Patents by Inventor Alessandro SORDONI
Alessandro SORDONI 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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Publication number: 20220327407Abstract: Described herein are systems and methods for providing a natural language comprehension system that employs a two-stage process for machine comprehension of text. The first stage indicates words in one or more text passages that potentially answer a question. The first stage outputs a set of candidate answers for the question, along with a first probability of correctness for each candidate answer. The second stage forms one or more hypotheses by inserting each candidate answer into the question and determines whether a sematic relationship exists between each hypothesis and each sentence in the text. The second processing circuitry generates a second probability of correctness for each candidate answer and combines the first probability with the second probability to produce a score that is used to rank the candidate answers. The candidate answer with the highest score is selected as a predicted answer.Type: ApplicationFiled: June 24, 2022Publication date: October 13, 2022Applicant: Microsoft Technology Licensing, LLCInventors: Adam TRISCHLER, Philip BACHMAN, Xingdi YUAN, Alessandro SORDONI, Zheng YE
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Patent number: 11379736Abstract: Described herein are systems and methods for providing a natural language comprehension system that employs a two-stage process for machine comprehension of text. The first stage indicates words in one or more text passages that potentially answer a question. The first stage outputs a set of candidate answers for the question, along with a first probability of correctness for each candidate answer. The second stage forms one or more hypotheses by inserting each candidate answer into the question and determines whether a sematic relationship exists between each hypothesis and each sentence in the text. The second processing circuitry generates a second probability of correctness for each candidate answer and combines the first probability with the second probability to produce a score that is used to rank the candidate answers. The candidate answer with the highest score is selected as a predicted answer.Type: GrantFiled: May 17, 2017Date of Patent: July 5, 2022Assignee: Microsoft Technology Licensing, LLCInventors: Adam Trischler, Philip Bachman, Xingdi Yuan, Alessandro Sordoni, Zheng Ye
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Patent number: 10592607Abstract: Described herein are systems and methods for providing a natural language comprehension system (NLCS) that iteratively performs an alternating search to gather information that may be used to predict the answer to the question. The NLCS first attends to a query glimpse of the question, and then finds one or more corresponding matches by attending to a text glimpse of the text.Type: GrantFiled: June 2, 2017Date of Patent: March 17, 2020Assignee: MICROSOFT TECHNOLOGY LICENSING, LLCInventors: Alessandro Sordoni, Philip Bachman, Adam Peter Trischler
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Patent number: 10536402Abstract: Examples are generally directed towards context-sensitive generation of conversational responses. Context-message-response n-tuples are extracted from at least one source of conversational data to generate a set of training context-message-response n-tuples. A response generation engine is trained on the set of training context-message-response n-tuples. The trained response generation engine automatically generates a context-sensitive response based on a user generated input message and conversational context data. A digital assistant utilizes the trained response generation engine to generate context-sensitive, natural language responses that are pertinent to user queries.Type: GrantFiled: August 24, 2018Date of Patent: January 14, 2020Assignee: Microsoft Technology Licensing, LLCInventors: Michel Galley, Alessandro Sordoni, Christopher John Brockett, Jianfeng Gao, William Brennan Dolan, Yangfeng Ji, Michael Auli, Margaret Ann Mitchell, Jian-Yun Nie
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Publication number: 20180367475Abstract: Examples are generally directed towards context-sensitive generation of conversational responses. Context-message-response n-tuples are extracted from at least one source of conversational data to generate a set of training context-message-response n-tuples. A response generation engine is trained on the set of training context-message-response n-tuples. The trained response generation engine automatically generates a context-sensitive response based on a user generated input message and conversational context data. A digital assistant utilizes the trained response generation engine to generate context-sensitive, natural language responses that are pertinent to user queries.Type: ApplicationFiled: August 24, 2018Publication date: December 20, 2018Inventors: Michel GALLEY, Alessandro SORDONI, Christopher John BROCKETT, Jianfeng GAO, William Brennan DOLAN, Yangfeng JI, Michael AULI, Margaret Ann MITCHELL, Jian-Yun NIE
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Patent number: 10091140Abstract: Examples are generally directed towards context-sensitive generation of conversational responses. Context-message-response n-tuples are extracted from at least one source of conversational data to generate a set of training context-message-response n-tuples. A response generation engine is trained on the set of training context-message-response n-tuples. The trained response generation engine automatically generates a context-sensitive response based on a user generated input message and conversational context data. A digital assistant utilizes the trained response generation engine to generate context-sensitive, natural language responses that are pertinent to user queries.Type: GrantFiled: May 31, 2015Date of Patent: October 2, 2018Assignee: MICROSOFT TECHNOLOGY LICENSING, LLCInventors: Michel Galley, Alessandro Sordoni, Christopher John Brockett, Jianfeng Gao, William Brennan Dolan, Yangfeng Ji, Michael Auli, Margaret Ann Mitchell, Jian-Yun Nie
