US 11,654,565 B2
Adapting simulation data to real-world conditions encountered by physical processes
Hui Li, San Francisco, CA (US); Evan Patrick Atherton, San Carlos, CA (US); Erin Bradner, San Francisco, CA (US); Nicholas Cote, San Francisco, CA (US); and Heather Kerrick, Oakland, CA (US)
Assigned to AUTODESK, INC., San Francisco, CA (US)
Filed by AUTODESK, INC., San Francisco, CA (US)
Filed on Jul. 27, 2020, as Appl. No. 16/940,288.
Application 16/940,288 is a continuation of application No. 15/995,005, filed on May 31, 2018, granted, now 10,751,879.
Claims priority of provisional application 62/515,456, filed on Jun. 5, 2017.
Prior Publication US 2020/0353621 A1, Nov. 12, 2020
This patent is subject to a terminal disclaimer.
Int. Cl. G06F 17/00 (2019.01); B25J 9/16 (2006.01); G06N 20/00 (2019.01); G05B 19/418 (2006.01); G06N 3/04 (2023.01); G06N 3/08 (2023.01); G06F 30/20 (2020.01); G06T 17/00 (2006.01)
CPC B25J 9/1671 (2013.01) [B25J 9/161 (2013.01); B25J 9/163 (2013.01); B25J 9/1605 (2013.01); G05B 19/41885 (2013.01); G06F 30/20 (2020.01); G06N 3/0445 (2013.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01); G06T 17/00 (2013.01); G05B 2219/32017 (2013.01); G05B 2219/35353 (2013.01)] 27 Claims
OG exemplary drawing
 
1. A method implemented by a machine learning model executing on a computing device, the method comprising:
receiving input data that includes simulated output data and real-world data;
performing one or more operations on the input data to generate augmented output; and
transmitting the augmented output to a physical process to control how the physical process performs a task in a physical world.