Software-defined vehicle and ai-convergence system of systems

Abstract

The present disclosure relates to transportation and related methods and systems including software-defined vehicles, vehicle operating states, an identity management system, an intelligent digital twin system that creates, manages, and provides digital twins for transportation systems using sensor and other data, and the integration of a transportation system with an AI convergence system of systems, providing a multi-layered system for intelligent automation and data-driven decision making across operational aspects of a transportation system.

Description

SFT-107-A-PCT SOFTWARE-DEFINED VEHICLE AND AI-CONVERGENCE SYSTEM OF SYSTEMS CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. provisional patent application 63 / 625,609, filed 26 January 2024. This application claims priority to U.S. provisional patent application 63 / 638,591, filed 25 April 2024. This application claims priority to U.S. provisional patent application 63 / 639,912, filed 29 April 2024. Each patent application referenced above is hereby incorporated by reference as if fully set forth herein in its entirety. TECHNICAL FIELD

[0002] The present disclosure relates to transportation and related methods and systems including the integration of a transportation system with an AI convergence system of systems, representing a multi-layered system for intelligent automation and data-driven decision making across operational aspects of a transportation system. BACKGROUND

[0003] Traditional enterprise operations in transportation often rely on separate, disconnected layers of technology infrastructure. Governance is largely manual, requiring significant human oversight to enforce policies, monitor compliance, and manage digital rights. Organizations struggle to maintain consistent oversight across different operational domains of a transportation system and often faced challenges in adapting to changing regulatory requirements.

[0004] Transportation systems typically operate with rigid, predefined offerings that lack the ability to dynamically adapt to user needs or environmental conditions. Such operations suffer from fragmented management of AI systems and technological resources. Organizations lack sufficient capabilities for AI system generation, training, verification, and deployment. The absence of coordinated operations modules means that AI systems were developed and deployed in isolation, without proper governance or optimization across the enterprise.

[0005] The lack of intelligent integration between different technological layers of a transportation system creates significant inefficiencies in operations. This fragmented approach limits the ability to leverage emerging technologies effectively and restricts the potential for innovation in transportation services delivery. These limitations in traditional transportation systems create a clear need for a more integrated, intelligent approach to technology infrastructure. SUMMARY

[0006] In some aspects, the techniques described herein relate to a computer-implemented system, the system including: an AI convergence system of systems.

[0007] In some aspects, the techniques described herein relate to a computer-implemented method, the method including: managing AI convergence system of systems.

[0008] In some aspects, the techniques described herein relate to an AI convergence system of systems substantially as shown and described.

[0009] In some aspects, the techniques described herein relate to a method for providing AI convergence system of systems substantially as shown and described.

[0010] In some aspects, the techniques described herein relate to a transportation AI convergence system of systems for optimizing powertrain performance, including: a vehicle having aSFT-107-A-PCT continuously variable powertrain; a hybrid neural network configured to optimize an operating state of the continuously variable powertrain, wherein: a first portion of the hybrid neural network is configured to classify a state of the vehicle; and a second portion of the hybrid neural network is configured to optimize at least one operating parameter of a transmission portion of the continuously variable powertrain based on the classified state of the vehicle.

[0011] In some aspects, the techniques described herein relate to a system, wherein the first portion of the hybrid neural network is configured to classify at least one of: a vehicle maintenance state, a vehicle health state, a vehicle operating state, a vehicle energy utilization state, a vehicle charging state, a vehicle satisfaction state, a vehicle component state, a vehicle sub-system state, a vehicle powertrain system state, a vehicle braking system state, a vehicle clutch system state, or a vehicle lubrication system state.

[0012] In some aspects, the techniques described herein relate to a system, wherein at least one of the first portion or the second portion of the hybrid neural network includes a convolutional neural network.

[0013] In some aspects, the techniques described herein relate to a system, further including a sensor system configured to provide sensor data to the hybrid neural network, wherein the sensor data includes at least one of: LIDAR data, RADAR data, vision-based system data, or temperature sensing data.

[0014] In some aspects, the techniques described herein relate to a system, wherein the second portion of the hybrid neural network optimizes the at least one operating parameter in real-time responsive to the classified state of the vehicle.

[0015] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network includes a plurality of connected nodes that form a directed cycle facilitating bi- directional flow of data among the connected nodes.

[0016] In some aspects, the techniques described herein relate to a system, wherein the at least one operating parameter affects at least one of: a speed of the vehicle, an acceleration of the vehicle, or a deceleration of the vehicle.

[0017] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network is configured to predict a future state of the vehicle based on the classified state.

[0018] In some aspects, the techniques described herein relate to a system, wherein the first portion of the hybrid neural network includes a structure-adaptive network configured to adapt its structure responsive to operational results.

[0019] In some aspects, the techniques described herein relate to a system, further including a feedback loop configured to provide operational feedback to the hybrid neural network for refining the optimization of the operating parameter.

[0020] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network is configured to process social data from social media sources to classify the state of the vehicle.

[0021] In some aspects, the techniques described herein relate to a transportation system having an AI convergence system of systems including: a sensor system configured to detect anSFT-107-A-PCT environmental condition; an artificial intelligence system configured to: receive sensor data from the sensor system; classify a plurality of operational states of a vehicle based on the sensor data; process an input descriptive of the vehicle and at least one detected condition associated with an occupant of the vehicle; and optimize at least one operating parameter of a powertrain based on the classified operational states and processed input.

[0022] In some aspects, the techniques described herein relate to a transportation AI convergence system of systems for transportation including: a vehicle having a powertrain system; a hybrid neural network configured to: classify a vehicle maintenance state, a vehicle health state, and a vehicle operating state; optimize powertrain operating parameters based on the classified states; and predict a future state of the vehicle based on the classified states and optimized operating parameters.

[0023] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network includes a convolutional neural network.

[0024] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network is configured to process sensor data from a plurality of vehicle-mounted sensors.

[0025] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network is configured to track conditions proximal to the vehicle using vehicle mounted sensors.

[0026] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network is configured to process data feeds from remote sensors contemporaneous to vehicle operation.

[0027] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network employs a workflow that involves decision-making and automated optimization.

[0028] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network is configured to optimize the powertrain operating parameters based on a correlation between vehicle operating state and rider emotional state.

[0029] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network is configured to optimize the powertrain operating parameters in real-time responsive to detected changes in vehicle state.

[0030] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network includes a plurality of connected nodes that form a directed cycle.

[0031] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network is configured to process social media data to classify the vehicle states.

[0032] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network is configured to optimize the powertrain operating parameters based on predicted traffic conditions.

[0033] In some aspects, the techniques described herein relate to a method for optimizing vehicle powertrain operation, including: executing a first network of a hybrid neural network to classify a plurality of operational states of a vehicle; executing a second network of the hybrid neural network to process inputs descriptive of the vehicle and at least one detected condition associated with anSFT-107-A-PCT occupant; and optimizing at least one operating parameter of a continuously variable powertrain based on the classified operational states and processed inputs.

[0034] In some aspects, the techniques described herein relate to a method, wherein classifying the operational states includes processing sensor data from vehicle-mounted sensors.

[0035] In some aspects, the techniques described herein relate to a method, wherein at least one of the first network or second network includes a convolutional neural network.

[0036] In some aspects, the techniques described herein relate to a method, further including tracking conditions proximal to the vehicle using vehicle-mounted sensors.

[0037] In some aspects, the techniques described herein relate to a method, further including processing data feeds from remote sensors contemporaneous to vehicle operation.

[0038] In some aspects, the techniques described herein relate to a method, wherein optimizing the operating parameter includes applying deep learning to optimize a margin of vehicle operational safety.

[0039] In some aspects, the techniques described herein relate to a method, wherein optimizing the operating parameter includes processing feedback from controlling the vehicle through machine learning.

[0040] In some aspects, the techniques described herein relate to a method, further including adapting the optimization based on detected emotional states of the occupant.

[0041] In some aspects, the techniques described herein relate to a method, wherein optimizing includes adjusting at least one of: vehicle speed, acceleration, or deceleration.

[0042] In some aspects, the techniques described herein relate to a method, further including predicting future operational states based on the classified states.

[0043] In some aspects, the techniques described herein relate to a method, wherein classifying includes processing social media data related to vehicle operation.

[0044] In some aspects, the techniques described herein relate to AI convergence system of systems for transportation including: a sensor system configured to detect vehicle operating conditions; an artificial intelligence system configured to: process sensor data to identify a vehicle operational state; optimize a powertrain parameter based on the identified operational state; and implement structured variation in the powertrain parameter through machine learning feedback processing.

[0045] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system includes a hybrid neural network.

[0046] In some aspects, the techniques described herein relate to a system, wherein the sensor system includes at least one of: LIDAR, RADAR, or vision-based sensors.

[0047] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system is configured to process environmental data contemporaneous with vehicle operation.

[0048] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system optimizes the powertrain parameter in real-time.SFT-107-A-PCT

[0049] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system implements the structured variation based on detected emotional states of vehicle occupants.

[0050] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system includes a plurality of connected nodes forming a directed cycle.

[0051] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system processes social media data to identify operational states.

[0052] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system predicts future operational states.

[0053] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system optimizes powertrain parameters based on weather conditions.

[0054] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system adapts optimization based on traffic conditions.

[0055] In some aspects, the techniques described herein relate to AI convergence system of systems for optimizing vehicle performance including: a hybrid neural network including: a structure-adaptive network configured to adapt its structure responsive to an operational result; and an optimization network configured to optimize a powertrain operating parameter based on the adapted structure.

[0056] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network includes a convolutional neural network.

[0057] In some aspects, the techniques described herein relate to a system, wherein the structure- adaptive network processes sensor data from vehicle-mounted sensors.

[0058] In some aspects, the techniques described herein relate to a system, wherein the optimization network processes environmental data.

[0059] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network optimizes parameters in real-time.

[0060] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network adapts based on detected emotional states of vehicle occupants.

[0061] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network includes nodes forming a directed cycle.

[0062] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network processes social media data.

[0063] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network predicts future operational states.

[0064] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network optimizes based on weather conditions.

[0065] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network adapts based on traffic conditions.

[0066] In some aspects, the techniques described herein relate to a transportation AI convergence system of systems including: a sensor system configured to detect an environmental condition; anSFT-107-A-PCT artificial intelligence system configured to: classify a weather or traffic condition based on sensor data; adjust a powertrain operating parameter based on the classified condition; and optimize the adjusted parameter through machine learning to maintain a safety margin.

[0067] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system includes a hybrid neural network.

[0068] In some aspects, the techniques described herein relate to a system, wherein the sensor system includes vision-based sensors.

[0069] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system processes real-time environmental data.

[0070] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system optimizes parameters in real-time.

[0071] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system adapts based on occupant emotional states.

[0072] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system includes directed cycle nodes.

[0073] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system processes social media data.

[0074] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system predicts future conditions.

[0075] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system maintains safety margins based on road conditions.

[0076] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system adapts to changing traffic patterns.

[0077] In some aspects, the techniques described herein relate to a transportation AI convergence system of systems for vehicle operation including: a vehicle having a continuously variable powertrain; a hybrid neural network configured to: process social data from a social media source to classify a vehicle operational state; optimize a powertrain operating parameter based on the classified state from social data processing.

[0078] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network includes a convolutional neural network.

[0079] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network processes sensor data.

[0080] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network processes environmental data.

[0081] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network optimizes in real-time.

[0082] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network adapts to occupant states.

[0083] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network includes directed cycle nodes.SFT-107-A-PCT

[0084] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network predicts future states.

[0085] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network optimizes for weather conditions.

[0086] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network adapts to traffic conditions.

[0087] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network maintains safety margins.

[0088] In some aspects, the techniques described herein relate to the method for optimizing vehicle powertrain operation including: receiving sensor data indicating a vehicle operating condition; processing the sensor data through a first neural network to classify a vehicle state; processing environmental data through a second neural network to identify an external condition; optimizing a powertrain operating parameter based on the classified state and identified condition.

[0089] In some aspects, the techniques described herein relate to a method, wherein at least one neural network is a convolutional network.

[0090] In some aspects, the techniques described herein relate to a method, further including processing social media data.

[0091] In some aspects, the techniques described herein relate to a method, wherein optimizing occurs in real-time.

[0092] In some aspects, the techniques described herein relate to a method, further including adapting to occupant emotional states.

[0093] In some aspects, the techniques described herein relate to a method, wherein the neural networks include directed cycle nodes.

[0094] In some aspects, the techniques described herein relate to a method, further including predicting future conditions.

[0095] In some aspects, the techniques described herein relate to a method, further including optimizing for weather conditions.

[0096] In some aspects, the techniques described herein relate to a method, further including adapting to traffic conditions.

[0097] In some aspects, the techniques described herein relate to a method, further including maintaining safety margins.

[0098] In some aspects, the techniques described herein relate to a method, further including processing feedback through machine learning.

[0099] In some aspects, the techniques described herein relate to a transportation AI convergence system of systems for transportation including: a hybrid neural network configured to: classify a vehicle operating state based on sensor data; predict a future operating condition based on the classified state; optimize a powertrain parameter based on the predicted condition; and adapt the optimized parameter through machine learning feedback.

[0100] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network includes a convolutional network.SFT-107-A-PCT

[0101] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network processes social media data.

[0102] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network optimizes in real-time.

[0103] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network adapts to occupant states.

[0104] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network includes directed cycle nodes.

[0105] In some aspects, the techniques described herein relate to a system, wherein optimization maintains safety margins.

[0106] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network optimizes for weather.

[0107] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network adapts to traffic.

[0108] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network processes environmental data.

[0109] In some aspects, the techniques described herein relate to a system, wherein adaptation occurs through structured variation. AI convergence system of systems ecosystem to optimize a vehicle charging system.

[0110] In some aspects, the techniques described herein relate to a transportation AI convergence system of systems for optimizing vehicle charging operations, including: a network-enabled vehicle information ingestion port configured to gather operational state and energy consumption information from a plurality of network-enabled vehicles; a charging infrastructure control system including cloud-based computing and local charging infrastructure systems; an artificial intelligence system configured to: determine at least one charging plan parameter upon which a charging plan for the plurality of network-enabled vehicles is dependent; and optimize the charging plan based on the operational state and energy consumption information.

[0111] In some aspects, the techniques described herein relate to a system, wherein the operational state information includes battery charge states of the plurality of vehicles.

[0112] In some aspects, the techniques described herein relate to a system, wherein the charging infrastructure control system is configured to adapt charging rates based on accumulated vehicles at charging locations.

[0113] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system includes a hybrid neural network.

[0114] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system is configured to predict vehicle geolocations within geographic regions.

[0115] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system is configured to automate negotiation of charging duration, quantity, and pricing.SFT-107-A-PCT

[0116] In some aspects, the techniques described herein relate to a system, wherein the charging plan parameter impacts vehicle routing to charging infrastructure.

[0117] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system optimizes electricity usage for vehicles and charging infrastructure.

[0118] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system processes market value indicators for charging.

[0119] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system processes available supply capacity data.

[0120] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system processes recharge demand data.

[0121] In some aspects, the techniques described herein relate to a transportation AI convergence system of systems for transportation, including: a plurality of network-enabled vehicles; a cloud- based artificial intelligence system configured to: receive inputs relating to the plurality of vehicles; determine at least one parameter of a charging plan for the plurality of vehicles based on the inputs; and optimize charging infrastructure operations based on the determined parameter.

[0122] In some aspects, the techniques described herein relate to a system, wherein the inputs include route plans for the plurality of vehicles.

[0123] In some aspects, the techniques described herein relate to a system, wherein the inputs include indicators of charging value.

[0124] In some aspects, the techniques described herein relate to a system, wherein the inputs include predicted traffic conditions.

[0125] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system includes a hybrid neural network.

[0126] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system predicts near-term charging needs.

[0127] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system optimizes charging time allocation.

[0128] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system optimizes charging location selection.

[0129] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system optimizes charging amounts.

[0130] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system processes environmental data.

[0131] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system processes marketplace factors.

[0132] In some aspects, the techniques described herein relate to a method for optimizing vehicle charging operations, including: receiving operational state information from a plurality of network- enabled vehicles; processing the operational state information through a first neural network to predict target energy renewal regions; processing infrastructure usage information through aSFT-107-A-PCT second neural network to optimize charging infrastructure operations within the target energy renewal regions.

[0133] In some aspects, the techniques described herein relate to a transportation AI convergence system of systems including: a plurality of vehicles having charging systems; a hybrid neural network including: a first neural network configured to process vehicle route and stored energy state information to predict target energy renewal regions; and a second neural network configured to process vehicle energy renewal infrastructure usage and demand information to determine charging infrastructure operational parameters.

[0134] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network includes a convolutional neural network.

[0135] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network processes real-time vehicle data.

[0136] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network optimizes charging infrastructure allocation.

[0137] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network processes marketplace factors.

[0138] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network predicts vehicle locations.

[0139] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network optimizes charging rates.

[0140] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network processes environmental data.

[0141] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network optimizes energy efficiency.

[0142] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network processes traffic data.

[0143] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network adapts to charging demand.

[0144] In some aspects, the techniques described herein relate to a method for optimizing vehicle charging operations, including: receiving battery status information from a plurality of vehicles; determining at least one charging plan parameter based on the battery status information; optimizing anticipated battery usage of the plurality of vehicles based on the charging plan parameter; and adapting charging infrastructure operations based on the optimized anticipated battery usage.

[0145] In some aspects, the techniques described herein relate to a method, wherein determining the charging plan parameter includes processing route plans.

[0146] In some aspects, the techniques described herein relate to a method, wherein determining includes processing traffic predictions.

[0147] In some aspects, the techniques described herein relate to a method, wherein optimizing includes predicting charging needs.SFT-107-A-PCT

[0148] In some aspects, the techniques described herein relate to a method, wherein adapting includes adjusting charging rates.

[0149] In some aspects, the techniques described herein relate to a method, wherein optimizing includes processing environmental data.

[0150] In some aspects, the techniques described herein relate to a method, wherein optimizing includes processing market data.

[0151] In some aspects, the techniques described herein relate to a method, wherein adapting includes coordinating multiple charging stations.

[0152] In some aspects, the techniques described herein relate to a method, wherein optimizing includes predicting vehicle locations.

[0153] In some aspects, the techniques described herein relate to a method, wherein adapting includes managing charging capacity.

[0154] In some aspects, the techniques described herein relate to a method, wherein optimizing includes processing feedback data.

[0155] In some aspects, the techniques described herein relate to a transportation AI convergence system of systems including: a vehicle charging infrastructure; an artificial intelligence system configured to: apply a vehicle recharging facility utilization optimization algorithm to vehicle- specific inputs; evaluate impacts of recharging plan parameters on the charging infrastructure; optimize energy usage based on the evaluation.

[0156] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system processes operational status data.

[0157] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system predicts charging needs.

[0158] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system optimizes charging time.

[0159] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system optimizes charging location.

[0160] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system processes environmental data.

[0161] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system processes market data.

[0162] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system coordinates charging stations.

[0163] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system predicts vehicle locations.

[0164] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system manages capacity.

[0165] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system processes feedback.SFT-107-A-PCT

[0166] In some aspects, the techniques described herein relate to a transportation AI convergence system of systems for optimizing vehicle charging including: a charging infrastructure control system; an artificial intelligence system configured to: predict geolocation of vehicles within a geographic region; optimize charging infrastructure allocation based on predicted vehicle locations; and adapt charging operations based on the optimization.

[0167] In some aspects, the techniques described herein relate to a system, wherein predicting includes processing route data.

[0168] In some aspects, the techniques described herein relate to a system, wherein predicting includes processing traffic data.

[0169] In some aspects, the techniques described herein relate to a system, wherein optimizing includes processing demand data.

[0170] In some aspects, the techniques described herein relate to a system, wherein adapting includes adjusting charging rates.

[0171] In some aspects, the techniques described herein relate to a system, wherein optimizing includes processing environmental data.

[0172] In some aspects, the techniques described herein relate to a system, wherein optimizing includes processing market data.

[0173] In some aspects, the techniques described herein relate to a system, wherein adapting includes coordinating charging stations.

[0174] In some aspects, the techniques described herein relate to a system, wherein optimizing includes managing capacity.

[0175] In some aspects, the techniques described herein relate to a system, wherein adapting includes processing feedback.

[0176] In some aspects, the techniques described herein relate to a system, wherein predicting includes processing historical data.

[0177] In some aspects, the techniques described herein relate to a method for vehicle charging optimization including: receiving inputs relating to charging states of vehicles within a geolocation range; predicting geolocations of the vehicles; optimizing at least one charging plan parameter based on the predicted geolocations; and implementing automated negotiation of charging parameters based on the optimization.

[0178] In some aspects, the techniques described herein relate to a method, wherein receiving includes processing route data.

[0179] In some aspects, the techniques described herein relate to a method, wherein predicting includes processing traffic data.

[0180] In some aspects, the techniques described herein relate to a method, wherein optimizing includes processing demand data.

[0181] In some aspects, the techniques described herein relate to a method, wherein implementing includes adjusting charging rates.

[0182] In some aspects, the techniques described herein relate to a method, wherein optimizing includes processing environmental data.SFT-107-A-PCT

[0183] In some aspects, the techniques described herein relate to a method, wherein optimizing includes processing market data.

[0184] In some aspects, the techniques described herein relate to a method, wherein implementing includes coordinating stations.

[0185] In some aspects, the techniques described herein relate to a method, wherein optimizing includes managing capacity.

[0186] In some aspects, the techniques described herein relate to a method, wherein implementing includes processing feedback.

[0187] In some aspects, the techniques described herein relate to a method, wherein predicting includes processing historical data.

[0188] In some aspects, the techniques described herein relate to a transportation AI convergence system of systems including: a charging infrastructure; a recharging plan update facility configured to: apply adjustment values to charging plan parameters; adjust the adjustment values based on feedback; and optimize charging operations based on adjusted values.

[0189] In some aspects, the techniques described herein relate to a system, wherein applying includes processing route data.

[0190] In some aspects, the techniques described herein relate to a system, wherein adjusting includes processing traffic data.

[0191] In some aspects, the techniques described herein relate to a system, wherein optimizing includes processing demand data.

[0192] In some aspects, the techniques described herein relate to a system, wherein optimizing includes adjusting charging rates.

[0193] In some aspects, the techniques described herein relate to a system, wherein optimizing includes processing environmental data.

[0194] In some aspects, the techniques described herein relate to a system, wherein optimizing includes processing market data.

[0195] In some aspects, the techniques described herein relate to a system, wherein optimizing includes coordinating stations.

[0196] In some aspects, the techniques described herein relate to a system, wherein optimizing includes managing capacity.

[0197] In some aspects, the techniques described herein relate to a system, wherein adjusting includes processing feedback.

[0198] In some aspects, the techniques described herein relate to a system, wherein optimizing includes processing historical data.

[0199] In some aspects, the techniques described herein relate to an AI convergence system of systems for transportation including: a charging infrastructure control system; an artificial intelligence system configured to: optimize electricity usage for vehicles and charging infrastructure; optimize charging infrastructure-specific recharging time, location, and amount; and adapt charging operations based on the optimizations.SFT-107-A-PCT

[0200] In some aspects, the techniques described herein relate to a system, wherein optimizing includes processing route data.

[0201] In some aspects, the techniques described herein relate to a system, wherein optimizing includes processing traffic data.

[0202] In some aspects, the techniques described herein relate to a system, wherein optimizing includes processing demand data.

[0203] In some aspects, the techniques described herein relate to a system, wherein adapting includes adjusting charging rates.

[0204] In some aspects, the techniques described herein relate to a system, wherein optimizing includes processing environmental data.

[0205] In some aspects, the techniques described herein relate to a system, wherein optimizing includes processing market data.

[0206] In some aspects, the techniques described herein relate to a system, wherein adapting includes coordinating stations.

[0207] In some aspects, the techniques described herein relate to a system, wherein optimizing includes managing capacity.

[0208] In some aspects, the techniques described herein relate to a system, wherein adapting includes processing feedback.

[0209] In some aspects, the techniques described herein relate to a system, wherein optimizing includes processing historical data. AI convergence system of systems ecosystem to perform data analysis and modeling related to the routing and navigation characteristics of a software-defined vehicle.

[0210] In some aspects, the techniques described herein relate to a transportation AI convergence system of systems for analyzing vehicle routing, including: a data processing system configured to process data from multiple sources including social media data, weather data, road profile data, and traffic data; an artificial intelligence system configured to: analyze a transportation network using a machine learning algorithm; predict traffic conditions using graph neural networks; optimize a routing decision using reinforcement learning; and generate a routing parameter based on an analysis and prediction.

[0211] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system implements clustering algorithms to segment data based on traffic patterns.

[0212] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system implements time-series forecasting to predict future traffic conditions.

[0213] In some aspects, the techniques described herein relate to a system, wherein the data processing system is configured to extract, transform, and load data to enable queries.

[0214] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system processes weather condition data to optimize routing.

[0215] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system processes road profile data.SFT-107-A-PCT

[0216] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system implements cognitive engagement for routing behavior analysis.

[0217] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system optimizes routes based on user satisfaction data.

[0218] In some aspects, the techniques described herein relate to a system, wherein an artificial intelligence system coordinates with infrastructure elements.

[0219] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system implements digital twins for simulating traffic patterns.

[0220] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system adapts routing based on real-time conditions.

[0221] In some aspects, the techniques described herein relate to the transportation system having an AI convergence system of systems including: a cognitive system configured to: facilitate negotiation among designated sets of vehicles; process inputs relating to value attributed by riders to route parameters; and optimize route selection based on multiple factors including traffic conditions and weather conditions.

[0222] In some aspects, the techniques described herein relate to a system, wherein the cognitive system implements game-based interfaces.

[0223] In some aspects, the techniques described herein relate to a system, wherein the cognitive system provides rewards for routing actions.

[0224] In some aspects, the techniques described herein relate to a system, wherein the cognitive system processes user preference data.

[0225] In some aspects, the techniques described herein relate to a system, wherein a cognitive system coordinates with traffic infrastructure.

[0226] In some aspects, the techniques described herein relate to a system, wherein the cognitive system analyzes congestion patterns.

[0227] In some aspects, the techniques described herein relate to a system, wherein the cognitive system optimizes fleet routing.

[0228] In some aspects, the techniques described herein relate to a system, wherein the cognitive system processes environmental data.

[0229] In some aspects, the techniques described herein relate to a system, wherein the cognitive system implements predictive modeling.

[0230] In some aspects, the techniques described herein relate to a system, wherein the cognitive system adapts to real-time conditions.

[0231] In some aspects, the techniques described herein relate to a system, wherein the cognitive system processes feedback data.

[0232] In some aspects, the techniques described herein relate to a method for analyzing vehicle routing, including: processing transportation network data using graph neural networks; implementing clustering algorithms to segment data based on traffic patterns; applying time-series forecasting to predict future traffic conditions; and optimizing routing decisions using reinforcement learning based on the predictions.SFT-107-A-PCT

[0233] In some aspects, the techniques described herein relate to a method, wherein processing includes analyzing social media data.

[0234] In some aspects, the techniques described herein relate to a method, wherein processing includes analyzing weather data.

[0235] In some aspects, the techniques described herein relate to a method, wherein processing includes analyzing road profile data.

[0236] In some aspects, the techniques described herein relate to a method, wherein implementing includes analyzing congestion patterns.

[0237] In some aspects, the techniques described herein relate to a method, wherein applying includes predicting infrastructure utilization.

[0238] In some aspects, the techniques described herein relate to a method, wherein optimizing includes processing user preferences.

[0239] In some aspects, the techniques described herein relate to a method, wherein optimizing includes coordinating multiple vehicles.

[0240] In some aspects, the techniques described herein relate to a method, wherein optimizing includes processing environmental data.

[0241] In some aspects, the techniques described herein relate to a method, wherein optimizing includes analyzing real-time conditions.

[0242] In some aspects, the techniques described herein relate to a method, wherein optimizing includes processing feedback data.

[0243] In some aspects, the techniques described herein relate to a transportation AI convergence system of systems for transportation routing including: a digital twin system configured to: create virtual representations of traffic patterns; simulate vehicle movements in real-time; predict congestion points; and optimize routing based on simulations and predictions.

[0244] In some aspects, the techniques described herein relate to a system, wherein the digital twin system processes sensor data.

[0245] In some aspects, the techniques described herein relate to a system, wherein the digital twin system analyzes infrastructure utilization.

[0246] In some aspects, the techniques described herein relate to a system, wherein the digital twin system processes weather data.

[0247] In some aspects, the techniques described herein relate to a system, wherein the digital twin system coordinates multiple vehicles.

[0248] In some aspects, the techniques described herein relate to a system, wherein the digital twin system processes environmental data.

[0249] In some aspects, the techniques described herein relate to a system, wherein the digital twin system analyzes user preferences.

[0250] In some aspects, the techniques described herein relate to a system, wherein the digital twin system implements predictive modeling.

[0251] In some aspects, the techniques described herein relate to a system, wherein the digital twin system adapts to real-time conditions.SFT-107-A-PCT

[0252] In some aspects, the techniques described herein relate to a system, wherein the digital twin system processes feedback data.

[0253] In some aspects, the techniques described herein relate to a system, wherein the digital twin system simulates future states.

[0254] In some aspects, the techniques described herein relate to a transportation AI convergence system of systems for analyzing vehicle routing including: an artificial intelligence system configured to: process graph data representing transportation networks; analyze traffic patterns using machine learning; optimize network efficiency through adaptive routing; and provide real- time routing suggestions based on an analysis.

[0255] In some aspects, the techniques described herein relate to an AI convergence system of systems for transportation routing including: an operations layer configured to: implement routing and control capabilities; monitor traffic patterns and resource utilization; optimize navigation based on real-time conditions; and coordinate with infrastructure elements for routing efficiency.

[0256] In some aspects, the techniques described herein relate to a system, wherein the operations layer processes sensor data.

[0257] In some aspects, the techniques described herein relate to a system, wherein the operations layer analyzes congestion patterns.

[0258] In some aspects, the techniques described herein relate to a system, wherein the operations layer processes weather data.

[0259] In some aspects, the techniques described herein relate to a system, wherein the operations layer coordinates multiple vehicles.

[0260] In some aspects, the techniques described herein relate to a system, wherein the operations layer processes environmental data.

[0261] In some aspects, the techniques described herein relate to a system, wherein the operations layer analyzes user preferences.

[0262] In some aspects, the techniques described herein relate to a system, wherein the operations layer implements predictive modeling.

[0263] In some aspects, the techniques described herein relate to a system, wherein the operations layer adapts to real-time conditions.

[0264] In some aspects, the techniques described herein relate to a system, wherein the operations layer processes feedback data.

[0265] In some aspects, the techniques described herein relate to a system, wherein the operations layer simulates future states.

[0266] In some aspects, the techniques described herein relate to a method for analyzing vehicle routing including: processing graph data representing transportation networks; implementing machine learning algorithms to analyze traffic patterns; predicting congestion using clustering algorithms; and optimizing routes based on predictions and real-time conditions.

[0267] In some aspects, the techniques described herein relate to a method, wherein processing includes analyzing sensor data.SFT-107-A-PCT

[0268] In some aspects, the techniques described herein relate to a method, wherein implementing includes analyzing weather data.

[0269] In some aspects, the techniques described herein relate to a method, wherein predicting includes analyzing road conditions.

[0270] In some aspects, the techniques described herein relate to a method, wherein optimizing includes coordinating multiple vehicles.

[0271] In some aspects, the techniques described herein relate to a method, wherein processing includes analyzing environmental data.

[0272] In some aspects, the techniques described herein relate to a method, wherein implementing includes analyzing user preferences.

[0273] In some aspects, the techniques described herein relate to a method, wherein predicting includes using time-series forecasting.

[0274] In some aspects, the techniques described herein relate to a method, wherein optimizing includes real-time adaptation.

[0275] In some aspects, the techniques described herein relate to a method, wherein processing includes analyzing feedback data.

[0276] In some aspects, the techniques described herein relate to a method, wherein implementing includes simulating future states.

[0277] In some aspects, the techniques described herein relate to an AI convergence system of systems for transportation routing including: a data layer configured to: process sensor and operations data; analyze market and environmental data; implement context-aware sensor fusion; and optimize routing based on analyzed data.

[0278] In some aspects, the techniques described herein relate to a system, wherein the data layer processes traffic pattern data.

[0279] In some aspects, the techniques described herein relate to a system, wherein the data layer analyzes weather conditions.

[0280] In some aspects, the techniques described herein relate to a system, wherein the data layer processes infrastructure data.

[0281] In some aspects, the techniques described herein relate to a system, wherein the data layer coordinates multiple vehicles.

[0282] In some aspects, the techniques described herein relate to a system, wherein the data layer processes environmental factors.

[0283] In some aspects, the techniques described herein relate to a system, wherein the data layer analyzes user preferences.

[0284] In some aspects, the techniques described herein relate to a system, wherein the data layer implements predictive modeling.