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Patent number: 9967211Abstract: Examples are generally directed towards automatic assessment of machine generated conversational responses. Context-message-response n-tuples are extracted from at least one source of conversational data to generate a set of multi-reference responses. A response in the set of multi-reference responses includes it context-message data pair and rating. The rating indicates a quality of the response relative to the context-message data pair. A response assessment engine generates a metric score for a machine-generated response based on an assessment metric and the set of multi-reference responses. The metric score indicates a quality of the machine-generated conversational response relative to a user-generated message and a context of the user-generated message. A response generation system of a computing device, such as a digital assistant, is optimized and adjusted based on the metric score to improve the accuracy, quality, and relevance of responses output to the user.Type: GrantFiled: May 31, 2015Date of Patent: May 8, 2018Assignee: Microsoft Technology Licensing, LLCInventors: Michel Galley, Alessandro Sordoni, Christopher John Brockett, Jianfeng Gao, William Brennan Dolan, Yangfeng Ji, Michael Auli, Margaret Ann Mitchell, Christopher Brian Quirk
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Publication number: 20170351663Abstract: Described herein are systems and methods for providing a natural language comprehension system (NLCS) that iteratively performs an alternating search to gather information that may be used to predict the answer to the question. The NLCS first attends to a query glimpse of the question, and then finds one or more corresponding matches by attending to a text glimpse of the text.Type: ApplicationFiled: June 2, 2017Publication date: December 7, 2017Applicant: Maluuba Inc.Inventors: Alessandro Sordoni, Philip Bachman, Adam Peter Trischler
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Publication number: 20170337479Abstract: Described herein are systems and methods for providing a natural language comprehension system that employs a two-stage process for machine comprehension of text. The first stage indicates words in one or more text passages that potentially answer a question. The first stage outputs a set of candidate answers for the question, along with a first probability of correctness for each candidate answer. The second stage forms one or more hypotheses by inserting each candidate answer into the question and determines whether a sematic relationship exists between each hypothesis and each sentence in the text. The second processing circuitry generates a second probability of correctness for each candidate answer and combines the first probability with the second probability to produce a score that is used to rank the candidate answers. The candidate answer with the highest score is selected as a predicted answer.Type: ApplicationFiled: May 17, 2017Publication date: November 23, 2017Applicant: Maluuba Inc.Inventors: Adam Trischler, Philip Bachman, Xingdi Yuan, Alessandro Sordoni, Zheng Ye
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Publication number: 20160352657Abstract: Examples are generally directed towards automatic assessment of machine generated conversational responses. Context-message-response n-tuples are extracted from at least one source of conversational data to generate a set of multi-reference responses. A response in the set of multi-reference responses includes it context-message data pair and rating. The rating indicates a quality of the response relative to the context-message data pair. A response assessment engine generates a metric score for a machine-generated response based on an assessment metric and the set of multi-reference responses. The metric score indicates a quality of the machine-generated conversational response relative to a user-generated message and a context of the user-generated message. A response generation system of a computing device, such as a digital assistant, is optimized and adjusted based on the metric score to improve the accuracy, quality, and relevance of responses output to the user.Type: ApplicationFiled: May 31, 2015Publication date: December 1, 2016Inventors: Michel GALLEY, Alessandro SORDONI, Christopher John BROCKETT, Jianfeng GAO, III, William Brennan DOLAN, Yangfeng JI, Michael AULI, Margaret Ann MITCHELL, Christopher Brian QUIRK
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Publication number: 20160352656Abstract: Examples are generally directed towards context-sensitive generation of conversational responses. Context-message-response n-tuples are extracted from at least one source of conversational data to generate a set of training context-message-response n-tuples. A response generation engine is trained on the set of training context-message-response n-tuples. The trained response generation engine automatically generates a context-sensitive response based on a user generated input message and conversational context data. A digital assistant utilizes the trained response generation engine to generate context-sensitive, natural language responses that are pertinent to user queries.Type: ApplicationFiled: May 31, 2015Publication date: December 1, 2016Inventors: Michel GALLEY, Alessandro SORDONI, Christopher John BROCKETT, Jianfeng GAO, III, William Brennan DOLAN, Yangfeng JI, Michael AULI, Margaret Ann MITCHELL, Jian-Yun NIE