Patents by Inventor Arul Menezes
Arul Menezes has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 8082143Abstract: In one embodiment of the present invention, a decoder receives a dependency tree as a source language input and accesses a set of statistical models that produce outputs combined in a log linear framework. The decoder also accesses a table of treelet translation pairs and returns a target dependency tree based on the source dependency tree, based on access to the table of treelet translation pairs, and based on the application of the statistical models.Type: GrantFiled: July 8, 2009Date of Patent: December 20, 2011Assignee: Microsoft CorporationInventors: Arul A. Menezes, Christopher B. Quirk, Colin A. Cherry
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Patent number: 8065307Abstract: The present invention may be used to analyze subject content, search and analyze reference content, compare the subject and reference content for similarity, and output comparison reports between the subject and reference content. The present invention may incorporate and utilize text from intrinsic and/or extrinsic subject documents. The analysis may employ a variety of metrics, including scores generated from a natural language processing system, scores based on classification similarity, scores based on proximity similarity, and in the case of analysis of patent documents, scores based on measurement of claims.Type: GrantFiled: December 20, 2006Date of Patent: November 22, 2011Assignee: Microsoft CorporationInventors: Brian Dean Haslam, Patrick Wayne John Evans, Arul Menezes, Patrick Santos
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Publication number: 20100223049Abstract: A computer-implemented machine translation system translates text from a first language to a second language. The system includes a plurality of mappings, each mapping indicative of associating a dependency structure of the first language with a dependency structure of the second language, wherein at least some of the mappings correspond to dependency structures of the first language having varying context with some common elements, and associated dependency structures of the second language to the dependency structures of the first language. A module receives input text in a first language and outputs output text in a second language based on accessing the plurality of mappings.Type: ApplicationFiled: May 4, 2010Publication date: September 2, 2010Applicant: Microsoft CorporationInventors: Arul A. Menezes, Stephen D. Richardson
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Patent number: 7734459Abstract: A method of aligning nodes of dependency structures obtained from a bilingual corpus includes a two-phase approach wherein a first phase comprises associating nodes of the dependency structures to form tentative correspondences. The nodes of the dependency structures are then aligned as a function of the tentative correspondences and structural considerations. Mappings are obtained from the aligned dependency structures. The mappings can be expanded with varying types and amounts of local context in order that a more fluent translation can be obtained when translation is performed.Type: GrantFiled: July 5, 2001Date of Patent: June 8, 2010Assignee: Microsoft CorporationInventors: Arul A. Menezes, Stephen D. Richardson
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Patent number: 7698124Abstract: In one embodiment of the present invention, a decoder receives a dependency tree as a source language input and accesses a set of statistical models that produce outputs combined in a log linear framework. The decoder also accesses a table of treelet translation pairs and returns a target dependency tree based on the source dependency tree, based on access to the table of treelet translation pairs, and based on the application of the statistical models.Type: GrantFiled: December 16, 2004Date of Patent: April 13, 2010Assignee: Microsoft CorporaitonInventors: Arul A. Menezes, Christopher B. Quirk, Colin A. Cherry
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Publication number: 20090326911Abstract: Many machine translation scenarios involve the generation of a language translation rule set based on parallel training corpuses (e.g., sentences in a first language and word-for-word translations into a second language.) However, the translation of a source corpus in a source language to a target corpus in a target language involves at least two aspects: selecting elements of the target language to match the elements of the source corpus, and ordering the target elements according to the semantic organization of the source corpus and the grammatic rules of the target language. The breadth of generalization of the translation rules derived from the training may be improved, while retaining contextual information, by formulating language order templates that specify orderings of small sets of target elements according to target element types.Type: ApplicationFiled: June 26, 2008Publication date: December 31, 2009Applicant: MICROSOFT CORPORATIONInventors: Arul A. Menezes, Christopher B. Quirk
