Invention Grant
- Patent Title: Generating ground truth for machine learning from time series elements
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Application No.: US16265729Application Date: 2019-02-01
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Publication No.: US10997461B2Publication Date: 2021-05-04
- Inventor: Ashok Kumar Elluswamy , Matthew Bauch , Christopher Payne , Andrej Karpathy , Joseph Polin
- Applicant: Tesla, Inc.
- Applicant Address: US CA Palo Alto
- Assignee: Tesla, Inc.
- Current Assignee: Tesla, Inc.
- Current Assignee Address: US CA Palo Alto
- Agency: Knobbe, Martens, Olson & Bear, LLP
- Main IPC: G06K9/62
- IPC: G06K9/62 ; G05D1/02 ; G06K9/00 ; G06N3/08 ; G06N3/04

Abstract:
Sensor data, including a group of time series elements, is received. A training data set is determined, including by determining for at least a selected time series element in the group of time series elements a corresponding ground truth. The corresponding ground truth is based on a plurality of time series elements in the group of time series elements. A processor is used to train a machine learning model using the training dataset.
Public/Granted literature
- US20200250473A1 GENERATING GROUND TRUTH FOR MACHINE LEARNING FROM TIME SERIES ELEMENTS Public/Granted day:2020-08-06
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