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US Patent 9792281 Contextual language generation by leveraging language understanding

Patent 9792281 was granted and assigned to Microsoft on October, 2017 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent

Patent attributes

Patent Applicant
Microsoft
Microsoft
Current Assignee
Microsoft
Microsoft
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
9792281
Date of Patent
October 17, 2017
Patent Application Number
14740082
Date Filed
June 15, 2015
Patent Citations Received
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US Patent 12131522 Contextual auto-completion for assistant systems
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US Patent 12125297 Task execution based on real-world text detection for assistant systems
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US Patent 12131733 Active listening for assistant systems
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US Patent 12131523 Multiple wake words for systems with multiple smart assistants
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US Patent 11658835 Using a single request for multi-person calling in assistant systems
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US Patent 11657094 Memory grounded conversational reasoning and question answering for assistant systems
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US Patent 11657333 Interpretability of deep reinforcement learning models in assistant systems
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US Patent 11669918 Dialog session override policies for assistant systems
0
...
Patent Primary Examiner
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Cam Linh Nguyen
Patent abstract

Technology is provided for improving digital assistant performance by generating and presenting suggestions to users for completing a task or a session. To generate the suggestions, a machine learned language prediction model is trained with features extracted from multiple sources, such as log data and session context. When input is received from a user, the trained machine learned language prediction model is used to determine the most likely suggestion to present to the user to lead to successful task completion. In generating the suggestion, intermediate suggestion data, such as a domain, intent, and/or slot, is generated for the suggestion. From the generated intermediate suggestion data for the suggestion, a surface form of the suggestion is generated that can be presented to the user. The resulting suggestion and related context may further be used to continue training the machine learned language prediction model.

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