Patents by Inventor Christopher B. Quirk
Christopher B. Quirk 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: 11477151Abstract: Generally discussed herein are devices, systems, and methods for identifying a purpose of a communication. A method can include receiving a communication including communication content and communication context, the communication content a first portion of the communication and the communication context a second, different portion of the communication. The method can include identifying, by a machine learning (ML) model, based on the communication content and the communication context, one or more purposes associated with the communication, the one or more purposes indicating respective actions to be performed by a user that generated or received the communication. The method can include providing data indicating the purpose of the first portion of the content.Type: GrantFiled: November 11, 2020Date of Patent: October 18, 2022Assignee: Microsoft Technology Licensing, LLCInventors: Saghar Hosseinisianaki, Wei Wang, Ahmed Awadallah, Paul N. Bennett, Christopher B. Quirk
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Publication number: 20210136018Abstract: Generally discussed herein are devices, systems, and methods for identifying a purpose of a communication. A method can include receiving a communication including communication content and communication context, the communication content a first portion of the communication and the communication context a second, different portion of the communication. The method can include identifying, by a machine learning (ML) model, based on the communication content and the communication context, one or more purposes associated with the communication, the one or more purposes indicating respective actions to be performed by a user that generated or received the communication. The method can include providing data indicating the purpose of the first portion of the content.Type: ApplicationFiled: November 11, 2020Publication date: May 6, 2021Inventors: Saghar HOSSEINISIANAKI, Wei WANG, Ahmed AWADALLAH, Paul N. BENNETT, Christopher B. QUIRK
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Patent number: 10868785Abstract: Generally discussed herein are devices, systems, and methods for identifying a purpose of a communication. A method can include receiving a communication including communication content and communication context, the communication content a first portion of the communication and the communication context a second, different portion of the communication. The method can include identifying, by a machine learning (ML) model, based on the communication content and the communication context, one or more purposes associated with the communication, the one or more purposes indicating respective actions to be performed by a user that generated or received the communication. The method can include providing data indicating the purpose of the first portion of the content.Type: GrantFiled: April 29, 2019Date of Patent: December 15, 2020Assignee: Microsoft Technology Licensing, LLCInventors: Saghar Hosseinisianaki, Wei Wang, Ahmed Hassan Awadallah, Paul N. Bennett, Christopher B. Quirk
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Publication number: 20200344194Abstract: Generally discussed herein are devices, systems, and methods for identifying a purpose of a communication. A method can include receiving a communication including communication content and communication context, the communication content a first portion of the communication and the communication context a second, different portion of the communication. The method can include identifying, by a machine learning (ML) model, based on the communication content and the communication context, one or more purposes associated with the communication, the one or more purposes indicating respective actions to be performed by a user that generated or received the communication. The method can include providing data indicating the purpose of the first portion of the content.Type: ApplicationFiled: April 29, 2019Publication date: October 29, 2020Inventors: Saghar Hosseinisianaki, Wei Wang, Ahmed Hassan Awadallah, Paul N. Bennett, Christopher B. Quirk
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Patent number: 10223349Abstract: A processing system is described which induces a context free grammar (CFG) based on a set of descriptions. The descriptions pertain to a particular subject. Thus, the CFG targets the particular subject, and is accordingly referred to as a subject-targeted context free grammar (ST-CFG). The processing system can use the ST-CFG to determine whether a new description is a proper description of the subject. The processing system also provides synthesizing functionality for building an ST-CFG based on one or more smaller component ST-CFGs.Type: GrantFiled: February 20, 2013Date of Patent: March 5, 2019Assignee: Microsoft Technology Licensing LLCInventors: Christopher B. Quirk, Pallavi Choudhury, Jurij Ganitkevic, Luke S. Zettlemoyer
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Publication number: 20170193157Abstract: Drug combinations offer promising treatment for some conditions such as cancer. However, the large number of available drug combinations makes it impractical to try all possible combinations. Machine-learning techniques described in this disclosure train a classification algorithm. Once trained, the classification algorithm uses genomic data from a specific patient to perform in silico tests of drugs and drug combinations against the genomic data to determine which therapies are likely to be effective for treating a condition of the specific patient.Type: ApplicationFiled: December 30, 2015Publication date: July 6, 2017Inventors: Christopher B. Quirk, Wen-tau Yih, Hoifung Poon, Kristina Toutanova, Stephen William Mayhew, Sheng Wang
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Patent number: 9116880Abstract: A processing system is described which generates stimulus information (SI) having one or more stimulus components (SCs) selected from an inventory of such components. The processing system then presents the SI to a group of human recipients, inviting those recipients to provide linguistic descriptions of the SI. The linguistic information that is received thereby has an implicit link to the SCs. Further, each linguistic component is associated with at least one feature of a target environment, such as a target computer system. Hence, the linguistic information also maps to the features of the target environment. These relationships allow applications to use the linguistic information to interact with the target environment in different ways. In one case, the processing system uses a challenge-response authentication task presentation to convey the stimulus information to the recipients.Type: GrantFiled: November 30, 2012Date of Patent: August 25, 2015Assignee: Microsoft Technology Licensing, LLCInventors: William B. Dolan, Christopher I. Charla, Christopher B. Quirk, Christopher J. Brockett, Noelle M. Sophy, Nicole Beaudry, Vikram Reddy Dendi, Pallavi Choudhury, Scott T. Laufer, Robert A. Sim, Thomas E. Woolsey, David Molnar
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Patent number: 8886516Abstract: 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: GrantFiled: March 1, 2012Date of Patent: November 11, 2014Assignee: Microsoft CorporationInventors: Arul A. Menezes, Christopher B. Quirk, Richard F. Rashid, Robert C. Moore
