to identify one or more of the references that are related to the set of one or more addresses by: determining whether a document, corresponding to one of the references in the set of references, is similar to the respective, particular document, corresponding to an address in the set of one or more addresses, based on whether text from a body of the document is similar to text from a body of the respective, particular document, and including the one of the references in a filtered set of references when the document is similar to the respective, particular document; and presenting, by one or more processors of the one or more devices, the filtered set of references. 13. The method of claim 1 , where obtaining the set of references includes: searching a network to locate documents relating to the search query, and including references to the located documents in the set of references. | 0.693056 |
9,246,708 | 25 | 32 | 25. The method of posting of claim 22 wherein creating comprises: prompting, by the mobile device, a user to create a new posting by the mobile device; recording, by the mobile device, an audio message provided by the user; recording, by the mobile device, a video message provided by the user; collecting, by the mobile device, a textual input provided by the user; and assembling, by the mobile device, the audio message, the video message and the textual input for delivery to the social networking website system. | 25. The method of posting of claim 22 wherein creating comprises: prompting, by the mobile device, a user to create a new posting by the mobile device; recording, by the mobile device, an audio message provided by the user; recording, by the mobile device, a video message provided by the user; collecting, by the mobile device, a textual input provided by the user; and assembling, by the mobile device, the audio message, the video message and the textual input for delivery to the social networking website system. 32. The method of posting of claim 25 wherein prompting comprises: playing, by the mobile device, an audio prompt in the local language, wherein the local language is identified based on a current local information determined by the mobile device; and showing a textual prompt, by the mobile device, in the local language. | 0.90266 |
10,095,749 | 9 | 11 | 9. A system comprising: memory storing: an information source; business knowledge comprising information about users and business objects comprising; and an application for: receiving a search query from a user, the search query querying information from the information source; identifying features associated with the user and/or the business objects and for which result sets are to include boosting of result items based on business knowledge associated with the user and the information source; identifying, for each identified feature, query terms in the received search query to which a particular feature applies; for each identified query term to which a particular feature applies, adding a significance factor to the identified query term to boost a result item by a particular significance, identifying a weight for one or more feature-value pairs associated with the query term, each weight based on the business knowledge associated with the user and the information source, the business knowledge used to identify one or more feature-value pairs to which the weight is applicable, and using the weights to adjust the effect of significance factors; generating a weighted search query that includes weighted query parts, each weighted query part being a function of a respective query term and an associated weight, wherein the weighted search query is a re-written version of the received search query, the weighted search query including: in-terms having high weights and including selection criteria matching a respective query term and including the values of the one or more feature-value pairs, the in-terms boosting ranks of corresponding result items in the ranked result set according to the weights associated with the one or more feature-value pairs; and out-terms having low weights and including selection criteria matching a respective query term and excluding the values of the one or more feature-value pairs, the out-terms not affecting ranks of corresponding result items in the ranked result set; executing the weighted search query to produce a ranked result set that includes result items ranked according to the weights, the ranked result set including identical, but ranked, result entries that would be produced by executing the received search query; and providing the ranked result set in response to the received search query. | 9. A system comprising: memory storing: an information source; business knowledge comprising information about users and business objects comprising; and an application for: receiving a search query from a user, the search query querying information from the information source; identifying features associated with the user and/or the business objects and for which result sets are to include boosting of result items based on business knowledge associated with the user and the information source; identifying, for each identified feature, query terms in the received search query to which a particular feature applies; for each identified query term to which a particular feature applies, adding a significance factor to the identified query term to boost a result item by a particular significance, identifying a weight for one or more feature-value pairs associated with the query term, each weight based on the business knowledge associated with the user and the information source, the business knowledge used to identify one or more feature-value pairs to which the weight is applicable, and using the weights to adjust the effect of significance factors; generating a weighted search query that includes weighted query parts, each weighted query part being a function of a respective query term and an associated weight, wherein the weighted search query is a re-written version of the received search query, the weighted search query including: in-terms having high weights and including selection criteria matching a respective query term and including the values of the one or more feature-value pairs, the in-terms boosting ranks of corresponding result items in the ranked result set according to the weights associated with the one or more feature-value pairs; and out-terms having low weights and including selection criteria matching a respective query term and excluding the values of the one or more feature-value pairs, the out-terms not affecting ranks of corresponding result items in the ranked result set; executing the weighted search query to produce a ranked result set that includes result items ranked according to the weights, the ranked result set including identical, but ranked, result entries that would be produced by executing the received search query; and providing the ranked result set in response to the received search query. 11. The system of claim 9 , wherein the respective feature-value pair identifies a value that is a constant, a range, a string, or a set. | 0.838061 |
10,019,526 | 5 | 8 | 5. The system of claim 1 , the instructions further comprising: authenticating whether the domain name controller is authorized to set up the matching keyword for the domain name. | 5. The system of claim 1 , the instructions further comprising: authenticating whether the domain name controller is authorized to set up the matching keyword for the domain name. 8. The system of claim 5 , the instructions further comprising: storing a flag with the domain name if the domain name controller is authenticated as being authorized to set up the matching keyword for the domain name. | 0.944016 |
9,043,265 | 15 | 16 | 15. A computer implemented method as in claim 1 , wherein non-overlapping square brackets are the circumfix symbols that represent planar nonintersecting marks of distinction between their inside and their outside, in the arrangement in which they appear, namely; [inside] outside. | 15. A computer implemented method as in claim 1 , wherein non-overlapping square brackets are the circumfix symbols that represent planar nonintersecting marks of distinction between their inside and their outside, in the arrangement in which they appear, namely; [inside] outside. 16. A computer implemented method as in claim 15 , wherein an arrangement is a formal language construct inductively defined as follows: 1. writing a void place “in the plane, i.e. there is no distinction, is an arrangement; 2. writing a square bracket, with nothing inside, ‘[ ]’ is an arrangement; 3. crossing an existing arrangement A, ‘[A]’ is an arrangement, which is called a package; 4. reuniting two existing arrangements A and B, ‘A B’ is an arrangement, which is called a bunch; and only language constructs defined by the four previous lines are arrangements. | 0.907528 |
9,978,022 | 1 | 8 | 1. A method comprising: maintaining social information for a social networking system user; receiving a search query from a user device associated with the user; receiving a location of the user device; determining a list of search results based on the search query, each search result comprising a search value, the search value indicating a quality of a match between the search query and the search result; associating each search result with a third-party content object; for each of the associated third-party content objects, identifying a set of connected users of the user associated with the third-party content object, a connected user being associated with a third-party content object when the connected user takes an action with respect to the third-party content object; for each connected user in the set of connected users, determining a weighing factor for the connected user based on characteristics of the user's relationship to the connected user, the weighing factor indicating a likelihood that the user will share an interest in the third-party content object with the connected user; and determining a connection value for the third-party content object based on the weighing factors of the connected users in the set of connected users; calculating a relevance score for each search result by matching the user location, social information, and search value to the third-party content object associated with the search result, wherein calculating the relevance score comprises: determining a location value for the third-party content object, the location value based on a proximity between a location assigned to the third-party content object and the user location; determining an interest value for the third-party content object, the interest value based on whether a category assigned to the third-party content object is included in one or more categories associated with affinity information for the user; determining a time value for the third-party content object, the time value based on whether a current time is within a delivery time range assigned to the third-party content object; and combining the search value, location value, interest value, connection value, and time value to determine the relevance score; adjusting the relevance score for each search result to determine an adjusted relevance score based on the connection value for the third-party content object associated with the search result; ranking the list of search results by the adjusted relevance scores of the third-party content objects associated with the search results; and providing the ranked list of search results to a notification controller. | 1. A method comprising: maintaining social information for a social networking system user; receiving a search query from a user device associated with the user; receiving a location of the user device; determining a list of search results based on the search query, each search result comprising a search value, the search value indicating a quality of a match between the search query and the search result; associating each search result with a third-party content object; for each of the associated third-party content objects, identifying a set of connected users of the user associated with the third-party content object, a connected user being associated with a third-party content object when the connected user takes an action with respect to the third-party content object; for each connected user in the set of connected users, determining a weighing factor for the connected user based on characteristics of the user's relationship to the connected user, the weighing factor indicating a likelihood that the user will share an interest in the third-party content object with the connected user; and determining a connection value for the third-party content object based on the weighing factors of the connected users in the set of connected users; calculating a relevance score for each search result by matching the user location, social information, and search value to the third-party content object associated with the search result, wherein calculating the relevance score comprises: determining a location value for the third-party content object, the location value based on a proximity between a location assigned to the third-party content object and the user location; determining an interest value for the third-party content object, the interest value based on whether a category assigned to the third-party content object is included in one or more categories associated with affinity information for the user; determining a time value for the third-party content object, the time value based on whether a current time is within a delivery time range assigned to the third-party content object; and combining the search value, location value, interest value, connection value, and time value to determine the relevance score; adjusting the relevance score for each search result to determine an adjusted relevance score based on the connection value for the third-party content object associated with the search result; ranking the list of search results by the adjusted relevance scores of the third-party content objects associated with the search results; and providing the ranked list of search results to a notification controller. 8. The method of claim 1 , wherein the characteristics of the connected users that vary the weighing factor comprise a degree of separation. | 0.867675 |
8,745,488 | 1 | 4 | 1. A system for editing both a webpage document and an image document online, the system comprising: a processor; circuitry for displaying an editing interface to a client device, via one or more networks, the displayed editing interface being configured for receiving editing data, via the client device, for editing both the webpage document and the image document, the webpage document being in one or more webpage file formats and the image document being in one or more image file formats, the displayed editing interface including at least a first portion and a second portion, the editing interface being configured for displaying with the first portion of the editing interface at least one or more webpage form documents, that are in one or more webpage file formats, for entering editing data and the editing interface being configured for displaying with the second portion of the editing interface at least one of the webpage document or the image document; circuitry for accepting the editing data; circuitry for updating, automatically and substantially concurrently, both the webpage document and the image document based, at least in part, on the same accepted editing data, substantially concurrently including within a window of time that is no greater than 15 seconds, wherein the circuitry for updating, automatically and substantially concurrently, both the webpage document and the image document includes at least: circuitry for updating the webpage document based at least in part on the accepted editing data; circuitry for updating the image document based at least in part on the accepted editing data, the circuitry for updating the image document including at least: circuitry for retrieving form data that is in one or more data management file formats from a database, for updating the form data based at least in part on the accepted editing data, for populating an image form document with the updated form data, the image form document being in one or more image file formats, and for storing the updated form data in the database; circuitry for updating the image document with the image form document; and circuitry for continuing to display the editing interface until an indication is received that the client device has terminated editing; circuitry for accepting first input data via the one or more networks from the client device, the accepted first input data including at least the webpage document and the one or more webpage form documents; circuitry for accepting second input data via the one or more networks from the client device, the accepted second input data including the image document and the image form document; and circuitry for generating the form data, for populating the image form document with the generated form data, and for storing the form data in the database; wherein the image document having been created by scanning a hardcopy document and the webpage document having been based at least in part on the image document and each of the image document and the webpage document thereby having substantially similar viewable content; and wherein the circuitry for displaying an editing interface is configured to display the first and second portions of the editing interface as, respectively, first and second windows, side-by-side for simultaneous viewing, in which: the first window is configured for displaying at least the webpage form document for entry of editing data for editing both the webpage document and the image document, and the second window displaying at least the image document to be edited. | 1. A system for editing both a webpage document and an image document online, the system comprising: a processor; circuitry for displaying an editing interface to a client device, via one or more networks, the displayed editing interface being configured for receiving editing data, via the client device, for editing both the webpage document and the image document, the webpage document being in one or more webpage file formats and the image document being in one or more image file formats, the displayed editing interface including at least a first portion and a second portion, the editing interface being configured for displaying with the first portion of the editing interface at least one or more webpage form documents, that are in one or more webpage file formats, for entering editing data and the editing interface being configured for displaying with the second portion of the editing interface at least one of the webpage document or the image document; circuitry for accepting the editing data; circuitry for updating, automatically and substantially concurrently, both the webpage document and the image document based, at least in part, on the same accepted editing data, substantially concurrently including within a window of time that is no greater than 15 seconds, wherein the circuitry for updating, automatically and substantially concurrently, both the webpage document and the image document includes at least: circuitry for updating the webpage document based at least in part on the accepted editing data; circuitry for updating the image document based at least in part on the accepted editing data, the circuitry for updating the image document including at least: circuitry for retrieving form data that is in one or more data management file formats from a database, for updating the form data based at least in part on the accepted editing data, for populating an image form document with the updated form data, the image form document being in one or more image file formats, and for storing the updated form data in the database; circuitry for updating the image document with the image form document; and circuitry for continuing to display the editing interface until an indication is received that the client device has terminated editing; circuitry for accepting first input data via the one or more networks from the client device, the accepted first input data including at least the webpage document and the one or more webpage form documents; circuitry for accepting second input data via the one or more networks from the client device, the accepted second input data including the image document and the image form document; and circuitry for generating the form data, for populating the image form document with the generated form data, and for storing the form data in the database; wherein the image document having been created by scanning a hardcopy document and the webpage document having been based at least in part on the image document and each of the image document and the webpage document thereby having substantially similar viewable content; and wherein the circuitry for displaying an editing interface is configured to display the first and second portions of the editing interface as, respectively, first and second windows, side-by-side for simultaneous viewing, in which: the first window is configured for displaying at least the webpage form document for entry of editing data for editing both the webpage document and the image document, and the second window displaying at least the image document to be edited. 4. The system of claim 1 , wherein the client device includes at least a first client device and a second client device, the circuitry for displaying an editing interface being configured for displaying the editing interface to the first client device, the circuitry for accepting first input data being configured for accepting the first input data from the second client device, and the circuitry for accepting second input data being configured to accept the second input data from the second client device. | 0.500978 |
9,298,437 | 5 | 6 | 5. The machine-readable medium of claim 1 , wherein unrolling the identified portions includes: determining that the identified portions match a multiple numbers of loops including at least a first number and a second number; creating a first new quantification of the first number minus one; creating a second new quantification with a ranged between one and the second number minus the first number plus one; and concatenating the result of repeating a sub-expression of the first new quantification for the first number minus one times to an unrolling of the second new quantification. | 5. The machine-readable medium of claim 1 , wherein unrolling the identified portions includes: determining that the identified portions match a multiple numbers of loops including at least a first number and a second number; creating a first new quantification of the first number minus one; creating a second new quantification with a ranged between one and the second number minus the first number plus one; and concatenating the result of repeating a sub-expression of the first new quantification for the first number minus one times to an unrolling of the second new quantification. 6. The machine-readable medium of claim 5 , wherein the unrolling of the second new quantification includes splitting the second new quantification based on a threshold transitions states to members of the split, the threshold denoting in-degree or out-degree transitions of an automaton. | 0.837288 |
