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US Patent 10133729 Semantically-relevant discovery of solutions

Patent 10133729 was granted and assigned to Microsoft on November, 2018 by the United States Patent and Trademark Office.

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Is a
Patent
Patent
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Patent attributes

Patent Applicant
Microsoft
Microsoft
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Current Assignee
Microsoft
Microsoft
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
101337290
Patent Inventor Names
Yelong Shen0
Li Deng0
Xiaodong He0
Xinying Song0
Hamid Palangi0
Jianfeng Gao0
Jianshu Chen0
Date of Patent
November 20, 2018
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Patent Application Number
148392810
Date Filed
August 28, 2015
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Patent Citations Received
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US Patent 10565305 Adaptive attention model for image captioning
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US Patent 10565306 Sentinel gate for modulating auxiliary information in a long short-term memory (LSTM) neural network
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US Patent 10740401 System for the automated semantic analysis processing of query strings
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US Patent 10846478 Spatial attention model for image captioning
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US Patent 10929448 Determining a category of a request by word vector representation of a natural language text string with a similarity value
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US Patent 11244111 Adaptive attention model for image captioning
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US Patent 11281867 Performing multi-objective tasks via primal networks trained with dual networks
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US Patent 10943068 N-ary relation prediction over text spans
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Patent Primary Examiner
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Bharatkumar S Shah
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Patent abstract

Systems, methods, and computer-readable media for providing semantically-relevant discovery of solutions are described herein. In some examples, a computing device can receive an input, such as a query. The computing device can process each word of the input sequentially to determine a semantic representation of the input. Techniques and technologies described herein determine a response to the input, such as an answer, based on the semantic representation of the input matching a semantic representation of the response. An output including one or more relevant responses to the request can then be provided to the requestor. Example techniques described herein can apply machine learning to train a model with click-through data to provide semantically-relevant discovery of solutions. Example techniques described herein can apply recurrent neural networks (RNN) and/or long short term memory (LSTM) cells in the machine learning model.

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