[0285] In some aspects, the techniques described herein relate to a system, wherein the data layer adapts to real-time conditions.

[0286] In some aspects, the techniques described herein relate to a system, wherein the data layer processes feedback data.SFT-107-A-PCT

[0287] In some aspects, the techniques described herein relate to a system, wherein the data layer simulates future states.

[0288] In some aspects, the techniques described herein relate to an AI convergence system of systems for transportation routing including: an artificial intelligence system configured to: analyze transportation network spatial structure; predict traffic conditions using neural networks; optimize routing using reinforcement learning; and adapt routes based on real-time conditions.

[0289] In some aspects, the techniques described herein relate to a system, wherein analyzing includes processing sensor data.

[0290] In some aspects, the techniques described herein relate to a system, wherein predicting includes analyzing weather data.

[0291] In some aspects, the techniques described herein relate to a system, wherein optimizing includes analyzing road conditions.

[0292] In some aspects, the techniques described herein relate to a system, wherein adapting includes coordinating multiple vehicles.

[0293] In some aspects, the techniques described herein relate to a system, wherein analyzing includes processing environmental data.

[0294] In some aspects, the techniques described herein relate to a system, wherein predicting includes analyzing user preferences.

[0295] In some aspects, the techniques described herein relate to a system, wherein optimizing includes time-series forecasting.

[0296] In some aspects, the techniques described herein relate to a system, wherein adapting includes real-time modification.

[0297] In some aspects, the techniques described herein relate to a system, wherein analyzing includes processing feedback data.

[0298] In some aspects, the techniques described herein relate to a system, wherein predicting includes simulating future states.

[0299] In some aspects, the techniques described herein relate to an AI convergence system of systems for transportation routing including: a hybrid neural network configured to: process transportation network graph data; analyze traffic patterns and congestion; predict future traffic conditions; and optimize routing based on predictions.

[0300] In some aspects, the techniques described herein relate to a system, wherein processing includes analyzing sensor data.

[0301] In some aspects, the techniques described herein relate to a system, wherein analyzing includes processing weather data.

[0302] In some aspects, the techniques described herein relate to a system, wherein predicting includes analyzing road conditions.

[0303] In some aspects, the techniques described herein relate to a system, wherein optimizing includes vehicle coordination.

[0304] In some aspects, the techniques described herein relate to a system, wherein processing includes environmental analysis.SFT-107-A-PCT

[0305] In some aspects, the techniques described herein relate to a system, wherein analyzing includes user preference processing.

[0306] In some aspects, the techniques described herein relate to a system, wherein predicting uses time-series forecasting.

[0307] In some aspects, the techniques described herein relate to a system, wherein optimizing includes real-time adaptation.

[0308] In some aspects, the techniques described herein relate to a system, wherein processing includes feedback analysis.

[0309] In some aspects, the techniques described herein relate to a system, wherein analyzing includes future state simulation. AI convergence system of systems ecosystem to perform data analysis and modeling related to the characteristics of a software-defined vehicle.

[0310] In some aspects, the techniques described herein relate to an AI convergence system of systems for analyzing vehicle characteristics, including: a data processing system configured to process data from multiple sources including social media data, weather data, road profile data, traffic data, and sensor data; an artificial intelligence system configured to: analyze vehicle operational states using machine learning algorithms; predict vehicle performance using neural networks; and optimize vehicle parameters using reinforcement learning.

[0311] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system implements hybrid neural networks to optimize distinct vehicle components.

[0312] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system implements time-series forecasting to predict future vehicle states.

[0313] In some aspects, the techniques described herein relate to a system, wherein the data processing system is configured to extract, transform, and load data to enable queries.

[0314] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system processes environmental condition data.

[0315] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system processes vehicle diagnostic data.

[0316] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system implements cognitive engagement for behavior analysis.

[0317] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system optimizes based on user satisfaction data.

[0318] In some aspects, the techniques described herein relate to a system, wherein an artificial intelligence system coordinates with vehicle subsystems.

[0319] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system implements digital twins for simulating vehicle operations.

[0320] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system adapts based on real-time conditions.

[0321] In some aspects, the techniques described herein relate to a transportation system having an AI convergence system of systems including: a data layer configured to: implement context-SFT-107-A-PCT aware sensor fusion; process sensor and energy operations data; analyze market and environmental data; and optimize vehicle operations based on analyzed data.

[0322] In some aspects, the techniques described herein relate to a system, wherein the data layer processes vehicle state data.

[0323] In some aspects, the techniques described herein relate to a system, wherein the data layer analyzes operational conditions.

[0324] In some aspects, the techniques described herein relate to a system, wherein the data layer processes performance metrics.

[0325] In some aspects, the techniques described herein relate to a system, wherein the data layer coordinates multiple vehicle systems.

[0326] In some aspects, the techniques described herein relate to a system, wherein the data layer analyzes usage patterns.

[0327] In some aspects, the techniques described herein relate to a system, wherein the data layer optimizes energy efficiency.

[0328] In some aspects, the techniques described herein relate to a system, wherein the data layer processes environmental data.

[0329] In some aspects, the techniques described herein relate to a system, wherein the data layer implements predictive modeling.

[0330] In some aspects, the techniques described herein relate to a system, wherein the data layer adapts to real-time conditions.

[0331] In some aspects, the techniques described herein relate to a system, wherein the data layer processes feedback data.

[0332] In some aspects, the techniques described herein relate to a method for analyzing vehicle characteristics, including: processing vehicle operational data using neural networks; implementing clustering algorithms to segment data based on usage patterns; applying time-series forecasting to predict future vehicle states; and optimizing vehicle parameters using reinforcement learning based on the predictions.

[0333] In some aspects, the techniques described herein relate to a method, wherein processing includes analyzing sensor data.

[0334] In some aspects, the techniques described herein relate to a method, wherein processing includes analyzing environmental data.

[0335] In some aspects, the techniques described herein relate to a method, wherein processing includes analyzing diagnostic data.

[0336] In some aspects, the techniques described herein relate to a method, wherein implementing includes analyzing usage patterns.

[0337] In some aspects, the techniques described herein relate to a method, wherein applying includes predicting maintenance needs.

[0338] In some aspects, the techniques described herein relate to a method, wherein optimizing includes processing user preferences.SFT-107-A-PCT

[0339] In some aspects, the techniques described herein relate to a method, wherein optimizing includes coordinating vehicle systems.

[0340] In some aspects, the techniques described herein relate to a method, wherein optimizing includes processing environmental data.

[0341] In some aspects, the techniques described herein relate to a method, wherein optimizing includes analyzing real-time conditions.

[0342] In some aspects, the techniques described herein relate to a method, wherein optimizing includes processing feedback data.

[0343] In some aspects, the techniques described herein relate to an AI convergence system of systems for vehicle analysis including: a digital twin system configured to: create virtual representations of vehicle components; simulate vehicle operations in real-time; predict maintenance needs; and optimize performance based on simulations and predictions.

[0344] In some aspects, the techniques described herein relate to a system, wherein the digital twin system processes sensor data.

[0345] In some aspects, the techniques described herein relate to a system, wherein the digital twin system analyzes component wear.

[0346] In some aspects, the techniques described herein relate to a system, wherein the digital twin system processes environmental data.

[0347] In some aspects, the techniques described herein relate to a system, wherein the digital twin system coordinates multiple systems.

[0348] In some aspects, the techniques described herein relate to a system, wherein the digital twin system processes usage patterns.

[0349] In some aspects, the techniques described herein relate to a system, wherein the digital twin system analyzes user preferences.

[0350] In some aspects, the techniques described herein relate to a system, wherein the digital twin system implements predictive modeling.

[0351] In some aspects, the techniques described herein relate to a system, wherein the digital twin system adapts to real-time conditions.

[0352] In some aspects, the techniques described herein relate to a system, wherein the digital twin system processes feedback data.

[0353] In some aspects, the techniques described herein relate to a system, wherein the digital twin system simulates future states.

[0354] In some aspects, the techniques described herein relate to a transportation AI convergence system of systems for analyzing vehicle characteristics including: an artificial intelligence system configured to: process vehicle operational data; analyze performance patterns using machine learning; optimize efficiency through adaptive control; and provide real-time operational suggestions based on an analysis.

[0355] In some aspects, the techniques described herein relate to a system, wherein processing includes analyzing sensor data.SFT-107-A-PCT

[0356] In some aspects, the techniques described herein relate to a system, wherein analyzing includes processing diagnostic data.

[0357] In some aspects, the techniques described herein relate to a system, wherein optimizing includes analyzing usage patterns.

[0358] In some aspects, the techniques described herein relate to a system, wherein providing includes coordinating vehicle systems.

[0359] In some aspects, the techniques described herein relate to a system, wherein processing includes environmental analysis.

[0360] In some aspects, the techniques described herein relate to a system, wherein analyzing includes user preference processing.

[0361] In some aspects, the techniques described herein relate to a system, wherein optimizing includes predictive modeling.

[0362] In some aspects, the techniques described herein relate to a system, wherein providing includes real-time adaptation.

[0363] In some aspects, the techniques described herein relate to a system, wherein processing includes feedback analysis.

[0364] In some aspects, the techniques described herein relate to a system, wherein analyzing includes future state simulation.

[0365] In some aspects, the techniques described herein relate to a transportation AI convergence system of systems for vehicle analysis including: an operations layer configured to: implement control and optimization capabilities; monitor performance patterns and resource utilization; optimize operations based on real-time conditions; and coordinate with vehicle subsystems for operational efficiency.

[0366] In some aspects, the techniques described herein relate to a system, wherein implementing includes processing sensor data.

[0367] In some aspects, the techniques described herein relate to a system, wherein monitoring includes analyzing usage patterns.

[0368] In some aspects, the techniques described herein relate to a system, wherein optimizing includes processing diagnostic data.

[0369] In some aspects, the techniques described herein relate to a system, wherein coordinating includes system integration.

[0370] In some aspects, the techniques described herein relate to a system, wherein implementing includes environmental analysis.

[0371] In some aspects, the techniques described herein relate to a system, wherein monitoring includes user preference processing.

[0372] In some aspects, the techniques described herein relate to a system, wherein optimizing includes predictive modeling.

[0373] In some aspects, the techniques described herein relate to a system, wherein coordinating includes real-time adaptation.SFT-107-A-PCT

[0374] In some aspects, the techniques described herein relate to a system, wherein implementing includes feedback analysis.

[0375] In some aspects, the techniques described herein relate to a system, wherein monitoring includes future state simulation.

[0376] In some aspects, the techniques described herein relate to a method for analyzing vehicle characteristics including: processing operational data representing vehicle states; implementing machine learning algorithms to analyze performance patterns; predicting maintenance needs using clustering algorithms; and optimizing parameters based on predictions and real-time conditions.

[0377] In some aspects, the techniques described herein relate to a method, wherein processing includes analyzing sensor data.

[0378] In some aspects, the techniques described herein relate to a method, wherein implementing includes analyzing usage patterns.

[0379] In some aspects, the techniques described herein relate to a method, wherein predicting includes analyzing component wear.

[0380] In some aspects, the techniques described herein relate to a method, wherein optimizing includes system coordination.

[0381] In some aspects, the techniques described herein relate to a method, wherein processing includes environmental analysis.

[0382] In some aspects, the techniques described herein relate to a method, wherein implementing includes preference processing.

[0383] In some aspects, the techniques described herein relate to a method, wherein predicting includes time-series forecasting.

[0384] In some aspects, the techniques described herein relate to a method, wherein optimizing includes real-time adaptation.

[0385] In some aspects, the techniques described herein relate to a method, wherein processing includes feedback analysis.

[0386] In some aspects, the techniques described herein relate to a method, wherein implementing includes state simulation.

[0387] In some aspects, the techniques described herein relate to a transportation AI convergence system of systems for vehicle analysis including: a hybrid neural network configured to: process vehicle operational data; analyze performance patterns and efficiency; predict future operational states; and optimize parameters based on predictions.

[0388] In some aspects, the techniques described herein relate to a system, wherein processing includes sensor analysis.

[0389] In some aspects, the techniques described herein relate to a system, wherein analyzing includes usage pattern processing.

[0390] In some aspects, the techniques described herein relate to a system, wherein predicting includes maintenance forecasting.

[0391] In some aspects, the techniques described herein relate to a system, wherein optimizing includes system coordination.SFT-107-A-PCT

[0392] In some aspects, the techniques described herein relate to a system, wherein processing includes environmental analysis.

[0393] In some aspects, the techniques described herein relate to a system, wherein analyzing includes preference processing.

[0394] In some aspects, the techniques described herein relate to a system, wherein predicting includes time-series forecasting.

[0395] In some aspects, the techniques described herein relate to a system, wherein optimizing includes real-time adaptation.

[0396] In some aspects, the techniques described herein relate to a system, wherein processing includes feedback analysis.

[0397] In some aspects, the techniques described herein relate to a system, wherein analyzing includes state simulation.

[0398] In some aspects, the techniques described herein relate to a transportation AI convergence system of systems for vehicle analysis including: an enterprise layer configured to: implement executive digital twins for vehicle operations; create vehicle digital twins for design and simulation; analyze fleet operations data; and optimize vehicle parameters based on analysis.

[0399] In some aspects, the techniques described herein relate to a system, wherein implementing includes sensor processing.

[0400] In some aspects, the techniques described herein relate to a system, wherein creating includes component modeling.

[0401] In some aspects, the techniques described herein relate to a system, wherein analyzing includes usage pattern processing.

[0402] In some aspects, the techniques described herein relate to a system, wherein optimizing includes system coordination.

[0403] In some aspects, the techniques described herein relate to a system, wherein implementing includes environmental analysis.

[0404] In some aspects, the techniques described herein relate to a system, wherein creating includes preference processing.

[0405] In some aspects, the techniques described herein relate to a system, wherein analyzing includes predictive modeling.

[0406] In some aspects, the techniques described herein relate to a system, wherein optimizing includes real-time adaptation.

[0407] In some aspects, the techniques described herein relate to a system, wherein implementing includes feedback analysis.

[0408] In some aspects, the techniques described herein relate to a system, wherein creating includes state simulation.

[0409] In some aspects, the techniques described herein relate to a transportation AI convergence system of systems for vehicle analysis including: an artificial intelligence system configured to: process feature vectors of vehicle operational data; determine operational states using patternSFT-107-A-PCT recognition; optimize vehicle parameters to improve operational states; and adapt parameters through machine learning feedback.

[0410] In some aspects, the techniques described herein relate to a system, wherein processing includes sensor analysis.

[0411] In some aspects, the techniques described herein relate to a system, wherein determining includes pattern processing.

[0412] In some aspects, the techniques described herein relate to a system, wherein optimizing includes efficiency analysis.

[0413] In some aspects, the techniques described herein relate to a system, wherein adapting includes system coordination.

[0414] In some aspects, the techniques described herein relate to a system, wherein processing includes environmental analysis.

[0415] In some aspects, the techniques described herein relate to a system, wherein determining includes preference processing.

[0416] In some aspects, the techniques described herein relate to a system, wherein optimizing includes predictive modeling.

[0417] In some aspects, the techniques described herein relate to a system, wherein adapting includes real-time modification.

[0418] In some aspects, the techniques described herein relate to a system, wherein processing includes feedback analysis.

[0419] In some aspects, the techniques described herein relate to a system, wherein determining includes state simulation. AI convergence system of systems ecosystem to perform simulations or predictions related to the characteristics of a software-defined vehicle or its usage.

[0420] In some aspects, the techniques described herein relate to a transportation AI convergence system of systems for simulating vehicle operations, including: a digital twin system configured to: create a digital replica of a vehicle; process substantially real-time sensor data to provide virtual representation of the vehicle; simulate possible future states of the vehicle; and predict vehicle behavior and performance under various conditions based on the simulations.

[0421] In some aspects, the techniques described herein relate to a system, wherein the digital twin system simulates physical elements and properties of the vehicle.

[0422] In some aspects, the techniques described herein relate to a system, wherein the digital twin system simulates vehicle dynamics throughout its lifecycle.

[0423] In some aspects, the techniques described herein relate to a system, wherein the digital twin system provides hypothetical simulations during vehicle design phases.

[0424] In some aspects, the techniques described herein relate to a system, wherein the digital twin system simulates high stress conditions.

[0425] In some aspects, the techniques described herein relate to a system, wherein the digital twin system simulates component wear scenarios.

[0426] In some aspects, the techniques described herein relate to a system, wherein the digital twin system simulates maximum throughput operation.SFT-107-A-PCT

[0427] In some aspects, the techniques described herein relate to a system, wherein the digital twin system simulates planned improvements.

[0428] In some aspects, the techniques described herein relate to a system, wherein the digital twin system processes environmental data.

[0429] In some aspects, the techniques described herein relate to a system, wherein the digital twin system processes marketplace factors.

[0430] In some aspects, the techniques described herein relate to a system, wherein the digital twin system adapts to real-time conditions.

[0431] In some aspects, the techniques described herein relate to a transportation AI convergence system of systems for transportation analysis including: an artificial intelligence system configured to: train predictive models using vehicle-related data; process vehicle specifications, environmental data, sensor data, and operational information; generate predictions regarding remaining vehicle life; and optimize vehicle operations based on predictions.

[0432] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system implements classification models.

[0433] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system implements regression models.

[0434] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system predicts failure within time windows.

[0435] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system predicts remaining useful life.

[0436] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system processes feedback data.

[0437] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system updates models based on outcomes.

[0438] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system processes environmental data.

[0439] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system implements supervised learning.

[0440] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system implements unsupervised learning.

[0441] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system implements reinforcement learning.

[0442] In some aspects, the techniques described herein relate to a transportation AI convergence system of systems for vehicle simulation including: a machine learning model configured to: perform analytics related to vehicle data processing; create simulations of vehicle operations; analyze simulation results; and make predictions based on analyzed results.

[0443] In some aspects, the techniques described herein relate to a system, wherein the machine learning model processes sensor data.SFT-107-A-PCT

[0444] In some aspects, the techniques described herein relate to a system, wherein the machine learning model processes event data.

[0445] In some aspects, the techniques described herein relate to a system, wherein the machine learning model processes state data.

[0446] In some aspects, the techniques described herein relate to a system, wherein the machine learning model implements neural networks.

[0447] In some aspects, the techniques described herein relate to a system, wherein the machine learning model implements decision trees.

[0448] In some aspects, the techniques described herein relate to a system, wherein the machine learning model implements support vector machines.

[0449] In some aspects, the techniques described herein relate to a system, wherein the machine learning model implements Bayesian networks.

[0450] In some aspects, the techniques described herein relate to a system, wherein the machine learning model implements genetic algorithms.

[0451] In some aspects, the techniques described herein relate to a system, wherein the machine learning model implements supervised learning.

[0452] In some aspects, the techniques described herein relate to a system, wherein the machine learning model implements reinforcement learning.

[0453] In some aspects, the techniques described herein relate to a transportation AI convergence system of systems for vehicle analysis including: an enterprise layer configured to: implement executive digital twins for vehicle fleet operations; create vehicle digital twins for design and simulation; predict fleet operational characteristics; and optimize fleet parameters based on predictions.

[0454] In some aspects, the techniques described herein relate to a system, wherein implementing includes processing sensor data.

[0455] In some aspects, the techniques described herein relate to a system, wherein creating includes component modeling.

[0456] In some aspects, the techniques described herein relate to a system, wherein predicting includes usage pattern analysis.

[0457] In some aspects, the techniques described herein relate to a system, wherein optimizing includes system coordination.

[0458] In some aspects, the techniques described herein relate to a system, wherein implementing includes environmental analysis.

[0459] In some aspects, the techniques described herein relate to a system, wherein creating includes preference processing.

[0460] In some aspects, the techniques described herein relate to a system, wherein predicting includes maintenance forecasting.

[0461] In some aspects, the techniques described herein relate to a system, wherein optimizing includes real-time adaptation.SFT-107-A-PCT

[0462] In some aspects, the techniques described herein relate to a system, wherein implementing includes feedback analysis.

[0463] In some aspects, the techniques described herein relate to a system, wherein creating includes state simulation.

[0464] In some aspects, the techniques described herein relate to a transportation AI convergence system of systems for vehicle simulation including: a hybrid neural network configured to: process vehicle operational data; simulate vehicle performance patterns; predict future operational states; and optimize parameters based on predictions.

[0465] In some aspects, the techniques described herein relate to a system, wherein processing includes sensor analysis.

[0466] In some aspects, the techniques described herein relate to a system, wherein simulating includes usage pattern processing.

[0467] In some aspects, the techniques described herein relate to a system, wherein predicting includes maintenance forecasting.

[0468] In some aspects, the techniques described herein relate to a system, wherein optimizing includes system coordination.

[0469] In some aspects, the techniques described herein relate to a system, wherein processing includes environmental analysis.

[0470] In some aspects, the techniques described herein relate to a system, wherein simulating includes preference processing.

[0471] In some aspects, the techniques described herein relate to a system, wherein predicting includes time-series forecasting.

[0472] In some aspects, the techniques described herein relate to a system, wherein optimizing includes real-time adaptation.

[0473] In some aspects, the techniques described herein relate to a system, wherein processing includes feedback analysis.

[0474] In some aspects, the techniques described herein relate to a system, wherein simulating includes state simulation.

[0475] In some aspects, the techniques described herein relate to a method for vehicle simulation including: creating a digital replica of a vehicle; processing real-time sensor data for virtual representation; simulating future vehicle states; and optimizing vehicle parameters based on simulations.

[0476] In some aspects, the techniques described herein relate to a method, wherein creating includes component modeling.

[0477] In some aspects, the techniques described herein relate to a method, wherein processing includes environmental data analysis.

[0478] In some aspects, the techniques described herein relate to a method, wherein simulating includes stress testing.

[0479] In some aspects, the techniques described herein relate to a method, wherein optimizing includes system coordination.SFT-107-A-PCT

[0480] In some aspects, the techniques described herein relate to a method, wherein creating includes physical property modeling.

[0481] In some aspects, the techniques described herein relate to a method, wherein processing includes operational data analysis.

[0482] In some aspects, the techniques described herein relate to a method, wherein simulating includes wear prediction.

[0483] In some aspects, the techniques described herein relate to a method, wherein optimizing includes real-time adaptation.

[0484] In some aspects, the techniques described herein relate to a method, wherein creating includes dynamic modeling.

[0485] In some aspects, the techniques described herein relate to a method, wherein processing includes feedback analysis.

[0486] In some aspects, the techniques described herein relate to a transportation AI convergence system of systems for vehicle prediction including: an artificial intelligence system configured to: analyze vehicle operational patterns; simulate vehicle performance scenarios; predict maintenance requirements; and optimize operational parameters based on predictions.

[0487] In some aspects, the techniques described herein relate to a system, wherein analyzing includes sensor processing.

[0488] In some aspects, the techniques described herein relate to a system, wherein simulating includes environmental modeling.

[0489] In some aspects, the techniques described herein relate to a system, wherein predicting includes component analysis.

[0490] In some aspects, the techniques described herein relate to a system, wherein optimizing includes system coordination.

[0491] In some aspects, the techniques described herein relate to a system, wherein analyzing includes usage pattern processing.

[0492] In some aspects, the techniques described herein relate to a system, wherein simulating includes stress testing.

[0493] In some aspects, the techniques described herein relate to a system, wherein predicting includes lifecycle analysis.

[0494] In some aspects, the techniques described herein relate to a system, wherein optimizing includes real-time adaptation.

[0495] In some aspects, the techniques described herein relate to a system, wherein analyzing includes feedback processing.

[0496] In some aspects, the techniques described herein relate to a system, wherein simulating includes state modeling.

[0497] In some aspects, the techniques described herein relate to a transportation AI convergence system of systems for vehicle analysis including: a data layer configured to: process sensor and operational data; simulate vehicle performance patterns; predict operational characteristics; and optimize parameters based on predictions.SFT-107-A-PCT

[0498] In some aspects, the techniques described herein relate to a system, wherein processing includes environmental analysis.

[0499] In some aspects, the techniques described herein relate to a system, wherein simulating includes component modeling.

[0500] In some aspects, the techniques described herein relate to a system, wherein predicting includes maintenance forecasting.

[0501] In some aspects, the techniques described herein relate to a system, wherein optimizing includes system coordination.

[0502] In some aspects, the techniques described herein relate to a system, wherein processing includes usage pattern analysis.

[0503] In some aspects, the techniques described herein relate to a system, wherein simulating includes stress testing.

[0504] In some aspects, the techniques described herein relate to a system, wherein predicting includes lifecycle analysis.

[0505] In some aspects, the techniques described herein relate to a system, wherein optimizing includes real-time adaptation.

[0506] In some aspects, the techniques described herein relate to a system, wherein processing includes feedback analysis.

[0507] In some aspects, the techniques described herein relate to a system, wherein simulating includes state modeling.

[0508] In some aspects, the techniques described herein relate to a method for vehicle prediction including: processing vehicle operational data; creating simulation models of vehicle performance; predicting future operational states; and optimizing vehicle parameters based on predictions.

[0509] In some aspects, the techniques described herein relate to a method, wherein processing includes sensor analysis.

[0510] In some aspects, the techniques described herein relate to a method, wherein creating includes environmental modeling.

[0511] In some aspects, the techniques described herein relate to a method, wherein predicting includes maintenance forecasting.

[0512] In some aspects, the techniques described herein relate to a method, wherein optimizing includes system coordination.

[0513] In some aspects, the techniques described herein relate to a method, wherein processing includes usage pattern analysis.

[0514] In some aspects, the techniques described herein relate to a method, wherein creating includes stress testing.

[0515] In some aspects, the techniques described herein relate to a method, wherein predicting includes lifecycle analysis.

[0516] In some aspects, the techniques described herein relate to a method, wherein optimizing includes real-time adaptation.SFT-107-A-PCT

[0517] In some aspects, the techniques described herein relate to a method, wherein processing includes feedback analysis.

[0518] In some aspects, the techniques described herein relate to a method, wherein creating includes state modeling.

[0519] In some aspects, the techniques described herein relate to a transportation AI convergence system of systems for vehicle simulation including: an operations layer configured to: process vehicle operational data; create performance simulations; predict future states; and optimize parameters based on predictions.

[0520] In some aspects, the techniques described herein relate to a system, wherein processing includes sensor analysis.

[0521] In some aspects, the techniques described herein relate to a system, wherein creating includes environmental modeling.

[0522] In some aspects, the techniques described herein relate to a system, wherein predicting includes maintenance forecasting.

[0523] In some aspects, the techniques described herein relate to a system, wherein optimizing includes system coordination.

[0524] In some aspects, the techniques described herein relate to a system, wherein processing includes usage pattern analysis.

[0525] In some aspects, the techniques described herein relate to a system, wherein creating includes stress testing.

[0526] In some aspects, the techniques described herein relate to a system, wherein predicting includes lifecycle analysis.

[0527] In some aspects, the techniques described herein relate to a system, wherein optimizing includes real-time adaptation.

[0528] In some aspects, the techniques described herein relate to a system, wherein processing includes feedback analysis.

[0529] In some aspects, the techniques described herein relate to a system, wherein creating includes state modeling. AI convergence system of systems ecosystem to alter user interface(s) characteristics for a driver of a software-defined vehicle.

[0530] In some aspects, the techniques described herein relate to a transportation AI convergence system of systems for optimizing vehicle user interfaces, including: a vehicle having a set of interfaces including steering systems, buttons, levers, touch screen interfaces, and audio interfaces; a sensor system configured to provide input to an expert system; an artificial intelligence system configured to: track vehicle operating states and user experience states; and modify interface characteristics based on current conditions and desired outcomes.

[0531] In some aspects, the techniques described herein relate to a system, wherein the set of interfaces includes a game interface.

[0532] In some aspects, the techniques described herein relate to a system, wherein the set of interfaces includes a navigation interface.SFT-107-A-PCT

[0533] In some aspects, the techniques described herein relate to a system, wherein the set of interfaces includes an entertainment interface.

[0534] In some aspects, the techniques described herein relate to a system, wherein the set of interfaces includes a vehicle settings interface.

[0535] In some aspects, the techniques described herein relate to a system, wherein the set of interfaces includes a search interface.

[0536] In some aspects, the techniques described herein relate to a system, wherein the set of interfaces includes an ecommerce interface.

[0537] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system processes voice patterns.

[0538] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system processes facial expressions.

[0539] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system processes physiological data.

[0540] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system adapts interface characteristics based on emotional state.

[0541] In some aspects, the techniques described herein relate to a transportation AI convergence system of systems for transportation including: a vehicle having user interfaces; an offering layer implementing expert systems and generative AI configured to: provide location-based offering systems; integrate advertising and marketplace functions; and implement customer-facing vehicle digital twins.

[0542] In some aspects, the techniques described herein relate to a system, wherein the offering layer processes user profiles.

[0543] In some aspects, the techniques described herein relate to a system, wherein the offering layer analyzes user behavior.

[0544] In some aspects, the techniques described herein relate to a system, wherein the offering layer processes environmental data.

[0545] In some aspects, the techniques described herein relate to a system, wherein the offering layer adapts to user location.

[0546] In some aspects, the techniques described herein relate to a system, wherein the offering layer processes user requests.

[0547] In some aspects, the techniques described herein relate to a system, wherein the offering layer customizes content delivery.

[0548] In some aspects, the techniques described herein relate to a system, wherein the offering layer personalizes interfaces.

[0549] In some aspects, the techniques described herein relate to a system, wherein the offering layer adapts to user preferences.

[0550] In some aspects, the techniques described herein relate to a system, wherein the offering layer processes real-time data.SFT-107-A-PCT

[0551] In some aspects, the techniques described herein relate to a system, wherein the offering layer implements feedback processing.

[0552] In some aspects, the techniques described herein relate to an AI convergence system of systems for vehicle interface optimization including: a cognitive system configured to: monitor driver interactions with vehicle controls; detect patterns indicating routine activities; generate cognitive challenges; and adapt interface characteristics based on detected patterns.

[0553] In some aspects, the techniques described herein relate to a system, wherein monitoring includes analyzing navigation system usage.

[0554] In some aspects, the techniques described herein relate to a system, wherein detecting includes analyzing spatial awareness.

[0555] In some aspects, the techniques described herein relate to a system, wherein generating includes memory recall challenges.

[0556] In some aspects, the techniques described herein relate to a system, wherein adapting includes modifying display characteristics.

[0557] In some aspects, the techniques described herein relate to a system, wherein monitoring includes analyzing voice patterns.

[0558] In some aspects, the techniques described herein relate to a system, wherein detecting includes analyzing facial expressions.

[0559] In some aspects, the techniques described herein relate to a system, wherein generating includes decision-making challenges.

[0560] In some aspects, the techniques described herein relate to a system, wherein adapting includes modifying audio feedback.

[0561] In some aspects, the techniques described herein relate to a system, wherein monitoring includes analyzing emotional states.

[0562] In some aspects, the techniques described herein relate to a system, wherein detecting includes analyzing user preferences.

[0563] In some aspects, the techniques described herein relate to an AI convergence system of systems for vehicle interface management including: an artificial intelligence system configured to: process feature vectors from facial images; determine driver emotional states; optimize interface parameters based on emotional states; and adapt information presentation to improve emotional states.

[0564] In some aspects, the techniques described herein relate to an AI convergence system of systems for vehicle interface adaptation including: a sensor system configured to detect driver state; an artificial intelligence system configured to: process voice patterns and physiological data; identify cognitive engagement levels; and modify interface characteristics to maintain optimal engagement.

[0565] In some aspects, the techniques described herein relate to a system, wherein processing includes analyzing speech patterns.

[0566] In some aspects, the techniques described herein relate to a system, wherein identifying includes analyzing facial expressions.SFT-107-A-PCT

[0567] In some aspects, the techniques described herein relate to a system, wherein modifying includes adjusting display parameters.

[0568] In some aspects, the techniques described herein relate to a system, wherein processing includes analyzing emotional states.

[0569] In some aspects, the techniques described herein relate to a system, wherein identifying includes analyzing attention levels.

[0570] In some aspects, the techniques described herein relate to a system, wherein modifying includes adjusting audio feedback.

[0571] In some aspects, the techniques described herein relate to a system, wherein processing includes analyzing user preferences.

[0572] In some aspects, the techniques described herein relate to a system, wherein identifying includes analyzing stress levels.

[0573] In some aspects, the techniques described herein relate to a system, wherein modifying includes real-time adaptation.

[0574] In some aspects, the techniques described herein relate to a system, wherein processing includes feedback analysis.

[0575] In some aspects, the techniques described herein relate to an AI convergence system of systems for vehicle interface optimization including: a multiplatform attention management system configured to: monitor driver attention patterns; analyze cognitive load levels; adapt interface presentations; and optimize information delivery based on attention patterns.

[0576] In some aspects, the techniques described herein relate to a system, wherein monitoring includes analyzing eye movements.

[0577] In some aspects, the techniques described herein relate to a system, wherein analyzing includes processing voice patterns.

[0578] In some aspects, the techniques described herein relate to a system, wherein adapting includes modifying display characteristics.