7,953,711 | 8 | 13 | 8. A system for garbage collection comprising: an old generation pool comprising memory for use in an old generation of an application; a garbage collector configured to: determine a plurality of old regions in the old generation obtain a root for the application, wherein the root is for traversing an object graph of live objects for the application; create a mark bitmap by traversing the object graph using the root, wherein the mark bitmap describes a plurality of marked words for the plurality of old regions and a plurality of unmarked words for the plurality of old regions; create a connectivity matrix by traversing the object graph using the root, wherein the connectivity matrix describes a plurality of cross-region references; scan the mark bitmap to obtain fragmentation information; select a first region of the plurality of old regions; determine that the first region satisfies a fragmentation threshold using the fragmentation information; in response to determining that the first region satisfies the fragmentation threshold: copy at least one of the plurality of marked words located in the first region to a new region allocated from a pool of free regions; adjust a cross-region reference to the first region in the plurality of marked words to point to the new region based on the connectivity matrix; and add the first region to the pool of free regions; determine that a second region from the plurality of old regions does not satisfy the fragmentation threshold; in response to determining that the second region does not satisfy the fragmentation threshold, designate each of the plurality of unmarked words in the second region as free. | 8. A system for garbage collection comprising: an old generation pool comprising memory for use in an old generation of an application; a garbage collector configured to: determine a plurality of old regions in the old generation obtain a root for the application, wherein the root is for traversing an object graph of live objects for the application; create a mark bitmap by traversing the object graph using the root, wherein the mark bitmap describes a plurality of marked words for the plurality of old regions and a plurality of unmarked words for the plurality of old regions; create a connectivity matrix by traversing the object graph using the root, wherein the connectivity matrix describes a plurality of cross-region references; scan the mark bitmap to obtain fragmentation information; select a first region of the plurality of old regions; determine that the first region satisfies a fragmentation threshold using the fragmentation information; in response to determining that the first region satisfies the fragmentation threshold: copy at least one of the plurality of marked words located in the first region to a new region allocated from a pool of free regions; adjust a cross-region reference to the first region in the plurality of marked words to point to the new region based on the connectivity matrix; and add the first region to the pool of free regions; determine that a second region from the plurality of old regions does not satisfy the fragmentation threshold; in response to determining that the second region does not satisfy the fragmentation threshold, designate each of the plurality of unmarked words in the second region as free. 13. The system of claim 8 , wherein the each of the plurality of unmarked words not in the second region designated as free are only available to the application, wherein the first region added to the pool of free regions is available to each of a plurality of applications, and wherein the application is one of the plurality of applications. | 0.501453 |
9,672,554 | 15 | 16 | 15. The system of claim 14 , the instructions further comprising a ranking score module configured to determine, for each of the item listings that satisfies the query, a ranking score based, a least in part, on the weighted sums of the predicted listing quality score and the observed listing quality score for the item listing. | 15. The system of claim 14 , the instructions further comprising a ranking score module configured to determine, for each of the item listings that satisfies the query, a ranking score based, a least in part, on the weighted sums of the predicted listing quality score and the observed listing quality score for the item listing. 16. The system of claim 15 , the instructions further comprising a presentation module configured to present the item listings that satisfy the query in accordance with the ranking scores. | 0.926217 |
8,108,371 | 1 | 9 | 1. A graphical user interface embodied on a tangible computer-readable medium and executable on a computer for displaying a search result received in response to a search-engine query, said graphical user interface comprising: a first display area of a search-results webpage that displays content received by a web browser running on the computer, wherein the content includes a hyperlink of a web page deemed to satisfy the search-engine query; a second display area of the search-results webpage that displays content retrieved in real time from a web server by another web browser running on a background thread of the web browser, wherein the content retrieved by the other web browser includes a thumbnail image of the web page displayed next to the hyperlink of the webpage, and wherein the hyperlink and the thumbnail image are displayed together as part of the search-results webpage; and a third display area of the search-results webpage that displays a preview-options GUI element, which allows a user to control a presentation of the thumbnail image in the second display area. | 1. A graphical user interface embodied on a tangible computer-readable medium and executable on a computer for displaying a search result received in response to a search-engine query, said graphical user interface comprising: a first display area of a search-results webpage that displays content received by a web browser running on the computer, wherein the content includes a hyperlink of a web page deemed to satisfy the search-engine query; a second display area of the search-results webpage that displays content retrieved in real time from a web server by another web browser running on a background thread of the web browser, wherein the content retrieved by the other web browser includes a thumbnail image of the web page displayed next to the hyperlink of the webpage, and wherein the hyperlink and the thumbnail image are displayed together as part of the search-results webpage; and a third display area of the search-results webpage that displays a preview-options GUI element, which allows a user to control a presentation of the thumbnail image in the second display area. 9. The graphical user interface of claim 1 , wherein the preview-options GUI element provides an option to present one or more graphical tabs associated with the web page. | 0.859143 |
3,947,708 | 1 | 4 | 1. An apparatus for converting a sequence of digital words representative of speech in a time division multiplexed format into an acoustic signal, each word having a plurality of magnitude bit locations comprising: a. means for converting the magnitude bits into a plurality of voltages, each of which represents the occurrence of a bit in one of the magnitude bit locations, b. a beam made of a piezoelectric material and having a free and fixed end, c. means for applying each of the plurality of voltages across each of a plurality of elements of the beam to create a deflection of the free end of the beam related to the magnitude of the digital word, the deflection being in a direction substantially perpendicular to the length of the beam, and d. means responsive to the deflection of the free end for generating an acoustic signal related to the sequence of digital words. | 1. An apparatus for converting a sequence of digital words representative of speech in a time division multiplexed format into an acoustic signal, each word having a plurality of magnitude bit locations comprising: a. means for converting the magnitude bits into a plurality of voltages, each of which represents the occurrence of a bit in one of the magnitude bit locations, b. a beam made of a piezoelectric material and having a free and fixed end, c. means for applying each of the plurality of voltages across each of a plurality of elements of the beam to create a deflection of the free end of the beam related to the magnitude of the digital word, the deflection being in a direction substantially perpendicular to the length of the beam, and d. means responsive to the deflection of the free end for generating an acoustic signal related to the sequence of digital words. 4. The apparatus according to claim 1, wherein the voltages have an absolute magnitude greater than zero volts and wherein the applying means includes a first layer of a grounded conductive material on a first surface of the beam and a second layer of a conductive material on a second surface of the beam, the second layer being divided into a plurality of electrically separated segments, each of which is connected to one of the voltage outputs of the converting means. | 0.835769 |
8,073,697 | 1 | 6 | 1. A method of establishing a multimodal personality for an application that provides vocal and visual output, the method comprising: with at least one processor: selecting, by the application that provides vocal and visual output, matching vocal and visual demeanors; and incorporating, by the application, the matching vocal and visual demeanors as a multimodal personality into the application by rendering a voice prompt and/or response generated by the application in the vocal demeanor and rendering a visual element generated by the application in the matching visual demeanor, the voice prompt or response being rendered in a voice having an age, gender and/or accent based on the selected vocal demeanor, wherein: selecting matching vocal and visual demeanors further comprises selecting a vocal demeanor in dependence upon a history of a user's navigation among web sites, the vocal demeanor being selected to match a visual demeanor determined based on at least one property of web pages previously visited, the at least one property comprising one or more of: text font; count of words on the web page; proportion of white space; ratio of graphics to screen area; or ratio of text space to graphic space. | 1. A method of establishing a multimodal personality for an application that provides vocal and visual output, the method comprising: with at least one processor: selecting, by the application that provides vocal and visual output, matching vocal and visual demeanors; and incorporating, by the application, the matching vocal and visual demeanors as a multimodal personality into the application by rendering a voice prompt and/or response generated by the application in the vocal demeanor and rendering a visual element generated by the application in the matching visual demeanor, the voice prompt or response being rendered in a voice having an age, gender and/or accent based on the selected vocal demeanor, wherein: selecting matching vocal and visual demeanors further comprises selecting a vocal demeanor in dependence upon a history of a user's navigation among web sites, the vocal demeanor being selected to match a visual demeanor determined based on at least one property of web pages previously visited, the at least one property comprising one or more of: text font; count of words on the web page; proportion of white space; ratio of graphics to screen area; or ratio of text space to graphic space. 6. The method of claim 1 wherein selecting matching vocal and visual demeanors further comprises retrieving a user profile from storage; selecting a vocal demeanor in dependence upon the retrieved user profile; and selecting a visual demeanor in dependence upon the retrieved user profile. | 0.802326 |
8,117,549 | 1 | 23 | 1. A method of capturing actions that are performed on at least one medical image of a patient during a medical imaging interpretation, the method being implemented using a computer system, the method comprising: (a) displaying a workflow template on a display of the computer system; (b) displaying the at least one medical image of the patient on said display; (c) automatically extracting data from an electronic medical record of the patient, or other data related to the patient, from a database, into said workflow template provided on said display; (d) capturing and storing one or more user actions as they are performed on the medical image of the patient, by an interpreting user during an entire medical imaging interpretation, using an auditing function of the computer system; (e) automatically generating user action information from the one or more captured actions, to prompt said user to perform certain actions; (f) storing the captured user actions and user action information, along with said data related to the patient, in said database, with the at least one medical image of the patient, as a new workflow sequence onto a new workflow template as a pre-defined protocol; (g) accessing said new workflow template having said pre-defined protocol from said database; (h) displaying to a new user, in said new workflow template, a recreation of the exact pre-defined protocol including said data related to the patient, stored by said previous interpreting user in said database, as a continuous replica of said previous interpreting user's actions and user action information stored in said workflow sequence, such that said new user may selectively review and modify clinically pertinent medical images and said data related to the patient, in a continuous manner that follows said stored workflow template as created by said previous interpreting user; and (i) repeating steps (b)-(f), such that a modified new workflow template is created and stored in said database: wherein each said modified new workflow template is a cumulative, refined, and dynamic workflow sequence of a series of said captured user actions and user action information, along with data related to each patient, with the at least one medical image of said patient, in order to provide best practice of said medical image interpretation for said user. | 1. A method of capturing actions that are performed on at least one medical image of a patient during a medical imaging interpretation, the method being implemented using a computer system, the method comprising: (a) displaying a workflow template on a display of the computer system; (b) displaying the at least one medical image of the patient on said display; (c) automatically extracting data from an electronic medical record of the patient, or other data related to the patient, from a database, into said workflow template provided on said display; (d) capturing and storing one or more user actions as they are performed on the medical image of the patient, by an interpreting user during an entire medical imaging interpretation, using an auditing function of the computer system; (e) automatically generating user action information from the one or more captured actions, to prompt said user to perform certain actions; (f) storing the captured user actions and user action information, along with said data related to the patient, in said database, with the at least one medical image of the patient, as a new workflow sequence onto a new workflow template as a pre-defined protocol; (g) accessing said new workflow template having said pre-defined protocol from said database; (h) displaying to a new user, in said new workflow template, a recreation of the exact pre-defined protocol including said data related to the patient, stored by said previous interpreting user in said database, as a continuous replica of said previous interpreting user's actions and user action information stored in said workflow sequence, such that said new user may selectively review and modify clinically pertinent medical images and said data related to the patient, in a continuous manner that follows said stored workflow template as created by said previous interpreting user; and (i) repeating steps (b)-(f), such that a modified new workflow template is created and stored in said database: wherein each said modified new workflow template is a cumulative, refined, and dynamic workflow sequence of a series of said captured user actions and user action information, along with data related to each patient, with the at least one medical image of said patient, in order to provide best practice of said medical image interpretation for said user. 23. The method according to claim 1 , wherein a unique identifier is used to allow user access to the workflow templates. | 0.91155 |
9,785,855 | 9 | 13 | 9. The system of claim 8 wherein said neural network comprises a CNN (Convolutional Neural Network). | 9. The system of claim 8 wherein said neural network comprises a CNN (Convolutional Neural Network). 13. The system of claim 9 wherein said instructions are further configured for adapting said neural network to perform tasks increasingly similar to license plate recognition ire a target set as a part of training said neural network. | 0.911229 |
9,946,750 | 1 | 11 | 1. A computer-implemented method for generating statistics for processing database queries, the method comprising: receiving, by a query compiler, from a client device, a plurality of database queries by a database system, the database queries processing data stored in database tables of the database system; identifying, by the query compiler, missing statistics while generating execution plans for database queries, the identifying comprising, for each of the plurality of database queries: requesting a statistical information useful for generating an execution plan for a database query; and determining that the requested statistical information is not available; and storing information describing the requested statistical information as missing statistics, responsive to determining that the requested statistical information is not available; ranking the identified missing statistics based on a number of times each missing statistics was identified as being not available during generation of execution plan; determining a subset of the identified missing statistics for use in generating execution plans for subsequent database queries, wherein determining the subset comprises selecting identified missing statistics based on the ranking; receiving, by the query compiler, the subsequent database queries; generating, by the query compiler, an execution plan for the one of the subsequent database queries using at least one of the identified missing statistics from the subset; executing, by an execution engine, the generated execution plan for the one of the subsequent queries to determine a result set; and sending the determined result set to the client device. | 1. A computer-implemented method for generating statistics for processing database queries, the method comprising: receiving, by a query compiler, from a client device, a plurality of database queries by a database system, the database queries processing data stored in database tables of the database system; identifying, by the query compiler, missing statistics while generating execution plans for database queries, the identifying comprising, for each of the plurality of database queries: requesting a statistical information useful for generating an execution plan for a database query; and determining that the requested statistical information is not available; and storing information describing the requested statistical information as missing statistics, responsive to determining that the requested statistical information is not available; ranking the identified missing statistics based on a number of times each missing statistics was identified as being not available during generation of execution plan; determining a subset of the identified missing statistics for use in generating execution plans for subsequent database queries, wherein determining the subset comprises selecting identified missing statistics based on the ranking; receiving, by the query compiler, the subsequent database queries; generating, by the query compiler, an execution plan for the one of the subsequent database queries using at least one of the identified missing statistics from the subset; executing, by an execution engine, the generated execution plan for the one of the subsequent queries to determine a result set; and sending the determined result set to the client device. 11. The computer-implemented method of claim 1 , wherein determining the subset of the identified missing statistics comprises selecting missing statistics that was identified as missing for queries associated with one of: a particular user, an application, or a work-load-management related identifier. | 0.741908 |
8,813,047 | 7 | 22 | 7. The non-transitory computer-readable medium of claim 6 , wherein the YATL statement is selected from the group consisting of: a foreach-match statement, a match-once statement, a match statement, a foreach-element statement, a debug statement, a native statement, a print statement, a log statement, an express “on” statement, an isolated statement, a continue statement, a die statement, an “on” statement, a statement insertion statement, a layout insertion statement, an if statement, a while statement, a do while statement, a for statement, a delete statement, a transform decl statement, a transform use statement, a replace with statement, a return statement, a try catch statement, an either or statement, a fail statement, an on file statement, and a pointer declare statement. | 7. The non-transitory computer-readable medium of claim 6 , wherein the YATL statement is selected from the group consisting of: a foreach-match statement, a match-once statement, a match statement, a foreach-element statement, a debug statement, a native statement, a print statement, a log statement, an express “on” statement, an isolated statement, a continue statement, a die statement, an “on” statement, a statement insertion statement, a layout insertion statement, an if statement, a while statement, a do while statement, a for statement, a delete statement, a transform decl statement, a transform use statement, a replace with statement, a return statement, a try catch statement, an either or statement, a fail statement, an on file statement, and a pointer declare statement. 22. The non-transitory computer-readable medium of claim 7 , wherein the YATL program includes an “on” statement having “on” followed by an on pointer dereference followed by the compound statement. | 0.902077 |
