US 12,321,170 B2
Robotic process automation for achieving an optimized margin of vehicle operational safety
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. 26, 2023, as Appl. No. 18/396,135.
Application 18/396,135 is a continuation of application No. 17/977,698, filed on Oct. 31, 2022.
Application 17/977,698 is a continuation of application No. 16/887,583, filed on May 29, 2020.
Application 16/887,583 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 2024/0142974 A1, May 2, 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 1/02 (2020.01); 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)] 19 Claims
OG exemplary drawing
 
1. A transportation system for optimizing a margin of safety when mimicking human operation of a vehicle, the transportation system comprising:
a robotic process automation system comprising:
an operator data collection module to capture human operator interaction data with a vehicle control system interface;
a vehicle data collection module to capture vehicle response and operating conditions associated at least contemporaneously with the human operator interaction; and
an environment data collection module to capture instances of environmental information associated at least contemporaneously with the human operator interactions; and
an artificial intelligence system to learn to control the vehicle with an optimized margin of safety while mimicking the human operator, wherein the artificial intelligence system is responsive to the robotic process automation system, wherein the artificial intelligence system is to detect data indicative of at least one of a plurality of the instances of environmental information associated with the contemporaneously captured vehicle response and operating conditions, wherein the optimized margin of safety is to be achieved by training the artificial intelligence system to control the vehicle based on the human operator interaction data collected at the robotic process automation system, and controlling the vehicle with the optimized margin of safety.