[0579] In some aspects, the techniques described herein relate to a system, wherein optimizing includes adjusting content timing.

[0580] In some aspects, the techniques described herein relate to a system, wherein monitoring includes analyzing facial expressions.

[0581] In some aspects, the techniques described herein relate to a system, wherein analyzing includes processing emotional states.

[0582] In some aspects, the techniques described herein relate to a system, wherein adapting includes modifying audio feedback.

[0583] In some aspects, the techniques described herein relate to a system, wherein optimizing includes content prioritization.

[0584] In some aspects, the techniques described herein relate to a system, wherein monitoring includes stress level analysis.

[0585] In some aspects, the techniques described herein relate to a system, wherein analyzing includes preference processing.SFT-107-A-PCT

[0586] In some aspects, the techniques described herein relate to a method for vehicle interface adaptation including: monitoring driver interactions with vehicle controls; analyzing cognitive engagement patterns; generating interface modifications; and implementing adaptive changes based on engagement patterns.

[0587] In some aspects, the techniques described herein relate to a method, wherein monitoring includes analyzing voice patterns.

[0588] In some aspects, the techniques described herein relate to a method, wherein analyzing includes processing facial expressions.

[0589] In some aspects, the techniques described herein relate to a method, wherein generating includes display modifications.

[0590] In some aspects, the techniques described herein relate to a method, wherein implementing includes audio adjustments.

[0591] In some aspects, the techniques described herein relate to a method, wherein monitoring includes emotional state analysis.

[0592] In some aspects, the techniques described herein relate to a method, wherein analyzing includes attention level processing.

[0593] In some aspects, the techniques described herein relate to a method, wherein generating includes content adaptation.

[0594] In some aspects, the techniques described herein relate to a method, wherein implementing includes real-time changes.

[0595] In some aspects, the techniques described herein relate to a method, wherein monitoring includes stress level analysis.

[0596] In some aspects, the techniques described herein relate to a method, wherein analyzing includes preference processing.

[0597] In some aspects, the techniques described herein relate to an AI convergence system of systems for vehicle interface management including: an artificial intelligence system configured to: process driver behavior patterns; analyze cognitive stimulation needs; generate interface modifications; and implement adaptive changes based on analyzed needs.

[0598] In some aspects, the techniques described herein relate to a system, wherein processing includes voice analysis.

[0599] In some aspects, the techniques described herein relate to a system, wherein analyzing includes facial expression processing.

[0600] In some aspects, the techniques described herein relate to a system, wherein generating includes display adaptations.

[0601] In some aspects, the techniques described herein relate to a system, wherein implementing includes audio modifications.

[0602] In some aspects, the techniques described herein relate to a system, wherein processing includes emotional state analysis.

[0603] In some aspects, the techniques described herein relate to a system, wherein analyzing includes attention level processing.SFT-107-A-PCT

[0604] In some aspects, the techniques described herein relate to a system, wherein generating includes content optimization.

[0605] In some aspects, the techniques described herein relate to a system, wherein implementing includes real-time adaptation.

[0606] In some aspects, the techniques described herein relate to a system, wherein processing includes stress level analysis.

[0607] In some aspects, the techniques described herein relate to a system, wherein analyzing includes preference processing.

[0608] In some aspects, the techniques described herein relate to an AI convergence system of systems for vehicle interface optimization including: a transaction layer configured to: implement user profiling and targeting; configure smart contracts for in-vehicle offers; automate transaction orchestration; and adapt interfaces based on user profiles.

[0609] In some aspects, the techniques described herein relate to a system, wherein implementing includes behavior analysis.

[0610] In some aspects, the techniques described herein relate to a system, wherein configuring includes preference processing.

[0611] In some aspects, the techniques described herein relate to a system, wherein automating includes content adaptation.

[0612] In some aspects, the techniques described herein relate to a system, wherein adapting includes display modifications.

[0613] In some aspects, the techniques described herein relate to a system, wherein implementing includes emotional state analysis.

[0614] In some aspects, the techniques described herein relate to a system, wherein configuring includes attention level processing.

[0615] In some aspects, the techniques described herein relate to a system, wherein automating includes interface optimization.

[0616] In some aspects, the techniques described herein relate to a system, wherein adapting includes real-time changes.

[0617] In some aspects, the techniques described herein relate to a system, wherein implementing includes stress level analysis.

[0618] In some aspects, the techniques described herein relate to a system, wherein configuring includes feedback processing.

[0619] In some aspects, the techniques described herein relate to an AI convergence system of systems for vehicle interface adaptation including: an operations layer configured to: monitor rider satisfaction; analyze interface effectiveness; generate interface modifications; and implement adaptive changes based on satisfaction analysis.

[0620] In some aspects, the techniques described herein relate to a system, wherein monitoring includes voice analysis.

[0621] In some aspects, the techniques described herein relate to a system, wherein analyzing includes facial expression processing.SFT-107-A-PCT

[0622] In some aspects, the techniques described herein relate to a system, wherein generating includes display adaptations.

[0623] In some aspects, the techniques described herein relate to a system, wherein implementing includes audio modifications.

[0624] In some aspects, the techniques described herein relate to a system, wherein monitoring includes emotional state analysis.

[0625] In some aspects, the techniques described herein relate to a system, wherein analyzing includes attention level processing.

[0626] In some aspects, the techniques described herein relate to a system, wherein generating includes content optimization.

[0627] In some aspects, the techniques described herein relate to a system, wherein implementing includes real-time adaptation.

[0628] In some aspects, the techniques described herein relate to a system, wherein monitoring includes stress level analysis.

[0629] In some aspects, the techniques described herein relate to a system, wherein analyzing includes preference processing. AI convergence system of systems ecosystem to alter a vehicle's interior or in-cabin characteristics.

[0630] In some aspects, the techniques described herein relate to an AI convergence system of systems for optimizing vehicle interior conditions, including: a vehicle having a set of interior systems including at least one of a seat, climate control system, or audio system; a sensor system configured to detect rider emotional states; an artificial intelligence system configured to: process physiological monitoring data from the rider; and optimize interior parameters based on the detected emotional states and physiological data.

[0631] In some aspects, the techniques described herein relate to a system, wherein the interior systems include seat positioning control.

[0632] In some aspects, the techniques described herein relate to a system, wherein the interior systems include lumbar support adjustment.

[0633] In some aspects, the techniques described herein relate to a system, wherein the interior systems include leg room adjustment.

[0634] In some aspects, the techniques described herein relate to a system, wherein the interior systems include seatback angle control.

[0635] In some aspects, the techniques described herein relate to a system, wherein the interior systems include ventilation control.

[0636] In some aspects, the techniques described herein relate to a system, wherein the interior systems include window control.

[0637] In some aspects, the techniques described herein relate to a system, wherein the interior systems include moonroof control.

[0638] In some aspects, the techniques described herein relate to a system, wherein the interior systems include temperature control.SFT-107-A-PCT

[0639] In some aspects, the techniques described herein relate to a system, wherein the interior systems include humidity control.

[0640] In some aspects, the techniques described herein relate to a system, wherein the interior systems include fan speed control.

[0641] In some aspects, the techniques described herein relate to an AI convergence system of systems for transportation including: a vehicle having interior environment controls; a physiological sensing system configured to monitor rider state; an artificial intelligence system configured to: detect changes in rider emotional state; and adjust interior environmental parameters to improve rider emotional state.

[0642] In some aspects, the techniques described herein relate to a system, wherein detecting includes processing vision system data.

[0643] In some aspects, the techniques described herein relate to a system, wherein detecting includes processing seat sensor data.

[0644] In some aspects, the techniques described herein relate to a system, wherein detecting includes processing steering wheel sensor data.

[0645] In some aspects, the techniques described herein relate to a system, wherein adjusting includes modifying audio content.

[0646] In some aspects, the techniques described herein relate to a system, wherein adjusting includes modifying climate settings.

[0647] In some aspects, the techniques described herein relate to a system, wherein adjusting includes modifying seat position.

[0648] In some aspects, the techniques described herein relate to a system, wherein detecting includes processing voice data.

[0649] In some aspects, the techniques described herein relate to a system, wherein detecting includes processing facial expressions.

[0650] In some aspects, the techniques described herein relate to a system, wherein adjusting includes modifying lighting conditions.

[0651] In some aspects, the techniques described herein relate to a system, wherein adjusting includes modifying ventilation settings.

[0652] In some aspects, the techniques described herein relate to an AI convergence system of systems for vehicle interior optimization including: a sensor system configured to detect rider state; an artificial intelligence system configured to: process physiological parameters; identify stress indicators; and modify cabin environment to reduce detected stress.

[0653] In some aspects, the techniques described herein relate to a system, wherein processing includes analyzing galvanic skin response.

[0654] In some aspects, the techniques described herein relate to a system, wherein processing includes analyzing cortisol levels.

[0655] In some aspects, the techniques described herein relate to a system, wherein identifying includes analyzing voice patterns.SFT-107-A-PCT

[0656] In some aspects, the techniques described herein relate to a system, wherein modifying includes adjusting audio content.

[0657] In some aspects, the techniques described herein relate to a system, wherein modifying includes adjusting climate settings.

[0658] In some aspects, the techniques described herein relate to a system, wherein modifying includes adjusting seat position.

[0659] In some aspects, the techniques described herein relate to a system, wherein processing includes analyzing facial expressions.

[0660] In some aspects, the techniques described herein relate to a system, wherein identifying includes analyzing emotional states.

[0661] In some aspects, the techniques described herein relate to a system, wherein modifying includes adjusting lighting.

[0662] In some aspects, the techniques described herein relate to a system, wherein modifying includes adjusting ventilation.

[0663] In some aspects, the techniques described herein relate to an AI convergence system of systems for vehicle interior management including: a hybrid neural network configured to: process rider physiological data; analyze cabin environmental conditions; predict optimal interior settings; and implement adaptive changes based on predictions.

[0664] In some aspects, the techniques described herein relate to a system, wherein processing includes voice analysis.

[0665] In some aspects, the techniques described herein relate to a system, wherein analyzing includes temperature monitoring.

[0666] In some aspects, the techniques described herein relate to a system, wherein predicting includes comfort optimization.

[0667] In some aspects, the techniques described herein relate to a system, wherein implementing includes seat adjustment.

[0668] In some aspects, the techniques described herein relate to a system, wherein processing includes stress level analysis.

[0669] In some aspects, the techniques described herein relate to a system, wherein analyzing includes humidity monitoring.

[0670] In some aspects, the techniques described herein relate to a system, wherein predicting includes ventilation optimization.

[0671] In some aspects, the techniques described herein relate to a system, wherein implementing includes audio adjustment.

[0672] In some aspects, the techniques described herein relate to a system, wherein processing includes emotional state analysis.

[0673] In some aspects, the techniques described herein relate to a system, wherein analyzing includes lighting conditions.

[0674] In some aspects, the techniques described herein relate to a method for vehicle interior optimization including: monitoring rider physiological state; analyzing cabin environmentalSFT-107-A-PCT conditions; predicting optimal comfort settings; and implementing adaptive changes based on predictions.

[0675] In some aspects, the techniques described herein relate to a method, wherein monitoring includes voice analysis.

[0676] In some aspects, the techniques described herein relate to a method, wherein analyzing includes temperature monitoring.

[0677] In some aspects, the techniques described herein relate to a method, wherein predicting includes comfort optimization.

[0678] In some aspects, the techniques described herein relate to a method, wherein implementing includes seat adjustment.

[0679] In some aspects, the techniques described herein relate to a method, wherein monitoring includes stress level analysis.

[0680] In some aspects, the techniques described herein relate to a method, wherein analyzing includes humidity monitoring.

[0681] In some aspects, the techniques described herein relate to a method, wherein predicting includes ventilation optimization.

[0682] In some aspects, the techniques described herein relate to a method, wherein implementing includes audio adjustment.

[0683] In some aspects, the techniques described herein relate to a method, wherein monitoring includes emotional state analysis.

[0684] In some aspects, the techniques described herein relate to a method, wherein analyzing includes lighting conditions.

[0685] In some aspects, the techniques described herein relate to an AI convergence system of systems for vehicle interior adaptation including: a sensor system configured to monitor cabin conditions; an artificial intelligence system configured to: analyze rider comfort indicators; predict optimal environmental settings; and implement adaptive changes based on predictions.

[0686] In some aspects, the techniques described herein relate to a system, wherein analyzing includes temperature monitoring.

[0687] In some aspects, the techniques described herein relate to a system, wherein analyzing includes humidity monitoring.

[0688] In some aspects, the techniques described herein relate to a system, wherein predicting includes ventilation optimization.

[0689] In some aspects, the techniques described herein relate to a system, wherein implementing includes seat adjustment.

[0690] In some aspects, the techniques described herein relate to a system, wherein analyzing includes stress level monitoring.

[0691] In some aspects, the techniques described herein relate to a system, wherein analyzing includes emotional state monitoring.

[0692] In some aspects, the techniques described herein relate to a system, wherein predicting includes lighting optimization.SFT-107-A-PCT

[0693] In some aspects, the techniques described herein relate to a system, wherein implementing includes audio adjustment.

[0694] In some aspects, the techniques described herein relate to a system, wherein analyzing includes voice pattern monitoring.

[0695] In some aspects, the techniques described herein relate to a system, wherein implementing includes climate control.

[0696] In some aspects, the techniques described herein relate to an AI convergence system of systems for vehicle interior optimization including: a digital twin system configured to: create virtual representations of cabin conditions; simulate environmental modifications; predict rider responses to modifications; and implement optimal environmental changes.

[0697] In some aspects, the techniques described herein relate to a system, wherein creating includes temperature modeling.

[0698] In some aspects, the techniques described herein relate to a system, wherein simulating includes humidity variations.

[0699] In some aspects, the techniques described herein relate to a system, wherein predicting includes comfort analysis.

[0700] In some aspects, the techniques described herein relate to a system, wherein implementing includes seat adjustment.

[0701] In some aspects, the techniques described herein relate to a system, wherein creating includes lighting modeling.

[0702] In some aspects, the techniques described herein relate to a system, wherein simulating includes ventilation changes.

[0703] In some aspects, the techniques described herein relate to a system, wherein predicting includes stress response.

[0704] In some aspects, the techniques described herein relate to a system, wherein implementing includes audio modification.

[0705] In some aspects, the techniques described herein relate to a system, wherein creating includes acoustic modeling.

[0706] In some aspects, the techniques described herein relate to a system, wherein simulating includes climate variations.

[0707] In some aspects, the techniques described herein relate to an AI convergence system of systems for vehicle interior management including: an operations layer configured to: monitor cabin environmental conditions; analyze rider comfort indicators; predict optimal settings; and implement adaptive changes.

[0708] In some aspects, the techniques described herein relate to a system, wherein monitoring includes temperature analysis.

[0709] In some aspects, the techniques described herein relate to a system, wherein analyzing includes humidity monitoring.

[0710] In some aspects, the techniques described herein relate to a system, wherein predicting includes ventilation optimization.SFT-107-A-PCT

[0711] In some aspects, the techniques described herein relate to a system, wherein implementing includes seat adjustment.

[0712] In some aspects, the techniques described herein relate to a system, wherein monitoring includes stress level analysis.

[0713] In some aspects, the techniques described herein relate to a system, wherein analyzing includes emotional state monitoring.

[0714] In some aspects, the techniques described herein relate to a system, wherein predicting includes lighting optimization.

[0715] In some aspects, the techniques described herein relate to a system, wherein implementing includes audio adjustment.

[0716] In some aspects, the techniques described herein relate to a system, wherein monitoring includes voice pattern analysis.

[0717] In some aspects, the techniques described herein relate to a system, wherein implementing includes climate control.

[0718] In some aspects, the techniques described herein relate to an AI convergence system of systems for vehicle interior optimization including: a hybrid neural network configured to: process cabin environmental data; analyze rider physiological responses; predict optimal comfort settings; and implement adaptive changes.

[0719] In some aspects, the techniques described herein relate to a system, wherein processing includes temperature analysis.

[0720] In some aspects, the techniques described herein relate to a system, wherein analyzing includes humidity monitoring.

[0721] In some aspects, the techniques described herein relate to a system, wherein predicting includes ventilation optimization.

[0722] In some aspects, the techniques described herein relate to a system, wherein implementing includes seat adjustment.

[0723] In some aspects, the techniques described herein relate to a system, wherein processing includes stress level analysis.

[0724] In some aspects, the techniques described herein relate to a system, wherein analyzing includes emotional state monitoring.

[0725] In some aspects, the techniques described herein relate to a system, wherein predicting includes lighting optimization.

[0726] In some aspects, the techniques described herein relate to a system, wherein implementing includes audio adjustment.

[0727] In some aspects, the techniques described herein relate to a system, wherein processing includes voice pattern analysis.

[0728] In some aspects, the techniques described herein relate to a system, wherein implementing includes climate control.

[0729] In some aspects, the techniques described herein relate to an AI convergence system of systems for vehicle interior adaptation including: an artificial intelligence system configured to:SFT-107-A-PCT monitor cabin environmental conditions; analyze rider comfort indicators; generate environmental modifications; and implement adaptive changes based on analysis.

[0730] In some aspects, the techniques described herein relate to a system, wherein monitoring includes temperature analysis.

[0731] In some aspects, the techniques described herein relate to a system, wherein analyzing includes humidity monitoring.

[0732] In some aspects, the techniques described herein relate to a system, wherein generating includes ventilation optimization.

[0733] In some aspects, the techniques described herein relate to a system, wherein implementing includes seat adjustment.

[0734] In some aspects, the techniques described herein relate to a system, wherein monitoring includes stress level analysis.

[0735] In some aspects, the techniques described herein relate to a system, wherein analyzing includes emotional state monitoring.

[0736] In some aspects, the techniques described herein relate to a system, wherein generating includes lighting optimization.

[0737] In some aspects, the techniques described herein relate to a system, wherein implementing includes audio adjustment.

[0738] In some aspects, the techniques described herein relate to a system, wherein monitoring includes voice pattern analysis.

[0739] In some aspects, the techniques described herein relate to a system, wherein implementing includes climate control. AI convergence system of systems ecosystem to alter a vehicle's performance or operating characteristics.

[0740] In some aspects, the techniques described herein relate to an AI convergence system of systems for optimizing vehicle performance, including: a set of sensors configured to provide input to an expert system; an artificial intelligence system configured to: track vehicle operating states including energy utilization state, maintenance state, and component state; optimize powertrain parameters based on the tracked states; and adapt vehicle performance characteristics in real-time.

[0741] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system implements hybrid neural networks.

[0742] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system processes environmental data.

[0743] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system optimizes fuel usage.

[0744] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system optimizes electricity usage.

[0745] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system optimizes refueling timing.

[0746] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system optimizes recharging timing.SFT-107-A-PCT

[0747] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system processes traffic predictions.

[0748] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system processes transportation predictions.

[0749] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system optimizes suspension profiles.

[0750] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system processes road profile data.

[0751] In some aspects, the techniques described herein relate to an AI convergence system of systems for transportation including: a hybrid neural network configured to: classify vehicle states including maintenance state, health state, and operating state; optimize powertrain operating parameters based on the classified states; and predict future vehicle states based on the optimization.

[0752] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network processes sensor data.

[0753] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network analyzes vehicle range.

[0754] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network analyzes powertrain parameters.

[0755] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network analyzes current gear state.

[0756] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network analyzes speed parameters.

[0757] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network analyzes acceleration parameters.

[0758] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network analyzes suspension profiles.

[0759] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network analyzes charge state.

[0760] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network analyzes fuel state.

[0761] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network implements feedback processing.

[0762] In some aspects, the techniques described herein relate to an AI convergence system of systems for vehicle performance optimization including: a data processing system configured to process data from vehicle sensors; an artificial intelligence system configured to: analyze vehicle operational states; optimize drivetrain parameters based on operational states; and implement adaptive changes based on real-time conditions.

[0763] In some aspects, the techniques described herein relate to a system, wherein analyzing includes processing transmission data.SFT-107-A-PCT

[0764] In some aspects, the techniques described herein relate to a system, wherein analyzing includes processing gear system data.

[0765] In some aspects, the techniques described herein relate to a system, wherein analyzing includes processing clutch system data.

[0766] In some aspects, the techniques described herein relate to a system, wherein analyzing includes processing braking system data.

[0767] In some aspects, the techniques described herein relate to a system, wherein analyzing includes processing fuel system data.

[0768] In some aspects, the techniques described herein relate to a system, wherein analyzing includes processing lubrication system data.

[0769] In some aspects, the techniques described herein relate to a system, wherein analyzing includes processing steering system data.

[0770] In some aspects, the techniques described herein relate to a system, wherein analyzing includes processing suspension system data.

[0771] In some aspects, the techniques described herein relate to a system, wherein analyzing includes processing lighting system data.

[0772] In some aspects, the techniques described herein relate to a system, wherein analyzing includes processing electrical system data.

[0773] In some aspects, the techniques described herein relate to an AI convergence system of systems for vehicle performance management including: a hybrid neural network configured to: process vehicle telemetry data; analyze component performance patterns; predict maintenance requirements; and optimize operational parameters based on predictions.

[0774] In some aspects, the techniques described herein relate to a system, wherein processing includes analyzing sensor data.

[0775] In some aspects, the techniques described herein relate to a system, wherein analyzing includes powertrain monitoring.

[0776] In some aspects, the techniques described herein relate to a system, wherein predicting includes component wear analysis.

[0777] In some aspects, the techniques described herein relate to a system, wherein optimizing includes transmission adjustment.

[0778] In some aspects, the techniques described herein relate to a system, wherein processing includes suspension analysis.

[0779] In some aspects, the techniques described herein relate to a system, wherein analyzing includes brake system monitoring.

[0780] In some aspects, the techniques described herein relate to a system, wherein predicting includes fuel efficiency optimization.

[0781] In some aspects, the techniques described herein relate to a system, wherein optimizing includes real-time adaptation.

[0782] In some aspects, the techniques described herein relate to a system, wherein processing includes environmental analysis.SFT-107-A-PCT

[0783] In some aspects, the techniques described herein relate to a system, wherein analyzing includes performance feedback.

[0784] In some aspects, the techniques described herein relate to a method for vehicle performance optimization including: monitoring vehicle operational states; analyzing component performance patterns; predicting maintenance requirements; and implementing adaptive changes based on predictions.

[0785] In some aspects, the techniques described herein relate to a method, wherein monitoring includes sensor data analysis.

[0786] In some aspects, the techniques described herein relate to a method, wherein analyzing includes powertrain monitoring.

[0787] In some aspects, the techniques described herein relate to a method, wherein predicting includes component wear analysis.

[0788] In some aspects, the techniques described herein relate to a method, wherein implementing includes transmission adjustment.

[0789] In some aspects, the techniques described herein relate to a method, wherein monitoring includes suspension analysis.

[0790] In some aspects, the techniques described herein relate to a method, wherein analyzing includes brake system monitoring.

[0791] In some aspects, the techniques described herein relate to a method, wherein predicting includes fuel efficiency optimization.

[0792] In some aspects, the techniques described herein relate to a method, wherein implementing includes real-time adaptation.

[0793] In some aspects, the techniques described herein relate to a method, wherein monitoring includes environmental analysis.

[0794] In some aspects, the techniques described herein relate to a method, wherein analyzing includes performance feedback.

[0795] In some aspects, the techniques described herein relate to an AI convergence system of systems for vehicle performance optimization including: an operations layer configured to: implement control and optimization capabilities; monitor performance patterns and resource utilization; optimize operations based on real-time conditions; and coordinate with vehicle subsystems for operational efficiency.

[0796] In some aspects, the techniques described herein relate to a system, wherein implementing includes powertrain control.

[0797] In some aspects, the techniques described herein relate to a system, wherein monitoring includes transmission analysis.

[0798] In some aspects, the techniques described herein relate to a system, wherein optimizing includes suspension adjustment.

[0799] In some aspects, the techniques described herein relate to a system, wherein coordinating includes brake system control.SFT-107-A-PCT

[0800] In some aspects, the techniques described herein relate to a system, wherein implementing includes fuel system optimization.

[0801] In some aspects, the techniques described herein relate to a system, wherein monitoring includes component wear analysis.

[0802] In some aspects, the techniques described herein relate to a system, wherein optimizing includes efficiency improvement.

[0803] In some aspects, the techniques described herein relate to a system, wherein coordinating includes real-time adaptation.

[0804] In some aspects, the techniques described herein relate to a system, wherein implementing includes environmental analysis.

[0805] In some aspects, the techniques described herein relate to a system, wherein monitoring includes performance feedback.

[0806] In some aspects, the techniques described herein relate to an AI convergence system of systems for vehicle performance management including: a digital twin system configured to: create virtual representations of vehicle components; simulate component operations in real-time; predict maintenance requirements; and optimize performance based on simulations.

[0807] In some aspects, the techniques described herein relate to a system, wherein creating includes powertrain modeling.

[0808] In some aspects, the techniques described herein relate to a system, wherein simulating includes transmission analysis.

[0809] In some aspects, the techniques described herein relate to a system, wherein predicting includes component wear analysis.

[0810] In some aspects, the techniques described herein relate to a system, wherein optimizing includes performance adjustment.

[0811] In some aspects, the techniques described herein relate to a system, wherein creating includes suspension modeling.

[0812] In some aspects, the techniques described herein relate to a system, wherein simulating includes brake system analysis.

[0813] In some aspects, the techniques described herein relate to a system, wherein predicting includes efficiency optimization.

[0814] In some aspects, the techniques described herein relate to a system, wherein optimizing includes real-time adaptation.

[0815] In some aspects, the techniques described herein relate to a system, wherein creating includes environmental modeling.

[0816] In some aspects, the techniques described herein relate to a system, wherein simulating includes performance feedback.

[0817] In some aspects, the techniques described herein relate to an AI convergence system of systems for vehicle performance optimization including: an artificial intelligence system configured to: process vehicle operational data; analyze performance patterns; predict maintenance requirements; and implement adaptive changes based on predictions.SFT-107-A-PCT

[0818] In some aspects, the techniques described herein relate to a system, wherein processing includes sensor analysis.

[0819] In some aspects, the techniques described herein relate to a system, wherein analyzing includes powertrain monitoring.

[0820] In some aspects, the techniques described herein relate to a system, wherein predicting includes component wear analysis.

[0821] In some aspects, the techniques described herein relate to a system, wherein implementing includes transmission adjustment.

[0822] In some aspects, the techniques described herein relate to a system, wherein processing includes suspension analysis.

[0823] In some aspects, the techniques described herein relate to a system, wherein analyzing includes brake system monitoring.

[0824] In some aspects, the techniques described herein relate to a system, wherein predicting includes efficiency optimization.

[0825] In some aspects, the techniques described herein relate to a system, wherein implementing includes real-time adaptation.

[0826] In some aspects, the techniques described herein relate to a system, wherein processing includes environmental analysis.

[0827] In some aspects, the techniques described herein relate to a system, wherein analyzing includes performance feedback.

[0828] In some aspects, the techniques described herein relate to an AI convergence system of systems for vehicle performance management including: a hybrid neural network configured to: analyze vehicle operational patterns; optimize component performance; predict maintenance requirements; and implement adaptive changes.

[0829] In some aspects, the techniques described herein relate to a system, wherein analyzing includes sensor processing.

[0830] In some aspects, the techniques described herein relate to a system, wherein optimizing includes powertrain control.

[0831] In some aspects, the techniques described herein relate to a system, wherein predicting includes component analysis.

[0832] In some aspects, the techniques described herein relate to a system, wherein implementing includes transmission adjustment.

[0833] In some aspects, the techniques described herein relate to a system, wherein analyzing includes suspension monitoring.

[0834] In some aspects, the techniques described herein relate to a system, wherein optimizing includes brake system control.

[0835] In some aspects, the techniques described herein relate to a system, wherein predicting includes efficiency analysis.

[0836] In some aspects, the techniques described herein relate to a system, wherein implementing includes real-time adaptation.SFT-107-A-PCT

[0837] In some aspects, the techniques described herein relate to a system, wherein analyzing includes environmental processing.

[0838] In some aspects, the techniques described herein relate to a system, wherein optimizing includes performance feedback.

[0839] In some aspects, the techniques described herein relate to an AI convergence system of systems for vehicle performance optimization including: an artificial intelligence system configured to: monitor vehicle operational states; analyze component performance; predict maintenance needs; and implement adaptive changes.

[0840] In some aspects, the techniques described herein relate to a system, wherein monitoring includes sensor analysis.

[0841] In some aspects, the techniques described herein relate to a system, wherein analyzing includes powertrain monitoring.

[0842] In some aspects, the techniques described herein relate to a system, wherein predicting includes wear analysis.

[0843] In some aspects, the techniques described herein relate to a system, wherein implementing includes transmission control.

[0844] In some aspects, the techniques described herein relate to a system, wherein monitoring includes suspension analysis.

[0845] In some aspects, the techniques described herein relate to a system, wherein analyzing includes brake system monitoring.

[0846] In some aspects, the techniques described herein relate to a system, wherein predicting includes efficiency optimization.

[0847] In some aspects, the techniques described herein relate to a system, wherein implementing includes real-time adaptation.

[0848] In some aspects, the techniques described herein relate to a system, wherein monitoring includes environmental analysis.

[0849] In some aspects, the techniques described herein relate to a system, wherein analyzing includes performance feedback. Using digital twins in an AI convergence system of systems

[0850] In some aspects, the techniques described herein relate to an AI convergence system of systems for transportation analysis including: a digital twin system configured to: create digital replicas of transportation entities; process real-time sensor data to provide virtual representations; simulate possible future states of the transportation entities; predict behavior and performance under various conditions; and optimize operations based on the simulations and predictions.

[0851] In some aspects, the techniques described herein relate to a system, wherein the digital twin system simulates physical elements and properties.

[0852] In some aspects, the techniques described herein relate to a system, wherein the digital twin system simulates dynamics throughout lifecycles.

[0853] In some aspects, the techniques described herein relate to a system, wherein the digital twin system provides hypothetical simulations.SFT-107-A-PCT

[0854] In some aspects, the techniques described herein relate to a system, wherein the digital twin system processes environmental data.

[0855] In some aspects, the techniques described herein relate to a system, wherein the digital twin system processes marketplace data.

[0856] In some aspects, the techniques described herein relate to a system, wherein the digital twin system adapts to real-time conditions.

[0857] In some aspects, the techniques described herein relate to a system, wherein the digital twin system processes feedback data.

[0858] In some aspects, the techniques described herein relate to a system, wherein the digital twin system simulates high stress conditions.

[0859] In some aspects, the techniques described herein relate to a system, wherein the digital twin system simulates component wear.

[0860] In some aspects, the techniques described herein relate to a system, wherein the digital twin system simulates planned improvements.

[0861] In some aspects, the techniques described herein relate to an AI convergence system of systems for transportation including: a digital twin management system configured to: interface with an environment; provide bi-directional transfer of data between coupled components; identify and store states related to transportation systems; and update properties based on client applications.

[0862] In some aspects, the techniques described herein relate to a system, wherein the states include vehicle operating states.

[0863] In some aspects, the techniques described herein relate to a system, wherein the states include user experience states.

[0864] In some aspects, the techniques described herein relate to a system, wherein the states include maintenance states.

[0865] In some aspects, the techniques described herein relate to a system, wherein the states include energy utilization states.

[0866] In some aspects, the techniques described herein relate to a system, wherein the states include component states.

[0867] In some aspects, the techniques described herein relate to a system, wherein the states include vehicle health states.

[0868] In some aspects, the techniques described herein relate to a system, wherein the states include charging states.

[0869] In some aspects, the techniques described herein relate to a system, wherein the states include satisfaction states.

[0870] In some aspects, the techniques described herein relate to a system, wherein the states include subsystem states.

[0871] In some aspects, the techniques described herein relate to a system, wherein the states include powertrain states.SFT-107-A-PCT

[0872] In some aspects, the techniques described herein relate to an AI convergence system of systems for transportation analysis including: an enterprise digital twin system configured to: create digital representations at various levels of abstraction; simulate operations from different points of view; process real-time operational data; and optimize system parameters based on simulations.

[0873] In some aspects, the techniques described herein relate to a system, wherein creating includes fleet operations modeling.

[0874] In some aspects, the techniques described herein relate to a system, wherein simulating includes maintenance operations.

[0875] In some aspects, the techniques described herein relate to a system, wherein processing includes performance data.

[0876] In some aspects, the techniques described herein relate to a system, wherein optimizing includes resource allocation.

[0877] In some aspects, the techniques described herein relate to a system, wherein creating includes infrastructure modeling.

[0878] In some aspects, the techniques described herein relate to a system, wherein simulating includes component operations.

[0879] In some aspects, the techniques described herein relate to a system, wherein processing includes environmental data.

[0880] In some aspects, the techniques described herein relate to a system, wherein optimizing includes efficiency parameters.

[0881] In some aspects, the techniques described herein relate to a system, wherein creating includes system integration modeling.

[0882] In some aspects, the techniques described herein relate to a system, wherein simulating includes future states.