8,315,870 | 14 | 17 | 14. A speech recognition method according to claim 11 , wherein the prosodic label is the tone label, and the prosodic interval is the vowel interval. | 14. A speech recognition method according to claim 11 , wherein the prosodic label is the tone label, and the prosodic interval is the vowel interval. 17. A speech recognition method according to claim 14 , wherein the step of outputting the second score comprises the step of performing prosodic recognition using a tone model which is modeled using a time-rate change of a pitch in the vowel interval as a feature amount. | 0.893833 |
8,412,637 | 1 | 24 | 1. A method for using a computer to facilitate and control access to a two-level information database created by inputs supplied by posting users, comprising the steps of: (a) receiving and storing in the database, a plurality of two-part descriptions each supplied by a user, each two-part description including a first-level element in the form of a basic description of each user's unmet need or unsolved problem, and a second-level element in the form of a detailed description of each user's unmet need or unsolved problem; (b) by a computer, permitting originators to access and select without restriction only the first-level elements of the stored two-part descriptions; (c) providing selecting originators with an option to access the second-level elements of the stored two-part descriptions that correspond to the selected first-level elements by agreeing to a license agreement specifying the terms of use for at least the second-level element; (d) providing license accepting originators with access to the second-level elements of the stored two-part description corresponding to the selected first-level elements; (e) permitting non-license-accepting originators to search the contents of the first-level and second-level elements of the two-part descriptions but disallowing visual access to the second-level elements; and (f) as a result of a search of the two-part descriptions by a non-license-accepting originator, sending the first-level element of the two-part descriptions. | 1. A method for using a computer to facilitate and control access to a two-level information database created by inputs supplied by posting users, comprising the steps of: (a) receiving and storing in the database, a plurality of two-part descriptions each supplied by a user, each two-part description including a first-level element in the form of a basic description of each user's unmet need or unsolved problem, and a second-level element in the form of a detailed description of each user's unmet need or unsolved problem; (b) by a computer, permitting originators to access and select without restriction only the first-level elements of the stored two-part descriptions; (c) providing selecting originators with an option to access the second-level elements of the stored two-part descriptions that correspond to the selected first-level elements by agreeing to a license agreement specifying the terms of use for at least the second-level element; (d) providing license accepting originators with access to the second-level elements of the stored two-part description corresponding to the selected first-level elements; (e) permitting non-license-accepting originators to search the contents of the first-level and second-level elements of the two-part descriptions but disallowing visual access to the second-level elements; and (f) as a result of a search of the two-part descriptions by a non-license-accepting originator, sending the first-level element of the two-part descriptions. 24. The method of claim 1 comprising: storing a two-part description, a first level element and its corresponding second-level element, at one server. | 0.830317 |
8,423,481 | 1 | 13 | 1. A self-learning method for keyword based human machine interaction, comprising: predetermining a set of keywords in a database of a portable navigation device; performing an initialization operation to initialize the set of keywords; sorting and displaying the set of keywords on a screen of the portable navigation device according to results of the initialization operation; selecting at least one of the keywords; assigning a weighted score to the selected keyword and performing a first calculation to refresh a priority score of the selected keyword; generating a weighted factor for the selected keyword and transmitting the weighted score and the weighted factor of the selected keyword to the keywords related to the selected keyword; refreshing, according to the weighted score and the weighted factor of the selected keyword, priority scores of the keywords related to the selected keyword and accordingly re-sorting all keywords; displaying the keywords on the screen according to results of the re-sorting; pushing the selected keyword to a keyword buffer of the portable navigation device; performing a second calculation to strengthen linkage strengths between the keywords stored in the keyword buffer; refreshing priority scores of all keywords according to the linkage strengths; and performing a reset operation to remove the selected keyword out of the keyword buffer when the selected keyword stored in the keyword buffer has been stored over a predetermined time period. | 1. A self-learning method for keyword based human machine interaction, comprising: predetermining a set of keywords in a database of a portable navigation device; performing an initialization operation to initialize the set of keywords; sorting and displaying the set of keywords on a screen of the portable navigation device according to results of the initialization operation; selecting at least one of the keywords; assigning a weighted score to the selected keyword and performing a first calculation to refresh a priority score of the selected keyword; generating a weighted factor for the selected keyword and transmitting the weighted score and the weighted factor of the selected keyword to the keywords related to the selected keyword; refreshing, according to the weighted score and the weighted factor of the selected keyword, priority scores of the keywords related to the selected keyword and accordingly re-sorting all keywords; displaying the keywords on the screen according to results of the re-sorting; pushing the selected keyword to a keyword buffer of the portable navigation device; performing a second calculation to strengthen linkage strengths between the keywords stored in the keyword buffer; refreshing priority scores of all keywords according to the linkage strengths; and performing a reset operation to remove the selected keyword out of the keyword buffer when the selected keyword stored in the keyword buffer has been stored over a predetermined time period. 13. The method as claimed in claim 1 , wherein the step of refreshing a priority score further comprises refreshing the linkage strengths between the keywords related to the selected keyword. | 0.877407 |
8,694,887 | 28 | 33 | 28. The article of claim 27 , wherein the computing platform comprises a server computing platform. | 28. The article of claim 27 , wherein the computing platform comprises a server computing platform. 33. The article of claim 28 , wherein the first subset of the plurality of elements comprises a relatively small number of highlighted elements and a larger number of non-highlighted elements, and wherein the storage medium has stored thereon further instructions executable by the processor to highlight one or more of the non-highlighted elements at least in part in response to the user pointing to the one or more of the non-highlighted elements. | 0.851779 |
8,233,714 | 14 | 16 | 14. A non-transitory computer-readable medium having stored thereon a sequence of instructions which when executed by a system, causes the system to perform a method, comprising: detecting data fields on an image; generating a flexible document description based on the detected data fields, including creating a set of search elements for each data field, each search element having one or more associated search criteria; and training the flexible document description using a search algorithm to detect the data fields on additional training images based on the set of search elements. | 14. A non-transitory computer-readable medium having stored thereon a sequence of instructions which when executed by a system, causes the system to perform a method, comprising: detecting data fields on an image; generating a flexible document description based on the detected data fields, including creating a set of search elements for each data field, each search element having one or more associated search criteria; and training the flexible document description using a search algorithm to detect the data fields on additional training images based on the set of search elements. 16. The non-transitory computer-readable medium of claim 14 , wherein the method further comprises receiving a user input indicating one or more of the data fields to be detected. | 0.823819 |
9,460,711 | 1 | 11 | 1. A computer-implemented method, comprising: receiving training data comprising a respective training data set for each of two or more languages; processing a monolingual deep neural network (DNN) acoustic model on a given training data set of the two or more languages; initializing one or more weights of a multilingual DNN acoustic model based on the processing of the monolingual DNN acoustic model, wherein the multilingual DNN acoustic model comprises a feedforward neural network having multiple layers of one or more nodes, wherein each node of a given layer connects with a respective weight to each node of a subsequent layer, and wherein the multiple layers of one or more nodes comprise one or more hidden layers of nodes that are shared between at least two of the two or more languages and a language-specific output layer of nodes corresponding to each of the two or more languages; after initializing the one or more weights of the multilingual DNN acoustic model based on the processing of the monolingual DNN acoustic model, processing, by one or more computing devices, the multilingual DNN acoustic model based on the training data to generate a trained multilingual DNN acoustic model; and using the trained multilingual DNN acoustic model in an automatic speech recognition (ASR) system. | 1. A computer-implemented method, comprising: receiving training data comprising a respective training data set for each of two or more languages; processing a monolingual deep neural network (DNN) acoustic model on a given training data set of the two or more languages; initializing one or more weights of a multilingual DNN acoustic model based on the processing of the monolingual DNN acoustic model, wherein the multilingual DNN acoustic model comprises a feedforward neural network having multiple layers of one or more nodes, wherein each node of a given layer connects with a respective weight to each node of a subsequent layer, and wherein the multiple layers of one or more nodes comprise one or more hidden layers of nodes that are shared between at least two of the two or more languages and a language-specific output layer of nodes corresponding to each of the two or more languages; after initializing the one or more weights of the multilingual DNN acoustic model based on the processing of the monolingual DNN acoustic model, processing, by one or more computing devices, the multilingual DNN acoustic model based on the training data to generate a trained multilingual DNN acoustic model; and using the trained multilingual DNN acoustic model in an automatic speech recognition (ASR) system. 11. The computer-implemented method of claim 1 , wherein the trained multilingual DNN acoustic model is configured to be used to perform ASR in multiple languages of the two or more languages. | 0.834483 |
6,067,517 | 15 | 24 | 15. A method for transcribing a segment of data that includes speech in one or more environments and non-speech data, comprising: inputting the data to a segmenter and producing a series of segments, each segment being given a type identifier tag selected from a predetermined set of classes, wherein it is assumed that the input data is produced by a parallel combination of models, each model corresponding to one of the predetermined classes, the identifier tag assigned to a segment being the class identifier tag of the model that gives the segment the highest probability, subject to certain constraints, wherein a number of classes that the acoustic input can be classified into are identified that represent the most acoustically dissimilar classes possible and, wherein the process of creating the models comprises identifying a feature space for the individual predetermined classes; and transcribing each type identifier tagged segment using a specific system created for that type. | 15. A method for transcribing a segment of data that includes speech in one or more environments and non-speech data, comprising: inputting the data to a segmenter and producing a series of segments, each segment being given a type identifier tag selected from a predetermined set of classes, wherein it is assumed that the input data is produced by a parallel combination of models, each model corresponding to one of the predetermined classes, the identifier tag assigned to a segment being the class identifier tag of the model that gives the segment the highest probability, subject to certain constraints, wherein a number of classes that the acoustic input can be classified into are identified that represent the most acoustically dissimilar classes possible and, wherein the process of creating the models comprises identifying a feature space for the individual predetermined classes; and transcribing each type identifier tagged segment using a specific system created for that type. 24. The method of claim 15, wherein the feature space for the model for clean speech is created by: taking a window of input speech every 10 milliseconds; computing the energy in the window, wherein the feature is related to the variation of energy across a plurality of consecutive windows. | 0.687097 |
9,836,284 | 1 | 4 | 1. A method of generating a user interface, the method comprising: retrieving structural information comprising a topology of virtual computing resources and application components executing thereon, wherein the structural information specifies a plurality of nodes corresponding to the virtual computing resources and a plurality of child elements of the plurality of nodes; generating a declarative layout associated with the plurality of child elements of the plurality of nodes according to the structural information, wherein the declarative layout is a text description of the structural information associated with the plurality of child elements; translating the declarative layout into a plurality of scalar vector graphics (SVG) elements that renders the plurality of child elements based on the declarative layout, comprising: parsing the text description to determine positional and sizing information for the plurality of child elements based on the declarative layout relative to the plurality of nodes; and rendering, on a display device, a graphical user interface comprising the plurality of SVG elements, wherein the graphical user interface visually represents the topology. | 1. A method of generating a user interface, the method comprising: retrieving structural information comprising a topology of virtual computing resources and application components executing thereon, wherein the structural information specifies a plurality of nodes corresponding to the virtual computing resources and a plurality of child elements of the plurality of nodes; generating a declarative layout associated with the plurality of child elements of the plurality of nodes according to the structural information, wherein the declarative layout is a text description of the structural information associated with the plurality of child elements; translating the declarative layout into a plurality of scalar vector graphics (SVG) elements that renders the plurality of child elements based on the declarative layout, comprising: parsing the text description to determine positional and sizing information for the plurality of child elements based on the declarative layout relative to the plurality of nodes; and rendering, on a display device, a graphical user interface comprising the plurality of SVG elements, wherein the graphical user interface visually represents the topology. 4. The method of claim 1 , further comprising: determining the declarative layout specifies a directional layout for a first set of child elements in sequence, wherein the directional layout is selected from a group consisting of a vertical layout and a horizontal layout. | 0.772575 |
9,753,908 | 20 | 23 | 20. A nontransitory computer readable storage medium having instructions stored thereon that when used to instruct a processor cause the processor to implement a method for transferring data from a receipt image directly into a spreadsheet, the instructions causing the processor to perform the steps of: selecting an XML map that maps types of expense data and associated expense data from the receipt to one or more cells in the spreadsheet in accordance with pre-designated mapping preferences of the user that map the types of expense data and associated expense data to XML data elements based on the types of expense data in the receipt; acquiring textual data from the receipt image and storing the textual data; parsing the textual data to identify the types of expense data and associated expense data for each type of expense data and to extract the types of expense data and associated expense data of each type of expense data from the textual data; and for each type of expense data extracted, determining if the each type of expense data is mapped in the selected XML map to one or more cells in the spreadsheet and, for each type of expense data found in the selected XML map, using the selected XML map to automatically transfer the extracted associated expense data to the cells in the spreadsheet designated for the types of extracted expense data with which the extracted associated expense data is associated. | 20. A nontransitory computer readable storage medium having instructions stored thereon that when used to instruct a processor cause the processor to implement a method for transferring data from a receipt image directly into a spreadsheet, the instructions causing the processor to perform the steps of: selecting an XML map that maps types of expense data and associated expense data from the receipt to one or more cells in the spreadsheet in accordance with pre-designated mapping preferences of the user that map the types of expense data and associated expense data to XML data elements based on the types of expense data in the receipt; acquiring textual data from the receipt image and storing the textual data; parsing the textual data to identify the types of expense data and associated expense data for each type of expense data and to extract the types of expense data and associated expense data of each type of expense data from the textual data; and for each type of expense data extracted, determining if the each type of expense data is mapped in the selected XML map to one or more cells in the spreadsheet and, for each type of expense data found in the selected XML map, using the selected XML map to automatically transfer the extracted associated expense data to the cells in the spreadsheet designated for the types of extracted expense data with which the extracted associated expense data is associated. 23. The medium of claim 20 wherein said instructions further cause the processor to pre-designate mapping preferences of the user by using a wizard to guide the user through the creation of a custom map that associates types of expense data of the receipt to desired cells in the spreadsheet. | 0.556231 |
9,690,773 | 6 | 7 | 6. The method of claim 5 , further comprising, subsequent to storing the additional value in association with the particular information field of the task entry: identifying user input provided in an interface of an application that enables modification of the task entry, wherein the user input is generated in response to interaction by the user with a user interface input device of a computing device of the user; determining a suggestion that is based on the additional value to provide for presentation to the user in response to the user input; and in response to the user input, providing the determined suggestion for presentation to the user in the interface. | 6. The method of claim 5 , further comprising, subsequent to storing the additional value in association with the particular information field of the task entry: identifying user input provided in an interface of an application that enables modification of the task entry, wherein the user input is generated in response to interaction by the user with a user interface input device of a computing device of the user; determining a suggestion that is based on the additional value to provide for presentation to the user in response to the user input; and in response to the user input, providing the determined suggestion for presentation to the user in the interface. 7. The method of claim 6 , further comprising: receiving an indication of a selection, by the user, of the suggestion; and in response to receiving the indication of the selection, replacing the given value with the additional value in the task entry. | 0.897718 |
4,625,287 | 6 | 11 | 6. The apparatus according to claim 5, wherein said first and second memory means respectively store the reference vowel patterns and the reference consonant patterns for reference monosyllables of the Japanese language. | 6. The apparatus according to claim 5, wherein said first and second memory means respectively store the reference vowel patterns and the reference consonant patterns for reference monosyllables of the Japanese language. 11. The apparatus according to claim 6, wherein said second processing means cuts, when the monosyllable of the input speech has a long vowel component longer than a predetermined time period, the vowel pattern representing the long vowel component after the predetermined time period, and omit to perform comparison of a cutoff portion of the vowel pattern of the long vowel pattern with the reference vowel patterns stored in said first memory. | 0.932095 |