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Publication number: 20140236571Abstract: A processing system is described which induces a context free grammar (CFG) based on a set of descriptions. The descriptions pertain to a particular subject. Thus, the CFG targets the particular subject, and is accordingly referred to as a subject-targeted context free grammar (ST-CFG). The processing system can use the ST-CFG to determine whether a new description is a proper description of the subject. The processing system also provides synthesizing functionality for building an ST-CFG based on one or more smaller component ST-CFGs.Type: ApplicationFiled: February 20, 2013Publication date: August 21, 2014Applicant: MICROSOFT CORPORATIONInventors: Christopher B. Quirk, Pallavi Choudhury, Jurij Ganitkevic, Luke S. Zettlemoyer
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Publication number: 20140156259Abstract: A processing system is described which generates stimulus information (SI) having one or more stimulus components (SCs) selected from an inventory of such components. The processing system then presents the SI to a group of human recipients, inviting those recipients to provide linguistic descriptions of the SI. The linguistic information that is received thereby has an implicit link to the SCs. Further, each linguistic component is associated with at least one feature of a target environment, such as a target computer system. Hence, the linguistic information also maps to the features of the target environment. These relationships allow applications to use the linguistic information to interact with the target environment in different ways. In one case, the processing system uses a challenge-response authentication task presentation to convey the stimulus information to the recipients.Type: ApplicationFiled: November 30, 2012Publication date: June 5, 2014Applicant: Microsoft CorporationInventors: William B. Dolan, Christopher I. Charla, Christopher B. Quirk, Christopher J. Brockett, Noelle M. Sophy, Nicole Beaudry, Vikram Reddy Dendi, Pallavi Choudhury, Scott T. Laufer, Robert A. Sim, Thomas E. Woolsey, David Molnar
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Patent number: 8504354Abstract: Machine translation algorithms for translating between a first language and a second language are often trained using parallel fragments, comprising a first language corpus and a second language corpus comprising an element-for-element translation of the first language corpus. Such training may involve large training sets that may be extracted from large bodies of similar sources, such as databases of news articles written in the first and second languages describing similar events; however, extracted fragments may be comparatively “noisy,” with extra elements inserted in each corpus. Extraction techniques may be devised that can differentiate between “bilingual” elements represented in both corpora and “monolingual” elements represented in only one corpus, and for extracting cleaner parallel fragments of bilingual elements.Type: GrantFiled: June 2, 2008Date of Patent: August 6, 2013Assignee: Microsoft CorporationInventors: Christopher B. Quirk, Raghavendra U. Udupa
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Publication number: 20120179450Abstract: 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: March 1, 2012Publication date: July 12, 2012Applicant: MICROSOFT CORPORATIONInventors: Arul A. Menezes, Christopher B. Quirk, Richard F. Rashid, Robert C. Moore
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Patent number: 8209162Abstract: 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: GrantFiled: May 1, 2006Date of Patent: June 26, 2012Assignee: Microsoft CorporationInventors: Arul A. Menezes, Christopher B. Quirk, Richard F. Rashid, Robert C. Moore
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Patent number: 8150677Abstract: 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: GrantFiled: June 26, 2008Date of Patent: April 3, 2012Assignee: Microsoft CorporationInventors: Arul A. Menezes, Christopher B. Quirk
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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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Publication number: 20110295897Abstract: Query-correction pairs can be extracted from search log data. Each query-correction pair can include an original query and a follow-up query, where the follow-up query meets one or more criteria for being identified as a correction of the original query, such as an indication of user input indicating the follow-up query is a correction for the original query. The query-correction pairs can be segmented to identify bi-phrases in the query-correction pairs. Probabilities of corrections between the bi-phrases can be estimated based on frequencies of matches in the query-correction pairs. Identifications of the bi-phrases and representations of the probabilities of those bi-phrases can be stored in a probabilistic model data structure.Type: ApplicationFiled: June 1, 2010Publication date: December 1, 2011Applicant: Microsoft CorporationInventors: Jianfeng Gao, Christopher B. Quirk, Daniel Micol Ponce, Andreas Bode, Xu Sun
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Patent number: 7725306Abstract: A method is provided for identifying phrase alignment pairs between a source sentence and a target sentence. Boundaries for a phrase in the source sentence are identified by requiring that a source word be aligned with at least one target word in a target sentence in order to form a boundary for the source phrase. Boundaries for a phrase in the target sentence are identified based on alignments between words in the source phrase and words in the target sentence. The words in the target phrase are examined to determine if any of the words are aligned with source words outside of the source phrase. If they are not aligned with source words outside of the source phrase, the source phrase and target phrase are determined to form an alignment pair and are stored as a phrase alignment pair.Type: GrantFiled: June 28, 2006Date of Patent: May 25, 2010Assignee: Microsoft CorporationInventors: Robert C. Moore, Christopher B. Quirk
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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: 20090299729Abstract: Machine translation algorithms for translating between a first language and a second language are often trained using parallel fragments, comprising a first language corpus and a second language corpus comprising an element-for-element translation of the first language corpus. Such training may involve large training sets that may be extracted from large bodies of similar sources, such as databases of news articles written in the first and second languages describing similar events; however, extracted fragments may be comparatively “noisy,” with extra elements inserted in each corpus. Extraction techniques may be devised that can differentiate between “bilingual” elements represented in both corpora and “monolingual” elements represented in only one corpus, and for extracting cleaner parallel fragments of bilingual elements.Type: ApplicationFiled: June 2, 2008Publication date: December 3, 2009Applicant: MICROSOFT CORPORATIONInventors: Christopher B. Quirk, Raghavendra U. Udupa