[0883] In some aspects, the techniques described herein relate to an AI convergence system of systems for transportation including: a digital twin creation module configured to: create digital twins using imported data; process image scans of transportation systems; analyze 3D data from sensing devices; and generate 3D representations of environments.

[0884] In some aspects, the techniques described herein relate to a system, wherein creating includes blueprint processing.

[0885] In some aspects, the techniques described herein relate to a system, wherein processing includes LIDAR data analysis.

[0886] In some aspects, the techniques described herein relate to a system, wherein analyzing includes SLAM sensor data.

[0887] In some aspects, the techniques described herein relate to a system, wherein generating includes object classification.

[0888] In some aspects, the techniques described herein relate to a system, wherein creating includes specification processing.SFT-107-A-PCT

[0889] In some aspects, the techniques described herein relate to a system, wherein processing includes IR scanner data.

[0890] In some aspects, the techniques described herein relate to a system, wherein analyzing includes radar device data.

[0891] In some aspects, the techniques described herein relate to a system, wherein generating includes pathway mapping.

[0892] In some aspects, the techniques described herein relate to a system, wherein creating includes equipment modeling.

[0893] In some aspects, the techniques described herein relate to a system, wherein processing includes EMF scanner data.

[0894] In some aspects, the techniques described herein relate to an AI convergence system of systems for transportation optimization including: a digital twin system configured to: alter traffic patterns through navigational data updates; achieve predetermined optimization criteria; coordinate mobile element interactions; and optimize process efficiency.

[0895] In some aspects, the techniques described herein relate to a system, wherein altering includes route optimization.

[0896] In some aspects, the techniques described herein relate to a system, wherein achieving includes safety criteria.

[0897] In some aspects, the techniques described herein relate to a system, wherein coordinating includes path planning.

[0898] In some aspects, the techniques described herein relate to a system, wherein optimizing includes resource utilization.

[0899] In some aspects, the techniques described herein relate to a system, wherein altering includes real-time adaptation.

[0900] In some aspects, the techniques described herein relate to a system, wherein achieving includes efficiency targets.

[0901] In some aspects, the techniques described herein relate to a system, wherein coordinating includes collision avoidance.

[0902] In some aspects, the techniques described herein relate to a system, wherein optimizing includes energy efficiency.

[0903] In some aspects, the techniques described herein relate to a system, wherein altering includes congestion management.

[0904] In some aspects, the techniques described herein relate to a system, wherein achieving includes performance goals.

[0905] In some aspects, the techniques described herein relate to an AI convergence system of systems for transportation analysis including: a digital twin dynamic model system configured to: calculate device property values; update transportation worker digital twins; implement psychometric models; and predict reactions to stimuli.

[0906] In some aspects, the techniques described herein relate to a system, wherein calculating includes status monitoring.SFT-107-A-PCT

[0907] In some aspects, the techniques described herein relate to a system, wherein updating includes location tracking.

[0908] In some aspects, the techniques described herein relate to a system, wherein implementing includes workflow models.

[0909] In some aspects, the techniques described herein relate to a system, wherein predicting includes behavioral responses.

[0910] In some aspects, the techniques described herein relate to a system, wherein calculating includes temperature analysis.

[0911] In some aspects, the techniques described herein relate to a system, wherein updating includes trajectory tracking.

[0912] In some aspects, the techniques described herein relate to a system, wherein implementing includes FMEA models.

[0913] In some aspects, the techniques described herein relate to a system, wherein predicting includes performance measures.

[0914] In some aspects, the techniques described herein relate to a system, wherein calculating includes stress measures.

[0915] In some aspects, the techniques described herein relate to a system, wherein updating includes task monitoring.

[0916] In some aspects, the techniques described herein relate to an AI convergence system of systems for transportation modeling including: a digital twin system configured to: adhere to physical laws and principles; implement dynamic models; conform to real-world conditions; and adapt based on behavioral differences.

[0917] In some aspects, the techniques described herein relate to a system, wherein adhering includes thermodynamic laws.

[0918] In some aspects, the techniques described herein relate to a system, wherein implementing includes motion laws.

[0919] In some aspects, the techniques described herein relate to a system, wherein conforming includes fluid dynamics.

[0920] In some aspects, the techniques described herein relate to a system, wherein adapting includes model correction.

[0921] In some aspects, the techniques described herein relate to a system, wherein adhering includes buoyancy laws.

[0922] In some aspects, the techniques described herein relate to a system, wherein implementing includes heat transfer laws.

[0923] In some aspects, the techniques described herein relate to a system, wherein conforming includes radiation laws.

[0924] In some aspects, the techniques described herein relate to a system, wherein adapting includes assumption validation.

[0925] In some aspects, the techniques described herein relate to a system, wherein adhering includes quantum dynamics.SFT-107-A-PCT

[0926] In some aspects, the techniques described herein relate to a system, wherein implementing includes aging principles.

[0927] In some aspects, the techniques described herein relate to an AI convergence system of systems for transportation analysis including: a digital twin interface system configured to: enable user inspection of digital twins; facilitate interaction with transportation entities; monitor processes being performed; and control digital twin properties.

[0928] In some aspects, the techniques described herein relate to a system, wherein enabling includes measurement monitoring.

[0929] In some aspects, the techniques described herein relate to a system, wherein facilitating includes movement tracking.

[0930] In some aspects, the techniques described herein relate to a system, wherein monitoring includes interaction analysis.

[0931] In some aspects, the techniques described herein relate to a system, wherein controlling includes loading operations.

[0932] In some aspects, the techniques described herein relate to a system, wherein enabling includes maintenance tracking.

[0933] In some aspects, the techniques described herein relate to a system, wherein facilitating includes cleaning operations.

[0934] In some aspects, the techniques described herein relate to a system, wherein monitoring includes fueling processes.

[0935] In some aspects, the techniques described herein relate to a system, wherein controlling includes resupply operations.

[0936] In some aspects, the techniques described herein relate to a system, wherein enabling includes painting operations.

[0937] In some aspects, the techniques described herein relate to a system, wherein facilitating includes process control.

[0938] In some aspects, the techniques described herein relate to an AI convergence system of systems for transportation optimization including: a digital twin generation system configured to: receive digital twin requests; determine required data types; structure collected data; and generate requested digital twins.

[0939] In some aspects, the techniques described herein relate to a system, wherein receiving includes type specification.

[0940] In some aspects, the techniques described herein relate to a system, wherein determining includes data classification.

[0941] In some aspects, the techniques described herein relate to a system, wherein structuring includes historical data.

[0942] In some aspects, the techniques described herein relate to a system, wherein generating includes real-time data.

[0943] In some aspects, the techniques described herein relate to a system, wherein receiving includes role specification.SFT-107-A-PCT

[0944] In some aspects, the techniques described herein relate to a system, wherein determining includes CRM data.

[0945] In some aspects, the techniques described herein relate to a system, wherein structuring includes market data.

[0946] In some aspects, the techniques described herein relate to a system, wherein generating includes configuration data.

[0947] In some aspects, the techniques described herein relate to a system, wherein receiving includes purpose specification.

[0948] In some aspects, the techniques described herein relate to a system, wherein determining includes operational data.

[0949] In some aspects, the techniques described herein relate to an AI convergence system of systems for transportation analysis including: an enterprise layer configured to: implement executive digital twins for vehicle fleet operations; create vehicle digital twins for design and simulation; provide enterprise access for fleet transactions; and manage software-defined vehicle fleets.

[0950] In some aspects, the techniques described herein relate to a system, wherein implementing includes contextual simulation.

[0951] In some aspects, the techniques described herein relate to a system, wherein creating includes performance forecasting.

[0952] In some aspects, the techniques described herein relate to a system, wherein providing includes transaction processing.

[0953] In some aspects, the techniques described herein relate to a system, wherein managing includes fleet optimization.

[0954] In some aspects, the techniques described herein relate to a system, wherein implementing includes operational modeling.

[0955] In some aspects, the techniques described herein relate to a system, wherein creating includes component simulation.

[0956] In some aspects, the techniques described herein relate to a system, wherein providing includes access control.

[0957] In some aspects, the techniques described herein relate to a system, wherein managing includes resource allocation.

[0958] In some aspects, the techniques described herein relate to a system, wherein implementing includes efficiency analysis.

[0959] In some aspects, the techniques described herein relate to a system, wherein creating includes maintenance planning.

[0960] In some aspects, the techniques described herein relate to a computer-implemented method substantially as hereinbefore described with reference to any of the examples and / or to the attached drawings.SFT-107-A-PCT

[0961] In some aspects, the techniques described herein relate to a computing system including one or more processors and one or more memories configured to perform operations substantially as hereinbefore described with reference to any of the examples and / or to the attached drawings.

[0962] In some aspects, the techniques described herein relate to a computer program product residing on a computer readable storage medium having a plurality of instructions stored thereon which, when executed across one or more processors, causes at least a portion of the one or more processors to perform operations substantially as hereinbefore described with reference to any of the examples and / or to the attached drawings.

[0963] In some aspects, the techniques described herein relate to a device configured substantially as hereinbefore described with reference to any of the examples and / or to the attached drawings.

[0964] It is to be understood that any combination of features from the methods disclosed herein and / or from the systems disclosed herein may be used together, and / or that any features from any or all of these aspects may be combined with any of the features of the embodiments and / or examples disclosed herein to achieve the benefits as described in this disclosure. BRIEF DESCRIPTION OF THE FIGURES

[0965] In the accompanying figures, like reference numerals refer to identical or functionally similar elements throughout the separate views and together with the detailed description below are incorporated in and form part of the specification, serve to further illustrate various embodiments and to explain various principles and advantages all in accordance with the systems and methods disclosed herein.

[0966] Fig. 1 is a diagrammatic view that illustrates an architecture for a transportation system showing certain illustrative components and arrangements relating to various embodiments of the present disclosure.

[0967] Fig.2 is a diagrammatic view that illustrates use of a hybrid neural network to optimize a powertrain component of a vehicle relating to various embodiments of the present disclosure.

[0968] Fig.3 is a diagrammatic view that illustrates a set of states that may be provided as inputs to and / or be governed by an expert system / Artificial Intelligence (AI) system relating to various embodiments of the present disclosure.

[0969] Fig. 4 is a diagrammatic view that illustrates a range of parameters that may be taken as inputs by an expert system or AI system, or component thereof, as described throughout this disclosure, or that may be provided as outputs from such a system and / or one or more sensors, cameras, or external systems relating to various embodiments of the present disclosure.

[0970] Fig. 5 is a diagrammatic view that illustrates a set of vehicle user interfaces relating to various embodiments of the present disclosure.

[0971] Fig. 6 is a diagrammatic view that illustrates a set of interfaces among transportation system components relating to various embodiments of the present disclosure.

[0972] Fig.7 is a diagrammatic view that illustrates a data processing system, which may process data from various sources relating to various embodiments of the present disclosure.SFT-107-A-PCT

[0973] Fig. 8 is a diagrammatic view that illustrates a set of algorithms that may be executed in connection with one or more of the many embodiments of transportation systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0974] Fig.9 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0975] Fig.10 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0976] Fig. 11 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0977] Fig.12 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0978] Fig. 13 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0979] Fig.14 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0980] Fig. 15 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0981] Fig.16 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0982] Fig. 17 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0983] Fig.18 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0984] Fig. 19 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0985] Fig. 20 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0986] Fig. 21 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0987] Fig.22 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0988] Fig. 23 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0989] Fig. 24 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0990] Fig.25 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0991] Fig. 26 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.SFT-107-A-PCT

[0992] Fig. 26A is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0993] Fig.27 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0994] Fig. 28 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0995] Fig.29 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0996] Fig.30 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0997] Fig.31 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0998] Fig.32 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0999] Fig. 33 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[1000] Fig.34 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[1001] Fig. 35 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[1002] Fig.36 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[1003] Fig.37 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[1004] Fig. 38 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[1005] Fig. 39 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[1006] Fig. 40 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[1007] Fig.41 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[1008] Fig. 42 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[1009] Fig. 43 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[1010] Fig.44 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[1011] Fig.45 is a diagrammatic view that illustrates systems and methods described throughout this disclosure relating to various embodiments of the present disclosure.SFT-107-A-PCT

[1012] Fig.46 is a diagrammatic view that illustrates systems and methods described throughout this disclosure relating to various embodiments of the present disclosure.

[1013] Fig.47 is a diagrammatic view that illustrates systems and methods described throughout this disclosure relating to various embodiments of the present disclosure.

[1014] Fig.48 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[1015] Fig. 49 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[1016] Fig. 50 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[1017] Fig.51 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[1018] Fig.52 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[1019] Fig.53 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[1020] Fig. 54 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[1021] Fig. 55 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[1022] Fig.56 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[1023] Fig.57 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[1024] Fig.58 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[1025] Fig. 59 is a diagrammatic view that illustrates an architecture for a transportation system including a digital twin system of a vehicle showing certain illustrative components and arrangements relating to various embodiments of the present disclosure.

[1026] Fig.60 shows a schematic illustration of the digital twin system integrated with an identity and access management system in accordance with certain embodiments of the present disclosure.

[1027] Fig.61 illustrates a schematic view of an interface of the digital twin system presented on the user device of a driver of the vehicle relating to various embodiments of the present disclosure.

[1028] Fig.62 is a schematic diagram showing the interaction between the driver and the digital twin using one or more views and modes of the interface in accordance with an example embodiment of the present disclosure.

[1029] Fig.63 illustrates a schematic view of an interface of the digital twin system presented on the user device of a manufacturer of the vehicle in accordance with various embodiments of the present disclosure.SFT-107-A-PCT

[1030] Fig.64 depicts a scenario in which the manufacturer uses the quality view of a digital twin interface to run simulations and generate what-if scenarios for quality testing a vehicle in accordance with an example embodiment of the present disclosure.

[1031] Fig.65 illustrates a schematic view of an interface of the digital twin system presented on the user device of a dealer of the vehicle.

[1032] Fig. 66 is a diagram illustrating the interaction between the dealer and the digital twin using one or more views with the goal of personalizing the experience of a customer purchasing a vehicle in accordance with an example embodiment.

[1033] Fig. 67 is a diagram illustrating the service & maintenance view presented to a user of a vehicle including a driver, a manufacturer and a dealer of the vehicle in accordance with various embodiments of the present disclosure.

[1034] Fig. 68 is a method used by the digital twin for detecting faults and predicting any future failures of the vehicle in accordance with an example embodiment.

[1035] Fig. 69 is a diagrammatic view that illustrates the architecture of a vehicle with a digital twin system for performing predictive maintenance on a vehicle in accordance with an example embodiment of the present disclosure.

[1036] Fig. 70 is a flow chart depicting a method for generating a digital twin of a vehicle in accordance with various embodiments of the disclosure.

[1037] Fig.71 is a diagrammatic view that illustrates an alternate architecture for a transportation system comprising a vehicle and a digital twin system in accordance with various embodiments of the present disclosure.

[1038] Fig.72 depicts a digital twin representing a combination of a set of states of both a vehicle and a driver of the vehicle in accordance with certain embodiments of the present disclosure.

[1039] Fig.73 illustrates a schematic diagram depicting a scenario in which the integrated vehicle and driver digital twin may configure the vehicle experience in accordance with an example embodiment.

[1040] Fig. 74 is a schematic illustrating an example of a portion of an information technology system for transportation artificial intelligence leveraging digital twins according to some embodiments of the present disclosure.

[1041] Fig. 75 is a schematic illustrating examples of architecture of a digital twin system according to embodiments of the present disclosure.

[1042] Fig. 76 is a schematic illustrating exemplary components of a digital twin management system according to embodiments of the present disclosure.

[1043] Fig. 77 is a schematic illustrating examples of a digital twin I / O system that interfaces with an environment, the digital twin system, and / or components thereof to provide bi-directional transfer of data between coupled components according to embodiments of the present disclosure.

[1044] Fig. 78 is a schematic illustrating an example set of identified states related to transportation systems that the digital twin system may identify and / or store for access by intelligent systems (e.g., a cognitive intelligence system) or users of the digital twin system according to embodiments of the present disclosure.SFT-107-A-PCT

[1045] Fig.79 is a schematic illustrating example embodiments of methods for updating a set of properties of a digital twin of the present disclosure on behalf of a client application and / or one or more embedded digital twins.

[1046] Fig. 80 illustrates example embodiments of a display interface of the present disclosure that renders a digital twin of a dryer centrifuge with information relating to the dryer centrifuge.

[1047] Fig.81 is a schematic illustrating an example embodiment of a method for updating a set of vibration fault level states of machine components such as bearings in the digital twin of a machine, on behalf of a client application.

[1048] Fig.82 is a schematic illustrating an example embodiment of a method for updating a set of vibration severity unit values of machine components such as bearings in the digital twin of a machine on behalf of a client application.

[1049] Fig.83 is a schematic illustrating an example embodiment of a method for updating a set of probability of failure values in the digital twins of machine components on behalf of a client application.

[1050] Fig.84 is a schematic illustrating an example embodiment of a method for updating a set of probability of downtime values of machines in the digital twin of a transportation system on behalf of a client application.

[1051] Fig. 85 is a schematic illustrating an example embodiment of a method for updating one or more probability of shutdown values of transportation entities in one or more transportation system digital twins.

[1052] Fig.86 is a schematic illustrating an example embodiment of a method for updating a set of cost of downtime values of machines in the digital twin of a transportation system.

[1053] Fig. 87 is a schematic illustrating an example embodiment of a method for updating one or more KPI values in a digital twin of a transportation system, on behalf of a client application.

[1054] Fig. 88 is a schematic illustrating an example embodiment of a method of the present disclosure.

[1055] Fig. 89 is a schematic illustrating examples of different types of enterprise digital twins, including executive digital twins, in relation to the data layer, processing layer, and application layer of an enterprise digital twin framework according to some embodiments of the present disclosure.

[1056] Fig. 90 is a schematic illustrating an example of a method for configuring role-based digital twins according to some embodiments of the present disclosure.

[1057] Fig. 91 is a schematic illustrating an example of a method for configuring a digital twin of a workforce according to some embodiments of the present disclosure.

[1058] Fig. 92 is a schematic view of an exemplary embodiment of the quantum computing service according to some embodiments of the present disclosure.

[1059] Fig. 93 illustrates quantum computing service request handling according to some embodiments of the present disclosure.

[1060] Fig. 94 is a diagrammatic view that illustrates embodiments of the biology-based system in accordance with the present disclosure.SFT-107-A-PCT

[1061] Fig.95 is a diagrammatic view of the thalamus service and how it coordinates within the modules in accordance with the present disclosure.

[1062] Fig.96 is a diagrammatic view of the dual process artificial neural network system.

[1063] Skilled artisans will appreciate that elements in the figures are illustrated for simplicity and clarity and have not necessarily been drawn to scale. For example, the dimensions of some of the elements in the figures may be exaggerated relative to other elements to help to improve understanding of the many embodiments of the systems and methods disclosed herein.

[1064] Fig.97 is a diagrammatic view of artificial intelligence capabilities, convergence technology stack capabilities and software-defined vehicle modules of a transportation system.

[1065] Fig. 98 is a diagrammatic view of software defined vehicle modules of a transportation system.

[1066] Fig. 99 depicts a block diagram of exemplary features, capabilities, and interfaces of a generative artificial intelligence platform of a transportation system.

[1067] Fig. 100 is a diagrammatic view of data and visualization methods and systems of a transportation system.

[1068] Fig. 101 is a diagrammatic view of data and visualization methods and systems of a transportation system.

[1069] Fig.102 is a schematic view of an example AI convergence system of systems.

[1070] Fig.103 is a schematic view of an example offering layer.

[1071] Fig.104 is a schematic view of an example transactions layer.

[1072] Fig.105 is a schematic view of an example operations layer.

[1073] Fig.106 is a schematic view of an example network layer.

[1074] Fig.107 is a schematic view of an example data layer.

[1075] Fig.108 is a schematic view of an example data layer.

[1076] Fig.109 is a schematic view of an example intelligent data layer architecture.

[1077] Fig.110 is a schematic view of an example network layer.

[1078] Fig.111 is a schematic view of an example AI subsystem integrator system.

[1079] Fig.112 is a schematic view of an example multiplatform attention management system.

[1080] Like reference symbols in the various drawings indicate like elements. DETAILED DESCRIPTION

[1081] The present disclosure will now be described in detail by describing various illustrative, non-limiting embodiments thereof with reference to the accompanying drawings and exhibits. The disclosure may, however, be embodied in many different forms and should not be construed as being limited to the illustrative embodiments set forth herein. Rather, the embodiments are provided so that this disclosure will be thorough and will fully convey the concept of the disclosure to those skilled in the art. The claims should be consulted to ascertain the true scope of the disclosure.

[1082] Before describing in detail embodiments that are in accordance with the systems and methods disclosed herein, it should be observed that the embodiments reside primarily in combinations of method and / or system components. Accordingly, the system components andSFT-107-A-PCT methods have been represented where appropriate by conventional symbols in the drawings, showing only those specific details that are pertinent to understanding the embodiments of the systems and methods disclosed herein.

[1083] All documents mentioned herein are hereby incorporated by reference in their entirety. References to items in the singular should be understood to include items in the plural, and vice versa, unless explicitly stated otherwise or clear from the context. Grammatical conjunctions are intended to express any and all disjunctive and conjunctive combinations of conjoined clauses, sentences, words, and the like, unless otherwise stated or clear from the context. Thus, the term “or” should generally be understood to mean “and / or” and so forth, except where the context clearly indicates otherwise.

[1084] Recitation of ranges of values herein are not intended to be limiting, referring instead individually to any and all values falling within the range, unless otherwise indicated herein, and each separate value within such a range is incorporated into the specification as if it were individually recited herein. The words “about,” “approximately,” or the like, when accompanying a numerical value, are to be construed as indicating a deviation as would be appreciated by one skilled in the art to operate satisfactorily for an intended purpose. Ranges of values and / or numeric values are provided herein as examples only, and do not constitute a limitation on the scope of the described embodiments. The use of any and all examples, or exemplary language (“e.g.,” “such as,” or the like) provided herein, is intended merely to better illuminate the embodiments and does not pose a limitation on the scope of the embodiments or the claims. No language in the specification should be construed as indicating any unclaimed element as essential to the practice of the embodiments.

[1085] In the following description, it is understood that terms such as “first,” “second,” “third,” “above,” “below,” and the like, are words of convenience and are not to be construed as implying a chronological order or otherwise limiting any corresponding element unless expressly stated otherwise. The term “set” should be understood to encompass a set with a single member or a plurality of members.

[1086] Referring to Fig. 1, an architecture for a transportation system 111 is depicted, showing certain illustrative components and arrangements relating to certain embodiments described herein. The transportation system 111 may include one or more vehicles 110, which may include various mechanical, electrical, and software components and systems, such as a powertrain 113, a suspension system 117, a steering system, a braking system, a fuel system, a charging system, seats 128, a combustion engine, an electric vehicle drive train, a transmission 119, a gear set, and the like. The vehicle may have a vehicle user interface 123, which may include a set of interfaces that include a steering system, buttons, levers, touch screen interfaces, audio interfaces, and the like as described throughout this disclosure. The vehicle may have a set of sensors 125 (including cameras 127), such as for providing input to expert system / artificial intelligence features described throughout this disclosure, such as one or more neural networks (which may include hybrid neural networks 147 as described herein). Sensors 125 and / or external information may be used to inform the expert system / Artificial Intelligence (AI) system 136 and to indicate or track one or moreSFT-107-A-PCT vehicle states 144, such as vehicle operating states 345 (Fig. 3), user experience states 346 (Fig. 3), and others described herein, which also may be as inputs to or taken as outputs from a set of expert system / AI components. Routing information 143 may inform and take input from the expert system / AI system 136, including using in-vehicle navigation capabilities and external navigation capabilities, such as Global Position System (GPS), routing by triangulation (such as cell towers), peer-to-peer routing with other vehicles 121, and the like. A collaboration engine 129 may facilitate collaboration among vehicles and / or among users of vehicles, such as for managing collective experiences, managing fleets and the like. Vehicles 110 may be networked among each other in a peer-to-peer manner, such as using cognitive radio, cellular, wireless or other networking features. An AI system 136 or other expert systems may take as input a wide range of vehicle parameters 130, such as from onboard diagnostic systems, telemetry systems, and other software systems, as well as from vehicle-located sensors 125 and from external systems. In embodiments, the system may manage a set of feedback / rewards 148, incentives, or the like, such as to induce certain user behavior and / or to provide feedback to the AI system 136, such as for learning on a set of outcomes to accomplish a given task or objective. The expert system or AI system 136 may inform, use, manage, or take output from a set of algorithms 149, including a wide variety as described herein. In the example of the present disclosure depicted in Fig. 1, a data processing system 162, is connected to the hybrid neural network 147. The data processing system 162 may process data from various sources (see Fig. 7). In the example of the present disclosure depicted in Fig. 1, a system user interface 163, is connected to the hybrid neural network 147. See the disclosure, below, relating to Fig.6 for further disclosure relating to interfaces. Fig.1 shows that vehicle surroundings 164 may be part of the transportation system 111. Vehicle surroundings may include roadways, weather conditions, lighting conditions, etc. Fig. 1 shows that devices 165, for example, mobile phones and computer systems, navigation systems, etc., may be connected to various elements of the transportation system 111, and therefore may be part of the transportation system 111 of the present disclosure.

[1087] Referring to Fig. 2, provided herein are transportation systems having a hybrid neural network 247 for optimizing a powertrain 213 of a vehicle, wherein at least two parts of the hybrid neural network 247 optimize distinct parts of the powertrain 213. An artificial intelligence system may control a powertrain component 215 based on an operational model (such as a physics model, an electrodynamic model, a hydrodynamic model, a chemical model, or the like for energy conversion, as well as a mechanical model for operation of various dynamically interacting system components). For example, the AI system may control a powertrain component 215 by manipulating a powertrain operating parameter 260 to achieve a powertrain state 261. The AI system may be trained to operate a powertrain component 215, such as by training on a data set of outcomes (e.g., fuel efficiency, safety, rider satisfaction, or the like) and / or by training on a data set of operator actions (e.g., driver actions sensed by a sensor set, camera or the like or by a vehicle information system). In embodiments, a hybrid approach may be used, where one neural network optimizes one part of a powertrain (e.g., for gear shifting operations), while another neural network optimizes another part (e.g., braking, clutch engagement, or energy discharge and recharging,SFT-107-A-PCT among others). Any of the powertrain components described throughout this disclosure may be controlled by a set of control instructions that consist of output from at least one component of a hybrid neural network 247.

[1088] Fig.3 illustrates a set of states that may be provided as inputs to and / or be governed by an expert system / AI system 336, as well as used in connection with various systems and components in various embodiments described herein. States 344 may include vehicle operating states 345, including vehicle configuration states, component states, diagnostic states, performance states, location states, maintenance states, and many others, as well as user experience states 346, such as experience-specific states, emotional states 366 for users, satisfaction states 367, location states, content / entertainment states and many others.

[1089] Fig.4 illustrates a range of parameters 430 that may be taken as inputs by an expert system or AI system 136 (Fig. 1), or component thereof, as described throughout this disclosure, or that may be provided as outputs from such a system and / or one or more sensors 125 (Fig.1), cameras 127 (Fig.1), or external systems. Parameters 430 may include one or more goals 431 or objectives (such as ones that are to be optimized by an expert system / AI system, such as by iteration and / or machine learning), such as a performance goal 433, such as relating to fuel efficiency, trip time, satisfaction, financial efficiency, safety, or the like. Parameters 430 may include market feedback parameters 435, such as relating to pricing, availability, location, or the like of goods, services, fuel, electricity, advertising, content, or the like. Parameters 430 may include rider state parameters 437, such as parameters relating to comfort 439, emotional state, satisfaction, goals, type of trip, fatigue and the like. Parameters 430 may include parameters of various transportation-relevant profiles, such as traffic profiles 440 (location, direction, density and patterns in time, among many others), road profiles 441 (elevation, curvature, direction, road surface conditions and many others), user profiles, and many others. Parameters 430 may include routing parameters 442, such as current vehicle locations, destinations, waypoints, points of interest, type of trip, goal for trip, required arrival time, desired user experience, and many others. Parameters 430 may include satisfaction parameters 443, such as for riders (including drivers), fleet managers, advertisers, merchants, owners, operators, insurers, regulators and others. Parameters 430 may include operating parameters 444, including the wide variety described throughout this disclosure.

[1090] Fig. 5 illustrates a set of vehicle user interfaces 523. Vehicle user interfaces 523 may include electromechanical interfaces 568, such as steering interfaces, braking interfaces, interfaces for seats, windows, moonroof, glove box and the like. Interfaces 523 may include various software interfaces (which may have touch screen, dials, knobs, buttons, icons or other features), such as a game interface 569, a navigation interface 570, an entertainment interface 571, a vehicle settings interface 572, a search interface 573, an ecommerce interface 574, and many others. Vehicle interfaces may be used to provide inputs to, and may be governed by, one or more AI systems / expert systems such as described in embodiments throughout this disclosure.

[1091] Fig. 6 illustrates a set of interfaces among transportation system components, including interfaces within a host system (such as governing a vehicle or fleet of vehicles) and host interfaces 650 between a host system and one or more third parties and / or external systems. Interfaces includeSFT-107-A-PCT third party interfaces 655 and end user interfaces 651 for users of the host system, including the in-vehicle interfaces that may be used by riders as noted in connection with Fig.5, as well as user interfaces for others, such as fleet managers, insurers, regulators, police, advertisers, merchants, content providers, and many others. Interfaces may include merchant interfaces 652, such as by which merchants may provide advertisements, content relating to offerings, and one or more rewards, such as to induce routing or other behavior on the part of users. Interfaces may include machine interfaces 653, such as application programming interfaces (API) 654, networking interfaces, peer-to-peer interfaces, connectors, brokers, extract-transform-load (ETL) system, bridges, gateways, ports and the like. Interfaces may include one or more host interfaces by which a host may manage and / or configure one or more of the many embodiments described herein, such as configuring neural network components, setting weight for models, setting one or more goals or objectives, setting reward parameters 656, and many others. Interfaces may include expert system / AI system configuration interfaces 657, such as for selecting one or more models 658, selecting and configuring data sets 659 (such as sensor data, external data and other inputs described herein), AI selection 660 and AI configuration 661 (such as selection of neural network category, parameter weighting and the like), feedback selection 662 for an expert system / AI system, such as for learning, and supervision configuration 663, among many others.

[1092] Fig. 7 illustrates a data processing system 758, which may process data from various sources, including social media data sources 769, weather data sources 770, road profile sources 771, traffic data sources 772, media data sources 773, sensors sets 774, and many others. The data processing system may be configured to extract data, transform data to a suitable format (such as for use by an interface system, an AI system / expert system, or other systems), load it to an appropriate location, normalize data, cleanse data, deduplicate data, store data (such as to enable queries) and perform a wide range of processing tasks as described throughout this disclosure.

[1093] Fig. 8 illustrates a set of algorithms 849 that may be executed in connection with one or more of the many embodiments of transportation systems described throughout this disclosure. Algorithms 849 may take input from, provide output to, and be managed by a set of AI systems / expert systems, such as of the many types described herein. Algorithms 849 may include algorithms for providing or managing user satisfaction 874, one or more genetic algorithms 875, such as for seeking favorable states, parameters, or combinations of states / parameters in connection with optimization of one or more of the systems described herein. Algorithms 849 may include vehicle routing algorithms 876, including ones that are sensitive to various vehicle operating parameters, user experience parameters, or other states, parameters, profiles, or the like described herein, as well as to various goals or objectives. Algorithms 849 may include object detection algorithms 881. Algorithms 849 may include energy calculation algorithms 877, such as for calculating energy parameters, for optimizing fuel usage, electricity usage or the like, for optimizing refueling or recharging time, location, amount or the like. Algorithms may include prediction algorithms 878, such as for a traffic prediction algorithm 879, a transportation prediction algorithm 880, and algorithms for predicting other states or parameters of transportation systems as described throughout this disclosure.SFT-107-A-PCT

[1094] In various embodiments, transportation systems 111 as described herein may include vehicles (including fleets and other sets of vehicles), as well as various infrastructure systems. Infrastructure systems may include Internet of Things systems (such as using cameras and other sensors, such as disposed on or in roadways, on or in traffic lights, utility poles, toll booths, signs and other roadside devices and systems, on or in buildings, and the like), refueling and recharging systems (such as at service stations, charging locations and the like, and including wireless recharging systems that use wireless power transfer), and many others.

[1095] Vehicle electrical, mechanical and / or powertrain components as described herein may include a wide range of systems, including transmission, gear system, clutch system, braking system, fuel system, lubrication system, steering system, suspension system, lighting system (including emergency lighting as well as interior and exterior lights), electrical system, and various subsystems and components thereof.