9,443,324 | 9 | 10 | 9. A system for rendering annotations associated with an electronic image comprising: a first data repository for storing the electronic image; the electronic image having a plurality of pixels, with one or more pixels annotated at a plurality of levels in ascending magnitude of descriptive characteristics, with each level containing one or more descriptive characteristics of the pixel, such that the descriptive characteristics at a subsequent level in the ascending magnitude is with reference to descriptive characteristics of one or more pixel surrounding the pixel retrieving and rendering the selected level annotation for the selected pixel; wherein a level selected by default is a lowest level available, wherein at a lowest level the descriptive characteristic of the pixel is a caption of the electronic image and at a highest level the descriptive characteristic of the pixel is a color of the pixel; a second data repository for storing the annotations associated with the electronic image wherein the annotations at the plurality of levels for the annotated pixels is collectively stored in a file separate from the electronic image; an image display module configured to display the electronic image on a display device, wherein the displayed electronic image is determined based on a cursor location relative to a plurality of pixel coordinates within a user interface; a pixel and level identification module configured to receive pixel selection and level selection details from a user interface; and an annotation retrieval module configured to retrieve from the second repository annotations corresponding to the pixel and level selected and render the retrieved annotations for the electronic image; wherein the pixel and level identification module is configured to receive new selection details for pixel or level or both, the system further configured to continue displaying the electronic image on a display device while retrieving from the second repository annotations corresponding to the new pixel or level or both. | 9. A system for rendering annotations associated with an electronic image comprising: a first data repository for storing the electronic image; the electronic image having a plurality of pixels, with one or more pixels annotated at a plurality of levels in ascending magnitude of descriptive characteristics, with each level containing one or more descriptive characteristics of the pixel, such that the descriptive characteristics at a subsequent level in the ascending magnitude is with reference to descriptive characteristics of one or more pixel surrounding the pixel retrieving and rendering the selected level annotation for the selected pixel; wherein a level selected by default is a lowest level available, wherein at a lowest level the descriptive characteristic of the pixel is a caption of the electronic image and at a highest level the descriptive characteristic of the pixel is a color of the pixel; a second data repository for storing the annotations associated with the electronic image wherein the annotations at the plurality of levels for the annotated pixels is collectively stored in a file separate from the electronic image; an image display module configured to display the electronic image on a display device, wherein the displayed electronic image is determined based on a cursor location relative to a plurality of pixel coordinates within a user interface; a pixel and level identification module configured to receive pixel selection and level selection details from a user interface; and an annotation retrieval module configured to retrieve from the second repository annotations corresponding to the pixel and level selected and render the retrieved annotations for the electronic image; wherein the pixel and level identification module is configured to receive new selection details for pixel or level or both, the system further configured to continue displaying the electronic image on a display device while retrieving from the second repository annotations corresponding to the new pixel or level or both. 10. The system as claimed in claim 9 , wherein the annotations stored in the second data repository are text or audio. | 0.714976 |
9,720,893 | 2 | 3 | 2. The method of claim 1 , wherein receiving data identifying one or several indicators of the insertion point comprises receiving an electrical signal containing information identifying coding creating an insertion point, wherein the electrical signal is received at an end-user server of an end-user network from a content network. | 2. The method of claim 1 , wherein receiving data identifying one or several indicators of the insertion point comprises receiving an electrical signal containing information identifying coding creating an insertion point, wherein the electrical signal is received at an end-user server of an end-user network from a content network. 3. The method of claim 2 , wherein identifying the presence of the at least one insertion point within the document further comprises associating a first Boolean value indicative of having found at least one insertion point in the document if the search engine identifies coding creating at least one insertion point within the document and associating a second Boolean value indicative of not having found at least one insertion point in the document if the search engine does not identify coding creating at least one insertion point. | 0.861642 |
10,055,397 | 12 | 14 | 12. The system of claim 1 further comprising: a plurality of devices, each device comprising a dynamic language model; wherein each of the plurality of devices is configured to transmit to the processor text data representing text that has been entered into that device. | 12. The system of claim 1 further comprising: a plurality of devices, each device comprising a dynamic language model; wherein each of the plurality of devices is configured to transmit to the processor text data representing text that has been entered into that device. 14. The system of claim 12 , wherein the system comprises a server and each of the plurality of devices is configured to download the one or more language models from the server and upload the text data onto the server. | 0.901262 |
10,140,358 | 13 | 14 | 13. The system of claim 12 , wherein the instructions, when executed by the one or more processors, cause the one or more processors to: assign different end lifetimes to each set of relational data items. | 13. The system of claim 12 , wherein the instructions, when executed by the one or more processors, cause the one or more processors to: assign different end lifetimes to each set of relational data items. 14. The system of claim 13 , wherein the instructions, when executed by the one or more processors, cause the one or more processors to: assign a common end lifetime for results provided by the stream engine when processing the relational data items. | 0.847188 |
8,457,409 | 10 | 11 | 10. The learning machine defined in claim 1 , said processing unit further comprising a pseudo-random vector generating means for generating a pseudo-random vector in accordance with said probability distribution produced by said estimation means, said pseudo-random vector being output from said processing unit as a label of said third feature vector. | 10. The learning machine defined in claim 1 , said processing unit further comprising a pseudo-random vector generating means for generating a pseudo-random vector in accordance with said probability distribution produced by said estimation means, said pseudo-random vector being output from said processing unit as a label of said third feature vector. 11. The learning machine defined in claim 10 , said processing unit further comprising unsupervised learning means for adjusting, in response to a sixth feature vector input to said processing unit, said at least one first expansion correlation matrix by using at least one outer product of a linear combination of a vector with components all equal to 1 and a label of said sixth feature vector and a sixth orthogonal expansion of a subvector of said sixth feature vector, said sixth orthogonal expansion comprising components of said subvector of said sixth feature vector and a plurality of products of said components of said subvector of said sixth feature vector, wherein said label of said sixth feature vector is a pseudo-random vector generated by said pseudo-random vector generating means as a label of said sixth feature vector. | 0.796807 |
9,098,568 | 1 | 6 | 1. A computer-implemented method, comprising: receiving at a client device a dictionary defining query triggers, each of the query triggers being one or more terms; identifying at the client device query triggers in a resource, the resource being a non-query resource; for each query trigger identified in the resource, calculating at the client device a rank score for the query trigger based on attributes of the query trigger, the attributes including at least one of: a context of the query trigger defined by a display format of the query trigger in the resource; and a frequency of occurrence of the query trigger in the resource; ranking at the client device the query triggers according to the rank scores; generating at the client device search query suggestions from the query triggers identified in the resource; and presenting at the client device the search query suggestions according to the ranking of the query triggers. | 1. A computer-implemented method, comprising: receiving at a client device a dictionary defining query triggers, each of the query triggers being one or more terms; identifying at the client device query triggers in a resource, the resource being a non-query resource; for each query trigger identified in the resource, calculating at the client device a rank score for the query trigger based on attributes of the query trigger, the attributes including at least one of: a context of the query trigger defined by a display format of the query trigger in the resource; and a frequency of occurrence of the query trigger in the resource; ranking at the client device the query triggers according to the rank scores; generating at the client device search query suggestions from the query triggers identified in the resource; and presenting at the client device the search query suggestions according to the ranking of the query triggers. 6. The computer-implemented method of claim 1 , wherein: the attributes of the query trigger further include a popularity score of the query trigger, the popularity score stored in the dictionary and being a measure of a popularity of the query trigger as a query for multiple users of a search engine; and calculating at the client device a rank score for the query trigger based on attributes of the query trigger comprises generating the rank score based on a function of the context of the query trigger, the frequency of occurrence of the query trigger, a length of the query trigger, and the popularity of the query trigger. | 0.773869 |
8,700,547 | 6 | 9 | 6. A method for uncovering hidden structures in data embodied on a storage device, comprising executing operations implementing a spectral clustering algorithm on at least one automated computer, the operations comprising: characterizing clustering of data of at least first, second, and third types using first, second and third tentative cluster characterization matrices, respectively; and iteratively improving each tentative cluster characterization matrix using linear combinations of other matrices, the other matrices characterizing relationships between data of different types, wherein iteratively improving comprises calculating a matrix M (P) , which is a function of at least: at least one relation matrix representing respective relations between distinct members of m sets to be clustered into k p disjoint clusters, where p is an index running from 1 to m; at least one feature matrix where each element denotes a feature value for an associated data object; the tentative cluster characterization matrices; and a set of weights for different types of relations and features. | 6. A method for uncovering hidden structures in data embodied on a storage device, comprising executing operations implementing a spectral clustering algorithm on at least one automated computer, the operations comprising: characterizing clustering of data of at least first, second, and third types using first, second and third tentative cluster characterization matrices, respectively; and iteratively improving each tentative cluster characterization matrix using linear combinations of other matrices, the other matrices characterizing relationships between data of different types, wherein iteratively improving comprises calculating a matrix M (P) , which is a function of at least: at least one relation matrix representing respective relations between distinct members of m sets to be clustered into k p disjoint clusters, where p is an index running from 1 to m; at least one feature matrix where each element denotes a feature value for an associated data object; the tentative cluster characterization matrices; and a set of weights for different types of relations and features. 9. The method of claim 6 , wherein the other matrices used to improve the first tentative cluster indication matrix include a transpose of at least one of the second and third tentative cluster indication matrices. | 0.917882 |
9,098,862 | 7 | 8 | 7. A computer-implemented method for obtaining human input regarding an item of interest to a user, the method comprising: identifying, by a computing system, a first plurality of human respondents by matching user-defined query criteria with human respondent profile information, wherein the user-defined query criteria identifies one or more characteristics of individual human respondents; based on a determination that the number of human respondents identified in the first plurality of the human respondents does not satisfy a threshold, modifying, by the computing system, the user-defined criteria; identifying, by the computing system, a second plurality of the human respondents by matching the modified user-defined query criteria with human respondent profile information, wherein the number of human respondents identified in the second plurality of human respondents satisfies the threshold; for individual human respondents of the second plurality of human respondents, providing one or more questions regarding the item of interest to a computing device associated with the human respondent; and receiving at least one response to the one or more questions from the human respondent. | 7. A computer-implemented method for obtaining human input regarding an item of interest to a user, the method comprising: identifying, by a computing system, a first plurality of human respondents by matching user-defined query criteria with human respondent profile information, wherein the user-defined query criteria identifies one or more characteristics of individual human respondents; based on a determination that the number of human respondents identified in the first plurality of the human respondents does not satisfy a threshold, modifying, by the computing system, the user-defined criteria; identifying, by the computing system, a second plurality of the human respondents by matching the modified user-defined query criteria with human respondent profile information, wherein the number of human respondents identified in the second plurality of human respondents satisfies the threshold; for individual human respondents of the second plurality of human respondents, providing one or more questions regarding the item of interest to a computing device associated with the human respondent; and receiving at least one response to the one or more questions from the human respondent. 8. The method of claim 7 , wherein the user-defined criteria is ranked. | 0.956227 |
8,930,339 | 1 | 4 | 1. In a computing environment, a method performed at least in part on at least one processor, comprising, applying a task-based assessment metric to a search engine implementation, including, examining user activities associated with the search engine implementation, classifying atomic events into tasks by measuring similarity between at least some of the user activities, and evaluating each task in accordance with the task-based assessment metric to determine indicia of search engine implementation performance comprising a task success rate for the search engine implementation, including applying the task-based assessment metric to the tasks to produce a statistical distribution and comparing the statistical distribution corresponding to the search engine implementation with a distribution corresponding to another search engine implementation to generate a comparison result. | 1. In a computing environment, a method performed at least in part on at least one processor, comprising, applying a task-based assessment metric to a search engine implementation, including, examining user activities associated with the search engine implementation, classifying atomic events into tasks by measuring similarity between at least some of the user activities, and evaluating each task in accordance with the task-based assessment metric to determine indicia of search engine implementation performance comprising a task success rate for the search engine implementation, including applying the task-based assessment metric to the tasks to produce a statistical distribution and comparing the statistical distribution corresponding to the search engine implementation with a distribution corresponding to another search engine implementation to generate a comparison result. 4. The method of claim 1 , wherein evaluating the each task further comprises computing a number of tasks per user for the search engine implementation. | 0.876221 |
8,972,322 | 8 | 13 | 8. A method for extending a default model for user context reasoning, the method comprising: generating first relationship information about a relationship between a concept included in the default model and a concept included in a linked model by matching the concept of the default model with the concept of the linked model based on concept relationship information, the concept relationship information comprising at least one of information about identical concepts, information about similar concepts, and information about upper and lower concepts; generating second relationship information about a relationship between a concept included in the linked model and a concept included in linked data by matching the concept of the linked model with the concept of the linked data; combining the default model and the linked model based on the first relationship information; and combining the linked model and the linked data based on the second relationship information. | 8. A method for extending a default model for user context reasoning, the method comprising: generating first relationship information about a relationship between a concept included in the default model and a concept included in a linked model by matching the concept of the default model with the concept of the linked model based on concept relationship information, the concept relationship information comprising at least one of information about identical concepts, information about similar concepts, and information about upper and lower concepts; generating second relationship information about a relationship between a concept included in the linked model and a concept included in linked data by matching the concept of the linked model with the concept of the linked data; combining the default model and the linked model based on the first relationship information; and combining the linked model and the linked data based on the second relationship information. 13. The method of claim 8 , wherein the first relationship information and the second relationship information include at least one of identity information, similarity information, and hierarchy information. | 0.836234 |
9,507,857 | 2 | 3 | 2. The apparatus according to claim 1 , wherein the extracting of the feature words comprises extracting words subjected to intention representation by using more than one type of intention representation and selecting words from the extracted words based on a predefined criteria, as the feature words. | 2. The apparatus according to claim 1 , wherein the extracting of the feature words comprises extracting words subjected to intention representation by using more than one type of intention representation and selecting words from the extracted words based on a predefined criteria, as the feature words. 3. The apparatus according to claim 2 , wherein the selecting of the words comprises selecting words, each word having a weight calculated based on appearance frequency that is equal to or greater than a predetermined value as the feature words. | 0.94414 |
7,657,935 | 4 | 5 | 4. The method as recited in claim 2 , which further comprises identifying each said attachment with a unique identifier. | 4. The method as recited in claim 2 , which further comprises identifying each said attachment with a unique identifier. 5. The method as recited in claim 4 , wherein gathering statistics relating to said transmission behavior of selected email through said computer system comprises recording the number of occurrences of each said attachment received by said client. | 0.907973 |
10,163,451 | 14 | 18 | 14. A non-transitory computer-readable medium having stored thereon a set of instructions, which if performed by a machine, causes the machine to perform operations comprising: determining first combined audio characteristics of a first accent by combining first sample audio characteristics of audio samples associated with the first accent; determining second combined audio characteristics of a second accent by combining second sample audio characteristics of audio samples associated with the second accent; determining third combined audio characteristics of a third accent by combining the first combined audio characteristics of the first accent and the second combined audio characteristics of the second accent; comparing the third combined audio characteristics of the third accent to other audio characteristics associated with one or more other accents; generating a translation model between a fourth accent and the third accent; receiving an input audio portion substantially associated with the fourth accent in a first spoken language; and outputting, based at least in part on the translation model, an output audio portion substantially associated with the third accent in the first spoken language. | 14. A non-transitory computer-readable medium having stored thereon a set of instructions, which if performed by a machine, causes the machine to perform operations comprising: determining first combined audio characteristics of a first accent by combining first sample audio characteristics of audio samples associated with the first accent; determining second combined audio characteristics of a second accent by combining second sample audio characteristics of audio samples associated with the second accent; determining third combined audio characteristics of a third accent by combining the first combined audio characteristics of the first accent and the second combined audio characteristics of the second accent; comparing the third combined audio characteristics of the third accent to other audio characteristics associated with one or more other accents; generating a translation model between a fourth accent and the third accent; receiving an input audio portion substantially associated with the fourth accent in a first spoken language; and outputting, based at least in part on the translation model, an output audio portion substantially associated with the third accent in the first spoken language. 18. The non-transitory computer-readable medium of claim 14 , wherein the operations further comprise calculating at least one of a fast Fourier transform or mel-frequency cepstral coefficients associated with the input audio portion to identify one or more audio characteristics of the audio input portion. | 0.7467 |
