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US Patent 10402689 Generating an image mask using machine learning

Patent 10402689 was granted and assigned to Snap! (company) on September, 2019 by the United States Patent and Trademark Office.

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Is a
Patent
Patent
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Patent attributes

Patent Applicant
Snap Inc.
Snap Inc.
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Current Assignee
Snap! (company)
Snap! (company)
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
104026890
Patent Inventor Names
Lidiia Bogdanovych0
Fedir Poliakov0
Guohui Wang0
Jianchao Yang0
Linjie Yang0
Samuel Edward Hare0
William Brendel0
Xuehan Xiong0
Date of Patent
September 3, 2019
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Patent Application Number
157060570
Date Filed
September 15, 2017
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Patent Citations Received
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US Patent 12137350 System, method, and apparatus for providing dynamic, prioritized spectrum management and utilization
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US Patent 12114172 System, method, and apparatus for providing dynamic, prioritized spectrum management and utilization
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US Patent 12114171 System, method, and apparatus for providing optimized network resources
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US Patent 12120528 System, method, and apparatus for providing optimized network resources
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US Patent 12120529 System, method, and apparatus for providing optimized network resources
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US Patent 12127008 System, method, and apparatus for providing optimized network resources
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US Patent 12127007 System, method, and apparatus for providing dynamic, prioritized spectrum management and utilization
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US Patent 12133082 System, method, and apparatus for providing dynamic, prioritized spectrum management and utilization
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Patent Primary Examiner
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Bernard Krasnic
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Patent abstract

A machine learning system can generate an image mask (e.g., a pixel mask) comprising pixel assignments for pixels. The pixels can be assigned to classes, including, for example, face, clothes, body skin, or hair. The machine learning system can be implemented using a convolutional neural network that is configured to execute efficiently on computing devices having limited resources, such as mobile phones. The pixel mask can be used to more accurately display video effects interacting with a user or subject depicted in the image.

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