US 12,235,641 B2
Hybrid neural networks sourcing social data sources to optimize satisfaction of rider in intelligent transportation systems
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 Oct. 31, 2022, as Appl. No. 17/978,093.
Application 17/978,093 is a continuation of application No. 16/887,547, filed on May 29, 2020, granted, now 11,694,288.
Application 16/887,547 is a continuation of application No. 16/694,733, filed on Nov. 25, 2019, granted, now 11,333,514.
Application 16/694,733 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 2023/0051185 A1, Feb. 16, 2023
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 1/692 (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 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 1/692 (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 20/59 (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); 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)] 20 Claims
OG exemplary drawing
 
1. A system for transportation, comprising:
a vehicle having at least one rider located in the vehicle;
a data processing system for taking data from a plurality of social data sources to determine a user profile of the at least one rider, wherein a portion of the data taken from the plurality of social data sources is specific to at least one of: interests, social relationships, or preferences of the at least one rider and includes at least one of: like activity, dislike activity, posts, comments, discussion threads, chats, or images, and wherein the data processing system includes an intelligent agent module; and
a hybrid neural network connected to the data processing system, wherein the hybrid neural network is configured to optimize satisfaction of the at least one rider by processing the portion of the data from the plurality of social data sources, wherein the hybrid neural network is configured to analyze keywords in the portion of the data to determine and execute at least one optimizing action likely to further optimize the satisfaction of the at least one rider,
wherein the at least one optimizing action includes adjusting an in-vehicle state of the vehicle by adjusting at least one of: seat positioning settings, climate control settings, window state, moonroof state, ventilation system state, temperature settings, humidity settings, fan speed setting, or in-vehicle entertainment system state including at least one of: video entertainment content, audio entertainment content, or sound system settings, and
wherein a characterization of the at least one rider's satisfaction resulting from the execution of the at least one optimizing action is used as feedback to improve the determination and the execution of the at least one optimizing action, wherein the feedback indicates an inconsistency between the at least one optimizing action and preferences of the at least one rider that are associated with the user profile, and wherein the improved determination of the at least one optimizing action is confirmed via a dialogue between the at least one rider and the intelligent agent module.