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Publication number: 20090271177Abstract: In one embodiment of the present invention, a decoder receives a dependency tree as a source language input and accesses a set of statistical models that produce outputs combined in a log linear framework. The decoder also accesses a table of treelet translation pairs and returns a target dependency tree based on the source dependency tree, based on access to the table of treelet translation pairs, and based on the application of the statistical models.Type: ApplicationFiled: July 8, 2009Publication date: October 29, 2009Applicant: Microsoft CorporationInventors: Arul A. Menezes, Christopher B. Quirk, Colin A. Cherry
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Patent number: 7593843Abstract: A method of decoding an input semantic structure to generate an output semantic structure. A set of transfer mappings are provided. A score is calculated for at least one transfer mapping in the set of transfer mappings using a statistical model. At least one transfer mapping is selected based on the score and used to construct the output semantic structure.Type: GrantFiled: March 30, 2004Date of Patent: September 22, 2009Assignee: Microsoft CorporationInventors: Anthony Aue, Eric K. Ringger, Christopher B. Quirk, Arul A. Menezes, Robert C. Moore
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Patent number: 7577562Abstract: In one embodiment of the present invention, a decoder receives a dependency tree as a source language input and accesses a set of statistical models that produce outputs combined in a log linear framework. The decoder also accesses a table of treelet translation pairs and returns a target dependency tree based on the source dependency tree, based on access to the table of treelet translation pairs, and based on the application of the statistical models.Type: GrantFiled: December 16, 2004Date of Patent: August 18, 2009Assignee: Microsoft CorporationInventors: Arul A. Menezes, Christopher B. Quirk, Colin A. Cherry
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Patent number: 7505894Abstract: In one embodiment of the present invention, a decoder receives a dependency tree as a source language input and accesses a set of statistical models that produce outputs combined in a log linear framework. The decoder also accesses a table of treelet translation pairs and returns a target dependency tree based on the source dependency tree, based on access to the table of treelet translation pairs, and based on the application of the statistical models.Type: GrantFiled: December 16, 2004Date of Patent: March 17, 2009Assignee: Microsoft CorporationInventors: Arul A. Menezes, Christopher B. Quirk, Colin A. Cherry
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Patent number: 7496496Abstract: A machine translation system is trained to generate confidence scores indicative of a quality of a translation result. A source string is translated with a machine translator to generate a target string. Features indicative of translation operations performed are extracted from the machine translator. A trusted entity-assigned translation score is obtained and is indicative of a trusted entity-assigned translation quality of the translated string. A relationship between a subset of the extracted features and the trusted entity-assigned translation score is identified.Type: GrantFiled: March 19, 2007Date of Patent: February 24, 2009Assignee: Microsoft CorporationInventors: Christopher B. Quirk, Arul A. Menezes, Stephen D. Richardson, Robert C. Moore
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Patent number: 7464334Abstract: In an operating system, a resource handler accepts resource requests from application modules. A resource request identifies a module from which the requested resource is to be obtained. Rather than providing the resource from the identified module, however, the resource handler provides the requested resource from an associated resource module. An association between an executable module and resource modules of different languages is created by a defined file naming convention, optionally using different directories for resource modules of different languages. Some executable modules contain a shared resource reference which can be used to create an association between multiple executable modules and a single set of shared resource modules. A language fallback mechanism allows alternative languages to be used where resource modules of the appropriate language are not available.Type: GrantFiled: January 27, 2004Date of Patent: December 9, 2008Assignee: Microsoft CorporationInventors: Edward S. Miller, Bjorn C. Retting, Gregory Wilson, Shan Xu, Arul A. Meneze, Michael J. Thomson, Sharad Mathur, Roberto Cazzaro, Michael Ginsberg
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Publication number: 20080154848Abstract: The present invention may be used to analyze subject content, search and analyze reference content, compare the subject and reference content for similarity, and output comparison reports between the subject and reference content. The present invention may incorporate and utilize text from intrinsic and/or extrinsic subject documents. The analysis may employ a variety of metrics, including scores generated from a natural language processing system, scores based on classification similarity, scores based on proximity similarity, and in the case of analysis of patent documents, scores based on measurement of claims.Type: ApplicationFiled: December 20, 2006Publication date: June 26, 2008Applicant: Microsoft CorporationInventors: Brian Dean Haslam, Patrick Wayne John Evans, Arul Menezes, Patrick Joseph Dinio Santos
