US 12,222,714 B2
Robotic process automation system trained to undertake actions with a vehicle based on user interactions with a user interface of the vehicle
Charles Howard Cella, Pembroke, MA (US)
Assigned to Strong Force TP Portfolio 2022, LLC, Fort Lauderdale, FL (US)
Filed by STRONG FORCE TP PORTFOLIO 2022, LLC, Fort Lauderdale, FL (US)
Filed on Dec. 22, 2023, as Appl. No. 18/395,424.
Application 18/395,424 is a continuation of application No. 17/977,791, filed on Oct. 31, 2022, granted, now 11,994,856.
Application 17/977,791 is a continuation of application No. 16/803,356, filed on Feb. 27, 2020, granted, now 11,782,435, issued on Oct. 10, 2023.
Application 16/803,356 is a continuation of application No. PCT/US2019/053857, filed on Sep. 30, 2019.
Claims priority of provisional application 62/739,335, filed on Sep. 30, 2018.
Prior Publication US 2024/0126288 A1, Apr. 18, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G05D 1/00 (2024.01); B60W 40/08 (2012.01); G01C 21/34 (2006.01); G01C 21/36 (2006.01); G05B 13/02 (2006.01); G05D 1/224 (2024.01); G05D 1/225 (2024.01); G05D 1/226 (2024.01); G05D 1/227 (2024.01); G05D 1/228 (2024.01); G05D 1/229 (2024.01); G05D 1/24 (2024.01); G05D 1/646 (2024.01); G05D 1/69 (2024.01); G05D 1/692 (2024.01); G05D 1/81 (2024.01); G06F 40/40 (2020.01); G06N 3/04 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01); G06N 3/086 (2023.01); G06N 20/00 (2019.01); G06Q 30/0208 (2023.01); G06Q 50/18 (2012.01); G06Q 50/40 (2024.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 20/56 (2022.01); G06V 20/59 (2022.01); G06V 20/64 (2022.01); G07C 5/00 (2006.01); G07C 5/02 (2006.01); G07C 5/08 (2006.01); G10L 15/16 (2006.01); G10L 25/63 (2013.01); G06N 3/02 (2006.01); G06Q 30/02 (2023.01); G06Q 50/00 (2012.01)
CPC G05D 1/0022 (2013.01) [B60W 40/08 (2013.01); G01C 21/3438 (2013.01); G01C 21/3461 (2013.01); G01C 21/3469 (2013.01); G01C 21/3617 (2013.01); G05B 13/027 (2013.01); G05D 1/0088 (2013.01); G05D 1/0212 (2013.01); G05D 1/0287 (2013.01); G05D 1/224 (2024.01); G05D 1/225 (2024.01); G05D 1/226 (2024.01); G05D 1/227 (2024.01); G05D 1/228 (2024.01); G05D 1/229 (2024.01); G05D 1/24 (2024.01); G05D 1/646 (2024.01); G05D 1/69 (2024.01); G05D 1/692 (2024.01); G05D 1/81 (2024.01); G06F 40/40 (2020.01); G06N 3/0418 (2013.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06N 3/086 (2013.01); G06N 20/00 (2019.01); G06Q 30/0208 (2013.01); G06Q 50/188 (2013.01); G06Q 50/40 (2024.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 20/56 (2022.01); G06V 20/59 (2022.01); G06V 20/597 (2022.01); G06V 20/64 (2022.01); G07C 5/006 (2013.01); G07C 5/008 (2013.01); G07C 5/02 (2013.01); G07C 5/08 (2013.01); G07C 5/0808 (2013.01); G07C 5/0816 (2013.01); G07C 5/0866 (2013.01); G07C 5/0891 (2013.01); G10L 15/16 (2013.01); G10L 25/63 (2013.01); B60W 2040/0881 (2013.01); G06N 3/02 (2013.01); G06Q 30/0281 (2013.01); G06Q 50/01 (2013.01)] 18 Claims
OG exemplary drawing
 
10. A method of robotic process automation to facilitate mimicking human operation of a vehicle, the method comprising:
tracking human interactions with a vehicle control-facilitating interface;
recording the tracked human interactions in a robotic process automation training data structure;
tracking vehicle operational state information of the vehicle, wherein the vehicle is controlled through the vehicle control-facilitating interface;
recording the vehicle operational state information in the robotic process automation training data structure;
training, through use of at least one neural network, an artificial intelligence system to operate the vehicle in a manner consistent with the human interactions based on the human interactions and the vehicle operational state information in the robotic process automation training data structure and based on feedback obtained from the robotic process automation training data structure,
wherein the training of the artificial intelligence system further involves training the artificial intelligence system to optimize a margin of safety and to control the vehicle on behalf of a user of the vehicle, wherein the optimizing and the controlling affect at least one of: a route of the vehicle, a speed of the vehicle, an acceleration of the vehicle, a deceleration of the vehicle, a proximity to other vehicles along the route of the vehicle, a proximity to objects along the route of the vehicle, braking intensity, steering sensitivity, or route selection; and
controlling, via the trained artificial intelligence system, the vehicle with the optimized margin of safety.