STRONG FORCE TP PORTFOLIO 2022, LLC

Systems for vehicles are provided. A system includes a brain-computer interface, an artificial intelligence system, and a vehicle control system. The brain-computer interface is configured to interact with the vehicle, to interact with a rider of the vehicle, and to generate output data. The artificial intelligence system is configured to receive the output data and to analyze an emotional state of the rider based on the output data. The vehicle control system is configured to optimize at least one operating parameter of the vehicle based on the emotional state of the rider as analyzed by the artificial intelligence system.

An artificial intelligence system for vehicle in-seat advertising includes a first portion of the artificial intelligence system that determines an operating state of the vehicle by processing inputs relating to at least one parameter of the vehicle. A second portion of the artificial intelligence system determines a state of a rider of the vehicle by processing inputs relating to at least one parameter of the rider. A third portion of the artificial intelligence system determines at least one of a price, classification, content and location of an advertisement to be delivered within an interface of the vehicle to a rider in a seat in the vehicle based on the vehicle state and the rider state.

Transportation systems can represent a set of operating states of a vehicle to a user of the vehicle and generally include a system for representing a set of operating states of a vehicle to a user of the vehicle. The system includes a portion of the vehicle having a vehicle operating state; a digital twin system receiving vehicle parameter data from one or more inputs to determine the vehicle operating state; and an interface for the digital twin system to present the vehicle operating state to the user of the vehicle.

A system for representing a set of operating states of a vehicle to a user of the vehicle includes a vehicle having a vehicle operating state, and a digital twin receiving vehicle parameter data from one or more inputs to determine the vehicle operating state. An interface for the digital twin presents the vehicle operating state to the user of the vehicle. An identity management system manages a set of identities and roles of the vehicle user and determines capabilities to view, modify, and configure the digital twin based on parsing of the set of identities and roles.

A transportation system includes: a neural network to determine current inferred hormonal state data of an occupant of the vehicle based in part on received sensor data relating to the occupant; and an artificial intelligence-based system trained on a set of outcomes related to occupant in-vehicle experience. The artificial intelligence-based system is configured to: retrieve sensor data of the occupant; identify a difference between the current inferred hormonal state data and a desired hormonal state; determine a variation including one of configuring the vehicle for aggressive driving performance or configuring the vehicle for non-aggressive driving performance to promote the desired hormonal state of the occupant responsive to the current inferred hormonal state; and induce the variation in one or more occupant experience parameters to achieve at least one desired outcome.

A system for transportation includes a vehicle occupied by a rider, and an artificial intelligence system for processing a voice of the rider to classify an emotional state of the rider and optimizing at least one operating parameter of the vehicle to improve the emotional state of the rider.

A method of optimizing an operating state of a vehicle includes classifying, using a first neural network of a hybrid neural network, social media data sourced from a plurality of social media sources as affecting a transportation system. The method further includes predicting, using a second neural network of the hybrid neural network, one or more effects of the classified social media data on the transportation system. The method further includes optimizing, using a third neural network of the hybrid neural network, a state of at least one vehicle of the transportation system, wherein the optimizing addresses an influence of the predicted one or more effects on the at least one vehicle.

A system for transportation includes a vehicle interface for gathering hormonal state data of a rider in the vehicle. The system further includes an artificial intelligence-based circuit that is trained on a set of outcomes related to rider in-vehicle experience and that induces, responsive to the sensed rider hormonal state data, variation in one or more of the user experience parameters to achieve at least one desired outcome in the set of outcomes. The set of outcomes includes at least one outcome that promotes rider safety. The inducing variation includes control of timing and extent of the variation.

A method of robotic process automation for achieving an optimized margin of vehicle operational safety includes tracking expert vehicle control human interactions with a vehicle control-facilitating interface, and recording the tracked expert vehicle control human interactions in a robotic process automation system training data structure. The method further includes tracking vehicle operational state information of a vehicle, and recording vehicle operational state information in the robotic process automation system training data structure. The method further includes training, via at least one neural network, the vehicle to operate with an optimized margin of vehicle operational safety in a manner consistent with the expert vehicle control human interactions based on the expert vehicle control human interactions and the vehicle operational state information in the robotic process automation system training data structure, and controlling at least one aspect of the vehicle with the trained artificial intelligence system.

A rider state modification system for improving a state of a rider in a vehicle includes a first neural network that operates to classify a state of the vehicle through analysis of information about the vehicle captured by an Internet-of-things device during operation of the vehicle. The rider state modification system further includes a second neural network that operates to optimize at least one operating parameter of the vehicle based on the classified state of the vehicle, information about a state of a rider occupying the vehicle, and information that correlates vehicle operation with an effect on rider state.