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US Patent 10109273 Efficient generation of personalized spoken language understanding models

Patent 10109273 was granted and assigned to Amazon on October, 2018 by the United States Patent and Trademark Office.

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Patent abstractTimelineTable: Further ResourcesReferences
Is a
Patent
Patent
1

Patent attributes

Current Assignee
Amazon
Amazon
1
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
1
Patent Number
101092731
Patent Inventor Names
Arushan Rajasekaram1
Madan Mohan Rao Jampani1
Nikko Strom1
Date of Patent
October 23, 2018
1
Patent Application Number
140141541
Date Filed
August 29, 2013
1
Patent Citations Received
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US Patent 12131522 Contextual auto-completion for assistant systems
2
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US Patent 11908179 Suggestions for fallback social contacts for assistant systems
3
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US Patent 12001862 Disambiguating user input with memorization for improved user assistance
4
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US Patent 12008802 Execution engine for compositional entity resolution for assistant systems
5
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US Patent 12112530 Execution engine for compositional entity resolution for assistant systems
6
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US Patent 12125272 Personalized gesture recognition for user interaction with assistant systems
7
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US Patent 12131523 Multiple wake words for systems with multiple smart assistants
8
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US Patent 11231946 Personalized gesture recognition for user interaction with assistant systems
...
Patent Primary Examiner
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Richemond Dorvil
1
Patent abstract

Features are disclosed for maintaining data that can be used to personalize spoken language understanding models, such as speech recognition or natural language understanding models. The personalization data can be used to update the models based on some or all of the data. The data may be obtained from various data sources, such as applications or services used by the user. Personalized spoken language understanding models may be generated or updated based on updates to the personalization data or some other portion of the stored personalization data. Generation of personalized spoken language understanding models may be prioritized such that the generation process accommodates multiple users.

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