9,020,941 | 1 | 2 | 1. A method of geocoding a multi-feature address, comprising: (a) receiving an address term comprising a region and a feature; (b) splitting the address term into the region and the feature, the feature including two or more components; (c) splitting the feature into two or more separate components, wherein each separate components includes at least one component, and each separate component is different from each other separate component, and for each separate component from the feature, determining a respective component geocode representing a location of a component and an associated component confidence score, the determining comprising: (i) determining at least one geocode based on the region and the component, (ii) determining the respective component geocode representing the location of the component based on the at least one geocode determined in (i), and (iii) determining a component confidence score for the respective component geocode inversely related to a number of geocodes determined in (i) for the component; and (d) selecting an address geocode representing a location of the address term as the component geocode determined in (c)(ii) according to the corresponding component confidence score determined in (c)(iii), wherein steps (a)-(d) are each performed on at least one computing device. | 1. A method of geocoding a multi-feature address, comprising: (a) receiving an address term comprising a region and a feature; (b) splitting the address term into the region and the feature, the feature including two or more components; (c) splitting the feature into two or more separate components, wherein each separate components includes at least one component, and each separate component is different from each other separate component, and for each separate component from the feature, determining a respective component geocode representing a location of a component and an associated component confidence score, the determining comprising: (i) determining at least one geocode based on the region and the component, (ii) determining the respective component geocode representing the location of the component based on the at least one geocode determined in (i), and (iii) determining a component confidence score for the respective component geocode inversely related to a number of geocodes determined in (i) for the component; and (d) selecting an address geocode representing a location of the address term as the component geocode determined in (c)(ii) according to the corresponding component confidence score determined in (c)(iii), wherein steps (a)-(d) are each performed on at least one computing device. 2. The method of claim 1 , further comprising parsing the address term into the region and the feature based on one or more delimiters in the address term. | 0.735495 |
7,644,000 | 6 | 7 | 6. The method according to claim 1 , wherein, the audio effect is a digital signal processing effect. | 6. The method according to claim 1 , wherein, the audio effect is a digital signal processing effect. 7. The method according to claim 6 , wherein, the digital signal processing effect includes a reverb effect. | 0.948669 |
9,009,163 | 1 | 16 | 1. A method for searching a database of digital media assets, comprising: a processor receiving a search query; the processor analyzing the search query to extract a specialized search condition corresponding to a specialized semantic concept; the processor selecting, from a pre-existing ontology of indexers, a specialized indexer based on a determination that the selected specialized indexer can be used to identify digital media assets that correspond to the specialized semantic concept; the processor analyzing the search query to extract a general search condition corresponding to a related higher level semantic concept; the processor identifying, from a database of digital media assets, a subset of digital media assets that satisfy the general search condition responsive to general metadata for each of the digital media assets; the processor applying the selected specialized indexer to the subset of digital media assets to determine specialized metadata for a digital media asset in the subset of digital media assets, wherein the specialized metadata provides an indication of whether the digital media asset corresponds to the specialized semantic concept; and the processor converting the specialized indexer into a general indexer based at least on a frequency with which the specialized semantic concept is queried, wherein the processor is configured to convert the specialized indexer into the general indexer based further on a computational cost associated with the specialized indexer, wherein the general indexer is applied to the digital media assets independent of a search query, wherein the general indexer is operable to identify a digital media asset that corresponds to a higher level semantic concept and determine the general metadata for the identified digital media asset based at least upon an analysis of pixel data associated with the digital media asset, and wherein the general metadata provides an indication of the higher level semantic concept to which the identified digital media asset corresponds. | 1. A method for searching a database of digital media assets, comprising: a processor receiving a search query; the processor analyzing the search query to extract a specialized search condition corresponding to a specialized semantic concept; the processor selecting, from a pre-existing ontology of indexers, a specialized indexer based on a determination that the selected specialized indexer can be used to identify digital media assets that correspond to the specialized semantic concept; the processor analyzing the search query to extract a general search condition corresponding to a related higher level semantic concept; the processor identifying, from a database of digital media assets, a subset of digital media assets that satisfy the general search condition responsive to general metadata for each of the digital media assets; the processor applying the selected specialized indexer to the subset of digital media assets to determine specialized metadata for a digital media asset in the subset of digital media assets, wherein the specialized metadata provides an indication of whether the digital media asset corresponds to the specialized semantic concept; and the processor converting the specialized indexer into a general indexer based at least on a frequency with which the specialized semantic concept is queried, wherein the processor is configured to convert the specialized indexer into the general indexer based further on a computational cost associated with the specialized indexer, wherein the general indexer is applied to the digital media assets independent of a search query, wherein the general indexer is operable to identify a digital media asset that corresponds to a higher level semantic concept and determine the general metadata for the identified digital media asset based at least upon an analysis of pixel data associated with the digital media asset, and wherein the general metadata provides an indication of the higher level semantic concept to which the identified digital media asset corresponds. 16. The method of claim 1 wherein a portion of the general metadata or the specialized metadata are specified by probability values. | 0.80814 |
9,792,038 | 1 | 7 | 1. A method for providing feedback via an input device, the method comprising: receiving a first input on an input surface via an input device, wherein the first input is received at a location corresponding to a margin of an electronic document; based on the location of the received first input on the input surface being in the margin of the electronic document and a length of time associated with drawing the received first input, determining if the received first input is associated with a scribble input intended to test pen attributes; in response to determining that the received first input is associated with the scribble input intended to test pen attributes, making a determination whether to erase the scribble input, wherein making the determination to erase the scribble input comprises: receiving a second input via the input device; determining that the second input is not in the margin of the electronic document; evaluating a size of the second input relative to the scribble input; and determining, based on the second input not being in the margin and the size of the second input, that the second input is not a continuation of the scribble input; and in response to making the determination to erase the scribble input, providing feedback via the input device that the scribble input is about to be erased. | 1. A method for providing feedback via an input device, the method comprising: receiving a first input on an input surface via an input device, wherein the first input is received at a location corresponding to a margin of an electronic document; based on the location of the received first input on the input surface being in the margin of the electronic document and a length of time associated with drawing the received first input, determining if the received first input is associated with a scribble input intended to test pen attributes; in response to determining that the received first input is associated with the scribble input intended to test pen attributes, making a determination whether to erase the scribble input, wherein making the determination to erase the scribble input comprises: receiving a second input via the input device; determining that the second input is not in the margin of the electronic document; evaluating a size of the second input relative to the scribble input; and determining, based on the second input not being in the margin and the size of the second input, that the second input is not a continuation of the scribble input; and in response to making the determination to erase the scribble input, providing feedback via the input device that the scribble input is about to be erased. 7. The method of claim 1 , wherein no portion of the second input overlaps the received first input. | 0.773756 |
9,076,039 | 10 | 12 | 10. A non-transitory computer readable storage medium having program instructions stored thereon that, when executed by a processor, cause the processor to: receive a first spectral signature corresponding to a region of interest (ROI) in the hyperspectral image; create a model search space including two or more models, wherein each of the two or more models corresponds to a subset of spectral signatures in a library; create, for each of the two or more models, a model spectral signature based on the corresponding subset of spectral signatures, wherein each of the model spectral signatures approximates the first spectral signature; calculate a cumulative probability of the first spectral signature indicating presence of a material within the ROI, wherein the cumulative probability is based on a sum of similarity probabilities of models that contain the material as an element; and determine the presence of the material in the ROI based on the cumulative probability. | 10. A non-transitory computer readable storage medium having program instructions stored thereon that, when executed by a processor, cause the processor to: receive a first spectral signature corresponding to a region of interest (ROI) in the hyperspectral image; create a model search space including two or more models, wherein each of the two or more models corresponds to a subset of spectral signatures in a library; create, for each of the two or more models, a model spectral signature based on the corresponding subset of spectral signatures, wherein each of the model spectral signatures approximates the first spectral signature; calculate a cumulative probability of the first spectral signature indicating presence of a material within the ROI, wherein the cumulative probability is based on a sum of similarity probabilities of models that contain the material as an element; and determine the presence of the material in the ROI based on the cumulative probability. 12. The computer readable storage medium of claim 10 , wherein the program instructions further comprise computer readable code that causes the processor to determine a background expected value of the model search space. | 0.786267 |
9,444,711 | 6 | 9 | 6. A method for providing content to a network browser based on a reported latency comprising: storing, in a memory, an item of content for a web page; receiving a latency measurement from the web browser or a plug-in of the web browser, the latency measurement being based on an amount of time that has passed between two network browser events, whether or not the network browser was using more than one window to display information between the two events, and a type of action that resulted in the network browser transitioning its display between the two events; and modifying data associated with the item of content for the web page with a different form of the data to account for the received latency measurement. | 6. A method for providing content to a network browser based on a reported latency comprising: storing, in a memory, an item of content for a web page; receiving a latency measurement from the web browser or a plug-in of the web browser, the latency measurement being based on an amount of time that has passed between two network browser events, whether or not the network browser was using more than one window to display information between the two events, and a type of action that resulted in the network browser transitioning its display between the two events; and modifying data associated with the item of content for the web page with a different form of the data to account for the received latency measurement. 9. The method of claim 6 , further comprising modifying the item of content when the received latency measurement indicates a high latency. | 0.81901 |
9,135,348 | 6 | 7 | 6. The computer implemented method of claim 1 wherein each user profile further comprises fine grain category activities for each category identified in the user profile, each fine grain category activity entry associating one or more timestamps with one or more text strings corresponding to classification information, the one or more timestamps representing a time when the classification information containing the text string was received. | 6. The computer implemented method of claim 1 wherein each user profile further comprises fine grain category activities for each category identified in the user profile, each fine grain category activity entry associating one or more timestamps with one or more text strings corresponding to classification information, the one or more timestamps representing a time when the classification information containing the text string was received. 7. The computer implemented method of claim 6 wherein when multiple instances of classification information containing the same text string have been received, each timestamp corresponds to receipt of a different instance of classification information, and wherein the profiling rules decay a significance of the text-timestamped classification information records over time. | 0.928923 |
8,346,534 | 63 | 81 | 63. An apparatus for automatically generating one or more keywords from an electronic document, the apparatus comprising; an interface to a document storage repository; a memory; and a processor communicably connected to the interface and the memory, wherein the processor (a) identifies candidate entries for the keywords by extracting all n-grams up to a specified length that do not cross sentence boundaries, reducing a size of a data set consisting of the extracted n-grams by applying one or more filters and balancing a distribution of positive and negative examples whenever the data set is derived from a training data set, (b) constructs a feature vector for each candidate entry, wherein the feature vector comprises at least one feature selected from the group consisting of one or more discourse comprehension features, one or more part-of-speech pattern features, and one or more encyclopedic annotation features, (c) assigns a numeric score to each candidate entry based on the feature vector for that candidate entry, and (d) selects a specified number of entries to be retained as the keywords. | 63. An apparatus for automatically generating one or more keywords from an electronic document, the apparatus comprising; an interface to a document storage repository; a memory; and a processor communicably connected to the interface and the memory, wherein the processor (a) identifies candidate entries for the keywords by extracting all n-grams up to a specified length that do not cross sentence boundaries, reducing a size of a data set consisting of the extracted n-grams by applying one or more filters and balancing a distribution of positive and negative examples whenever the data set is derived from a training data set, (b) constructs a feature vector for each candidate entry, wherein the feature vector comprises at least one feature selected from the group consisting of one or more discourse comprehension features, one or more part-of-speech pattern features, and one or more encyclopedic annotation features, (c) assigns a numeric score to each candidate entry based on the feature vector for that candidate entry, and (d) selects a specified number of entries to be retained as the keywords. 81. The apparatus as recited in claim 63 , wherein one of the one or more part-of-speech pattern features is derived by the steps of: providing a candidate phrase from a list of candidate entries; labeling each word in the candidate phrase with a tag indicating part-of-speech to form a tag pattern; using the training data set to calculate a number of distinct phrases C(pattern) having this tag pattern; using the training data set to calculate a number of phrases C(pattern, positive) having this tag pattern and also having a positive label indicating presence in a manually-constructed collection of keywords associated with the training data set; and calculating a probability of the tag pattern being positive as P(pattern)=C(pattern, positive)/C(pattern), whereby the probability may be used as the part-of-speech pattern feature. | 0.748499 |
9,875,022 | 9 | 15 | 9. An electronic terminal device using a touch pen, the electronic terminal device comprising: a touch screen device configured to display an execution screen of an application in response to the application being executed; and a processor configured to: in response to a handwriting image being input on an area of a handwriting input layer by the touch pen, determine a data type to recognize the handwriting image based on an area in which the handwriting image is input among a plurality of areas of the handwriting input layer; recognize the handwriting image as the determined data type; and apply the recognized handwriting image to the executed application differently according to the determined data type of the handwriting image, wherein the handwriting input layer overlaps with the execution screen of the application, and the processor is further configured to: determine the data type for recognizing the handwriting image as a text in response to the handwriting image being input in a first area among the plurality of areas of the handwriting input layer, and determine the data type for recognizing the handwriting image as an image in response to the handwriting image being input in a second area different from the first area among the plurality of areas of the handwriting input layer. | 9. An electronic terminal device using a touch pen, the electronic terminal device comprising: a touch screen device configured to display an execution screen of an application in response to the application being executed; and a processor configured to: in response to a handwriting image being input on an area of a handwriting input layer by the touch pen, determine a data type to recognize the handwriting image based on an area in which the handwriting image is input among a plurality of areas of the handwriting input layer; recognize the handwriting image as the determined data type; and apply the recognized handwriting image to the executed application differently according to the determined data type of the handwriting image, wherein the handwriting input layer overlaps with the execution screen of the application, and the processor is further configured to: determine the data type for recognizing the handwriting image as a text in response to the handwriting image being input in a first area among the plurality of areas of the handwriting input layer, and determine the data type for recognizing the handwriting image as an image in response to the handwriting image being input in a second area different from the first area among the plurality of areas of the handwriting input layer. 15. The electronic terminal device of claim 9 , wherein in response to the area of the execution screen comprising a table comprising a plurality of cells, the processor is further configured to recognize the handwriting image as a command which selects at least one of the plurality of cells and, in response to a handwriting image being additionally input on the handwriting input layer using the touch pen, the processor is further configured to recognize the additionally input handwriting image as the text or the image to display the text or the image on at least one selected cell. | 0.613158 |
