US 12,223,428 B2
Generating ground truth for machine learning from time series elements
Ashok Kumar Elluswamy, Sunnyvale, CA (US); Matthew Bauch, San Francisco, CA (US); Christopher Payne, San Francisco, CA (US); Andrej Karpathy, San Francisco, CA (US); and Joseph Polin, San Francisco, CA (US)
Assigned to Tesla, Inc., Austin, TX (US)
Filed by Tesla, Inc., Austin, TX (US)
Filed on Sep. 1, 2023, as Appl. No. 18/459,954.
Application 18/459,954 is a continuation of application No. 17/301,965, filed on Apr. 20, 2021, granted, now 11,748,620.
Application 17/301,965 is a continuation of application No. 16/265,729, filed on Feb. 1, 2019, granted, now 10,997,461, issued on May 4, 2021.
Prior Publication US 2024/0070460 A1, Feb. 29, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06N 3/08 (2023.01); G05D 1/00 (2006.01); G06F 18/28 (2023.01); G06V 20/56 (2022.01); G16Y 20/10 (2020.01); G06N 3/04 (2023.01)
CPC G06N 3/08 (2013.01) [G05D 1/0221 (2013.01); G06F 18/28 (2023.01); G06V 20/588 (2022.01); G16Y 20/10 (2020.01); G06N 3/04 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method, comprising: receiving, by one or more processors, sensor data of a group of time series elements; determining, by the one or more processors, a training dataset, including by determining for at least a selected time series element in the group of time series elements a corresponding ground truth based on a plurality of time series elements in the group of time series elements; and training, by the one or more processors, using the training dataset, a machine learning model to output a predicted ground truth based on a single time series element, the training dataset comprising the selected time series element in the group of time series elements and the corresponding ground truth.