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Publication number: 20070255550Abstract: A method of translation includes uploading a source text portion to a back end processor. The back end processor identifies a subset of translation knowledge associated with the source text portion. The back end processor downloads the subset to a front end processor. A translation engine runs on the front end processor. The translation engine generates a translation of the source text portion as a function of the subset.Type: ApplicationFiled: May 1, 2006Publication date: November 1, 2007Applicant: Microsoft CorporationInventors: Arul Menezes, Christopher Quirk, Richard Rashid, Robert Moore
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Publication number: 20070219774Abstract: A machine translation system is trained to generate confidence scores indicative of a quality of a translation result. A source string is translated with a machine translator to generate a target string. Features indicative of translation operations performed are extracted from the machine translator. A trusted entity-assigned translation score is obtained and is indicative of a trusted entity-assigned translation quality of the translated string. A relationship between a subset of the extracted features and the trusted entity-assigned translation score is identified.Type: ApplicationFiled: March 19, 2007Publication date: September 20, 2007Applicant: Microsoft CorporationInventors: Christopher Quirk, Arul Menezes, Stephen Richardson, Robert Moore
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Patent number: 7233892Abstract: A method of training a natural language processing unit applies a candidate learning set to at least one component of the natural language unit. The natural language unit is then used to generate a meaning set from a first corpus. A second meaning set is generated from a second corpus using a second natural language unit and the two meaning sets are compared to each other to form a score for the candidate learning set. This score is used to determine whether to modify the natural language unit based on the candidate learning set.Type: GrantFiled: August 15, 2005Date of Patent: June 19, 2007Assignee: Microsoft CorporationInventors: Eric D. Brill, Arul A. Menezes
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Patent number: 7209875Abstract: A machine translation system is trained to generate confidence scores indicative of a quality of a translation result. A source string is translated with a machine translator to generate a target string. Features indicative of translation operations performed are extracted from the machine translator. A trusted entity-assigned translation score is obtained and is indicative of a trusted entity-assigned translation quality of the translated string. A relationship between a subset of the extracted features and the trusted entity-assigned translation score is identified.Type: GrantFiled: December 4, 2002Date of Patent: April 24, 2007Assignee: Microsoft CorporationInventors: Christopher B. Quirk, Arul A. Menezes, Steven D. Richardson, Robert C. Moore
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Patent number: 7200550Abstract: In one embodiment of the present invention, a decoder receives a dependency tree as a source language input and accesses a set of statistical models that produce outputs combined in a log linear framework. The decoder also accesses a table of treelet translation pairs and returns a target dependency tree based on the source dependency tree, based on access to the table of treelet translation pairs, and based on the application of the statistical models.Type: GrantFiled: December 16, 2004Date of Patent: April 3, 2007Assignee: Microsoft CorporationInventors: Arul A. Menezes, Christopher B. Quirk, Colin A. Cherry
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Publication number: 20060111892Abstract: In one embodiment of the present invention, a decoder receives a dependency tree as a source language input and accesses a set of statistical models that produce outputs combined in a log linear framework. The decoder also accesses a table of treelet translation pairs and returns a target dependency tree based on the source dependency tree, based on access to the table of treelet translation pairs, and based on the application of the statistical models.Type: ApplicationFiled: December 16, 2004Publication date: May 25, 2006Applicant: Microsoft CorporationInventors: Arul Menezes, Christopher Quirk, Colin Cherry
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Publication number: 20060111891Abstract: In one embodiment of the present invention, a decoder receives a dependency tree as a source language input and accesses a set of statistical models that produce outputs combined in a log linear framework. The decoder also accesses a table of treelet translation pairs and returns a target dependency tree based on the source dependency tree, based on access to the table of treelet translation pairs, and based on the application of the statistical models.Type: ApplicationFiled: December 16, 2004Publication date: May 25, 2006Applicant: Microsoft CorporationInventors: Arul Menezes, Christopher Quirk, Colin Cherry