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US Patent 11922726 Systems for and methods of creating a library of facial expressions

Patent 11922726 was granted and assigned to Prof Jim Inc. on March, 2024 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent
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Patent attributes

Patent Applicant
Prof Jim Inc
Prof Jim Inc
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Current Assignee
Prof Jim Inc
Prof Jim Inc
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
119227260
Patent Inventor Names
Pranav Mehta0
Deepak Chandra Sekar0
Date of Patent
March 5, 2024
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Patent Application Number
182435270
Date Filed
September 7, 2023
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Patent Citations
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US Patent 10169905 Systems and methods for animating models from audio data
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US Patent 10249207 Educational teaching system and method utilizing interactive avatars with learning manager and authoring manager functions
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US Patent 10339822 System and method for automated course individualization via learning behaviors and natural language processing
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US Patent 10586369 Using dialog and contextual data of a virtual reality environment to create metadata to drive avatar animation
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US Patent 10810409 Identifying facial expressions in acquired digital images
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US Patent 10825227 Artificial intelligence for generating structured descriptions of scenes
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US Patent 11010656 System and method for implementing an artificially intelligent virtual assistant using machine learning
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US Patent 11042800 System and method for implementing an artificially intelligent virtual assistant using machine learning
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Patent Primary Examiner
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Phuoc Tran
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CPC Code
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G06V 10/772
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G06V 10/82
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G06V 20/41
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G06V 40/165
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G06V 10/7715
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G06V 40/176
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G06V 40/174
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G06V 40/166
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Patent abstract

Methods, systems, and computer readable storage media for using image processing to develop a library of facial expressions. The system can receive digital video of at least one speaker, then execute image processing on the video to identify landmarks within facial features of the speaker. The system can also identify vectors based on the landmarks, then assign each vector to an expression, resulting in a plurality of speaker expressions. The system then scores the expressions based on similarity to one another, and creates subsets based on the similarity scores.

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