[1096] Vehicle operating states and parameters may include route, purpose of trip, geolocation, orientation, vehicle range, powertrain parameters, current gear, speed / acceleration, suspension profile (including various parameters, such as for each wheel), charge state for electric and hybrid vehicles, fuel state for fueled vehicles, and many others as described throughout this disclosure.

[1097] Rider and / or user experience states and parameters as described throughout this disclosure may include emotional states, comfort states, psychological states (e.g., anxiety, nervousness, relaxation or the like), awake / asleep states, and / or states related to satisfaction, alertness, health, wellness, one or more goals or objectives, and many others. User experience parameters as described herein may further include ones related to driving, braking, curve approach, seat positioning, window state, ventilation system, climate control, temperature, humidity, sound level, entertainment content type (e.g., news, music, sports, comedy, or the like), route selection (such as for POIs, scenic views, new sites and the like), and many others.

[1098] In embodiments, a route may be ascribed various parameters of value, such as parameters of value that may be optimized to improve user experience or other factors, such as under control of an AI system / expert system. Parameters of value of a route may include speed, duration, on time arrival, length (e.g., in miles), goals (e.g., to see a Point of Interest (POI), to complete a task (e.g., complete a shopping list, complete a delivery schedule, complete a meeting, or the like), refueling or recharging parameters, game-based goals, and others. As one of many examples, a route may be attributed value, such as in a model and / or as an input or feedback to an AI system or expert system that is configured to optimize a route, for task completion. A user may, for example, indicate a goal to meet up with at least one of a set of friends during a weekend, such as by interacting with a user interface or menu that allows setting of objectives. A route may be configured (including with inputs that provide awareness of friend locations, such as by interacting with systems that include location information for other vehicles and / or awareness of social relationships, such as through social data feeds) to increase the likelihood of meeting up, such as by intersecting with predicted locations of friends (which may be predicted by a neural network or other AI system / expert system as described throughout this disclosure) and by providing in-vehicle messages (or messages to a mobile device) that indicates possible opportunities for meeting up.SFT-107-A-PCT

[1099] Market feedback factors may be used to optimize various elements of transportation systems as described throughout this disclosure, such as current and predicted pricing and / or cost (e.g., of fuel, electricity and the like, as well as of goods, services, content and the like that may be available along the route and / or in a vehicle), current and predicted capacity, supply and / or demand for one or more transportation related factors (such as fuel, electricity, charging capacity, maintenance, service, replacement parts, new or used vehicles, capacity to provide ride sharing, self-driving vehicle capacity or availability, and the like), and many others.

[1100] An interface in or on a vehicle may include a negotiation system, such as a bidding system, a price-negotiating system, a reward-negotiating system, or the like. For example, a user may negotiate for a higher reward in exchange for agreeing to re-route to a merchant location, a user may name a price the user is willing to pay for fuel (which may be provided to nearby refueling stations that may offer to meet the price), or the like. Outputs from negotiation (such as agreed prices, trips and the like) may automatically result in reconfiguration of a route, such as one governed by an AI system / expert system.

[1101] Rewards, such as provided by a merchant or a host, among others, as described herein may include one or more coupons, such as redeemable at a location, provision of higher priority (such as in collective routing of multiple vehicles), permission to use a “Fast Lane,” priority for charging or refueling capacity, among many others. Actions that can lead to rewards in a vehicle may include playing a game, downloading an app, driving to a location, taking a photograph of a location or object, visiting a website, viewing or listening to an advertisement, watching a video, and many others.

[1102] In embodiments, an AI system / expert system may use or optimize one or more parameters for a charging plan, such as for charging a battery of an electric or hybrid vehicle. Charging plan parameters may include routing (such as to charging locations), amount of charge or fuel provided, duration of time for charging, battery state, battery charging profile, time required to charge, value of charging, indicators of value, market price, bids for charging, available supply capacity (such as within a geofence or within a range of a set of vehicles), demand (such as based on detected charge / refueling state, based on requested demand, or the like), supply, and others. A neural network or other systems (optionally a hybrid system as described herein), using a model or algorithm (such as a genetic algorithm) may be used (such as by being trained over a set of trials on outcomes, and / or using a training set of human created or human supervised inputs, or the like) may provide a favorable and / or optimized charging plan for a vehicle or a set of vehicles based on the parameters. Other inputs may include priority for certain vehicles (e.g., for emergency responders or for those who have been rewarded priority in connection with various embodiments described herein).

[1103] In embodiments, a processor, as described herein, may comprise a neural processing chip, such as one employing a fabric, such as a LambdaFabric. Such a chip may have a plurality of cores, such as 256 cores, where each core is configured in a neuron-like arrangement with other cores on the same chip. Each core may comprise a micro-scale digital signal processor, and the fabric may enable the cores to readily connect to the other cores on the chip. In embodiments, the fabric maySFT-107-A-PCT connect a large number of cores (e.g., more than 500,000 cores) and / or chips, thereby facilitating use in computational environments that require, for example, large scale neural networks, massively parallel computing, and large-scale, complex conditional logic. In embodiments, a low- latency fabric is used, such as one that has latency of 400 nanoseconds, 300 nanoseconds, 200 nanoseconds, 100 nanoseconds, or less from device-to-device, rack-to-rack, or the like. The chip may be a low power chip, such as one that can be powered by energy harvesting from the environment, from an inspection signal, from an onboard antenna, or the like. In embodiments, the cores may be configured to enable application of a set of sparse matrix heterogeneous machine learning algorithms. The chip may run an object-oriented programming language, such as C++, Java, or the like. In embodiments, a chip may be programmed to run each core with a different algorithm, thereby enabling heterogeneity in algorithms, such as to enable one or more of the hybrid neural network embodiments described throughout this disclosure. A chip can thereby take multiple inputs (e.g., one per core) from multiple data sources, undertake massively parallel processing using a large set of distinct algorithms, and provide a plurality of outputs (such as one per core or per set of cores).

[1104] In embodiments, a chip may contain or enable a security fabric, such as a fabric for performing content inspection, packet inspection (such as against a black list, white list, or the like), and the like, in addition to undertaking processing tasks, such as for a neural network, hybrid AI solution, or the like.

[1105] In embodiments, the platform described herein may include, integrate with, or connect with a system for robotic process automation (RPA), whereby an artificial intelligence / machine learning system may be trained on a training set of data that consists of tracking and recording sets of interactions of humans as the humans interact with a set of interfaces, such as graphical user interfaces (e.g., via interactions with mouse, trackpad, keyboard, touch screen, joystick, remote control devices); audio system interfaces (such as by microphones, smart speakers, voice response interfaces, intelligent agent interfaces (e.g., Siri and Alexa) and the like); human-machine interfaces (such as involving robotic systems, prosthetics, cybernetic systems, exoskeleton systems, wearables (including clothing, headgear, headphones, watches, wrist bands, glasses, arm bands, torso bands, belts, rings, necklaces and other accessories); physical or mechanical interfaces (e.g., buttons, dials, toggles, knobs, touch screens, levers, handles, steering systems, wheels, and many others); optical interfaces (including ones triggered by eye tracking, facial recognition, gesture recognition, emotion recognition, and the like); sensor-enabled interfaces (such as ones involving cameras, EEG or other electrical signal sensing (such as for brain-computer interfaces), magnetic sensing, accelerometers, galvanic skin response sensors, optical sensors, IR sensors, LIDAR and other sensor sets that are capable of recognizing thoughts, gestures (facial, hand, posture, or other), utterances, and the like, and others. In addition to tracking and recording human interactions, the RPA system may also track and record a set of states, actions, events and results that occur by, within, from or about the systems and processes with which the humans are engaging. For example, the RPA system may record mouse clicks on a frame of video that appears within a process by which a human review the video, such as where the human highlights pointsSFT-107-A-PCT of interest within the video, tags objects in the video, captures parameters (such as sizes, dimensions, or the like), or otherwise operates on the video within a graphical user interface. The RPA system may also record system or process states and events, such as recording what elements were the subject of interaction, what the state of a system was before, during and after interaction, and what outputs were provided by the system or what results were achieved. Through a large training set of observation of human interactions and system states, events, and outcomes, the RPA system may learn to interact with the system in a fashion that mimics that of the human. Learning may be reinforced by training and supervision, such as by having a human correct the RPA system as it attempts in a set of trials to undertake the action that the human would have undertaken (e.g., tagging the right object, labeling an item correctly, selecting the correct button to trigger a next step in a process, or the like), such that over a set of trials the RPA system becomes increasingly effective at replicating the action the human would have taken. Learning may include deep learning, such as by reinforcing learning based on outcomes, such as successful outcomes (such as based on successful process completion, financial yield, and many other outcome measures described throughout this disclosure). In embodiments, an RPA system may be seeded during a learning phase with a set of expert human interactions, such that the RPA system begins to be able to replicate expert interaction with a system. For example, an expert driver's interactions with a robotic system, such as a remote-controlled vehicle or a UAV, may be recorded along with information about the vehicles state (e.g., the surrounding environment, navigation parameters, and purpose), such that the RPA system may learn to drive the vehicle in a way that reflects the same choices as an expert driver. After being taught to replicate the skills or expertise of an expert human, the RPA system may be transitioned to a deep learning mode, where the system further improves based on a set of outcomes, such as by being configured to attempt some level of variation in approach (e.g., trying different navigation paths to optimize time of arrival, or trying different approaches to deceleration and acceleration in curves) and tracking outcomes (with feedback), such that the RPA system can learn, by variation / experimentation (which may be randomized, rule- based, or the like, such as using genetic programming techniques, random-walk techniques, random forest techniques, and others) and selection, to exceed the expertise of the human expert. Thus, the RPA system learns from a human expert, acquires expertise in interacting with a system or process, facilitates automation of the process (such as by taking over some of the more repetitive tasks, including ones that require consistent execution of acquired skills), and provides a very effective seed for artificial intelligence, such as by providing a seed model or system that can be improved by machine learning with feedback on outcomes of a system or process.

[1106] RPA systems may have particular value in situations where human expertise or knowledge is acquired with training and experience, as well as in situations where the human brain and sensory systems are particularly adapted and evolved to solve problems that are computationally difficult or highly complex. Thus, in embodiments, RPA systems may be used to learn to undertake, among other things: visual pattern recognition tasks with respect to the various systems, processes, workflows and environments described herein (such as recognizing the meaning of dynamic interactions of objects or entities within a video stream (e.g., to understand what is taking place asSFT-107-A-PCT humans and objects interact in a video); recognition of the significance of visual patterns (e.g., recognizing objects, structures, defects and conditions in a photograph or radiography image); tagging of relevant objects within a visual pattern (e.g., tagging or labeling objects by type, category, or specific identity (such as person recognition); indication of metrics in a visual pattern (such as dimensions of objects indicated by clicking on dimensions in an x-ray or the like); labeling activities in a visual pattern by category (e.g., what work process is being done); recognizing a pattern that is displayed as a signal (e.g., a wave or similar pattern in a frequency domain, time domain, or other signal processing representation); anticipate a n future state based on a current state (e.g., anticipating motion of a flying or rolling object, anticipating a next action by a human in a process, anticipating a next step by a machine, anticipating a reaction by a person to an event, and many others); recognize and predicting emotional states and reactions (such as based on facial expression, posture, body language or the like); apply a heuristic to achieve a favorable state without deterministic calculation (e.g., selecting a favorable strategy in sport or game, selecting a business strategy, selecting a negotiating strategy, setting a price for a product, developing a message to promote a product or idea, generating creative content, recognizing a favorable style or fashion, and many others); and many others. In embodiments, an RPA system may automate workflows that involve visual inspection of people, systems, and objects (including internal components), workflows that involve performing software tasks, such as involving sequential interactions with a series of screens in a software interface, workflows that involve remote control of robots and other systems and devices, workflows that involve content creation (such as selecting, editing and sequencing content), workflows that involve financial decision-making and negotiation (such as setting prices and other terms and conditions of financial and other transactions), workflows that involve decision-making (such as selecting an optimal configuration for a system or sub-system, selecting an optimal path or sequence of actions in a workflow, process or other activity that involves dynamic decision-making), and many others.

[1107] In embodiments, an RPA system may use a set of IoT devices and systems (such as cameras and sensors), to track and record human actions and interactions with respect to various interfaces and systems in an environment. The RPA system may also use data from onboard sensors, telemetry, and event recording systems, such as telemetry systems on vehicles and event logs on computers). The RPA system may thus generate and / or receive a large data set (optionally distributed) for an environment (such as any of the environments described throughout this disclosure) including data recording the various entities (human and non-human), systems, processes, applications (e.g., software applications used to enable workflows), states, events, and outcomes, which can be used to train the RPA system (or a set of RPA systems dedicated to automating various processes and workflows) to accomplish processes and workflows in a way that reflects and mimics accumulated human expertise, and that eventually improves on the results of that human expertise by further machine learning.

[1108] Referring to Fig. 9, in embodiments provided herein are systems for transportation 911 having an artificial intelligence system 936 that uses at least one genetic algorithm 975 to explore a set of possible vehicle operating states 945 to determine at least one optimized operating state.SFT-107-A-PCT In embodiments, the genetic algorithm 975 takes inputs relating to at least one vehicle performance parameter 982 and at least one rider state 937.

[1109] An aspect provided herein includes a system for transportation 911, comprising: a vehicle 910 having a vehicle operating state 945; an artificial intelligence system 936 to execute a genetic algorithm 975 to generate mutations from an initial vehicle operating state to determine at least one optimized vehicle operating state. In embodiments, the vehicle operating state 945 includes a set of vehicle parameter values 984. In embodiments, the genetic algorithm 975 is to: vary the set of vehicle parameter values 984 for a set of corresponding time periods such that the vehicle 910 operates according to the set of vehicle parameter values 984 during the corresponding time periods; evaluate the vehicle operating state 945 for each of the corresponding time periods according to a set of measures 983 to generate evaluations; and select, for future operation of the vehicle 910, an optimized set of vehicle parameter values based on the evaluations.

[1110] In embodiments, the vehicle operating state 945 includes the rider state 937 of a rider of the vehicle. In embodiments, the at least one optimized vehicle operating state includes an optimized state of the rider. In embodiments, the genetic algorithm 975 is to optimize the state of the rider. In embodiments, the evaluating according to the set of measures 983 is to determine the state of the rider corresponding to the vehicle parameter values 984.

[1111] In embodiments, the vehicle operating state 945 includes a state of the rider of the vehicle. In embodiments, the set of vehicle parameter values 984 includes a set of vehicle performance control values. In embodiments, the at least one optimized vehicle operating state includes an optimized state of performance of the vehicle. In embodiments, the genetic algorithm 975 is to optimize the state of the rider and the state of performance of the vehicle. In embodiments, the evaluating according to the set of measures 983 is to determine the state of the rider and the state of performance of the vehicle corresponding to the vehicle performance control values.

[1112] In embodiments, the set of vehicle parameter values 984 includes a set of vehicle performance control values. In embodiments, the at least one optimized vehicle operating state includes an optimized state of performance of the vehicle. In embodiments, the genetic algorithm 975 is to optimize the state of performance of the vehicle. In embodiments, the evaluating according to the set of measures 983 is to determine the state of performance of the vehicle corresponding to the vehicle performance control values.

[1113] In embodiments, the set of vehicle parameter values 984 includes a rider-occupied parameter value. In embodiments, the rider-occupied parameter value affirms a presence of a rider in the vehicle 910. In embodiments, the vehicle operating state 945 includes the rider state 937 of a rider of the vehicle. In embodiments, the at least one optimized vehicle operating state includes an optimized state of the rider. In embodiments, the genetic algorithm 975 is to optimize the state of the rider. In embodiments, the evaluating according to the set of measures 983 is to determine the state of the rider corresponding to the vehicle parameter values 984. In embodiments, the state of the rider includes a rider satisfaction parameter. In embodiments, the state of the rider includes an input representative of the rider. In embodiments, the input representative of the rider is selected from the group consisting of: a rider state parameter, a rider comfort parameter, a rider emotionalSFT-107-A-PCT state parameter, a rider satisfaction parameter, a rider goals parameter, a classification of the trip, and combinations thereof.

[1114] In embodiments, the set of vehicle parameter values 984 includes a set of vehicle performance control values. In embodiments, the at least one optimized vehicle operating state includes an optimized state of performance of the vehicle. In embodiments, the genetic algorithm 975 is to optimize the state of the rider and the state of performance of the vehicle. In embodiments, the evaluating according to the set of measures 983 is to determine the state of the rider and the state of performance of the vehicle corresponding to the vehicle performance control values. In embodiments, the set of vehicle parameter values 984 includes a set of vehicle performance control values. In embodiments, the at least one optimized vehicle operating state includes an optimized state of performance of the vehicle. In embodiments, the genetic algorithm 975 is to optimize the state of performance of the vehicle. In embodiments, the evaluating according to the set of measures 983 is to determine the state of performance of the vehicle corresponding to the vehicle performance control values.

[1115] In embodiments, the set of vehicle performance control values are selected from the group consisting of: a fuel efficiency; a trip duration; a vehicle wear; a vehicle make; a vehicle model; a vehicle energy consumption profiles; a fuel capacity; a real-time fuel level; a charge capacity; a recharging capability; a regenerative braking state; and combinations thereof. In embodiments, at least a portion of the set of vehicle performance control values is sourced from at least one of an on-board diagnostic system, a telemetry system, a software system, a vehicle-located sensor, and a system external to the vehicle 910. In embodiments, the set of measures 983 relates to a set of vehicle operating criteria. In embodiments, the set of measures 983 relates to a set of rider satisfaction criteria. In embodiments, the set of measures 983 relates to a combination of vehicle operating criteria and rider satisfaction criteria. In embodiments, each evaluation uses feedback indicative of an effect on at least one of a state of performance of the vehicle and a state of the rider.

[1116] An aspect provided herein includes a system for transportation 911, comprising: an artificial intelligence system 936 to process inputs representative of a state of a vehicle and inputs representative of a rider state 937 of a rider occupying the vehicle during the state of the vehicle with the genetic algorithm 975 to optimize a set of vehicle parameters that affects the state of the vehicle or the rider state 937. In embodiments, the genetic algorithm 975 is to perform a series of evaluations using variations of the inputs. In embodiments, each evaluation in the series of evaluations uses feedback indicative of an effect on at least one of a vehicle operating state 945 and the rider state 937. In embodiments, the inputs representative of the rider state 937 indicate that the rider is absent from the vehicle 910. In embodiments, the state of the vehicle includes the vehicle operating state 945. In embodiments, a vehicle parameter in the set of vehicle parameters includes a vehicle performance parameter 982. In embodiments, the genetic algorithm 975 is to optimize the set of vehicle parameters for the state of the rider.

[1117] In embodiments, optimizing the set of vehicle parameters is responsive to an identifying, by the genetic algorithm 975, of at least one vehicle parameter that produces a favorable rider state.SFT-107-A-PCT In embodiments, the genetic algorithm 975 is to optimize the set of vehicle parameters for vehicle performance. In embodiments, the genetic algorithm 975 can optimize the set of vehicle parameters for the state of the rider and can optimize the set of vehicle parameters for vehicle performance. In embodiments, optimizing the set of vehicle parameters is responsive to the genetic algorithm 975 identifying at least one of a favorable vehicle operating state, and favorable vehicle performance that maintains the rider state 937. In embodiments, the artificial intelligence system 936 further includes a neural network selected from a plurality of different neural networks. In embodiments, the selection of the neural network involves the genetic algorithm 975. In embodiments, the selection of the neural network is based on a structured competition among the plurality of different neural networks. In embodiments, the genetic algorithm 975 facilitates training a neural network to process interactions among a plurality of vehicle operating systems and riders to produce the optimized set of vehicle parameters.

[1118] In embodiments, a set of inputs relating to at least one vehicle parameter are provided by at least one of an on-board diagnostic system, a telemetry system, a vehicle-located sensor, and a system external to the vehicle. In embodiments, the inputs representative of the rider state 937 comprise at least one of comfort, emotional state, satisfaction, goals, classification of trip, or fatigue. In embodiments, the inputs representative of the rider state 937 reflect a satisfaction parameter of at least one of a driver, a fleet manager, an advertiser, a merchant, an owner, an operator, an insurer, and a regulator. In embodiments, the inputs representative of the rider state 937 comprise inputs relating to a user that, when processed with a cognitive system yield the rider state 937.

[1119] Referring to Fig.10, in embodiments provided herein are systems for transportation 1011 having a hybrid neural network 1047 for optimizing the operating state of a continuously variable powertrain 1013 of a vehicle 1010. In embodiments, at least one part of the hybrid neural network 1047 operates to classify a state of the vehicle 1010 and another part of the hybrid neural network 1047 operates to optimize at least one operating parameter 1087 of the transmission 1019. In embodiments, the vehicle 1010 may be a self-driving vehicle. In an example, the first portion 1085 of the hybrid neural network may classify the vehicle 1010 as operating in a high-traffic state (such as by use of LIDAR, RADAR, or the like that indicates the presence of other vehicles, or by taking input from a traffic monitoring system, or by detecting the presence of a high density of mobile devices, or the like) and a bad weather state (such as by taking inputs indicating wet roads (such as using vision-based systems), precipitation (such as determined by radar), presence of ice (such as by temperature sensing, vision-based sensing, or the like), hail (such as by impact detection, sound-sensing, or the like), lightning (such as by vision-based systems, sound-based systems, or the like), or the like. Once classified, another neural network 1086 (optionally of another type) may optimize the vehicle operating parameter based on the classified state, such as by putting the vehicle 1010 into a safe-driving mode (e.g., by providing forward-sensing alerts at greater distances and / lower speeds than in good weather, by providing automated braking earlier and more aggressively than in good weather, and the like).SFT-107-A-PCT

[1120] An aspect provided herein includes a system for transportation 1011, comprising: a hybrid neural network 1047 for optimizing an operating state of a continuously variable powertrain 1013 of a vehicle 1010. In embodiments, a portion 1085 of the hybrid neural network 1047 is to operate to classify a state 1044 of the vehicle 1010 thereby generating a classified state of the vehicle, and another neural network 1086 portion of the hybrid neural network 1047 is to operate to optimize at least one operating parameter 1060 of a transmission 1019 portion of the continuously variable powertrain 1013.

[1121] In embodiments, the system for transportation 1011 further comprises: an artificial intelligence system 1036 operative on at least one processor 1088, the artificial intelligence system 1036 to operate the portion 1085 of the hybrid neural network 1047 to operate to classify the state of the vehicle and the artificial intelligence system 1036 to operate the other neural network 1086 portion of the hybrid neural network 1047 to optimize the at least one operating parameter 1087 of the transmission 1019 portion of the continuously variable powertrain 1013 based on the classified state of the vehicle. In embodiments, the vehicle 1010 comprises a system for automating at least one control parameter of the vehicle. In embodiments, the vehicle 1010 is at least a semi- autonomous vehicle. In embodiments, the vehicle 1010 is to be automatically routed. In embodiments, the vehicle 1010 is a self-driving vehicle. In embodiments, the classified state of the vehicle is: a vehicle maintenance state; a vehicle health state; a vehicle operating state; a vehicle energy utilization state; a vehicle charging state; a vehicle satisfaction state; a vehicle component state; a vehicle sub-system state; a vehicle powertrain system state; a vehicle braking system state; a vehicle clutch system state; a vehicle lubrication system state; a vehicle transportation infrastructure system state; or a vehicle rider state. In embodiments, at least a portion of the hybrid neural network 1047 is a convolutional neural network.

[1122] Fig. 11 illustrates a method 1100 for optimizing operation of a continuously variable vehicle powertrain of a vehicle in accordance with embodiments of the systems and methods disclosed herein. At 1102, the method includes executing a first network of a hybrid neural network on at least one processor, the first network classifying a plurality of operational states of the vehicle. In embodiments, at least a portion of the operational states is based on a state of the continuously variable powertrain of the vehicle. At 1104, the method includes executing a second network of the hybrid neural network on the at least one processor, the second network processing inputs that are descriptive of the vehicle and of at least one detected condition associated with an occupant of the vehicle for at least one of the plurality of classified operational states of the vehicle. In embodiments, the processing of the inputs by the second network can cause optimization of at least one operating parameter of the continuously variable powertrain of the vehicle for a plurality of the operational states of the vehicle.

[1123] Referring to Fig. 10 and Fig. 11 together, in embodiments, the vehicle comprises an artificial intelligence system 1036, the method further comprising automating at least one control parameter of the vehicle by the artificial intelligence system 1036. In embodiments, the vehicle 1010 is at least a semi-autonomous vehicle. In embodiments, the vehicle 1010 is to be automatically routed. In embodiments, the vehicle 1010 is a self-driving vehicle. In embodiments,SFT-107-A-PCT the method further comprises optimizing, by the artificial intelligence system 1036, an operating state of the continuously variable powertrain 1013 of the vehicle based on the optimized at least one operating parameter 1060 of the continuously variable powertrain 1013 by adjusting at least one other operating parameter 1087 of a transmission 1019 portion of the continuously variable powertrain 1013.

[1124] In embodiments, the method further comprises optimizing, by the artificial intelligence system 1036, the operating state of the continuously variable powertrain 1013 by processing social data from a plurality of social data sources. In embodiments, the method further comprises optimizing, by the artificial intelligence system 1036, the operating state of the continuously variable powertrain 1013 by processing data sourced from a stream of data from unstructured data sources. In embodiments, the method further comprises optimizing, by the artificial intelligence system 1036, the operating state of the continuously variable powertrain 1013 by processing data sourced from wearable devices. In embodiments, the method further comprises optimizing, by the artificial intelligence system 1036, the operating state of the continuously variable powertrain 1013 by processing data sourced from in-vehicle sensors. In embodiments, the method further comprises optimizing, by the artificial intelligence system 1036, the operating state of the continuously variable powertrain 1013 by processing data sourced from a rider helmet.

[1125] In embodiments, the method further comprises optimizing, by the artificial intelligence system 1036, the operating state of the continuously variable powertrain 1013 by processing data sourced from rider headgear. In embodiments, the method further comprises optimizing, by the artificial intelligence system 1036, the operating state of the continuously variable powertrain 1013 by processing data sourced from a rider voice system. In embodiments, the method further comprises operating, by the artificial intelligence system 1036, a third network of the hybrid neural network 1047 to predict a state of the vehicle based at least in part on at least one of the classified plurality of operational states of the vehicle and at least one operating parameter of the transmission 1019. In embodiments, the first network of the hybrid neural network 1047 comprises a structure- adaptive network to adapt a structure of the first network responsive to a result of operating the first network of the hybrid neural network 1047. In embodiments, the first network of the hybrid neural network 1047 is to process a plurality of social data from social data sources to classify the plurality of operational states of the vehicle.

[1126] In embodiments, at least a portion of the hybrid neural network 1047 is a convolutional neural network. In embodiments, at least one of the classified plurality of operational states of the vehicle is: a vehicle maintenance state; or a vehicle health state. In embodiments, at least one of the classified states of the vehicle is: a vehicle operating state; a vehicle energy utilization state; a vehicle charging state; a vehicle satisfaction state; a vehicle component state; a vehicle sub-system state; a vehicle powertrain system state; a vehicle braking system state; a vehicle clutch system state; a vehicle lubrication system state; or a vehicle transportation infrastructure system state. In embodiments, the at least one of classified states of the vehicle is a vehicle driver state. In embodiments, the at least one of classified states of the vehicle is a vehicle rider state.SFT-107-A-PCT

[1127] Referring to Fig. 12, in embodiments, provided herein are transportation systems 1211 having a cognitive system for routing at least one vehicle 1210 within a set of vehicles 1294 based on a routing parameter determined by facilitating negotiation among a designated set of vehicles. In embodiments, negotiation accepts inputs relating to the value attributed by at least one rider to at least one parameter 1230 of a route 1295. A user 1290 may express value by a user interface that rates one or more parameters (e.g., any of the parameters noted throughout), by behavior (e.g., undertaking behavior that reflects or indicates value ascribed to arriving on time, following a given route 1295, or the like), or by providing or offering value (e.g., offering currency, tokens, points, cryptocurrency, rewards, or the like). For example, a user 1290 may negotiate for a preferred route by offering tokens to the system that are awarded if the user 1290 arrives at a designated time, while others may offer to accept tokens in exchange for taking alternative routes (and thereby reducing congestion). Thus, an artificial intelligence system may optimize a combination of offers to provide rewards or to undertake behavior in response to rewards, such that the reward system optimizes a set of outcomes. Negotiation may include explicit negotiation, such as where a driver offers to reward drivers ahead of the driver on the road in exchange for their leaving the route temporarily as the driver passes.

[1128] An aspect provided herein includes a transportation system 1211, comprising: a cognitive system for routing at least one vehicle 1210 within a set of vehicles 1294 based on a routing parameter determined by facilitating a negotiation among a designated set of vehicles, wherein the negotiation accepts inputs relating to a value attributed by at least one user 1290 to at least one parameter of a route 1295.

[1129] Fig.13 illustrates a method 1300 of negotiation-based vehicle routing in accordance with embodiments of the systems and methods disclosed herein. At 1302, the method includes facilitating a negotiation of a route-adjustment value for a plurality of parameters used by a vehicle routing system to route at least one vehicle in a set of vehicles. At 1304, the method includes determining a parameter in the plurality of parameters for optimizing at least one outcome based on the negotiation.

[1130] Referring to Fig.12 and Fig.13, in embodiments, a user 1290 is an administrator for a set of roadways to be used by the at least one vehicle 1210 in the set of vehicles 1294. In embodiments, a user 1290 is an administrator for a fleet of vehicles including the set of vehicles 1294. In embodiments, the method further comprises offering a set of offered user-indicated values for the plurality of parameters 1230 to users 1290 with respect to the set of vehicles 1294. In embodiments, the route-adjustment value 1224 is based at least in part on the set of offered user-indicated values 1297. In embodiments, the route-adjustment value 1224 is further based on at least one user response to the offering. In embodiments, the route-adjustment value 1224 is based at least in part on the set of offered user-indicated values 1297 and at least one response thereto by at least one user of the set of vehicles 1294. In embodiments, the determined parameter facilitates adjusting a route 1295 of at least one of the vehicles 1210 in the set of vehicles 1294. In embodiments, adjusting the route includes prioritizing the determined parameter for use by the vehicle routing system.SFT-107-A-PCT

[1131] In embodiments, the facilitating negotiation includes facilitating negotiation of a price of a service. In embodiments, the facilitating negotiation includes facilitating negotiation of a price of fuel. In embodiments, the facilitating negotiation includes facilitating negotiation of a price of recharging. In embodiments, the facilitating negotiation includes facilitating negotiation of a reward for taking a routing action.

[1132] An aspect provided herein includes a transportation system 1211 for negotiation-based vehicle routing comprising: a route adjustment negotiation system 1236 through which users 1290 in a set of users 1291 negotiate a route-adjustment value 1224 for at least one of a plurality of parameters 1230 used by a vehicle routing system 1292 to route at least one vehicle 1210 in a set of vehicles 1294; and a user route optimizing circuit 1245 to optimize a portion of a route 1295 of at least one user 1290 of the set of vehicles 1294 based on the route-adjustment value 1224 for the at least one of the plurality of parameters 1230. In embodiments, the route-adjustment value 1224 is based at least in part on user-indicated values 1297 and at least one negotiation response thereto by at least one user of the set of vehicles 1294. In embodiments, the transportation system 1211 further comprises a vehicle-based route negotiation interface 1296 through which user-indicated values 1297 for the plurality of parameters 1230 used by the vehicle routing system are captured. In embodiments, a user 1290 is a rider of the at least one vehicle 1210. In embodiments, a user 1290 is an administrator for a set of roadways to be used by the at least one vehicle 1210 in the set of vehicles 1294.

[1133] In embodiments, a user 1290 is an administrator for a fleet of vehicles including the set of vehicles 1294. In embodiments, the at least one of the plurality of parameters 1230 facilitates adjusting a route 1295 of the at least one vehicle 1210. In embodiments, adjusting the route 1295 includes prioritizing a determined parameter for use by the vehicle routing system. In embodiments, at least one of the user-indicated values 1297 is attributed to at least one of the plurality of parameters 1230 through an interface to facilitate expression of rating one or more route parameters. In embodiments, the vehicle-based route negotiation interface facilitates expression of rating one or more route parameters. In embodiments, the user-indicated values 1297 are derived from a behavior of the user 1290. In embodiments, the vehicle-based route negotiation interface facilitates converting user behavior to the user-indicated values 1297. In embodiments, the user behavior reflects value ascribed to the at least one parameter used by the vehicle routing system to influence a route 1295 of at least one vehicle 1210 in the set of vehicles 1294. In embodiments, the user-indicated value indicated by at least one user 1290 correlates to an item of value provided by the user 1290. In embodiments, the item of value is provided by the user 1290 through an offering of the item of value in exchange for a result of routing based on the at least one parameter. In embodiments, the negotiating of the route-adjustment value 1224 includes offering an item of value to the users of the set of vehicles 1294.