9,203,881 | 1 | 5 | 1. A method for adapting entity mentions in a feed item to requirements for posting on multiple online social environments, the method including: receiving a request to post a feed item on multiple online social environments, wherein the request mentions at least one identifier that identifies an entity; receiving a selection of the multiple online social environments for posting the feed item; matching the identifier to a particular contact stored in a database, wherein the contact holds supplemental information that includes multiple social handles of the entity for at least some of the multiple online social environments; retrieving the multiple social handles associated with the entity; creating different instances of the feed item, wherein the different instances include social handles that are specific to the multiple online environments; and embedding the different instances of the feed item in the online social environments. | 1. A method for adapting entity mentions in a feed item to requirements for posting on multiple online social environments, the method including: receiving a request to post a feed item on multiple online social environments, wherein the request mentions at least one identifier that identifies an entity; receiving a selection of the multiple online social environments for posting the feed item; matching the identifier to a particular contact stored in a database, wherein the contact holds supplemental information that includes multiple social handles of the entity for at least some of the multiple online social environments; retrieving the multiple social handles associated with the entity; creating different instances of the feed item, wherein the different instances include social handles that are specific to the multiple online environments; and embedding the different instances of the feed item in the online social environments. 5. The method of claim 1 , further including creating a canonical entity identifier linked to multiple social handles previously specified by a user and using the canonical entity identifier in the retrieving of the multiple social handles. | 0.557196 |
7,831,600 | 1 | 6 | 1. A method of managing internal cluster communications in a cluster of application server instances, comprising: executing a plurality of work processes in the application server instances; establishing internal connections for internal cluster communication between a message server and each of the plurality of work processes executing in the application server instances, wherein internal cluster communication comprises internal communications relating to at least one of events occurring in the cluster and an exchange of messages between work processes executing in the application server instances, the internal connections separate from connections for external cluster communication; providing a message service for the internal cluster communication, the internal cluster communication including inter-application server instance communication via the message server in accordance with a connection-based message passing protocol in which an internal connection established in the message server receives a notification message for each event occurring in the cluster and sends acknowledgement messages to internal connections established in the application server instances responsive to receiving messages being exchanged between work processes executing in the application server instances, the message server being shared by the application server instances through the established internal connections for internal cluster communication between the message server and each of the plurality of work processes executing in the application server instances; operating a cluster manager to manage the internal cluster communication in accordance with the connection-based message passing protocol, the internal cluster communication sent over the established internal connections for internal cluster communications between the message server and each of the plurality of work processes executing in the application server instances, the cluster manager: communicating an event in the cluster to the message server using an event handler, the event handler sending a notification message of the event to the message server; relaying the notification message of the event in the cluster to one or more of the application server instances using a notification handler, the notification handler causing a function to be performed in one or more of the application server instances responsive to receiving the notification message; and communicating messages to be exchanged between work processes executing in the application server instances to and from the message server using a message server communication handler, the message server communication handler: acknowledging receipt of messages received from the work processes; and relaying messages received from an originating work process to a destination work process, including replicating a multicast message received from the originating work process for broadcast to all other work processes executing in the application server instances. | 1. A method of managing internal cluster communications in a cluster of application server instances, comprising: executing a plurality of work processes in the application server instances; establishing internal connections for internal cluster communication between a message server and each of the plurality of work processes executing in the application server instances, wherein internal cluster communication comprises internal communications relating to at least one of events occurring in the cluster and an exchange of messages between work processes executing in the application server instances, the internal connections separate from connections for external cluster communication; providing a message service for the internal cluster communication, the internal cluster communication including inter-application server instance communication via the message server in accordance with a connection-based message passing protocol in which an internal connection established in the message server receives a notification message for each event occurring in the cluster and sends acknowledgement messages to internal connections established in the application server instances responsive to receiving messages being exchanged between work processes executing in the application server instances, the message server being shared by the application server instances through the established internal connections for internal cluster communication between the message server and each of the plurality of work processes executing in the application server instances; operating a cluster manager to manage the internal cluster communication in accordance with the connection-based message passing protocol, the internal cluster communication sent over the established internal connections for internal cluster communications between the message server and each of the plurality of work processes executing in the application server instances, the cluster manager: communicating an event in the cluster to the message server using an event handler, the event handler sending a notification message of the event to the message server; relaying the notification message of the event in the cluster to one or more of the application server instances using a notification handler, the notification handler causing a function to be performed in one or more of the application server instances responsive to receiving the notification message; and communicating messages to be exchanged between work processes executing in the application server instances to and from the message server using a message server communication handler, the message server communication handler: acknowledging receipt of messages received from the work processes; and relaying messages received from an originating work process to a destination work process, including replicating a multicast message received from the originating work process for broadcast to all other work processes executing in the application server instances. 6. The method of claim 1 , wherein the message server is in a central services instance in the cluster. | 0.953729 |
9,737,759 | 11 | 17 | 11. Non-transitory computer readable media embodying executable instructions for controlling a computer for an automatic exercise tracking method, comprising: receiving text from a user that describes an exercise that is to be tracked; parsing the received text into text segments; identifying automatically in each parsed text segment an exercise quantity value and an exercise quantity unit for said exercise that is to be tracked, said identifying comprising searching said parsed text segment for a quantity value followed directly by a quantity unit, and assigning said quantity value and said quantity unit to be said exercise quantity value and said exercise quantity unit for the exercise to be tracked, and upon not finding a quantity value followed directly by a quantity unit, selecting as said exercise quantity value and said exercise quantity unit a most frequently occurring quantity value and quantity unit for said exercise to be tracked; cleaning the parsed text segments to identify and remove words, connected spaces, and punctuation that are not used to identify exercise and produce parsed cleaned text; processing the parsed cleaned text segments using a text match algorithm to find said exercise that is to be tracked in each parsed cleaned text segment, comprising ranking each exercise text match found using a ranking process, and selecting the ranked exercise with a predetermined rank to be the exercise that is to be tracked; and reporting exercise tracking information for said exercise to be tracked. | 11. Non-transitory computer readable media embodying executable instructions for controlling a computer for an automatic exercise tracking method, comprising: receiving text from a user that describes an exercise that is to be tracked; parsing the received text into text segments; identifying automatically in each parsed text segment an exercise quantity value and an exercise quantity unit for said exercise that is to be tracked, said identifying comprising searching said parsed text segment for a quantity value followed directly by a quantity unit, and assigning said quantity value and said quantity unit to be said exercise quantity value and said exercise quantity unit for the exercise to be tracked, and upon not finding a quantity value followed directly by a quantity unit, selecting as said exercise quantity value and said exercise quantity unit a most frequently occurring quantity value and quantity unit for said exercise to be tracked; cleaning the parsed text segments to identify and remove words, connected spaces, and punctuation that are not used to identify exercise and produce parsed cleaned text; processing the parsed cleaned text segments using a text match algorithm to find said exercise that is to be tracked in each parsed cleaned text segment, comprising ranking each exercise text match found using a ranking process, and selecting the ranked exercise with a predetermined rank to be the exercise that is to be tracked; and reporting exercise tracking information for said exercise to be tracked. 17. The non-transitory computer readable media of claim 11 , wherein said searching for an exercise quantity value and an exercise quantity unit comprises performing a fuzzy logic search of said parsed text for a textual number or a numeric value, and upon finding said textual number or numeric value, performing a further fuzzy logic search of said parsed text for a match of a quantity unit term directly following said textual number or numeric quantity and, if found, assigning said textual number or numeric quantity to said exercise quantity value and assigning said quantity unit term to said exercise quantity unit. | 0.5008 |
9,336,299 | 1 | 3 | 1. A method performed by a computing device, the method comprising: creating seed distribution data comprising a seed phrase and associated seed probabilities that the seed phrase corresponds to a plurality of different lexicons having associated meanings, the seed probabilities including at least: a first seed probability assigned to the seed phrase, the first seed probability indicative of a first likelihood that the seed phrase corresponds to a first lexicon associated with a first meaning of the seed phrase, and a second seed probability assigned to the seed phrase, the second seed probability indicative of a second likelihood that the seed phrase corresponds to a second lexicon associated with a second meaning of the seed phrase, the first seed probability different than the second seed probability, the first lexicon different than the second lexicon; and identifying, from a plurality of web documents, a set of lists of phrases that include the seed phrase as well as other phrases; and based at least on the seed probabilities, determining other probabilities that the other phrases correspond to the plurality of different lexicons having the associated meanings. | 1. A method performed by a computing device, the method comprising: creating seed distribution data comprising a seed phrase and associated seed probabilities that the seed phrase corresponds to a plurality of different lexicons having associated meanings, the seed probabilities including at least: a first seed probability assigned to the seed phrase, the first seed probability indicative of a first likelihood that the seed phrase corresponds to a first lexicon associated with a first meaning of the seed phrase, and a second seed probability assigned to the seed phrase, the second seed probability indicative of a second likelihood that the seed phrase corresponds to a second lexicon associated with a second meaning of the seed phrase, the first seed probability different than the second seed probability, the first lexicon different than the second lexicon; and identifying, from a plurality of web documents, a set of lists of phrases that include the seed phrase as well as other phrases; and based at least on the seed probabilities, determining other probabilities that the other phrases correspond to the plurality of different lexicons having the associated meanings. 3. The method of claim 1 , further comprising: obtaining a set of labeled training data having different semantic labels for the seed phrase, including a first instance of training data labeling the seed phrase with the first meaning and a second instance of training data labeling the seed phrase with the second meaning, wherein the creating the seed distribution data is performed using the set of labeled training data. | 0.756617 |
7,620,245 | 1 | 8 | 1. A method for improving cursive handwriting recognition comprising the steps of: receiving a cursive handwriting input from a user; performing a first same space search against a mixed database having print and cursive handwriting samples to generate a first mixed alternate list of possible matches to the cursive handwriting input; performing a second same space search against a cursive database having cursive samples to generate a first cursive alternate list of possible matches to the cursive handwriting input; merging the first mixed alternate list with the first cursive alternate list to form a first combined alternate list; using the first combined alternate list as a constraint, performing a first dynamic time warp search against the mixed database to generate a second mixed alternate list of possible matches to the cursive handwriting input; using the first combined alternate list as a constraint, performing a second dynamic time warp search against the cursive database to generate a second cursive alternate list of possible matches to the cursive handwriting input; and merging the second mixed alternate list with the second cursive alternate list to form a second combined alternate list. | 1. A method for improving cursive handwriting recognition comprising the steps of: receiving a cursive handwriting input from a user; performing a first same space search against a mixed database having print and cursive handwriting samples to generate a first mixed alternate list of possible matches to the cursive handwriting input; performing a second same space search against a cursive database having cursive samples to generate a first cursive alternate list of possible matches to the cursive handwriting input; merging the first mixed alternate list with the first cursive alternate list to form a first combined alternate list; using the first combined alternate list as a constraint, performing a first dynamic time warp search against the mixed database to generate a second mixed alternate list of possible matches to the cursive handwriting input; using the first combined alternate list as a constraint, performing a second dynamic time warp search against the cursive database to generate a second cursive alternate list of possible matches to the cursive handwriting input; and merging the second mixed alternate list with the second cursive alternate list to form a second combined alternate list. 8. The method of claim 1 , wherein the cursive handwriting input from the user was written in at least four strokes. | 0.57971 |
7,617,492 | 18 | 19 | 18. The method of claim 17 , wherein parsing the command line comprises: comparing a number of characters of the first command line option on the received command line with a minimum number of characters required to uniquely identify the first allowable option in the option list. | 18. The method of claim 17 , wherein parsing the command line comprises: comparing a number of characters of the first command line option on the received command line with a minimum number of characters required to uniquely identify the first allowable option in the option list. 19. The method of claim 18 , further comprising returning a failure result in response to determining that the minimum number of characters required to uniquely identify the first allowable option in the option list is greater than the number of characters of the first command line option on the command line. | 0.878622 |
8,732,153 | 7 | 8 | 7. A system, comprising: one or more computers including one or more storage devices storing instructions that when executed by the one or more computers cause the one or more computers to perform operations comprising: generating a first image based on a first search query; generating a second image based on a second search query; generating a first set of wavelets corresponding to the first image; generating a second set of wavelets corresponding to the second image; generating a first signature for at least some of the wavelets corresponding to the first image; generating a second signature for at least some of the wavelets corresponding to the second image; and determining whether the first search query and the second search query are related queries based on comparing at least part of the first signature and at least part of the second signature. | 7. A system, comprising: one or more computers including one or more storage devices storing instructions that when executed by the one or more computers cause the one or more computers to perform operations comprising: generating a first image based on a first search query; generating a second image based on a second search query; generating a first set of wavelets corresponding to the first image; generating a second set of wavelets corresponding to the second image; generating a first signature for at least some of the wavelets corresponding to the first image; generating a second signature for at least some of the wavelets corresponding to the second image; and determining whether the first search query and the second search query are related queries based on comparing at least part of the first signature and at least part of the second signature. 8. The system of claim 7 , wherein the at least some wavelets corresponding to the first image are wavelets corresponding to the first image that have a magnitude that is greater than a predetermined threshold. | 0.571429 |
8,725,552 | 11 | 12 | 11. A system comprising: at least one programmable processor; and a non-transitory machine-readable medium storing instructions that, when executed by the at least one programmable processor, perform the method comprising: importing data of a publication; transforming the data into a structured schema including dividing a section of the publication into a plurality of sub-sections; ingesting the structured schema to determine a context of the data and one or more key concepts, wherein ingesting comprises: targeting one or more of the sub-sections using natural language and one or more controlled vocabulary phrases, and drawing associations between the data and a plurality of profiles; and generating a score based on the associations between the raw data and the profiles; wherein the at least one of the above is performed on at least one processor. | 11. A system comprising: at least one programmable processor; and a non-transitory machine-readable medium storing instructions that, when executed by the at least one programmable processor, perform the method comprising: importing data of a publication; transforming the data into a structured schema including dividing a section of the publication into a plurality of sub-sections; ingesting the structured schema to determine a context of the data and one or more key concepts, wherein ingesting comprises: targeting one or more of the sub-sections using natural language and one or more controlled vocabulary phrases, and drawing associations between the data and a plurality of profiles; and generating a score based on the associations between the raw data and the profiles; wherein the at least one of the above is performed on at least one processor. 12. The system according to claim 11 , further comprising generating meta data based on the determined context of the raw data. | 0.942793 |
9,367,591 | 3 | 4 | 3. The server according to claim 1 , wherein the communication section further receives support information to a specific question or answer. | 3. The server according to claim 1 , wherein the communication section further receives support information to a specific question or answer. 4. The server according to claim 3 , further comprising a question information update section for updating the question support numbers based on the received user ID and support information. | 0.959848 |
8,832,140 | 5 | 6 | 5. The method according to claim 1 , further comprising determining a measurement of distinctiveness based on a statistical distribution of at least one identifying characteristic within the set of results and a statistical distribution of at least one identifying characteristic within another set. | 5. The method according to claim 1 , further comprising determining a measurement of distinctiveness based on a statistical distribution of at least one identifying characteristic within the set of results and a statistical distribution of at least one identifying characteristic within another set. 6. The method according to claim 5 , further comprising generating the another set from the collection of information. | 0.974247 |
