Invention Grant
- Patent Title: Updating machine learning systems using previous output data
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Application No.: US16945289Application Date: 2020-07-31
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Publication No.: US11887046B1Publication Date: 2024-01-30
- Inventor: Christopher Andrew Stephens , Alexander Clark Prater , Alexander Michael McNamara , Sridhar Boyapati , David Echevarria Ignacio , David William Bettis , Korwin Jon Smith , Kevin Alexander Lee , Aaron Craig Thompson , Gary Paolo Raden , Sudarshan Narasimha Raghavan , Dilip Kumar , Félix Joseph Étienne Pageau
- Applicant: AMAZON TECHNOLOGIES, INC.
- Applicant Address: US WA Seattle
- Assignee: AMAZON TECHNOLOGIES, INC.
- Current Assignee: AMAZON TECHNOLOGIES, INC.
- Current Assignee Address: US WA Seattle
- Agency: Lindauer Law, PLLC
- Main IPC: G06Q10/087
- IPC: G06Q10/087 ; G06Q10/08 ; G06T7/00 ; G06V40/10 ; H04N7/18

Abstract:
A system may use sensor data from a facility to generate tentative values associated with an event, such as the identification of an item removed from a shelf of the facility. A confidence value associated with each of the tentative values may be less than a confidence threshold. In response, inquiry data seeking confirmation of a tentative value from an associate is generated and sent to one or more associates in the facility. Responses from the associates are collected to determine a selection of one of the tentative values. The selected tentative value is designated as output data for the system. Thereafter, the output data and the original sensor data are designated as training data, which can then be used to train or update machine learning systems. Subsequent use of the updated machine learning systems can yield more accurate results.
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