[1134] Referring to Fig. 14, in embodiments provided herein are transportation systems 1411 having a cognitive system for routing at least one vehicle 1410 within a set of vehicles 1494 based on a routing parameter determined by facilitating coordination among a designated set of vehicles 1498. In embodiments, the coordination is accomplished by taking at least one input from at leastSFT-107-A-PCT one game-based interface 1499 for riders of the vehicles. A game-based interface 1499 may include rewards for undertaking game-like actions (i.e., game activities 14101) that provide an ancillary benefit. For example, a rider in a vehicle 1410 may be rewarded for routing the vehicle 1410 to a point of interest off a highway (such as to collect a coin, to capture an item, or the like), while the rider’s departure clears space for other vehicles that are seeking to achieve other objectives, such as on-time arrival. For example, a game like Pokemon Go™ may be configured to indicate the presence of rare Pokemon™ creatures in locations that attract traffic away from congested locations. Others may provide rewards (e.g., currency, cryptocurrency or the like) that may be pooled to attract users 1490 away from congested roads.

[1135] An aspect provided herein includes a transportation system 1411, comprising: a cognitive system for routing at least one vehicle 1410 within a set of vehicles 1494 based on a set of routing parameters 1430 determined by facilitating coordination among a designated set of vehicles 1498, wherein the coordination is accomplished by taking at least one input from at least one game-based interface 1499 for a user 1490 of users 1491 of a vehicle 1410 in the designated set of vehicles 1498.

[1136] In embodiments, the system for transportation further comprises: a vehicle routing system 1492 to route the at least one vehicle 1410 based on the set of routing parameters 1430; and the game-based interface 1499 through which the user 1490 indicates a routing preference 14100 for at least one vehicle 1410 within the set of vehicles 1494 to undertake a game activity 14101 offered in the game-based interface 1499; wherein the game-based interface 1499 is to induce the user 1490 to undertake a set of favorable routing choices based on the set of routing parameters 1430. As used herein, “to route” means to select a route 1495.

[1137] In embodiments, the vehicle routing system 1492 accounts for the routing preference 14100 of the user 1490 when routing the at least one vehicle 1410 within the set of vehicles 1494. In embodiments, the game-based interface 1499 is disposed for in-vehicle use as indicated in Fig. 14 by the line extending from the Game-Based Interface into the box for Vehicle 1. In embodiments, the user 1490 is a rider of the at least one vehicle 1410. In embodiments, the user 1490 is an administrator for a set of roadways to be used by the at least one vehicle 1410 in the set of vehicles 1494. In embodiments, the user 1490 is an administrator for a fleet of vehicles including the set of vehicles 1494. In embodiments, the set of routing parameters 1430 includes at least one of traffic congestion, desired arrival times, preferred routes, fuel efficiency, pollution reduction, accident avoidance, avoiding bad weather, avoiding bad road conditions, reduced fuel consumption, reduced carbon footprint, reduced noise in a region, avoiding high-crime regions, collective satisfaction, maximum speed limit, avoidance of toll roads, avoidance of city roads, avoidance of undivided highways, avoidance of left turns, avoidance of driver-operated vehicles. In embodiments, the game activity 14101 offered in the game-based interface 1499 includes contests. In embodiments, the game activity 14101 offered in the game-based interface 1499 includes entertainment games.

[1138] In embodiments, the game activity 14101 offered in the game-based interface 1499 includes competitive games. In embodiments, the game activity 14101 offered in the game-basedSFT-107-A-PCT interface 1499 includes strategy games. In embodiments, the game activity 14101 offered in the game-based interface 1499 includes scavenger hunts. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system 1492 achieves a fuel efficiency objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system 1492 achieves a reduced traffic objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system 1492 achieves a reduced pollution objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system 1492 achieves a reduced carbon footprint objective.

[1139] In embodiments, the set of favorable routing choices is configured so that the vehicle routing system 1492 achieves a reduced noise in neighborhoods objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system 1492 achieves a collective satisfaction objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system 1492 achieves an avoiding accident scenes objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system 1492 achieves an avoiding high-crime areas objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system 1492 achieves a reduced traffic congestion objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system 1492 achieves a bad weather avoidance objective.

[1140] In embodiments, the set of favorable routing choices is configured so that the vehicle routing system 1492 achieves a maximum travel time objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system 1492 achieves a maximum speed limit objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system 1492 achieves an avoidance of toll road’s objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system 1492 achieves an avoidance of city road’s objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system 1492 achieves an avoidance of undivided highway’s objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system 1492 achieves an avoidance of left turns objective. In embodiments, the set of favorable routing choices is configured so that the vehicle routing system 1492 achieves an avoidance of driver-operated vehicles objective.

[1141] Fig.15 illustrates a method 1500 of game-based coordinated vehicle routing in accordance with embodiments of the systems and methods disclosed herein. At 1502, the method includes presenting, in a game-based interface, a vehicle route preference-affecting game activity. At 1504, the method includes receiving, through the game-based interface, a user response to the presented game activity. At 1506, the method includes adjusting a routing preference for the user responsive to the received response. At 1508, the method includes determining at least one vehicle-routing parameter used to route vehicles to reflect the adjusted routing preference for routing vehicles. At 1509, the method includes routing, with a vehicle routing system, vehicles in a set of vehicles responsive to the at least one determined vehicle routing parameter adjusted to reflect the adjustedSFT-107-A-PCT routing preference, wherein routing of the vehicles includes adjusting the determined routing parameter for at least a plurality of vehicles in the set of vehicles.

[1142] Referring to Fig.14 and Fig.15, in embodiments, the method further comprises indicating, by the game-based interface 1499, a reward value 14102 for accepting the game activity 14101. In embodiments, the game-based interface 1499 further comprises a routing preference negotiation system 1436 for a rider to negotiate the reward value 14102 for accepting the game activity 14101. In embodiments, the reward value 14102 is a result of pooling contributions of value from riders in the set of vehicles. In embodiments, at least one routing parameter 1430 used by the vehicle routing system 1492 to route the vehicles 1410 in the set of vehicles 1494 is associated with the game activity 14101 and a user acceptance of the game activity 14101 adjusts (e.g., by the routing adjustment value 1424) the at least one routing parameter 1430 to reflect the routing preference. In embodiments, the user response to the presented game activity 14101 is derived from a user interaction with the game-based interface 1499. In embodiments, the at least one routing parameter used by the vehicle routing system 1492 to route the vehicles 1410 in the set of vehicles 1494 includes at least one of: traffic congestion, desired arrival times, preferred routes, fuel efficiency, pollution reduction, accident avoidance, avoiding bad weather, avoiding bad road conditions, reduced fuel consumption, reduced carbon footprint, reduced noise in a region, avoiding high- crime regions, collective satisfaction, maximum speed limit, avoidance of toll roads, avoidance of city roads, avoidance of undivided highways, avoidance of left turns, and avoidance of driver- operated vehicles.

[1143] In embodiments, the game activity 14101 presented in the game-based interface 1499 includes contests. In embodiments, the game activity 14101 presented in the game-based interface 1499 includes entertainment games. In embodiments, the game activity 14101 presented in the game-based interface 1499 includes competitive games. In embodiments, the game activity 14101 presented in the game-based interface 1499 includes strategy games. In embodiments, the game activity 14101 presented in the game-based interface 1499 includes scavenger hunts. In embodiments, the routing responsive to the at least one determined vehicle routing parameter 14103 achieves a fuel efficiency objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter 14103 achieves a reduced traffic objective.

[1144] In embodiments, the routing responsive to the at least one determined vehicle routing parameter 14103 achieves a reduced pollution objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter 14103 achieves a reduced carbon footprint objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter 14103 achieves a reduced noise in neighborhoods objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter 14103 achieves a collective satisfaction objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter 14103 achieves an avoiding accident scene’s objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter 14103 achieves an avoiding high-crime areas objective. In embodiments, the routing responsive toSFT-107-A-PCT the at least one determined vehicle routing parameter 14103 achieves a reduced traffic congestion objective.

[1145] In embodiments, the routing responsive to the at least one determined vehicle routing parameter 14103 achieves a bad weather avoidance objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter 14103 achieves a maximum travel time objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter 14103 achieves a maximum speed limit objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter 14103 achieves an avoidance of toll road’s objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter 14103 achieves an avoidance of city road’s objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter 14103 achieves an avoidance of undivided highway’s objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter 14103 achieves an avoidance of left turns objective. In embodiments, the routing responsive to the at least one determined vehicle routing parameter 14103 achieves an avoidance of driver-operated vehicles objective.

[1146] Referring to Fig. 16, in embodiments, provided herein are transportation systems 1611 having a cognitive system for routing at least one vehicle, wherein the routing is determined at least in part by processing at least one input from a rider interface wherein a rider can obtain a reward 16102 by undertaking an action while in the vehicle. In embodiments, the rider interface may display a set of available rewards for undertaking various actions, such that the rider may select (such as by interacting with a touch screen or audio interface), a set of rewards to pursue, such as by allowing a navigation system of the vehicle (or of a ride-share system of which the user 1690 has at least partial control) or a routing system 1692 of a self-driving vehicle to use the actions that result in rewards to govern routing. For example, selection of a reward for attending a site may result in sending a signal to a navigation or routing system 1692 to set an intermediate destination at the site. As another example, indicating a willingness to watch a piece of content may cause a routing system 1692 to select a route that permits adequate time to view or hear the content.

[1147] An aspect provided herein includes a transportation system 1611, comprising: a cognitive system for routing at least one vehicle 1610, wherein the routing is based, at least in part, by processing at least one input from a rider interface, wherein a reward 16102 is made available to a rider in response to the rider undertaking a predetermined action while in the at least one vehicle 1610.

[1148] An aspect provided herein includes a transportation system 1611 for reward-based coordinated vehicle routing comprising: a reward-based interface 1696 to offer a reward 16102 and through which a user 1690 of users 1691 related to a set of vehicles 1694 indicates a routing preference of the user 1690 related to the reward 16102 by responding to the reward 16102 offered in the reward-based interface 1696; a reward offer response processing circuit 16105 to determine at least one user action resulting from the user response to the reward 16102 and to determine a corresponding effect 16106 on at least one routing parameter 1630; and a vehicle routing systemSFT-107-A-PCT 1692 to use the routing preference 16100 of the user 1690 and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles 1694.

[1149] In embodiments, the user 1690 is a rider of at least one vehicle 1610 in the set of vehicles 1694. In embodiments, the user 1690 is an administrator for a set of roadways to be used by at least one vehicle 1610 in the set of vehicles 1694. In embodiments, the user 1690 is an administrator for a fleet of vehicles including the set of vehicles 1694. In embodiments, the reward-based interface 1696 is disposed for in-vehicle use. In embodiments, the at least one routing parameter 1630 includes at least one of: traffic congestion, desired arrival times, preferred routes, fuel efficiency, pollution reduction, accident avoidance, avoiding bad weather, avoiding bad road conditions, reduced fuel consumption, reduced carbon footprint, reduced noise in a region, avoiding high- crime regions, collective satisfaction, maximum speed limit, avoidance of toll roads, avoidance of city roads, avoidance of undivided highways, avoidance of left turns, and avoidance of driver- operated vehicles. In embodiments, the vehicle routing system 1692 is to use the routing preference of the user 1690 and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve a fuel efficiency objective. In embodiments, the vehicle routing system 1692 is to use the routing preference of the user 1690 and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve a reduced traffic objective. In embodiments, the vehicle routing system 1692 is to use the routing preference of the user 1690 and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve` a reduced pollution objective. In embodiments, the vehicle routing system 1692 is to use the routing preference of the user 1690 and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve a reduced carbon footprint objective.

[1150] In embodiments, the vehicle routing system 1692 is to use the routing preference of the user 1690 and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve a reduced noise in neighborhoods objective. In embodiments, the vehicle routing system 1692 is to use the routing preference of the user 1690 and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve a collective satisfaction objective. In embodiments, the vehicle routing system 1692 is to use the routing preference of the user 1690 and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve` an avoiding accident scenes objective. In embodiments, the vehicle routing system 1692 is to use the routing preference of the user 1690 and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve an avoiding high-crime areas objective. In embodiments, the vehicle routing system 1692 is to use the routing preference of the user 1690 and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve a reduced traffic congestion objective.

[1151] In embodiments, the vehicle routing system 1692 is to use the routing preference of the user 1690 and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve a bad weather avoidance objective. In embodiments, the vehicleSFT-107-A-PCT routing system 1692 is to use the routing preference of the user 1690 and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve a maximum travel time objective. In embodiments, the vehicle routing system 1692 is to use the routing preference of the user 1690 and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve a maximum speed limit objective. In embodiments, the vehicle routing system 1692 is to use the routing preference of the user 1690 and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve an avoidance of toll road’s objective. In embodiments, the vehicle routing system 1692 is to use the routing preference of the user 1690 and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve an avoidance of city road’s objective.

[1152] In embodiments, the vehicle routing system 1692 is to use the routing preference of the user 1690 and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve an avoidance of undivided highway’s objective. In embodiments, the vehicle routing system 1692 is to use the routing preference of the user 1690 and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve an avoidance of left turns objective. In embodiments, the vehicle routing system 1692 is to use the routing preference of the user 1690 and the corresponding effect on the at least one routing parameter to govern routing of the set of vehicles to achieve an avoidance of driver-operated vehicles objective.

[1153] Fig. 17 illustrates a method 1700 of reward-based coordinated vehicle routing in accordance with embodiments of the systems and methods disclosed herein. At 1702, the method includes receiving through a reward-based interface a response of a user related to a set of vehicles to a reward offered in the reward-based interface. At 1704, the method includes determining a routing preference based on the response of the user. At 1706, the method includes determining at least one user action resulting from the response of the user to the reward. At 1708, the method includes determining a corresponding effect of the at least one user action on at least one routing parameter. At 1709, the method includes governing routing of the set of vehicles responsive to the routing preference and the corresponding effect on the at least one routing parameter.

[1154] In embodiments, the user 1690 is a rider of at least one vehicle 1610 in the set of vehicles 1694. In embodiments, the user 1690 is an administrator for a set of roadways to be used by at least one vehicle 1610 in the set of vehicles 1694. In embodiments, the user 1690 is an administrator for a fleet of vehicles including the set of vehicles 1694.

[1155] In embodiments, the reward-based interface 1696 is disposed for in-vehicle use. In embodiments, the at least one routing parameter 1630 includes at least one of: traffic congestion, desired arrival times, preferred routes, fuel efficiency, pollution reduction, accident avoidance, avoiding bad weather, avoiding bad road conditions, reduced fuel consumption, reduced carbon footprint, reduced noise in a region, avoiding high-crime regions, collective satisfaction, maximum speed limit, avoidance of toll roads, avoidance of city roads, avoidance of undivided highways, avoidance of left turns, and avoidance of driver-operated vehicles. In embodiments, the user 1690SFT-107-A-PCT responds to the reward 16102 offered in the reward-based interface 1696 by accepting the reward 16102 offered in the interface, rejecting the reward 16102 offered in the reward-based interface 1696, or ignoring the reward 16102 offered in the reward-based interface 1696. In embodiments, the user 1690 indicates the routing preference by either accepting or rejecting the reward 16102 offered in the reward-based interface 1696. In embodiments, the user 1690 indicates the routing preference by undertaking an action in at least one vehicle 1610 in the set of vehicles 1694 that facilitates transferring the reward 16102 to the user 1690.

[1156] In embodiments, the method further comprises sending, via a reward offer response processing circuit 16105, a signal to the vehicle routing system 1692 to select a vehicle route that permits adequate time for the user 1690 to perform the at least one user action. In embodiments, the method further comprises: sending, via a reward offer response processing circuit 16105, a signal to a vehicle routing system 1692, the signal indicating a destination of a vehicle associated with the at least one user action; and adjusting, by the vehicle routing system 1692, a route of the vehicle 1695 associated with the at least one user action to include the destination. In embodiments, the reward 16102 is associated with achieving a vehicle routing fuel efficiency objective.

[1157] In embodiments, the reward 16102 is associated with achieving a vehicle routing reduced traffic objective. In embodiments, the reward 16102 is associated with achieving a vehicle routing reduced pollution objective. In embodiments, the reward 16102 is associated with achieving a vehicle routing reduced carbon footprint objective. In embodiments, the reward 16102 is associated with achieving a vehicle routing reduced noise in neighborhoods objective. In embodiments, reward 16102 is associated with achieving a vehicle routing collective satisfaction objective. In embodiments, the reward 16102 is associated with achieving a vehicle routing avoiding accident scene’s objective.

[1158] In embodiments, the reward 16102 is associated with achieving a vehicle routing avoiding high-crime areas objective. In embodiments, the reward 16102 is associated with achieving a vehicle routing reduced traffic congestion objective. In embodiments, the reward 16102 is associated with achieving a vehicle routing bad weather avoidance objective. In embodiments, the reward 16102 is associated with achieving a vehicle routing maximum travel time objective. In embodiments, the reward 16102 is associated with achieving a vehicle routing maximum speed limit objective. In embodiments, the reward 16102 is associated with achieving a vehicle routing avoidance of toll road’s objective. In embodiments, the reward 16102 is associated with achieving a vehicle routing avoidance of city road’s objective. In embodiments, the reward 16102 is associated with achieving a vehicle routing avoidance of undivided highway’s objective. In embodiments, the reward 16102 is associated with achieving a vehicle routing avoidance of left turns objective. In embodiments, the reward 16102 is associated with achieving a vehicle routing avoidance of driver-operated vehicles objective.

[1159] Referring to Fig. 18, in embodiments provided herein are transportation systems 1811 having a data processing system 1862 for taking data 18114 from a plurality 1869 of social data sources 18107 and using a neural network 18108 to predict an emerging transportation need 18112 for a group of individuals. Among the various social data sources 18107, such as those describedSFT-107-A-PCT above, a large amount of data is available relating to social groups, such as friend groups, families, workplace colleagues, club members, people having shared interests or affiliations, political groups, and others. The expert system described above can be trained, as described throughout, such as using a training data set of human predictions and / or a model, with feedback of outcomes, to predict the transportation needs of a group. For example, based on a discussion thread of a social group as indicated at least in part on a social network feed, it may become evident that a group meeting or trip will take place, and the system may (such as using location information for respective members, as well as indicators of a set of destinations of the trip), predict where and when each member would need to travel in order to participate. Based on such a prediction, the system could automatically identify and show options for travel, such as available public transportation options, flight options, ride share options, and the like. Such options may include ones by which the group may share transportation, such as indicating a route that results in picking up a set of members of the group for travel together. Social media information may include posts, tweets, comments, chats, photographs, and the like and may be processed as noted above.

[1160] An aspect provided herein includes a system 1811 for transportation, comprising: a data processing system 1862 for taking data 18114 from a plurality 1869 of social data sources 18107 and using a neural network 18108 to predict an emerging transportation need 18112 for a group of individuals 18110.

[1161] Fig.19 illustrates a method 1900 of predicting a common transportation need for a group in accordance with embodiments of the systems and methods disclosed herein. At 1902, the method includes gathering social media-sourced data about a plurality of individuals, the data being sourced from a plurality of social media sources. At 1904, the method includes processing the data to identify a subset of the plurality of individuals who form a social group based on group affiliation references in the data. At 1906, the method includes detecting keywords in the data indicative of a transportation need. At 1908, the method includes using a neural network trained to predict transportation needs based on the detected keywords to identify the common transportation need for the subset of the plurality of individuals.

[1162] Referring to Fig. 18 and Fig. 19, in embodiments, the neural network 18108 is a convolutional neural network 18113. In embodiments, the neural network 18108 is trained based on a model that facilitates matching phrases in social media with transportation activity. In embodiments, the neural network 18108 predicts at least one of a destination and an arrival time for the subset 18110 of the plurality of individuals sharing the common transportation need. In embodiments, the neural network 18108 predicts the common transportation need based on analysis of transportation need-indicative keywords detected in a discussion thread among a portion of individuals in the social group. In embodiments, the method further comprises identifying at least one shared transportation service 18111 that facilitates a portion of the social group meeting the predicted common transportation need 18112. In embodiments, the at least one shared transportation service comprises generating a vehicle route that facilitates picking up the portion of the social group.SFT-107-A-PCT

[1163] Fig.20 illustrates a method 2000 of predicting a group transportation need for a group in accordance with embodiments of the systems and methods disclosed herein. At 2002, the method includes gathering social media-sourced data about a plurality of individuals, the data being sourced from a plurality of social media sources. At 2004, the method includes processing the data to identify a subset of the plurality of individuals who share the group transportation need. At 2006, the method includes detecting keywords in the data indicative of the group transportation need for the subset of the plurality of individuals. At 2008, the method includes predicting the group transportation need using a neural network trained to predict transportation needs based on the detected keywords. At 2009, the method includes directing a vehicle routing system to meet the group transportation need.

[1164] Referring to Fig. 18 and Fig. 20, in embodiments, the neural network 18108 is a convolutional neural network 18113. In embodiments, directing the vehicle routing system to meet the group transportation need involves routing a plurality of vehicles to a destination derived from the social media-sourced data 18114. In embodiments, the neural network 18108 is trained based on a model that facilitates matching phrases in the social media-sourced data 18114 with transportation activities. In embodiments, the method further comprises predicting, by the neural network 18108, at least one of a destination and an arrival time for the subset 18110 of the plurality 18109 of individuals sharing the group transportation need. In embodiments, the method further comprises predicting, by the neural network 18108, the group transportation need based on an analysis of transportation need-indicative keywords detected in a discussion thread in the social media-sourced data 18114. In embodiments, the method further comprises identifying at least one shared transportation service 18111 that facilitates meeting the predicted group transportation need for at least a portion of the subset 18110 of the plurality of individuals. In embodiments, the at least one shared transportation service 18111 comprises generating a vehicle route that facilitates picking up the at least the portion of the subset 18110 of the plurality of individuals.

[1165] Fig.21 illustrates a method 2100 of predicting a group transportation need in accordance with embodiments of the systems and methods disclosed herein. At 2102, the method includes gathering social media-sourced data from a plurality of social media sources. At 2104, the method includes processing the data to identify an event. At 2106, the method includes detecting keywords in the data indicative of the event to determine a transportation need associated with the event. At 2108, the method includes using a neural network trained to predict transportation needs based at least in part on social media-sourced data to direct a vehicle routing system to meet the transportation need.

[1166] Referring to Fig. 18 and Fig. 21, in embodiments, the neural network 18108 is a convolutional neural network 18113. In embodiments, the vehicle routing system is directed to meet the transportation need by routing a plurality of vehicles to a location associated with the event. In embodiments, the vehicle routing system is directed to meet the transportation need by routing a plurality of vehicles to avoid a region proximal to a location associated with the event. In embodiments, the vehicle routing system is directed to meet the transportation need by routing vehicles associated with users whose social media-sourced data 18114 do not indicate theSFT-107-A-PCT transportation need to avoid a region proximal to a location associated with the event. In embodiments, the method further comprises presenting at least one transportation service for satisfying the transportation need. In embodiments, the neural network 18108 is trained based on a model that facilitates matching phrases in social media-sourced data 18114 with transportation activity.

[1167] In embodiments, the neural network 18108 predicts at least one of a destination and an arrival time for individuals attending the event. In embodiments, the neural network 18108 predicts the transportation need based on analysis of transportation need-indicative keywords detected in a discussion thread in the social media-sourced data 18114. In embodiments, the method further comprises identifying at least one shared transportation service that facilitates meeting the predicted transportation need for at least a subset of individuals identified in the social media- sourced data 18114. In embodiments, the at least one shared transportation service comprises generating a vehicle route that facilitates picking up the portion of the subset of individuals identified in the social media-sourced data 18114.

[1168] Referring to Fig. 22, in embodiments provided herein are transportation systems 2211 having a data processing system 2262 for taking social media data 22114 from a plurality 2269 of social data sources 22107 and using a hybrid neural network 2247 to optimize an operating state of a transportation system 22111 based on processing the social data sources 22107 with the hybrid neural network 2247. A hybrid neural network 2247 may have, for example, a neural network component that makes a classification or prediction based on processing social media data 22114 (such as predicting a high level of attendance of an event by processing images on many social media feeds that indicate interest in the event by many people, prediction of traffic, classification of interest by an individual in a topic, and many others) and another component that optimizes an operating state of a transportation system, such as an in-vehicle state, a routing state (for an individual vehicle 2210 or a set of vehicles 2294), a user-experience state, or other state described throughout this disclosure (e.g., routing an individual early to a venue like a music festival where there is likely to be very high attendance, playing music content in a vehicle 2210 for bands who will be at the music festival, or the like).

[1169] An aspect provided herein includes a system for transportation, comprising: a data processing system 2262 for taking social media data 22114 from a plurality 2269 of social data sources 22107 and using a hybrid neural network 2247 to optimize an operating state of a transportation system based on processing the data 22114 from the plurality 2269 of social data sources 22107 with the hybrid neural network 2247.

[1170] An aspect provided herein includes a hybrid neural network system 22115 for transportation system optimization, the hybrid neural network system 22115 comprising a hybrid neural network 2247, including: a first neural network 2222 that predicts a localized effect 22116 on a transportation system through analysis of social medial data 22114 sourced from a plurality 2269 of social media data sources 22107; and a second neural network 2220 that optimizes an operating state of the transportation system based on the predicted localized effect 22116.SFT-107-A-PCT

[1171] In embodiments, at least one of the first neural network 2222 and the second neural network 2220 is a convolutional neural network. In embodiments, the second neural network 2220 is to optimize an in-vehicle rider experience state. In embodiments, the first neural network 2222 identifies a set of vehicles 2294 contributing to the localized effect 22116 based on correlation of vehicle location and an area of the localized effect 22116. In embodiments, the second neural network 2220 is to optimize a routing state of the transportation system for vehicles proximal to a location of the localized effect 22116. In embodiments, the hybrid neural network 2247 is trained for at least one of the predicting and optimizing based on keywords in the social media data indicative of an outcome of a transportation system optimization action. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on social media posts.

[1172] In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on social media feeds. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on ratings derived from the social media data 22114. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on like or dislike activity detected in the social media data 22114. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on indications of relationships in the social media data 22114. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on user behavior detected in the social media data 22114. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on discussion threads in the social media data 22114.

[1173] In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on chats in the social media data 22114. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on photographs in the social media data 22114. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on traffic-affecting information in the social media data 22114. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on an indication of a specific individual at a location in the social media data 22114. In embodiments, the specific individual is a celebrity. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based a presence of a rare or transient phenomena at a location in the social media data 22114.

[1174] In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based a commerce-related event at a location in the social media data 22114. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based an entertainment event at a location in the social media data 22114. In embodiments, the social media data analyzed to predict a localized effect on a transportation system includes traffic conditions. In embodiments, the social media data analyzed to predict a localized effect on a transportation system includes weather conditions. In embodiments, the socialSFT-107-A-PCT media data analyzed to predict a localized effect on a transportation system includes entertainment options.

[1175] In embodiments, the social media data analyzed to predict a localized effect on a transportation system includes risk-related conditions. In embodiments, the risk-related conditions include crowds gathering for potentially dangerous reasons. In embodiments, the social media data analyzed to predict a localized effect on a transportation system includes commerce-related conditions. In embodiments, the social media data analyzed to predict a localized effect on a transportation system includes goal-related conditions.

[1176] In embodiments, the social media data analyzed to predict a localized effect on a transportation system includes estimates of attendance at an event. In embodiments, the social media data analyzed to predict a localized effect on a transportation system includes predictions of attendance at an event. In embodiments, the social media data analyzed to predict a localized effect on a transportation system includes modes of transportation. In embodiments, the modes of transportation include car traffic. In embodiments, the modes of transportation include public transportation options.

[1177] In embodiments, the social media data analyzed to predict a localized effect on a transportation system includes hash tags. In embodiments, the social media data analyzed to predict a localized effect on a transportation system includes trending of topics. In embodiments, an outcome of a transportation system optimization action is reducing fuel consumption. In embodiments, an outcome of a transportation system optimization action is reducing traffic congestion. In embodiments, an outcome of a transportation system optimization action is reduced pollution. In embodiments, an outcome of a transportation system optimization action is bad weather avoidance. In embodiments, an operating state of the transportation system being optimized includes an in-vehicle state. In embodiments, an operating state of the transportation system being optimized includes a routing state.

[1178] In embodiments, the routing state is for an individual vehicle 2210. In embodiments, the routing state is for a set of vehicles 2294. In embodiments, an operating state of the transportation system being optimized includes a user-experience state.

[1179] Fig. 23 illustrates a method 2300 of optimizing an operating state of a transportation system in accordance with embodiments of the systems and methods disclosed herein. At 2302 the method includes gathering social media-sourced data about a plurality of individuals, the data being sourced from a plurality of social media sources. At 2304 the method includes optimizing, using a hybrid neural network, the operating state of the transportation system. At 2306 the method includes predicting, by a first neural network of the hybrid neural network, an effect on the transportation system through an analysis of the social media-sourced data. At 2308 the method includes optimizing, by a second neural network of the hybrid neural network, at least one operating state of the transportation system responsive to the predicted effect thereon.

[1180] Referring to Fig.22 and Fig.23, in embodiments, at least one of the first neural network 2222 and the second neural network 2220 is a convolutional neural network. In embodiments, the second neural network 2220 optimizes an in-vehicle rider experience state. In embodiments, theSFT-107-A-PCT first neural network 2222 identifies a set of vehicles contributing to the effect based on correlation of vehicle location and an effect area. In embodiments, the second neural network 2220 optimizes a routing state of the transportation system for vehicles proximal to a location of the effect.

[1181] In embodiments, the hybrid neural network 2247 is trained for at least one of the predicting and optimizing based on keywords in the social media data indicative of an outcome of a transportation system optimization action. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on social media posts. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on social media feeds. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on ratings derived from the social media data 22114. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on like or dislike activity detected in the social media data 22114. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on indications of relationships in the social media data 22114.

[1182] In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on user behavior detected in the social media data 22114. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on discussion threads in the social media data 22114. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on chats in the social media data 22114. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on photographs in the social media data 22114. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on traffic- affecting information in the social media data 22114.

[1183] In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based on an indication of a specific individual at a location in the social media data. In embodiments, the specific individual is a celebrity. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based a presence of a rare or transient phenomena at a location in the social media data. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based a commerce-related event at a location in the social media data. In embodiments, the hybrid neural network 2247 is trained for at least one of predicting and optimizing based an entertainment event at a location in the social media data. In embodiments, the social media data analyzed to predict an effect on a transportation system includes traffic conditions.

[1184] In embodiments, the social media data analyzed to predict an effect on a transportation system includes weather conditions. In embodiments, the social media data analyzed to predict an effect on a transportation system includes entertainment options. In embodiments, the social media data analyzed to predict an effect on a transportation system includes risk-related conditions. In embodiments, the risk-related conditions include crowds gathering for potentially dangerous reasons. In embodiments, the social media data analyzed to predict an effect on a transportationSFT-107-A-PCT system includes commerce-related conditions. In embodiments, the social media data analyzed to predict an effect on a transportation system includes goal-related conditions.

[1185] In embodiments, the social media data analyzed to predict an effect on a transportation system includes estimates of attendance at an event. In embodiments, the social media data analyzed to predict an effect on a transportation system includes predictions of attendance at an event. In embodiments, the social media data analyzed to predict an effect on a transportation system includes modes of transportation. In embodiments, the modes of transportation include car traffic. In embodiments, the modes of transportation include public transportation options. In embodiments, the social media data analyzed to predict an effect on a transportation system includes hash tags. In embodiments, the social media data analyzed to predict an effect on a transportation system includes trending of topics.

[1186] In embodiments, an outcome of a transportation system optimization action is reducing fuel consumption. In embodiments, an outcome of a transportation system optimization action is reducing traffic congestion. In embodiments, an outcome of a transportation system optimization action is reduced pollution. In embodiments, an outcome of a transportation system optimization action is bad weather avoidance. In embodiments, the operating state of the transportation system being optimized includes an in-vehicle state. In embodiments, the operating state of the transportation system being optimized includes a routing state. In embodiments, the routing state is for an individual vehicle. In embodiments, the routing state is for a set of vehicles. In embodiments, the operating state of the transportation system being optimized includes a user- experience state.

[1187] Fig. 24 illustrates a method 2400 of optimizing an operating state of a transportation system in accordance with embodiments of the systems and methods disclosed herein. At 2402 the method includes using a first neural network of a hybrid neural network to classify social media data sourced from a plurality of social media sources as affecting a transportation system. At 2404 the method includes using a second network of the hybrid neural network to predict at least one operating objective of the transportation system based on the classified social media data. At 2406 the method includes using a third network of the hybrid neural network to optimize the operating state of the transportation system to achieve the at least one operating objective of the transportation system.

[1188] Referring to Fig. 22 and Fig. 24, in embodiments, at least one of the neural networks in the hybrid neural network 2247 is a convolutional neural network.