8,078,554 | 8 | 9 | 8. In a computer readable storage medium having stored therein data representing instructions executable by a programmed processor for knowledge-based interpretable predictive modeling of patients, the instructions comprising: receiving diagram information representing relationships between variables of lung cancer, wherein the variables comprise at least two of tumor load, T-stage, N-stage, number of lymph node stations, WHO performance, and survival, the predictive model trained to predict the survival; seeding a predictive model with the diagram information; training the predictive model, as seeded with the diagram information, with training data, the data comprising values for the variables of lung cancer; and displaying a graphical representation of the predictive model after the training, the graphical representation showing at least one of the relationships. | 8. In a computer readable storage medium having stored therein data representing instructions executable by a programmed processor for knowledge-based interpretable predictive modeling of patients, the instructions comprising: receiving diagram information representing relationships between variables of lung cancer, wherein the variables comprise at least two of tumor load, T-stage, N-stage, number of lymph node stations, WHO performance, and survival, the predictive model trained to predict the survival; seeding a predictive model with the diagram information; training the predictive model, as seeded with the diagram information, with training data, the data comprising values for the variables of lung cancer; and displaying a graphical representation of the predictive model after the training, the graphical representation showing at least one of the relationships. 9. The computer readable storage medium of claim 8 further comprising instructions for: applying the predictive model after the training to patient information for a patient; and displaying a prediction output by the predictive model as a result of the applying and displaying the graphical representation. | 0.688391 |
9,299,339 | 12 | 16 | 12. A non-transitory computer readable storage medium storing instructions executable by a data processing apparatus and that upon such execution causes the data processing apparatus to perform operations comprising: accessing command input logs storing data defining user device sessions; identifying, from the command input logs, user device sessions that each respectively store: a sequence of two or more input sentences, each input sentence being one or more n-grams, and each input sentence having a respective ordinal position in the sequence, and wherein the sequence of two or more input sentences includes at least one pair of a first input sentence that precedes a second input sentence in ordinal position in the sequence; and data indicating a first action performed by a user device from which the user device session occurred, the first action having been performed in response to the second input sentence being successfully parsed by a parsing rule that is associated with the first action; identifying pairs of first and second input sentences from respective user device sessions that are indicative of an augmentation signal, comprising identifying pairs of first and second input sentence for which the first action is not performed in response to the first input sentence and is performed in response to the second input sentence; and determining, from the identified pairs of first and second input sentences, first input sentences for which a parsing rule that is associated with the first action is to be generated; wherein the determining first input sentences for which the parsing rule that is associated with the first action is to be generated comprises, for each first input sentence: determining a co-occurrence measure for the occurrence of first input sentence and the occurrence of the first action for the second input sentence; and determining a parsing rule that is associated with the first action is to be generated for the first input sentence when co-occurrence measure for the first input sentence and the first action meets a threshold value. | 12. A non-transitory computer readable storage medium storing instructions executable by a data processing apparatus and that upon such execution causes the data processing apparatus to perform operations comprising: accessing command input logs storing data defining user device sessions; identifying, from the command input logs, user device sessions that each respectively store: a sequence of two or more input sentences, each input sentence being one or more n-grams, and each input sentence having a respective ordinal position in the sequence, and wherein the sequence of two or more input sentences includes at least one pair of a first input sentence that precedes a second input sentence in ordinal position in the sequence; and data indicating a first action performed by a user device from which the user device session occurred, the first action having been performed in response to the second input sentence being successfully parsed by a parsing rule that is associated with the first action; identifying pairs of first and second input sentences from respective user device sessions that are indicative of an augmentation signal, comprising identifying pairs of first and second input sentence for which the first action is not performed in response to the first input sentence and is performed in response to the second input sentence; and determining, from the identified pairs of first and second input sentences, first input sentences for which a parsing rule that is associated with the first action is to be generated; wherein the determining first input sentences for which the parsing rule that is associated with the first action is to be generated comprises, for each first input sentence: determining a co-occurrence measure for the occurrence of first input sentence and the occurrence of the first action for the second input sentence; and determining a parsing rule that is associated with the first action is to be generated for the first input sentence when co-occurrence measure for the first input sentence and the first action meets a threshold value. 16. The non-transitory computer readable storage medium of claim 12 , wherein determining the co-occurrence measure for the first input sentence comprises: grouping first input sentences that are determined to be similar to each other; and determining that a cardinality of the group of first input sentences meets a threshold cardinality. | 0.704704 |
9,465,793 | 26 | 27 | 26. The method of claim 23 , wherein the plurality of human proofreaders are monitored for quality of proofreading to update corresponding rankings of the plurality of human proofreaders. | 26. The method of claim 23 , wherein the plurality of human proofreaders are monitored for quality of proofreading to update corresponding rankings of the plurality of human proofreaders. 27. The method of claim 26 , wherein a determined quality of proofreading for a human proofreader determines a type of detected error the proofreader is subsequently transmitted. | 0.972248 |
9,679,569 | 3 | 4 | 3. The method of claim 1 , comprising: prompting the user to verify their identity using a technique other than voice-based speaker identification. | 3. The method of claim 1 , comprising: prompting the user to verify their identity using a technique other than voice-based speaker identification. 4. The method of claim 3 , wherein the technique other than voice-based speaker identification comprises prompting the user for a passcode. | 0.943312 |
8,952,961 | 21 | 27 | 21. A method for servicing photon map queries, comprising: accepting photon queries from one or more code modules or shaders, each of the photon queries defining a spatially located volume and criteria for photons that are responsive to that photon query, at least some of the photon queries defining a locus and a number of photons (k) closest to the locus as the criteria, wherein k>=1; associating each of the accepted queries with a collection of photon queries that is associated with a node of an acceleration structure, the acceleration structure including nodes that respectively define surfaces that each spatially bound a respective selection of photons located in a 3-D scene, the selections of varying relative granularity, and the nodes arranged in a graph with edges connecting pairs of nodes; traversing the acceleration structure with collections of photon queries from the accepted photon queries, wherein the traversal is performed, collection-by-collection, by testing, in an acceleration structure resource comprising one or more processors, each of a plurality of child nodes of the node associated with a selected collection of photon queries for overlap with each of the spatially located volumes of the photon queries of that collection, and updating a status of collections maintained in a memory by referencing each photon query in a respective collection associated with each child node found to overlap with the spatially located volume of that photon query; and returning, for each of the photon queries, identifiers for photons satisfying the criteria specified for that photon query. | 21. A method for servicing photon map queries, comprising: accepting photon queries from one or more code modules or shaders, each of the photon queries defining a spatially located volume and criteria for photons that are responsive to that photon query, at least some of the photon queries defining a locus and a number of photons (k) closest to the locus as the criteria, wherein k>=1; associating each of the accepted queries with a collection of photon queries that is associated with a node of an acceleration structure, the acceleration structure including nodes that respectively define surfaces that each spatially bound a respective selection of photons located in a 3-D scene, the selections of varying relative granularity, and the nodes arranged in a graph with edges connecting pairs of nodes; traversing the acceleration structure with collections of photon queries from the accepted photon queries, wherein the traversal is performed, collection-by-collection, by testing, in an acceleration structure resource comprising one or more processors, each of a plurality of child nodes of the node associated with a selected collection of photon queries for overlap with each of the spatially located volumes of the photon queries of that collection, and updating a status of collections maintained in a memory by referencing each photon query in a respective collection associated with each child node found to overlap with the spatially located volume of that photon query; and returning, for each of the photon queries, identifiers for photons satisfying the criteria specified for that photon query. 27. The method of claim 21 , wherein the accepting photon queries comprises accepting data defining each photon query through an application programming interface, the data comprising data identifying another photon query from which one or more attributes are to be copied. | 0.840909 |
8,712,850 | 24 | 25 | 24. The method of claim 1 further comprising determining when promotion should occur including evaluating one or more metrics associated with the creative or delivery of the creative. | 24. The method of claim 1 further comprising determining when promotion should occur including evaluating one or more metrics associated with the creative or delivery of the creative. 25. The method of claim 24 wherein one metric relates to a location of where the creative is to be served. | 0.953873 |
8,117,644 | 6 | 7 | 6. The method of claim 1 , further comprising the step of: applying the modification made by the second user to the document. | 6. The method of claim 1 , further comprising the step of: applying the modification made by the second user to the document. 7. The method of claim 6 , further comprising the step of: storing the identity of the second user. | 0.971923 |
8,225,273 | 11 | 12 | 11. A method for displaying unified model (UM) data in a UM visualization, comprising: assigning weights to UM elements; clustering the weighted UM elements into clusters and assigning weights to the clusters; displaying the weighted clusters and the weighted UM elements in the weighted clusters within a conceptual framework; and outputting the UM visualization containing the conceptual framework; wherein a size of each weighted UM element displayed within the conceptual framework is proportional to the weight assigned to the weighted UM element, and wherein a size of each weighted cluster displayed within the conceptual framework is proportional to the weight assigned to the weighted cluster. | 11. A method for displaying unified model (UM) data in a UM visualization, comprising: assigning weights to UM elements; clustering the weighted UM elements into clusters and assigning weights to the clusters; displaying the weighted clusters and the weighted UM elements in the weighted clusters within a conceptual framework; and outputting the UM visualization containing the conceptual framework; wherein a size of each weighted UM element displayed within the conceptual framework is proportional to the weight assigned to the weighted UM element, and wherein a size of each weighted cluster displayed within the conceptual framework is proportional to the weight assigned to the weighted cluster. 12. The method of claim 11 , wherein weights are manually assigned based on user input. | 0.819502 |
9,696,877 | 1 | 3 | 1. A system comprising: one or more processors; and memory having one or more instructions stored thereon that, responsive to execution by the one or more processors, causes the one or more processors to perform operations comprising: presenting, based on a selected character and in a user interface, multiple character strings representing words or later parts of words or acronyms of which the selected character is a part, at least one of the multiple character strings being a multi-string having two or more character strings of the multiple character strings, the multi-string having or completing a long word and at least one of the two or more character strings having or completing a short word, the short word being shorter than the long word and being a constituent part of the long word, the short word and the long word being presented at the same time in the user interface; enabling selection, through the user interface and with a gesture, to select the short word or the long word through selection of one or more of the multiple character strings, wherein the selection of the multiple character string for the short word would be selecting a portion of the multiple character string for the long word; receiving selection, through the user interface, of the short word or the long word of the one of the multiple character strings through the gesture; and providing or presenting the selected short word or long word. | 1. A system comprising: one or more processors; and memory having one or more instructions stored thereon that, responsive to execution by the one or more processors, causes the one or more processors to perform operations comprising: presenting, based on a selected character and in a user interface, multiple character strings representing words or later parts of words or acronyms of which the selected character is a part, at least one of the multiple character strings being a multi-string having two or more character strings of the multiple character strings, the multi-string having or completing a long word and at least one of the two or more character strings having or completing a short word, the short word being shorter than the long word and being a constituent part of the long word, the short word and the long word being presented at the same time in the user interface; enabling selection, through the user interface and with a gesture, to select the short word or the long word through selection of one or more of the multiple character strings, wherein the selection of the multiple character string for the short word would be selecting a portion of the multiple character string for the long word; receiving selection, through the user interface, of the short word or the long word of the one of the multiple character strings through the gesture; and providing or presenting the selected short word or long word. 3. The system as recited in claim 1 , wherein the multi-string is determined to be presented based on an amount of display real estate taken up by the multi-string relative to other potential character strings. | 0.754098 |
8,848,088 | 22 | 24 | 22. An electronic device, comprising: an imaging device; a processor; a display; and a computer-readable memory containing a model identifying application comprising programming instructions that, when executed, instruct the processor to: cause the imaging device to receive a first image of a target object, analyze the first image to identify a first feature of the target object, access a model database to identify one or more models of objects that include the first feature, and in response to identifying more than one model: identify that one of the identified models includes a distinguishing point, determine, by analyzing a second image of the target object, that the target object comprises the distinguishing point, and in response to determining that the target object comprises the distinguishing point, retrieve one or more document files that correspond to the identified model that contains the distinguishing point, wherein the instructions that cause the processor to determine that the target object comprises the distinguishing point comprise instructions to: identify a location on the target object where the distinguishing point may appear, display the target object on a display of the electronic device, when an area that contains the location appears on the display, cause the display to indicate the location by displaying one or more boundary indicators, receive the second image, and analyze the second image to determine whether the second image includes the distinguishing point. | 22. An electronic device, comprising: an imaging device; a processor; a display; and a computer-readable memory containing a model identifying application comprising programming instructions that, when executed, instruct the processor to: cause the imaging device to receive a first image of a target object, analyze the first image to identify a first feature of the target object, access a model database to identify one or more models of objects that include the first feature, and in response to identifying more than one model: identify that one of the identified models includes a distinguishing point, determine, by analyzing a second image of the target object, that the target object comprises the distinguishing point, and in response to determining that the target object comprises the distinguishing point, retrieve one or more document files that correspond to the identified model that contains the distinguishing point, wherein the instructions that cause the processor to determine that the target object comprises the distinguishing point comprise instructions to: identify a location on the target object where the distinguishing point may appear, display the target object on a display of the electronic device, when an area that contains the location appears on the display, cause the display to indicate the location by displaying one or more boundary indicators, receive the second image, and analyze the second image to determine whether the second image includes the distinguishing point. 24. The device of claim 22 , wherein the instructions that cause the imaging device to receive the first image of the target object and that cause the processor to analyze the first image comprise instructions to: receive a series of images of the target object; select, from the series of images, the first image; compare information from the first image with information from the database to identify a subset of objects containing similar features; obtain information from the database regarding one or more locations of one or more specific features; and analyze the first image to seek the one or more locations and determine whether the target object as shown in the first image matches any of the objects in the subset. | 0.500688 |
8,856,056 | 1 | 10 | 1. A sentiment calculator using social media messages for the real-time evaluation of publicly traded assets wherein a sentiment is a computed integer per asset comprising: means for determining polarity in the social media messages based upon pairs of lexical items in local syntactic context; and means for determining a strength value in the social media messages based upon the pairs of lexical items in local syntactic context. | 1. A sentiment calculator using social media messages for the real-time evaluation of publicly traded assets wherein a sentiment is a computed integer per asset comprising: means for determining polarity in the social media messages based upon pairs of lexical items in local syntactic context; and means for determining a strength value in the social media messages based upon the pairs of lexical items in local syntactic context. 10. The sentiment calculator according to claim 1 , wherein the means for determining a strength value derives strength of syntactic constituents by application of an arithmetic operation. | 0.638462 |
