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US Patent 10127438 Predicting inventory events using semantic diffing

Patent 10127438 was granted and assigned to Standard Cognition, Corp. on November, 2018 by the United States Patent and Trademark Office.

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Is a
Patent
Patent

Patent attributes

Patent Applicant
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Standard Cognition, Corp.
Current Assignee
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Standard Cognition, Corp.
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
10127438
Date of Patent
November 13, 2018
Patent Application Number
15945466
Date Filed
April 4, 2018
Patent Citations Received
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US Patent 11501537 Multiple-factor verification for vision-based systems
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US Patent 11501453 Sensor array for scalable position tracking system
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US Patent 11501454 Mapping wireless weight sensor array for item detection and identification
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US Patent 11501455 System and method for position tracking using edge computing
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US Patent 11506483 Method, system and apparatus for support structure depth determination
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US Patent 11507103 Method, system and apparatus for localization-based historical obstacle handling
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US Patent 11507933 Cashier interface for linking customers to virtual data
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US Patent 12126940 Detection of object removal and replacement from a shelf
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...
Patent Primary Examiner
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Anner N Holder
Patent abstract

Systems and techniques are provided for tracking puts and takes of inventory items by subjects in an area of real space. A plurality of cameras with overlapping fields of view produce respective sequences of images of corresponding fields of view in the real space. In one embodiment, the system includes first image processors, including subject image recognition engines, receiving corresponding sequences of images from the plurality of cameras. The first image processors process images to identify subjects represented in the images in the corresponding sequences of images. The system includes second image processors, including background image recognition engines, receiving corresponding sequences of images from the plurality of cameras. The second image processors mask the identified subjects to generate masked images. Following this, the second image processors process the masked images to identify and classify background changes represented in the images in the corresponding sequences of images.

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