[1189] Referring to Fig. 25, in embodiments provided herein are transportation systems 2511 having a data processing system 2562 for taking social media data 25114 from a plurality of social data sources 25107 and using a hybrid neural network 2547 to optimize an operating state 2545 of a vehicle 2510 based on processing the social data sources with the hybrid neural network 2547. In embodiments, the hybrid neural network 2547 can include one neural network category for prediction, another for classification, and another for optimization of one or more operating states, such as based on optimizing one or more desired outcomes (such a providing efficient travel, highly satisfying rider experiences, comfortable rides, on-time arrival, or the like). Social data sourcesSFT-107-A-PCT 2569 may be used by distinct neural network categories (such as any of the types described herein) to predict travel times, to classify content such as for profiling interests of a user, to predict objectives for a transportation plan (such as what will provide overall satisfaction for an individual or a group) and the like. Social data sources 2569 may also inform optimization, such as by providing indications of successful outcomes (e.g., a social data source 25107 like a Facebook feed might indicate that a trip was “amazing” or “horrible,” a Yelp review might indicate a restaurant was terrible, or the like). Thus, social data sources 2569, by contributing to outcome tracking, can be used to train a system to optimize transportation plans, such as relating to timing, destinations, trip purposes, what individuals should be invited, what entertainment options should be selected, and many others.

[1190] An aspect provided herein includes a transportation system 2511, comprising: a data processing system 2562 for taking social media data 25114 from a plurality of social data sources 25107 and using a hybrid neural network 2547 to optimize an operating state 2545 of a vehicle 2510 based on processing the data 25114 from the plurality of social data sources 25107 with the hybrid neural network 2547.

[1191] Fig. 26 illustrates a method 2600 of optimizing an operating state of a vehicle in accordance with embodiments of the systems and methods disclosed herein. At 2602 the method includes classifying, using a first neural network 2522 (Fig.25) of a hybrid neural network, social media data 25119 (Fig. 25) sourced from a plurality of social media sources as affecting a transportation system. At 2604 the method includes predicting, using a second neural network 2520 (Fig.25) of the hybrid neural network, one or more effects 25118 (Fig.25) of the classified social media data on the transportation system. At 2606 the method includes optimizing, using a third neural network 25117 (Fig. 25) 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.

[1192] Referring to Fig. 25 and Fig. 26, in embodiments, at least one of the neural networks in the hybrid neural network 2547 is a convolutional neural network. In embodiments, the social media data 25114 includes social media posts. In embodiments, the social media data 25114 includes social media feeds. In embodiments, the social media data 25114 includes like or dislike activity detected in the social media. In embodiments, the social media data 25114 includes indications of relationships. In embodiments, the social media data 25114 includes user behavior. In embodiments, the social media data 25114 includes discussion threads. In embodiments, the social media data 25114 includes chats. In embodiments, the social media data 25114 includes photographs.

[1193] In embodiments, the social media data 25114 includes traffic-affecting information. In embodiments, the social media data 25114 includes an indication of a specific individual at a location. In embodiments, the social media data 25114 includes an indication of a celebrity at a location. In embodiments, the social media data 25114 includes presence of a rare or transient phenomena at a location. In embodiments, the social media data 25114 includes a commerce- related event. In embodiments, the social media data 25114 includes an entertainment event at aSFT-107-A-PCT location. In embodiments, the social media data 25114 includes traffic conditions. In embodiments, the social media data 25114 includes weather conditions. In embodiments, the social media data 25114 includes entertainment options.

[1194] In embodiments, the social media data 25114 includes risk-related conditions. In embodiments, the social media data 25114 includes predictions of attendance at an event. In embodiments, the social media data 25114 includes estimates of attendance at an event. In embodiments, the social media data 25114 includes modes of transportation used with an event. In embodiments, the effect 25118 on the transportation system includes reducing fuel consumption. In embodiments, the effect 25118 on the transportation system includes reducing traffic congestion. In embodiments, the effect 25118 on the transportation system includes reduced carbon footprint. In embodiments, the effect 25118 on the transportation system includes reduced pollution.

[1195] In embodiments, the optimized state 2544 of the at least one vehicle 2510 is an operating state 2545 of the vehicle. In embodiments, the optimized state of the at least one vehicle includes an in-vehicle state. In embodiments, the optimized state of the at least one vehicle includes a rider state. In embodiments, the optimized state of the at least one vehicle includes a routing state. In embodiments, the optimized state of the at least one vehicle includes user experience state. In embodiments, a characterization of an outcome of the optimizing in the social media data 25114 is used as feedback to improve the optimizing. In embodiments, the feedback includes likes and dislikes of the outcome. In embodiments, the feedback includes social medial activity referencing the outcome.

[1196] In embodiments, the feedback includes trending of social media activity referencing the outcome. In embodiments, the feedback includes hash tags associated with the outcome. In embodiments, the feedback includes ratings of the outcome. In embodiments, the feedback includes requests for the outcome.

[1197] Fig. 26A illustrates a method 26A00 of optimizing an operating state of a vehicle in accordance with embodiments of the systems and methods disclosed herein. At 26A02 the method 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. At 26A04 the method includes predicting, using a second neural network of the hybrid neural network, at least one vehicle-operating objective of the transportation system based on the classified social media data. At 26A06 the method includes optimizing, using a third neural network of the hybrid neural network, a state of a vehicle in the transportation system to achieve the at least one vehicle- operating objective of the transportation system.

[1198] Referring to Fig.25 and Fig.26A, in embodiments, at least one of the neural networks in the hybrid neural network 2547 is a convolutional neural network. In embodiments, the vehicle- operating objective comprises achieving a rider state of at least one rider in the vehicle. In embodiments, the social media data 25114 includes social media posts.

[1199] In embodiments, the social media data 25114 includes social media feeds. In embodiments, the social media data 25114 includes like and dislike activity detected in the socialSFT-107-A-PCT media. In embodiments, the social media data 25114 includes indications of relationships. In embodiments, the social media data 25114 includes user behavior. In embodiments, the social media data 25114 includes discussion threads. In embodiments, the social media data 25114 includes chats. In embodiments, the social media data 25114 includes photographs. In embodiments, the social media data 25114 includes traffic-affecting information.

[1200] In embodiments, the social media data 25114 includes an indication of a specific individual at a location. In embodiments, the social media data 25114 includes an indication of a celebrity at a location. In embodiments, the social media data 25114 includes presence of a rare or transient phenomena at a location. In embodiments, the social media data 25114 includes a commerce-related event. In embodiments, the social media data 25114 includes an entertainment event at a location. In embodiments, the social media data 25114 includes traffic conditions. In embodiments, the social media data 25114 includes weather conditions. In embodiments, the social media data 25114 includes entertainment options.

[1201] In embodiments, the social media data 25114 includes risk-related conditions. In embodiments, the social media data 25114 includes predictions of attendance at an event. In embodiments, the social media data 25114 includes estimates of attendance at an event. In embodiments, the social media data 25114 includes modes of transportation used with an event. In embodiments, the effect on the transportation system includes reducing fuel consumption. In embodiments, the effect on the transportation system includes reducing traffic congestion. In embodiments, the effect on the transportation system includes reduced carbon footprint. In embodiments, the effect on the transportation system includes reduced pollution. In embodiments, the optimized state of the vehicle is an operating state of the vehicle.

[1202] In embodiments, the optimized state of the vehicle includes an in-vehicle state. In embodiments, the optimized state of the vehicle includes a rider state. In embodiments, the optimized state of the vehicle includes a rou...

Claims

SFT-107-A-PCT CLAIMS What is claimed is: AI convergence system of systems ecosystem to optimize a powertrain of a software-defined vehicle 1. A transportation AI convergence system of systems for optimizing powertrain performance, comprising: a vehicle having a continuously variable powertrain; a hybrid neural network configured to optimize an operating state of the continuously variable powertrain, wherein: a first portion of the hybrid neural network is configured to classify a state of the vehicle; and a second portion of the hybrid neural network is configured to optimize at least one operating parameter of a transmission portion of the continuously variable powertrain based on the classified state of the vehicle.

2. The system of claim 1, wherein the first portion of the hybrid neural network is configured to classify at least one of: a vehicle maintenance state, a vehicle health state, a vehicle operating state, a vehicle energy utilization state, a vehicle charging state, a vehicle satisfaction state, a vehicle component state, a vehicle sub-system state, a vehicle powertrain system state, a vehicle braking system state, a vehicle clutch system state, or a vehicle lubrication system state.

3. The system of claim 1, wherein at least one of the first portion or the second portion of the hybrid neural network comprises a convolutional neural network.

4. The system of claim 1, further comprising a sensor system configured to provide sensor data to the hybrid neural network, wherein the sensor data includes at least one of: LIDAR data, RADAR data, vision-based system data, or temperature sensing data.

5. The system of claim 1, wherein the second portion of the hybrid neural network optimizes the at least one operating parameter in real-time responsive to the classified state of the vehicle.

6. The system of claim 1, wherein the hybrid neural network comprises a plurality of connected nodes that form a directed cycle facilitating bi-directional flow of data among the connected nodes.

7. The system of claim 1, wherein the at least one operating parameter affects at least one of: a speed of the vehicle, an acceleration of the vehicle, or a deceleration of the vehicle.

8. The system of claim 1, wherein the hybrid neural network is configured to predict a future state of the vehicle based on the classified state.

9. The system of claim 1, wherein the first portion of the hybrid neural network comprises a structure-adaptive network configured to adapt its structure responsive to operational results.

10. The system of claim 1, further comprising a feedback loop configured to provide operational feedback to the hybrid neural network for refining the optimization of the operating parameter.

11. The system of claim 1, wherein the hybrid neural network is configured to process social data from social media sources to classify the state of the vehicle.SFT-107-A-PCT 12. A transportation system having an AI convergence system of systems comprising: a sensor system configured to detect an environmental condition; an artificial intelligence system configured to: receive sensor data from the sensor system; classify a plurality of operational states of a vehicle based on the sensor data; process an input descriptive of the vehicle and at least one detected condition associated with an occupant of the vehicle; and optimize at least one operating parameter of a powertrain based on the classified operational states and processed input.

13. A transportation AI convergence system of systems for transportation comprising: a vehicle having a powertrain system; a hybrid neural network configured to: classify a vehicle maintenance state, a vehicle health state, and a vehicle operating state; optimize powertrain operating parameters based on the classified states; and predict a future state of the vehicle based on the classified states and optimized operating parameters.

14. The system of claim 13, wherein the hybrid neural network comprises a convolutional neural network.

15. The system of claim 13, wherein the hybrid neural network is configured to process sensor data from a plurality of vehicle-mounted sensors.

16. The system of claim 13, wherein the hybrid neural network is configured to track conditions proximal to the vehicle using vehicle mounted sensors.

17. The system of claim 13, wherein the hybrid neural network is configured to process data feeds from remote sensors contemporaneous to vehicle operation.

18. The system of claim 13, wherein the hybrid neural network employs a workflow that involves decision-making and automated optimization.

19. The system of claim 13, wherein the hybrid neural network is configured to optimize the powertrain operating parameters based on a correlation between vehicle operating state and rider emotional state.

20. The system of claim 13, wherein the hybrid neural network is configured to optimize the powertrain operating parameters in real-time responsive to detected changes in vehicle state.

21. The system of claim 13, wherein the hybrid neural network comprises a plurality of connected nodes that form a directed cycle.

22. The system of claim 13, wherein the hybrid neural network is configured to process social media data to classify the vehicle states.

23. The system of claim 13, wherein the hybrid neural network is configured to optimize the powertrain operating parameters based on predicted traffic conditions.

24. A method for optimizing vehicle powertrain operation, comprising: executing a first network of a hybrid neural network to classify a plurality of operational states of a vehicle;SFT-107-A-PCT executing a second network of the hybrid neural network to process inputs descriptive of the vehicle and at least one detected condition associated with an occupant; and optimizing at least one operating parameter of a continuously variable powertrain based on the classified operational states and processed inputs.

25. The method of claim 24, wherein classifying the operational states comprises processing sensor data from vehicle-mounted sensors.

26. The method of claim 24, wherein at least one of the first network or second network comprises a convolutional neural network.

27. The method of claim 24, further comprising tracking conditions proximal to the vehicle using vehicle-mounted sensors.

28. The method of claim 24, further comprising processing data feeds from remote sensors contemporaneous to vehicle operation.

29. The method of claim 24, wherein optimizing the operating parameter comprises applying deep learning to optimize a margin of vehicle operational safety.

30. The method of claim 24, wherein optimizing the operating parameter comprises processing feedback from controlling the vehicle through machine learning.

31. The method of claim 24, further comprising adapting the optimization based on detected emotional states of the occupant.

32. The method of claim 24, wherein optimizing comprises adjusting at least one of: vehicle speed, acceleration, or deceleration.

33. The method of claim 24, further comprising predicting future operational states based on the classified states.

34. The method of claim 24, wherein classifying comprises processing social media data related to vehicle operation.

35. AI convergence system of systems for transportation comprising: a sensor system configured to detect vehicle operating conditions; an artificial intelligence system configured to: process sensor data to identify a vehicle operational state; optimize a powertrain parameter based on the identified operational state; and implement structured variation in the powertrain parameter through machine learning feedback processing.

36. The system of claim 35, wherein the artificial intelligence system comprises a hybrid neural network.

37. The system of claim 35, wherein the sensor system comprises at least one of: LIDAR, RADAR, or vision-based sensors.

38. The system of claim 35, wherein the artificial intelligence system is configured to process environmental data contemporaneous with vehicle operation.

39. The system of claim 35, wherein the artificial intelligence system optimizes the powertrain parameter in real-time.SFT-107-A-PCT 40. The system of claim 35, wherein the artificial intelligence system implements the structured variation based on detected emotional states of vehicle occupants.

41. The system of claim 35, wherein the artificial intelligence system comprises a plurality of connected nodes forming a directed cycle.

42. The system of claim 35, wherein the artificial intelligence system processes social media data to identify operational states.

43. The system of claim 35, wherein the artificial intelligence system predicts future operational states.

44. The system of claim 35, wherein the artificial intelligence system optimizes powertrain parameters based on weather conditions.

45. The system of claim 35, wherein the artificial intelligence system adapts optimization based on traffic conditions.

46. AI convergence system of systems for optimizing vehicle performance comprising: a hybrid neural network including: a structure-adaptive network configured to adapt its structure responsive to an operational result; and an optimization network configured to optimize a powertrain operating parameter based on the adapted structure.

47. The system of claim 46, wherein the hybrid neural network comprises a convolutional neural network.

48. The system of claim 46, wherein the structure-adaptive network processes sensor data from vehicle-mounted sensors.

49. The system of claim 46, wherein the optimization network processes environmental data.

50. The system of claim 46, wherein the hybrid neural network optimizes parameters in real- time.

51. The system of claim 46, wherein the hybrid neural network adapts based on detected emotional states of vehicle occupants.

52. The system of claim 46, wherein the hybrid neural network comprises nodes forming a directed cycle.

53. The system of claim 46, wherein the hybrid neural network processes social media data.

54. The system of claim 46, wherein the hybrid neural network predicts future operational states.

55. The system of claim 46, wherein the hybrid neural network optimizes based on weather conditions.

56. The system of claim 46, wherein the hybrid neural network adapts based on traffic conditions.

57. A transportation AI convergence system of systems comprising: a sensor system configured to detect an environmental condition; an artificial intelligence system configured to: classify a weather or traffic condition based on sensor data;SFT-107-A-PCT adjust a powertrain operating parameter based on the classified condition; and optimize the adjusted parameter through machine learning to maintain a safety margin.

58. The system of claim 57, wherein the artificial intelligence system comprises a hybrid neural network.

59. The system of claim 57, wherein the sensor system includes vision-based sensors.

60. The system of claim 57, wherein the artificial intelligence system processes real-time environmental data.

61. The system of claim 57, wherein the artificial intelligence system optimizes parameters in real-time.

62. The system of claim 57, wherein the artificial intelligence system adapts based on occupant emotional states.

63. The system of claim 57, wherein the artificial intelligence system comprises directed cycle nodes.

64. The system of claim 57, wherein the artificial intelligence system processes social media data.

65. The system of claim 57, wherein the artificial intelligence system predicts future conditions.

66. The system of claim 57, wherein the artificial intelligence system maintains safety margins based on road conditions.

67. The system of claim 57, wherein the artificial intelligence system adapts to changing traffic patterns.

68. A transportation AI convergence system of systems for vehicle operation comprising: a vehicle having a continuously variable powertrain; a hybrid neural network configured to: process social data from a social media source to classify a vehicle operational state; optimize a powertrain operating parameter based on the classified state from social data processing.

69. The system of claim 68, wherein the hybrid neural network comprises a convolutional neural network.

70. The system of claim 68, wherein the hybrid neural network processes sensor data.

71. The system of claim 68, wherein the hybrid neural network processes environmental data.

72. The system of claim 68, wherein the hybrid neural network optimizes in real-time.

73. The system of claim 68, wherein the hybrid neural network adapts to occupant states.

74. The system of claim 68, wherein the hybrid neural network comprises directed cycle nodes.

75. The system of claim 68, wherein the hybrid neural network predicts future states.

76. The system of claim 68, wherein the hybrid neural network optimizes for weather conditions.

77. The system of claim 68, wherein the hybrid neural network adapts to traffic conditions.

78. The system of claim 68, wherein the hybrid neural network maintains safety margins.

79. A method for optimizing vehicle powertrain operation comprising: receiving sensor data indicating a vehicle operating condition; processing the sensor data through a first neural network to classify a vehicle state;SFT-107-A-PCT processing environmental data through a second neural network to identify an external condition; optimizing a powertrain operating parameter based on the classified state and identified condition.

80. The method of claim 79, wherein at least one neural network is a convolutional network.

81. The method of claim 79, further comprising processing social media data.

82. The method of claim 79, wherein optimizing occurs in real-time.

83. The method of claim 79, further comprising adapting to occupant emotional states.

84. The method of claim 79, wherein the neural networks comprise directed cycle nodes.

85. The method of claim 79, further comprising predicting future conditions.

86. The method of claim 79, further comprising optimizing for weather conditions.

87. The method of claim 79, further comprising adapting to traffic conditions.

88. The method of claim 79, further comprising maintaining safety margins.

89. The method of claim 79, further comprising processing feedback through machine learning.

90. A transportation AI convergence system of systems for transportation comprising: a hybrid neural network configured to: classify a vehicle operating state based on sensor data; predict a future operating condition based on the classified state; optimize a powertrain parameter based on the predicted condition; and adapt the optimized parameter through machine learning feedback.

91. The system of claim 90, wherein the hybrid neural network comprises a convolutional network.

92. The system of claim 90, wherein the hybrid neural network processes social media data.

93. The system of claim 90, wherein the hybrid neural network optimizes in real-time.

94. The system of claim 90, wherein the hybrid neural network adapts to occupant states.

95. The system of claim 90, wherein the hybrid neural network comprises directed cycle nodes.

96. The system of claim 90, wherein optimization maintains safety margins.

97. The system of claim 90, wherein the hybrid neural network optimizes for weather.

98. The system of claim 90, wherein the hybrid neural network adapts to traffic.

99. The system of claim 90, wherein the hybrid neural network processes environmental data.

100. The system of claim 90, wherein adaptation occurs through structured variation. AI convergence system of systems ecosystem to optimize a vehicle charging system 101. A transportation AI convergence system of systems for optimizing vehicle charging operations, comprising: a network-enabled vehicle information ingestion port configured to gather operational state and energy consumption information from a plurality of network-enabled vehicles; a charging infrastructure control system including cloud-based computing and local charging infrastructure systems; an artificial intelligence system configured to: determine at least one charging plan parameter upon which a charging plan for the plurality ofSFT-107-A-PCT network-enabled vehicles is dependent; and optimize the charging plan based on the operational state and energy consumption information.

102. The system of claim 101, wherein the operational state information includes battery charge states of the plurality of vehicles.

103. The system of claim 101, wherein the charging infrastructure control system is configured to adapt charging rates based on accumulated vehicles at charging locations.

104. The system of claim 101, wherein the artificial intelligence system includes a hybrid neural network.

105. The system of claim 101, wherein the artificial intelligence system is configured to predict vehicle geolocations within geographic regions.

106. The system of claim 101, wherein the artificial intelligence system is configured to automate negotiation of charging duration, quantity, and pricing.

107. The system of claim 101, wherein the charging plan parameter impacts vehicle routing to charging infrastructure.

108. The system of claim 101, wherein the artificial intelligence system optimizes electricity usage for vehicles and charging infrastructure.

109. The system of claim 101, wherein the artificial intelligence system processes market value indicators for charging.

110. The system of claim 101, wherein the artificial intelligence system processes available supply capacity data.

111. The system of claim 101, wherein the artificial intelligence system processes recharge demand data.

112. A transportation AI convergence system of systems for transportation, comprising: a plurality of network-enabled vehicles; a cloud-based artificial intelligence system configured to: receive inputs relating to the plurality of vehicles; determine at least one parameter of a charging plan for the plurality of vehicles based on the inputs; and optimize charging infrastructure operations based on the determined parameter.

113. The system of claim 112, wherein the inputs include route plans for the plurality of vehicles.

114. The system of claim 112, wherein the inputs include indicators of charging value.

115. The system of claim 112, wherein the inputs include predicted traffic conditions.

116. The system of claim 112, wherein the artificial intelligence system includes a hybrid neural network.

117. The system of claim 112, wherein the artificial intelligence system predicts near-term charging needs.

118. The system of claim 112, wherein the artificial intelligence system optimizes charging time allocation.

119. The system of claim 112, wherein the artificial intelligence system optimizes charging location selection.SFT-107-A-PCT 120. The system of claim 112, wherein the artificial intelligence system optimizes charging amounts.

121. The system of claim 112, wherein the artificial intelligence system processes environmental data.

122. The system of claim 112, wherein the artificial intelligence system processes marketplace factors.

123. A method for optimizing vehicle charging operations, comprising: receiving operational state information from a plurality of network-enabled vehicles; processing the operational state information through a first neural network to predict target energy renewal regions; processing infrastructure usage information through a second neural network to optimize charging infrastructure operations within the target energy renewal regions.

124. A transportation AI convergence system of systems comprising: a plurality of vehicles having charging systems; a hybrid neural network including: a first neural network configured to process vehicle route and stored energy state information to predict target energy renewal regions; and a second neural network configured to process vehicle energy renewal infrastructure usage and demand information to determine charging infrastructure operational parameters.

125. The system of claim 124, wherein the hybrid neural network comprises a convolutional neural network.

126. The system of claim 124, wherein the hybrid neural network processes real-time vehicle data.

127. The system of claim 124, wherein the hybrid neural network optimizes charging infrastructure allocation.

128. The system of claim 124, wherein the hybrid neural network processes marketplace factors.

129. The system of claim 124, wherein the hybrid neural network predicts vehicle locations.

130. The system of claim 124, wherein the hybrid neural network optimizes charging rates.

131. The system of claim 124, wherein the hybrid neural network processes environmental data.

132. The system of claim 124, wherein the hybrid neural network optimizes energy efficiency.

133. The system of claim 124, wherein the hybrid neural network processes traffic data.

134. The system of claim 124, wherein the hybrid neural network adapts to charging demand.

135. A method for optimizing vehicle charging operations, comprising: receiving battery status information from a plurality of vehicles; determining at least one charging plan parameter based on the battery status information; optimizing anticipated battery usage of the plurality of vehicles based on the charging plan parameter; and adapting charging infrastructure operations based on the optimized anticipated battery usage.

136. The method of claim 135, wherein determining the charging plan parameter includes processing route plans.SFT-107-A-PCT 137. The method of claim 135, wherein determining includes processing traffic predictions.

138. The method of claim 135, wherein optimizing includes predicting charging needs.

139. The method of claim 135, wherein adapting includes adjusting charging rates.

140. The method of claim 135, wherein optimizing includes processing environmental data.

141. The method of claim 135, wherein optimizing includes processing market data.

142. The method of claim 135, wherein adapting includes coordinating multiple charging stations.

143. The method of claim 135, wherein optimizing includes predicting vehicle locations.

144. The method of claim 135, wherein adapting includes managing charging capacity.

145. The method of claim 135, wherein optimizing includes processing feedback data.

146. A transportation AI convergence system of systems comprising: a vehicle charging infrastructure; an artificial intelligence system configured to: apply a vehicle recharging facility utilization optimization algorithm to vehicle-specific inputs; evaluate impacts of recharging plan parameters on the charging infrastructure; optimize energy usage based on the evaluation.

147. The system of claim 146, wherein the artificial intelligence system processes operational status data.

148. The system of claim 146, wherein the artificial intelligence system predicts charging needs.

149. The system of claim 146, wherein the artificial intelligence system optimizes charging time.

150. The system of claim 146, wherein the artificial intelligence system optimizes charging location.

151. The system of claim 146, wherein the artificial intelligence system processes environmental data.

152. The system of claim 146, wherein the artificial intelligence system processes market data.

153. The system of claim 146, wherein the artificial intelligence system coordinates charging stations.

154. The system of claim 146, wherein the artificial intelligence system predicts vehicle locations.

155. The system of claim 146, wherein the artificial intelligence system manages capacity.

156. The system of claim 146, wherein the artificial intelligence system processes feedback.

157. A transportation AI convergence system of systems for optimizing vehicle charging comprising: a charging infrastructure control system; an artificial intelligence system configured to: predict geolocation of vehicles within a geographic region; optimize charging infrastructure allocation based on predicted vehicle locations; and adapt charging operations based on the optimization.

158. The system of claim 157, wherein predicting includes processing route data.

159. The system of claim 157, wherein predicting includes processing traffic data.SFT-107-A-PCT 160. The system of claim 157, wherein optimizing includes processing demand data.

161. The system of claim 157, wherein adapting includes adjusting charging rates.

162. The system of claim 157, wherein optimizing includes processing environmental data.

163. The system of claim 157, wherein optimizing includes processing market data.

164. The system of claim 157, wherein adapting includes coordinating charging stations.

165. The system of claim 157, wherein optimizing includes managing capacity.

166. The system of claim 157, wherein adapting includes processing feedback.

167. The system of claim 157, wherein predicting includes processing historical data.

168. A method for vehicle charging optimization comprising: receiving inputs relating to charging states of vehicles within a geolocation range; predicting geolocations of the vehicles; optimizing at least one charging plan parameter based on the predicted geolocations; and implementing automated negotiation of charging parameters based on the optimization.

169. The method of claim 168, wherein receiving includes processing route data.

170. The method of claim 168, wherein predicting includes processing traffic data.

171. The method of claim 168, wherein optimizing includes processing demand data.

172. The method of claim 168, wherein implementing includes adjusting charging rates.

173. The method of claim 168, wherein optimizing includes processing environmental data.

174. The method of claim 168, wherein optimizing includes processing market data.

175. The method of claim 168, wherein implementing includes coordinating stations.

176. The method of claim 168, wherein optimizing includes managing capacity.

177. The method of claim 168, wherein implementing includes processing feedback.

178. The method of claim 168, wherein predicting includes processing historical data.

179. A transportation AI convergence system of systems comprising: a charging infrastructure; a recharging plan update facility configured to: apply adjustment values to charging plan parameters; adjust the adjustment values based on feedback; and optimize charging operations based on adjusted values.

180. The system of claim 179, wherein applying includes processing route data.

181. The system of claim 179, wherein adjusting includes processing traffic data.

182. The system of claim 179, wherein optimizing includes processing demand data.

183. The system of claim 179, wherein optimizing includes adjusting charging rates.

184. The system of claim 179, wherein optimizing includes processing environmental data.

185. The system of claim 179, wherein optimizing includes processing market data.

186. The system of claim 179, wherein optimizing includes coordinating stations.

187. The system of claim 179, wherein optimizing includes managing capacity.

188. The system of claim 179, wherein adjusting includes processing feedback.

189. The system of claim 179, wherein optimizing includes processing historical data.SFT-107-A-PCT 190. An AI convergence system of systems for transportation comprising: a charging infrastructure control system; an artificial intelligence system configured to: optimize electricity usage for vehicles and charging infrastructure; optimize charging infrastructure-specific recharging time, location, and amount; and adapt charging operations based on the optimizations.

191. The system of claim 190, wherein optimizing includes processing route data.

192. The system of claim 190, wherein optimizing includes processing traffic data.

193. The system of claim 190, wherein optimizing includes processing demand data.

194. The system of claim 190, wherein adapting includes adjusting charging rates.

195. The system of claim 190, wherein optimizing includes processing environmental data.

196. The system of claim 190, wherein optimizing includes processing market data.

197. The system of claim 190, wherein adapting includes coordinating stations.

198. The system of claim 190, wherein optimizing includes managing capacity.

199. The system of claim 190, wherein adapting includes processing feedback.

200. The system of claim 190, wherein optimizing includes processing historical data. AI convergence system of systems ecosystem to perform data analysis and modeling related to the routing and navigation characteristics of a software-defined vehicle 201. A transportation AI convergence system of systems for analyzing vehicle routing, comprising: a data processing system configured to process data from multiple sources including social media data, weather data, road profile data, and traffic data; an artificial intelligence system configured to: analyze a transportation network using a machine learning algorithm; predict traffic conditions using graph neural networks; optimize a routing decision using reinforcement learning; and generate a routing parameter based on an analysis and prediction.

202. The system of claim 201, wherein the artificial intelligence system implements clustering algorithms to segment data based on traffic patterns.

203. The system of claim 201, wherein the artificial intelligence system implements time-series forecasting to predict future traffic conditions.

204. The system of claim 201, wherein the data processing system is configured to extract, transform, and load data to enable queries.

205. The system of claim 201, wherein the artificial intelligence system processes weather condition data to optimize routing.

206. The system of claim 201, wherein the artificial intelligence system processes road profile data.

207. The system of claim 201, wherein the artificial intelligence system implements cognitive engagement for routing behavior analysis.SFT-107-A-PCT 208. The system of claim 201, wherein the artificial intelligence system optimizes routes based on user satisfaction data.

209. The system of claim 201, wherein an artificial intelligence system coordinates with infrastructure elements.

210. The system of claim 201, wherein the artificial intelligence system implements digital twins for simulating traffic patterns.

211. The system of claim 201, wherein the artificial intelligence system adapts routing based on real-time conditions.

212. The transportation system having an AI convergence system of systems comprising: a cognitive system configured to: facilitate negotiation among designated sets of vehicles; process inputs relating to value attributed by riders to route parameters; and optimize route selection based on multiple factors including traffic conditions and weather conditions.

213. The system of claim 212, wherein the cognitive system implements game-based interfaces.

214. The system of claim 212, wherein the cognitive system provides rewards for routing actions.

215. The system of claim 212, wherein the cognitive system processes user preference data.

216. The system of claim 212, wherein a cognitive system coordinates with traffic infrastructure.

217. The system of claim 212, wherein the cognitive system analyzes congestion patterns.

218. The system of claim 212, wherein the cognitive system optimizes fleet routing.

219. The system of claim 212, wherein the cognitive system processes environmental data.

220. The system of claim 212, wherein the cognitive system implements predictive modeling.

221. The system of claim 212, wherein the cognitive system adapts to real-time conditions.

222. The system of claim 212, wherein the cognitive system processes feedback data.

223. A method for analyzing vehicle routing, comprising: processing transportation network data using graph neural networks; implementing clustering algorithms to segment data based on traffic patterns; applying time-series forecasting to predict future traffic conditions; and optimizing routing decisions using reinforcement learning based on the predictions.

224. The method of claim 223, wherein processing includes analyzing social media data.

225. The method of claim 223, wherein processing includes analyzing weather data.

226. The method of claim 223, wherein processing includes analyzing road profile data.

227. The method of claim 223, wherein implementing includes analyzing congestion patterns.

228. The method of claim 223, wherein applying includes predicting infrastructure utilization.

229. The method of claim 223, wherein optimizing includes processing user preferences.

230. The method of claim 223, wherein optimizing includes coordinating multiple vehicles.

231. The method of claim 223, wherein optimizing includes processing environmental data.

232. The method of claim 223, wherein optimizing includes analyzing real-time conditions.

233. The method of claim 223, wherein optimizing includes processing feedback data.SFT-107-A-PCT 234. A transportation AI convergence system of systems for transportation routing comprising: a digital twin system configured to: create virtual representations of traffic patterns; simulate vehicle movements in real-time; predict congestion points; and optimize routing based on simulations and predictions.

235. The system of claim 234, wherein the digital twin system processes sensor data.

236. The system of claim 234, wherein the digital twin system analyzes infrastructure utilization.

237. The system of claim 234, wherein the digital twin system processes weather data.

238. The system of claim 234, wherein the digital twin system coordinates multiple vehicles.

239. The system of claim 234, wherein the digital twin system processes environmental data.

240. The system of claim 234, wherein the digital twin system analyzes user preferences.

241. The system of claim 234, wherein the digital twin system implements predictive modeling.

242. The system of claim 234, wherein the digital twin system adapts to real-time conditions.

243. The system of claim 234, wherein the digital twin system processes feedback data.

244. The system of claim 234, wherein the digital twin system simulates future states.

245. A transportation AI convergence system of systems for analyzing vehicle routing comprising: an artificial intelligence system configured to: process graph data representing transportation networks; analyze traffic patterns using machine learning; optimize network efficiency through adaptive routing; and provide real-time routing suggestions based on an analysis.