8,164,762 | 9 | 13 | 9. A system for text driven document sizing for a plurality of differently-sized documents in a rendering device, comprising: a data-processing apparatus; a module executed by said data-processing apparatus, said module and said data-processing apparatus being operable in combination with one another to: automatically detect document sizes for said plurality of differently-sized documents by pre-scanning all sides of said plurality of differently-sized documents to determine said minimum output document sizes needed to accommodate all sides of said plurality of differently-sized documents, wherein reproduced output document sizes of said plurality of differently-sized documents are larger sides of said plurality of differently-sized documents; automatically detect minimum text size of text contained in plurality of differently-sized documents; record said minimum text sizes of said text contained in said plurality of differently-sized documents; detect output document sizes available to said rendering device in response to automatically detecting said minimum text sizes of said text contained in said plurality of differently-sized documents and specifying a particular said minimum set text sizes; utilize said detected said minimum text sizes and said particular minimum set text sizes to automatically determine image magnification ratios and document sizes required to achieve said particular minimum text sizes in rendered documents via said rendering device, wherein text sizes achieved in said rendered documents are as close as possible to said particular minimum set text sizes if said particular minimum set text sizes specified are unachievable; recalculating said magnification ratio to fill a largest loaded paper size; and automatically optimizing said plurality of differently-sized documents reproduction factors including at least one of original document size, image size, magnification ratios, and output document sizes. | 9. A system for text driven document sizing for a plurality of differently-sized documents in a rendering device, comprising: a data-processing apparatus; a module executed by said data-processing apparatus, said module and said data-processing apparatus being operable in combination with one another to: automatically detect document sizes for said plurality of differently-sized documents by pre-scanning all sides of said plurality of differently-sized documents to determine said minimum output document sizes needed to accommodate all sides of said plurality of differently-sized documents, wherein reproduced output document sizes of said plurality of differently-sized documents are larger sides of said plurality of differently-sized documents; automatically detect minimum text size of text contained in plurality of differently-sized documents; record said minimum text sizes of said text contained in said plurality of differently-sized documents; detect output document sizes available to said rendering device in response to automatically detecting said minimum text sizes of said text contained in said plurality of differently-sized documents and specifying a particular said minimum set text sizes; utilize said detected said minimum text sizes and said particular minimum set text sizes to automatically determine image magnification ratios and document sizes required to achieve said particular minimum text sizes in rendered documents via said rendering device, wherein text sizes achieved in said rendered documents are as close as possible to said particular minimum set text sizes if said particular minimum set text sizes specified are unachievable; recalculating said magnification ratio to fill a largest loaded paper size; and automatically optimizing said plurality of differently-sized documents reproduction factors including at least one of original document size, image size, magnification ratios, and output document sizes. 13. The system of claim 9 wherein said module and said data-processing apparatus are further operable in combination with one another to: collect at least one user reproduction preference utilizing a user interface associated with said rendering device; and specify particular minimum set text sizes, wherein said particular minimum set text sizes are achievable in a rendered document based on text sizes of said plurality of differently-sized documents. | 0.506508 |
8,930,356 | 19 | 32 | 19. A non-transitory computer-readable storage medium storing instructions for handling queries, wherein the instructions include instructions which, when executed by one or more processors, cause the one or more processors to perform the steps of: storing associations that indicate how to modify user-submitted queries that are to be sent to a plurality of searchable sources; wherein the associations include a first association between a first modification, one or more keywords, and a first searchable source; wherein the associations include a second association between a second modification, the one or more keywords, and a second searchable source; after storing the associations that indicate how to modify user-submitted quires, receiving a user-submitted query, comprising the one or more keywords, to be sent to the plurality of searchable sources; in response to receiving the user-submitted query, performing the steps of: generating a first modified query to be sent to the first searchable source of the plurality of searchable sources; wherein the first modified query is generated by adding a first additional text to the user-submitted query based on existence of the one or more keywords in the user-submitted query and the first association; generating a second modified query to be sent to the second searchable source of the plurality of searchable sources; wherein the second modified query is generated by adding a second additional text to the user-submitted query based on existence of the one or more keywords in the user-submitted query and the second association; wherein the first association is different than the second association; and wherein the first modification is different than the second modification; wherein the first searchable source is separate from the second searchable source. | 19. A non-transitory computer-readable storage medium storing instructions for handling queries, wherein the instructions include instructions which, when executed by one or more processors, cause the one or more processors to perform the steps of: storing associations that indicate how to modify user-submitted queries that are to be sent to a plurality of searchable sources; wherein the associations include a first association between a first modification, one or more keywords, and a first searchable source; wherein the associations include a second association between a second modification, the one or more keywords, and a second searchable source; after storing the associations that indicate how to modify user-submitted quires, receiving a user-submitted query, comprising the one or more keywords, to be sent to the plurality of searchable sources; in response to receiving the user-submitted query, performing the steps of: generating a first modified query to be sent to the first searchable source of the plurality of searchable sources; wherein the first modified query is generated by adding a first additional text to the user-submitted query based on existence of the one or more keywords in the user-submitted query and the first association; generating a second modified query to be sent to the second searchable source of the plurality of searchable sources; wherein the second modified query is generated by adding a second additional text to the user-submitted query based on existence of the one or more keywords in the user-submitted query and the second association; wherein the first association is different than the second association; and wherein the first modification is different than the second modification; wherein the first searchable source is separate from the second searchable source. 32. The non-transitory computer-readable storage medium of claim 19 , further comprising instructions for: determining that said user-submitted query matches an initial query that was received before receiving said user-submitted query; determining the first association based on the initial query that matches said user-submitted query. | 0.827357 |
7,483,831 | 22 | 23 | 22. The method of claim 21 , wherein the performing step includes re-adjusting the current candidate frequency-wise gain to remove at least some of the adjustments made in noise-minimizing frequency-wise gain adjustment step. | 22. The method of claim 21 , wherein the performing step includes re-adjusting the current candidate frequency-wise gain to remove at least some of the adjustments made in noise-minimizing frequency-wise gain adjustment step. 23. The method of claim 22 , wherein the performing step includes selecting as a current candidate frequency-wise gain any of a re-adjusted candidate frequency-wise gain and one or more prior candidate frequency-wise gains, where such selection is a function of which of such gains is associated with the highest intelligibility metric. | 0.891262 |
8,990,769 | 7 | 8 | 7. The method of claim 1 , and further comprising, subsequent to said transforming, employing operations with respect to said first represented expression. | 7. The method of claim 1 , and further comprising, subsequent to said transforming, employing operations with respect to said first represented expression. 8. The method of claim 7 , and further comprising, subsequent to said transforming, employing operations with respect to said second represented expression. | 0.903941 |
9,665,713 | 1 | 8 | 1. A computer-implemented method for improved zero-day malware detection comprising: receiving, at a computer that includes one or more processors and memory, a set of training files which are each known to be either malign or benign, wherein the training files comprise one or more types of computer files; partitioning, using the one or more computer processors, the set of training files into a plurality of categories wherein the categories are based on a type of file in each category; and training, using the one or more computer processors, category-specific classifiers that distinguish between malign and benign files in a category of files, wherein the training comprises: selecting one of the plurality of categories of training files, wherein each of the one or more categories corresponds to a type of file; identifying features present in the training files in the selected category of training files, wherein the identifying identifies n-gram features and the n-gram features include n-bytes of code; evaluating the identified features to determine the identified features most effective at distinguishing between malign and benign files; and building a category-specific classifier based on the evaluated features. | 1. A computer-implemented method for improved zero-day malware detection comprising: receiving, at a computer that includes one or more processors and memory, a set of training files which are each known to be either malign or benign, wherein the training files comprise one or more types of computer files; partitioning, using the one or more computer processors, the set of training files into a plurality of categories wherein the categories are based on a type of file in each category; and training, using the one or more computer processors, category-specific classifiers that distinguish between malign and benign files in a category of files, wherein the training comprises: selecting one of the plurality of categories of training files, wherein each of the one or more categories corresponds to a type of file; identifying features present in the training files in the selected category of training files, wherein the identifying identifies n-gram features and the n-gram features include n-bytes of code; evaluating the identified features to determine the identified features most effective at distinguishing between malign and benign files; and building a category-specific classifier based on the evaluated features. 8. The method of claim 1 wherein the partitioning of the training files includes determining the file type of each training file, from the one or more categories of training files, and dividing the training files into groups of same-type files. | 0.684755 |
8,762,408 | 1 | 4 | 1. A method performed with a computing system for optimizing a performance of an enterprise application, the method comprising: receiving, at the local computing system that comprises an enterprise computing system, from a remote computing system that executes a hosted application, one or more configuration tables defining a predefined query scenario for querying and retrieving data from the hosted application for data stored on a database table; replicating a database table from a main database to a secondary database, the main database comprising updated data relative to data contained in the database table of the secondary database, the secondary database comprising an in-memory database and the main database comprising a magnetic memory; receiving, at the local computing system, a query from the enterprise application for data stored on the database table, the query comprising a query return time and a context; comparing the context of the query to a context of the predefined query scenario, the context of the query comprising at least one of a database table name, a scenario application name, or a scenario job name; based on the context of the query matching the context of the predefined query scenario, retrieving data stored on the secondary database replicated from data stored on the main database; and passing the retrieved data from the secondary database to the enterprise application. | 1. A method performed with a computing system for optimizing a performance of an enterprise application, the method comprising: receiving, at the local computing system that comprises an enterprise computing system, from a remote computing system that executes a hosted application, one or more configuration tables defining a predefined query scenario for querying and retrieving data from the hosted application for data stored on a database table; replicating a database table from a main database to a secondary database, the main database comprising updated data relative to data contained in the database table of the secondary database, the secondary database comprising an in-memory database and the main database comprising a magnetic memory; receiving, at the local computing system, a query from the enterprise application for data stored on the database table, the query comprising a query return time and a context; comparing the context of the query to a context of the predefined query scenario, the context of the query comprising at least one of a database table name, a scenario application name, or a scenario job name; based on the context of the query matching the context of the predefined query scenario, retrieving data stored on the secondary database replicated from data stored on the main database; and passing the retrieved data from the secondary database to the enterprise application. 4. The method of claim 1 , wherein the predefined query scenario further comprises a scenario job name, and comparing the context of the query to a context of a predefined query scenario further comprises: comparing a name of a background job to the scenario job name. | 0.79542 |
9,805,126 | 1 | 6 | 1. A method comprising, by one or more computer systems of a social-networking system: receiving, from a client device of a first user of the social-networking system, a search query comprising one or more characters inputted by the first user, the one or more characters being received as the first user inputs the one or more characters at the client device into a user interface of the social-networking system; identifying one or more entities associated with the social-networking system matching the one or more characters of the search query; ranking each of the identified entities matching the search query based on a calculated likelihood that the user will interact with a search result corresponding to the identified entity, wherein the calculated likelihood that the user will interact with the search result corresponding to the identified entity is based at least in part on a historical behavior of the user in selecting one or more prior search results related to the identified entity; boosting one or more ranks of one or more of the identified entities matching the search query based on: one or more business objectives of the social-networking system, wherein at least one of the business objectives comprises encouraging the first user to interact with the identified entity based on a level of social relevance of the identified entity with respect to the first user; and a level of the user's interaction with one or more features of the social-networking system associated with the identified entity, wherein the boosting is designed to increase the level of the user's interaction with the one or more features of the social-networking system associated with the identified entity; and sending, to the client device of the first user and in response to the received search query, one or more search results for presentation to the user as the user inputs the one or more characters at the client device into the user interface, each search result corresponding to one of the identified entities, the search results being presented according to the ranking and boosting of the corresponding identified entities, the presentation of the search results to the user enabling the user to select one or more of the search results to interact with. | 1. A method comprising, by one or more computer systems of a social-networking system: receiving, from a client device of a first user of the social-networking system, a search query comprising one or more characters inputted by the first user, the one or more characters being received as the first user inputs the one or more characters at the client device into a user interface of the social-networking system; identifying one or more entities associated with the social-networking system matching the one or more characters of the search query; ranking each of the identified entities matching the search query based on a calculated likelihood that the user will interact with a search result corresponding to the identified entity, wherein the calculated likelihood that the user will interact with the search result corresponding to the identified entity is based at least in part on a historical behavior of the user in selecting one or more prior search results related to the identified entity; boosting one or more ranks of one or more of the identified entities matching the search query based on: one or more business objectives of the social-networking system, wherein at least one of the business objectives comprises encouraging the first user to interact with the identified entity based on a level of social relevance of the identified entity with respect to the first user; and a level of the user's interaction with one or more features of the social-networking system associated with the identified entity, wherein the boosting is designed to increase the level of the user's interaction with the one or more features of the social-networking system associated with the identified entity; and sending, to the client device of the first user and in response to the received search query, one or more search results for presentation to the user as the user inputs the one or more characters at the client device into the user interface, each search result corresponding to one of the identified entities, the search results being presented according to the ranking and boosting of the corresponding identified entities, the presentation of the search results to the user enabling the user to select one or more of the search results to interact with. 6. The method of claim 1 , wherein one or more of the business objectives is associated with one or more of the features. | 0.89292 |
9,996,620 | 1 | 10 | 1. A method comprising: initiating displaying, via control logic, on one common page, an interface comprising a search element and at least two independent topic interfaces comprising a first independent topic interface for a first topic of a plurality of topics and at least a second independent topic interface for at least a second topic of the plurality of topics, wherein of the at least two independent topic interfaces: a first topic interface displaying at least one first topic and at least two different first types of content relating to the at least one first topic, the at least two different first types of content being selectable by a user via a user input, and a second topic interface displaying at least one second topic and at least two different second types of content relating to the at least one second topic, the at least two different second types of content being selectable by a user via a user input, the at least two different first types of content being different from the at least two different second types of content; wherein at least one of the first or second topic interfaces is to: display one or more suggested topics at least partially responsive to a first user interaction with a first position on a display device, and hide the one or more suggested topics from display at least partially in response to a second user interaction with a second position on the display device; and wherein in response to receiving an input, via a network, to combine or modify the at least one first topic with one or more suggested topics, displaying a selection of the one or more portions via the first topic interface, by the control logic, to display two or more items of different types of content and respective links to portions of the two or more items of different types of content relating to the first topic after being modified or combined. | 1. A method comprising: initiating displaying, via control logic, on one common page, an interface comprising a search element and at least two independent topic interfaces comprising a first independent topic interface for a first topic of a plurality of topics and at least a second independent topic interface for at least a second topic of the plurality of topics, wherein of the at least two independent topic interfaces: a first topic interface displaying at least one first topic and at least two different first types of content relating to the at least one first topic, the at least two different first types of content being selectable by a user via a user input, and a second topic interface displaying at least one second topic and at least two different second types of content relating to the at least one second topic, the at least two different second types of content being selectable by a user via a user input, the at least two different first types of content being different from the at least two different second types of content; wherein at least one of the first or second topic interfaces is to: display one or more suggested topics at least partially responsive to a first user interaction with a first position on a display device, and hide the one or more suggested topics from display at least partially in response to a second user interaction with a second position on the display device; and wherein in response to receiving an input, via a network, to combine or modify the at least one first topic with one or more suggested topics, displaying a selection of the one or more portions via the first topic interface, by the control logic, to display two or more items of different types of content and respective links to portions of the two or more items of different types of content relating to the first topic after being modified or combined. 10. The method of claim 1 and further comprising selecting one or more types of the content via the interface. | 0.763948 |
Subsets and Splits