246. An AI convergence system of systems for transportation routing comprising: an operations layer configured to: implement routing and control capabilities; monitor traffic patterns and resource utilization; optimize navigation based on real-time conditions; and coordinate with infrastructure elements for routing efficiency.

247. The system of claim 246, wherein the operations layer processes sensor data.

248. The system of claim 246, wherein the operations layer analyzes congestion patterns.

249. The system of claim 246, wherein the operations layer processes weather data.

250. The system of claim 246, wherein the operations layer coordinates multiple vehicles.

251. The system of claim 246, wherein the operations layer processes environmental data.

252. The system of claim 246, wherein the operations layer analyzes user preferences.

253. The system of claim 246, wherein the operations layer implements predictive modeling.

254. The system of claim 246, wherein the operations layer adapts to real-time conditions.

255. The system of claim 246, wherein the operations layer processes feedback data.

256. The system of claim 246, wherein the operations layer simulates future states.SFT-107-A-PCT 257. A method for analyzing vehicle routing comprising: processing graph data representing transportation networks; implementing machine learning algorithms to analyze traffic patterns; predicting congestion using clustering algorithms; and optimizing routes based on predictions and real-time conditions.

258. The method of claim 257, wherein processing includes analyzing sensor data.

259. The method of claim 257, wherein implementing includes analyzing weather data.

260. The method of claim 257, wherein predicting includes analyzing road conditions.

261. The method of claim 257, wherein optimizing includes coordinating multiple vehicles.

262. The method of claim 257, wherein processing includes analyzing environmental data.

263. The method of claim 257, wherein implementing includes analyzing user preferences.

264. The method of claim 257, wherein predicting includes using time-series forecasting.

265. The method of claim 257, wherein optimizing includes real-time adaptation.

266. The method of claim 257, wherein processing includes analyzing feedback data.

267. The method of claim 257, wherein implementing includes simulating future states.

268. An AI convergence system of systems for transportation routing comprising: a data layer configured to: process sensor and operations data; analyze market and environmental data; implement context-aware sensor fusion; and optimize routing based on analyzed data.

269. The system of claim 268, wherein the data layer processes traffic pattern data.

270. The system of claim 268, wherein the data layer analyzes weather conditions.

271. The system of claim 268, wherein the data layer processes infrastructure data.

272. The system of claim 268, wherein the data layer coordinates multiple vehicles.

273. The system of claim 268, wherein the data layer processes environmental factors.

274. The system of claim 268, wherein the data layer analyzes user preferences.

275. The system of claim 268, wherein the data layer implements predictive modeling.

276. The system of claim 268, wherein the data layer adapts to real-time conditions.

277. The system of claim 268, wherein the data layer processes feedback data.

278. The system of claim 268, wherein the data layer simulates future states.

279. An AI convergence system of systems for transportation routing comprising: an artificial intelligence system configured to: analyze transportation network spatial structure; predict traffic conditions using neural networks; optimize routing using reinforcement learning; and adapt routes based on real-time conditions.

280. The system of claim 279, wherein analyzing includes processing sensor data.

281. The system of claim 279, wherein predicting includes analyzing weather data.

282. The system of claim 279, wherein optimizing includes analyzing road conditions.SFT-107-A-PCT 283. The system of claim 279, wherein adapting includes coordinating multiple vehicles.

284. The system of claim 279, wherein analyzing includes processing environmental data.

285. The system of claim 279, wherein predicting includes analyzing user preferences.

286. The system of claim 279, wherein optimizing includes time-series forecasting.

287. The system of claim 279, wherein adapting includes real-time modification.

288. The system of claim 279, wherein analyzing includes processing feedback data.

289. The system of claim 279, wherein predicting includes simulating future states.

290. An AI convergence system of systems for transportation routing comprising: a hybrid neural network configured to: process transportation network graph data; analyze traffic patterns and congestion; predict future traffic conditions; and optimize routing based on predictions.

291. The system of claim 290, wherein processing includes analyzing sensor data.

292. The system of claim 290, wherein analyzing includes processing weather data.

293. The system of claim 290, wherein predicting includes analyzing road conditions.

294. The system of claim 290, wherein optimizing includes vehicle coordination.

295. The system of claim 290, wherein processing includes environmental analysis.

296. The system of claim 290, wherein analyzing includes user preference processing.

297. The system of claim 290, wherein predicting uses time-series forecasting.

298. The system of claim 290, wherein optimizing includes real-time adaptation.

299. The system of claim 290, wherein processing includes feedback analysis.

300. The system of claim 290, wherein analyzing includes future state simulation. AI convergence system of systems ecosystem to perform data analysis and modeling related to the characteristics of a software-defined vehicle 301. An AI convergence system of systems for analyzing vehicle characteristics, comprising: a data processing system configured to process data from multiple sources including social media data, weather data, road profile data, traffic data, and sensor data; an artificial intelligence system configured to: analyze vehicle operational states using machine learning algorithms; predict vehicle performance using neural networks; and optimize vehicle parameters using reinforcement learning.

302. The system of claim 301, wherein the artificial intelligence system implements hybrid neural networks to optimize distinct vehicle components.

303. The system of claim 301, wherein the artificial intelligence system implements time-series forecasting to predict future vehicle states.

304. The system of claim 301, wherein the data processing system is configured to extract, transform, and load data to enable queries.SFT-107-A-PCT 305. The system of claim 301, wherein the artificial intelligence system processes environmental condition data.

306. The system of claim 301, wherein the artificial intelligence system processes vehicle diagnostic data.

307. The system of claim 301, wherein the artificial intelligence system implements cognitive engagement for behavior analysis.

308. The system of claim 301, wherein the artificial intelligence system optimizes based on user satisfaction data.

309. The system of claim 301, wherein an artificial intelligence system coordinates with vehicle subsystems.

310. The system of claim 301, wherein the artificial intelligence system implements digital twins for simulating vehicle operations.

311. The system of claim 301, wherein the artificial intelligence system adapts based on real-time conditions.

312. A transportation system having an AI convergence system of systems comprising: a data layer configured to: implement context-aware sensor fusion; process sensor and energy operations data; analyze market and environmental data; and optimize vehicle operations based on analyzed data.

313. The system of claim 312, wherein the data layer processes vehicle state data.

314. The system of claim 312, wherein the data layer analyzes operational conditions.

315. The system of claim 312, wherein the data layer processes performance metrics.

316. The system of claim 312, wherein the data layer coordinates multiple vehicle systems.

317. The system of claim 312, wherein the data layer analyzes usage patterns.

318. The system of claim 312, wherein the data layer optimizes energy efficiency.

319. The system of claim 312, wherein the data layer processes environmental data.

320. The system of claim 312, wherein the data layer implements predictive modeling.

321. The system of claim 312, wherein the data layer adapts to real-time conditions.

322. The system of claim 312, wherein the data layer processes feedback data.

323. A method for analyzing vehicle characteristics, comprising: processing vehicle operational data using neural networks; implementing clustering algorithms to segment data based on usage patterns; applying time-series forecasting to predict future vehicle states; and optimizing vehicle parameters using reinforcement learning based on the predictions.

324. The method of claim 323, wherein processing includes analyzing sensor data.

325. The method of claim 323, wherein processing includes analyzing environmental data.

326. The method of claim 323, wherein processing includes analyzing diagnostic data.

327. The method of claim 323, wherein implementing includes analyzing usage patterns.

328. The method of claim 323, wherein applying includes predicting maintenance needs.SFT-107-A-PCT 329. The method of claim 323, wherein optimizing includes processing user preferences.

330. The method of claim 323, wherein optimizing includes coordinating vehicle systems.

331. The method of claim 323, wherein optimizing includes processing environmental data.

332. The method of claim 323, wherein optimizing includes analyzing real-time conditions.

333. The method of claim 323, wherein optimizing includes processing feedback data.

334. An AI convergence system of systems for vehicle analysis comprising: a digital twin system configured to: create virtual representations of vehicle components; simulate vehicle operations in real-time; predict maintenance needs; and optimize performance based on simulations and predictions.

335. The system of claim 334, wherein the digital twin system processes sensor data.

336. The system of claim 334, wherein the digital twin system analyzes component wear.

337. The system of claim 334, wherein the digital twin system processes environmental data.

338. The system of claim 334, wherein the digital twin system coordinates multiple systems.

339. The system of claim 334, wherein the digital twin system processes usage patterns.

340. The system of claim 334, wherein the digital twin system analyzes user preferences.

341. The system of claim 334, wherein the digital twin system implements predictive modeling.

342. The system of claim 334, wherein the digital twin system adapts to real-time conditions.

343. The system of claim 334, wherein the digital twin system processes feedback data.

344. The system of claim 334, wherein the digital twin system simulates future states.

345. A transportation AI convergence system of systems for analyzing vehicle characteristics comprising: an artificial intelligence system configured to: process vehicle operational data; analyze performance patterns using machine learning; optimize efficiency through adaptive control; and provide real-time operational suggestions based on an analysis.

346. The system of claim 345, wherein processing includes analyzing sensor data.

347. The system of claim 345, wherein analyzing includes processing diagnostic data.

348. The system of claim 345, wherein optimizing includes analyzing usage patterns.

349. The system of claim 345, wherein providing includes coordinating vehicle systems.

350. The system of claim 345, wherein processing includes environmental analysis.

351. The system of claim 345, wherein analyzing includes user preference processing.

352. The system of claim 345, wherein optimizing includes predictive modeling.

353. The system of claim 345, wherein providing includes real-time adaptation.

354. The system of claim 345, wherein processing includes feedback analysis.

355. The system of claim 345, wherein analyzing includes future state simulation.

356. A transportation AI convergence system of systems for vehicle analysis comprising: an operations layer configured to:SFT-107-A-PCT implement control and optimization capabilities; monitor performance patterns and resource utilization; optimize operations based on real-time conditions; and coordinate with vehicle subsystems for operational efficiency.

357. The system of claim 356, wherein implementing includes processing sensor data.

358. The system of claim 356, wherein monitoring includes analyzing usage patterns.

359. The system of claim 356, wherein optimizing includes processing diagnostic data.

360. The system of claim 356, wherein coordinating includes system integration.

361. The system of claim 356, wherein implementing includes environmental analysis.

362. The system of claim 356, wherein monitoring includes user preference processing.

363. The system of claim 356, wherein optimizing includes predictive modeling.

364. The system of claim 356, wherein coordinating includes real-time adaptation.

365. The system of claim 356, wherein implementing includes feedback analysis.

366. The system of claim 356, wherein monitoring includes future state simulation.

367. A method for analyzing vehicle characteristics comprising: processing operational data representing vehicle states; implementing machine learning algorithms to analyze performance patterns; predicting maintenance needs using clustering algorithms; and optimizing parameters based on predictions and real-time conditions.

368. The method of claim 367, wherein processing includes analyzing sensor data.

369. The method of claim 367, wherein implementing includes analyzing usage patterns.

370. The method of claim 367, wherein predicting includes analyzing component wear.

371. The method of claim 367, wherein optimizing includes system coordination.

372. The method of claim 367, wherein processing includes environmental analysis.

373. The method of claim 367, wherein implementing includes preference processing.

374. The method of claim 367, wherein predicting includes time-series forecasting.

375. The method of claim 367, wherein optimizing includes real-time adaptation.

376. The method of claim 367, wherein processing includes feedback analysis.

377. The method of claim 367, wherein implementing includes state simulation.

378. A transportation AI convergence system of systems for vehicle analysis comprising: a hybrid neural network configured to: process vehicle operational data; analyze performance patterns and efficiency; predict future operational states; and optimize parameters based on predictions.

379. The system of claim 378, wherein processing includes sensor analysis.

380. The system of claim 378, wherein analyzing includes usage pattern processing.

381. The system of claim 378, wherein predicting includes maintenance forecasting.

382. The system of claim 378, wherein optimizing includes system coordination.

383. The system of claim 378, wherein processing includes environmental analysis.SFT-107-A-PCT 384. The system of claim 378, wherein analyzing includes preference processing.

385. The system of claim 378, wherein predicting includes time-series forecasting.

386. The system of claim 378, wherein optimizing includes real-time adaptation.

387. The system of claim 378, wherein processing includes feedback analysis.

388. The system of claim 378, wherein analyzing includes state simulation.

389. A transportation AI convergence system of systems for vehicle analysis comprising: an enterprise layer configured to: implement executive digital twins for vehicle operations; create vehicle digital twins for design and simulation; analyze fleet operations data; and optimize vehicle parameters based on analysis.

390. The system of claim 389, wherein implementing includes sensor processing.

391. The system of claim 389, wherein creating includes component modeling.

392. The system of claim 389, wherein analyzing includes usage pattern processing.

393. The system of claim 389, wherein optimizing includes system coordination.

394. The system of claim 389, wherein implementing includes environmental analysis.

395. The system of claim 389, wherein creating includes preference processing.

396. The system of claim 389, wherein analyzing includes predictive modeling.

397. The system of claim 389, wherein optimizing includes real-time adaptation.

398. The system of claim 389, wherein implementing includes feedback analysis.

399. The system of claim 389, wherein creating includes state simulation.

400. A transportation AI convergence system of systems for vehicle analysis comprising: an artificial intelligence system configured to: process feature vectors of vehicle operational data; determine operational states using pattern recognition; optimize vehicle parameters to improve operational states; and adapt parameters through machine learning feedback.

401. The system of claim 400, wherein processing includes sensor analysis.

402. The system of claim 400, wherein determining includes pattern processing.

403. The system of claim 400, wherein optimizing includes efficiency analysis.

404. The system of claim 400, wherein adapting includes system coordination.

405. The system of claim 400, wherein processing includes environmental analysis.

406. The system of claim 400, wherein determining includes preference processing.

407. The system of claim 400, wherein optimizing includes predictive modeling.

408. The system of claim 400, wherein adapting includes real-time modification.

409. The system of claim 400, wherein processing includes feedback analysis.

410. The system of claim 400, wherein determining includes state simulation. AI convergence system of systems ecosystem to perform simulations or predictions related to the characteristics of a software-defined vehicle or its usageSFT-107-A-PCT 411. A transportation AI convergence system of systems for simulating vehicle operations, comprising: a digital twin system configured to: create a digital replica of a vehicle; process substantially real-time sensor data to provide virtual representation of the vehicle; simulate possible future states of the vehicle; and predict vehicle behavior and performance under various conditions based on the simulations.

412. The system of claim 411, wherein the digital twin system simulates physical elements and properties of the vehicle.

413. The system of claim 411, wherein the digital twin system simulates vehicle dynamics throughout its lifecycle.

414. The system of claim 411, wherein the digital twin system provides hypothetical simulations during vehicle design phases.

415. The system of claim 411, wherein the digital twin system simulates high stress conditions.

416. The system of claim 411, wherein the digital twin system simulates component wear scenarios.

417. The system of claim 411, wherein the digital twin system simulates maximum throughput operation.

418. The system of claim 411, wherein the digital twin system simulates planned improvements.

419. The system of claim 411, wherein the digital twin system processes environmental data.

420. The system of claim 411, wherein the digital twin system processes marketplace factors.

421. The system of claim 411, wherein the digital twin system adapts to real-time conditions.

422. A transportation AI convergence system of systems for transportation analysis comprising: an artificial intelligence system configured to: train predictive models using vehicle-related data; process vehicle specifications, environmental data, sensor data, and operational information; generate predictions regarding remaining vehicle life; and optimize vehicle operations based on predictions.

423. The system of claim 422, wherein the artificial intelligence system implements classification models.

424. The system of claim 422, wherein the artificial intelligence system implements regression models.

425. The system of claim 422, wherein the artificial intelligence system predicts failure within time windows.

426. The system of claim 422, wherein the artificial intelligence system predicts remaining useful life.

427. The system of claim 422, wherein the artificial intelligence system processes feedback data.

428. The system of claim 422, wherein the artificial intelligence system updates models based on outcomes.SFT-107-A-PCT 429. The system of claim 422, wherein the artificial intelligence system processes environmental data.

430. The system of claim 422, wherein the artificial intelligence system implements supervised learning.

431. The system of claim 422, wherein the artificial intelligence system implements unsupervised learning.

432. The system of claim 422, wherein the artificial intelligence system implements reinforcement learning.

433. A transportation AI convergence system of systems for vehicle simulation comprising: a machine learning model configured to: perform analytics related to vehicle data processing; create simulations of vehicle operations; analyze simulation results; and make predictions based on analyzed results.

434. The system of claim 433, wherein the machine learning model processes sensor data.

435. The system of claim 433, wherein the machine learning model processes event data.

436. The system of claim 433, wherein the machine learning model processes state data.

437. The system of claim 433, wherein the machine learning model implements neural networks.

438. The system of claim 433, wherein the machine learning model implements decision trees.

439. The system of claim 433, wherein the machine learning model implements support vector machines.

440. The system of claim 433, wherein the machine learning model implements Bayesian networks.

441. The system of claim 433, wherein the machine learning model implements genetic algorithms.

442. The system of claim 433, wherein the machine learning model implements supervised learning.

443. The system of claim 433, wherein the machine learning model implements reinforcement learning.

444. A transportation AI convergence system of systems for vehicle analysis comprising: an enterprise layer configured to: implement executive digital twins for vehicle fleet operations; create vehicle digital twins for design and simulation; predict fleet operational characteristics; and optimize fleet parameters based on predictions.

445. The system of claim 444, wherein implementing includes processing sensor data.

446. The system of claim 444, wherein creating includes component modeling.

447. The system of claim 444, wherein predicting includes usage pattern analysis.

448. The system of claim 444, wherein optimizing includes system coordination.

449. The system of claim 444, wherein implementing includes environmental analysis.SFT-107-A-PCT 450. The system of claim 444, wherein creating includes preference processing.

451. The system of claim 444, wherein predicting includes maintenance forecasting.

452. The system of claim 444, wherein optimizing includes real-time adaptation.

453. The system of claim 444, wherein implementing includes feedback analysis.

454. The system of claim 444, wherein creating includes state simulation.

455. A transportation AI convergence system of systems for vehicle simulation comprising: a hybrid neural network configured to: process vehicle operational data; simulate vehicle performance patterns; predict future operational states; and optimize parameters based on predictions.

456. The system of claim 455, wherein processing includes sensor analysis.

457. The system of claim 455, wherein simulating includes usage pattern processing.

458. The system of claim 455, wherein predicting includes maintenance forecasting.

459. The system of claim 455, wherein optimizing includes system coordination.

460. The system of claim 455, wherein processing includes environmental analysis.

461. The system of claim 455, wherein simulating includes preference processing.

462. The system of claim 455, wherein predicting includes time-series forecasting.

463. The system of claim 455, wherein optimizing includes real-time adaptation.

464. The system of claim 455, wherein processing includes feedback analysis.

465. The system of claim 455, wherein simulating includes state simulation.

466. A method for vehicle simulation comprising: creating a digital replica of a vehicle; processing real-time sensor data for virtual representation; simulating future vehicle states; and optimizing vehicle parameters based on simulations.

467. The method of claim 466, wherein creating includes component modeling.

468. The method of claim 466, wherein processing includes environmental data analysis.

469. The method of claim 466, wherein simulating includes stress testing.

470. The method of claim 466, wherein optimizing includes system coordination.

471. The method of claim 466, wherein creating includes physical property modeling.

472. The method of claim 466, wherein processing includes operational data analysis.

473. The method of claim 466, wherein simulating includes wear prediction.

474. The method of claim 466, wherein optimizing includes real-time adaptation.

475. The method of claim 466, wherein creating includes dynamic modeling.

476. The method of claim 466, wherein processing includes feedback analysis.

477. A transportation AI convergence system of systems for vehicle prediction comprising: an artificial intelligence system configured to: analyze vehicle operational patterns; simulate vehicle performance scenarios;SFT-107-A-PCT predict maintenance requirements; and optimize operational parameters based on predictions.

478. The system of claim 477, wherein analyzing includes sensor processing.

479. The system of claim 477, wherein simulating includes environmental modeling.

480. The system of claim 477, wherein predicting includes component analysis.

481. The system of claim 477, wherein optimizing includes system coordination.

482. The system of claim 477, wherein analyzing includes usage pattern processing.

483. The system of claim 477, wherein simulating includes stress testing.

484. The system of claim 477, wherein predicting includes lifecycle analysis.

485. The system of claim 477, wherein optimizing includes real-time adaptation.

486. The system of claim 477, wherein analyzing includes feedback processing.

487. The system of claim 477, wherein simulating includes state modeling.

488. A transportation AI convergence system of systems for vehicle analysis comprising: a data layer configured to: process sensor and operational data; simulate vehicle performance patterns; predict operational characteristics; and optimize parameters based on predictions.

489. The system of claim 488, wherein processing includes environmental analysis.

490. The system of claim 488, wherein simulating includes component modeling.

491. The system of claim 488, wherein predicting includes maintenance forecasting.

492. The system of claim 488, wherein optimizing includes system coordination.

493. The system of claim 488, wherein processing includes usage pattern analysis.

494. The system of claim 488, wherein simulating includes stress testing.

495. The system of claim 488, wherein predicting includes lifecycle analysis.

496. The system of claim 488, wherein optimizing includes real-time adaptation.

497. The system of claim 488, wherein processing includes feedback analysis.

498. The system of claim 488, wherein simulating includes state modeling.

499. A method for vehicle prediction comprising: processing vehicle operational data; creating simulation models of vehicle performance; predicting future operational states; and optimizing vehicle parameters based on predictions.

500. The method of claim 499, wherein processing includes sensor analysis.

501. The method of claim 499, wherein creating includes environmental modeling.

502. The method of claim 499, wherein predicting includes maintenance forecasting.

503. The method of claim 499, wherein optimizing includes system coordination.

504. The method of claim 499, wherein processing includes usage pattern analysis.

505. The method of claim 499, wherein creating includes stress testing.

506. The method of claim 499, wherein predicting includes lifecycle analysis.SFT-107-A-PCT 507. The method of claim 499, wherein optimizing includes real-time adaptation.

508. The method of claim 499, wherein processing includes feedback analysis.

509. The method of claim 499, wherein creating includes state modeling.

510. A transportation AI convergence system of systems for vehicle simulation comprising: an operations layer configured to: process vehicle operational data; create performance simulations; predict future states; and optimize parameters based on predictions.

511. The system of claim 510, wherein processing includes sensor analysis.

512. The system of claim 510, wherein creating includes environmental modeling.

513. The system of claim 510, wherein predicting includes maintenance forecasting.

514. The system of claim 510, wherein optimizing includes system coordination.

515. The system of claim 510, wherein processing includes usage pattern analysis.

516. The system of claim 510, wherein creating includes stress testing.

517. The system of claim 510, wherein predicting includes lifecycle analysis.

518. The system of claim 510, wherein optimizing includes real-time adaptation.

519. The system of claim 510, wherein processing includes feedback analysis.

520. The system of claim 510, wherein creating includes state modeling. AI convergence system of systems ecosystem to alter user interface(s) characteristics for a driver of a software-defined vehicle 521. A transportation AI convergence system of systems for optimizing vehicle user interfaces, comprising: a vehicle having a set of interfaces including steering systems, buttons, levers, touch screen interfaces, and audio interfaces; a sensor system configured to provide input to an expert system; an artificial intelligence system configured to: track vehicle operating states and user experience states; and modify interface characteristics based on current conditions and desired outcomes.

522. The system of claim 521, wherein the set of interfaces includes a game interface.

523. The system of claim 521, wherein the set of interfaces includes a navigation interface.

524. The system of claim 521, wherein the set of interfaces includes an entertainment interface.

525. The system of claim 521, wherein the set of interfaces includes a vehicle settings interface.

526. The system of claim 521, wherein the set of interfaces includes a search interface.

527. The system of claim 521, wherein the set of interfaces includes an ecommerce interface.

528. The system of claim 521, wherein the artificial intelligence system processes voice patterns.

529. The system of claim 521, wherein the artificial intelligence system processes facial expressions.

530. The system of claim 521, wherein the artificial intelligence system processes physiological data.SFT-107-A-PCT 531. The system of claim 521, wherein the artificial intelligence system adapts interface characteristics based on emotional state.

532. A transportation AI convergence system of systems for transportation comprising: a vehicle having user interfaces; an offering layer implementing expert systems and generative AI configured to: provide location-based offering systems; integrate advertising and marketplace functions; and implement customer-facing vehicle digital twins.

533. The system of claim 532, wherein the offering layer processes user profiles.

534. The system of claim 532, wherein the offering layer analyzes user behavior.

535. The system of claim 532, wherein the offering layer processes environmental data.

536. The system of claim 532, wherein the offering layer adapts to user location.

537. The system of claim 532, wherein the offering layer processes user requests.

538. The system of claim 532, wherein the offering layer customizes content delivery.

539. The system of claim 532, wherein the offering layer personalizes interfaces.

540. The system of claim 532, wherein the offering layer adapts to user preferences.

541. The system of claim 532, wherein the offering layer processes real-time data.

542. The system of claim 532, wherein the offering layer implements feedback processing.

543. An AI convergence system of systems for vehicle interface optimization comprising: a cognitive system configured to: monitor driver interactions with vehicle controls; detect patterns indicating routine activities; generate cognitive challenges; and adapt interface characteristics based on detected patterns.

544. The system of claim 543, wherein monitoring includes analyzing navigation system usage.

545. The system of claim 543, wherein detecting includes analyzing spatial awareness.

546. The system of claim 543, wherein generating includes memory recall challenges.

547. The system of claim 543, wherein adapting includes modifying display characteristics.

548. The system of claim 543, wherein monitoring includes analyzing voice patterns.

549. The system of claim 543, wherein detecting includes analyzing facial expressions.

550. The system of claim 543, wherein generating includes decision-making challenges.

551. The system of claim 543, wherein adapting includes modifying audio feedback.

552. The system of claim 543, wherein monitoring includes analyzing emotional states.

553. The system of claim 543, wherein detecting includes analyzing user preferences.

554. An AI convergence system of systems for vehicle interface management comprising: an artificial intelligence system configured to: process feature vectors from facial images; determine driver emotional states; optimize interface parameters based on emotional states; and adapt information presentation to improve emotional states.SFT-107-A-PCT 555. An AI convergence system of systems for vehicle interface adaptation comprising: a sensor system configured to detect driver state; an artificial intelligence system configured to: process voice patterns and physiological data; identify cognitive engagement levels; and modify interface characteristics to maintain optimal engagement.

556. The system of claim 555, wherein processing includes analyzing speech patterns.

557. The system of claim 555, wherein identifying includes analyzing facial expressions.

558. The system of claim 555, wherein modifying includes adjusting display parameters.

559. The system of claim 555, wherein processing includes analyzing emotional states.

560. The system of claim 555, wherein identifying includes analyzing attention levels.

561. The system of claim 555, wherein modifying includes adjusting audio feedback.

562. The system of claim 555, wherein processing includes analyzing user preferences.

563. The system of claim 555, wherein identifying includes analyzing stress levels.

564. The system of claim 555, wherein modifying includes real-time adaptation.

565. The system of claim 555, wherein processing includes feedback analysis.

566. An AI convergence system of systems for vehicle interface optimization comprising: a multiplatform attention management system configured to: monitor driver attention patterns; analyze cognitive load levels; adapt interface presentations; and optimize information delivery based on attention patterns.

567. The system of claim 566, wherein monitoring includes analyzing eye movements.

568. The system of claim 566, wherein analyzing includes processing voice patterns.

569. The system of claim 566, wherein adapting includes modifying display characteristics.

570. The system of claim 566, wherein optimizing includes adjusting content timing.

571. The system of claim 566, wherein monitoring includes analyzing facial expressions.

572. The system of claim 566, wherein analyzing includes processing emotional states.

573. The system of claim 566, wherein adapting includes modifying audio feedback.

574. The system of claim 566, wherein optimizing includes content prioritization.

575. The system of claim 566, wherein monitoring includes stress level analysis.

576. The system of claim 566, wherein analyzing includes preference processing.

577. A method for vehicle interface adaptation comprising: monitoring driver interactions with vehicle controls; analyzing cognitive engagement patterns; generating interface modifications; and implementing adaptive changes based on engagement patterns.

578. The method of claim 577, wherein monitoring includes analyzing voice patterns.

579. The method of claim 577, wherein analyzing includes processing facial expressions.

580. The method of claim 577, wherein generating includes display modifications.SFT-107-A-PCT 581. The method of claim 577, wherein implementing includes audio adjustments.

582. The method of claim 577, wherein monitoring includes emotional state analysis.

583. The method of claim 577, wherein analyzing includes attention level processing.

584. The method of claim 577, wherein generating includes content adaptation.

585. The method of claim 577, wherein implementing includes real-time changes.

586. The method of claim 577, wherein monitoring includes stress level analysis.

587. The method of claim 577, wherein analyzing includes preference processing.

588. An AI convergence system of systems for vehicle interface management comprising: an artificial intelligence system configured to: process driver behavior patterns; analyze cognitive stimulation needs; generate interface modifications; and implement adaptive changes based on analyzed needs.

589. The system of claim 588, wherein processing includes voice analysis.

590. The system of claim 588, wherein analyzing includes facial expression processing.

591. The system of claim 588, wherein generating includes display adaptations.

592. The system of claim 588, wherein implementing includes audio modifications.

593. The system of claim 588, wherein processing includes emotional state analysis.

594. The system of claim 588, wherein analyzing includes attention level processing.

595. The system of claim 588, wherein generating includes content optimization.

596. The system of claim 588, wherein implementing includes real-time adaptation.

597. The system of claim 588, wherein processing includes stress level analysis.

598. The system of claim 588, wherein analyzing includes preference processing.

599. An AI convergence system of systems for vehicle interface optimization comprising: a transaction layer configured to: implement user profiling and targeting; configure smart contracts for in-vehicle offers; automate transaction orchestration; and adapt interfaces based on user profiles.

600. The system of claim 599, wherein implementing includes behavior analysis.

601. The system of claim 599, wherein configuring includes preference processing.

602. The system of claim 599, wherein automating includes content adaptation.

603. The system of claim 599, wherein adapting includes display modifications.

604. The system of claim 599, wherein implementing includes emotional state analysis.

605. The system of claim 599, wherein configuring includes attention level processing.

606. The system of claim 599, wherein automating includes interface optimization.

607. The system of claim 599, wherein adapting includes real-time changes.

608. The system of claim 599, wherein implementing includes stress level analysis.

609. The system of claim 599, wherein configuring includes feedback processing.SFT-107-A-PCT 610. An AI convergence system of systems for vehicle interface adaptation comprising: an operations layer configured to: monitor rider satisfaction; analyze interface effectiveness; generate interface modifications; and implement adaptive changes based on satisfaction analysis.

611. The system of claim 610, wherein monitoring includes voice analysis.

612. The system of claim 610, wherein analyzing includes facial expression processing.

613. The system of claim 610, wherein generating includes display adaptations.

614. The system of claim 610, wherein implementing includes audio modifications.

615. The system of claim 610, wherein monitoring includes emotional state analysis.

616. The system of claim 610, wherein analyzing includes attention level processing.

617. The system of claim 610, wherein generating includes content optimization.

618. The system of claim 610, wherein implementing includes real-time adaptation.

619. The system of claim 610, wherein monitoring includes stress level analysis.

620. The system of claim 610, wherein analyzing includes preference processing. AI convergence system of systems ecosystem to alter a vehicle’s interior or in-cabin characteristics 621. An AI convergence system of systems for optimizing vehicle interior conditions, comprising: a vehicle having a set of interior systems including at least one of a seat, climate control system, or audio system; a sensor system configured to detect rider emotional states; an artificial intelligence system configured to: process physiological monitoring data from the rider; and optimize interior parameters based on the detected emotional states and physiological data.

622. The system of claim 621, wherein the interior systems include seat positioning control.

623. The system of claim 621, wherein the interior systems include lumbar support adjustment.

624. The system of claim 621, wherein the interior systems include leg room adjustment.

625. The system of claim 621, wherein the interior systems include seatback angle control.

626. The system of claim 621, wherein the interior systems include ventilation control.

627. The system of claim 621, wherein the interior systems include window control.

628. The system of claim 621, wherein the interior systems include moonroof control.

629. The system of claim 621, wherein the interior systems include temperature control.

630. The system of claim 621, wherein the interior systems include humidity control.

631. The system of claim 621, wherein the interior systems include fan speed control.

632. An AI convergence system of systems for transportation comprising: a vehicle having interior environment controls; a physiological sensing system configured to monitor rider state; an artificial intelligence system configured to:SFT-107-A-PCT detect changes in rider emotional state; and adjust interior environmental parameters to improve rider emotional state.

633. The system of claim 632, wherein detecting includes processing vision system data.

634. The system of claim 632, wherein detecting includes processing seat sensor data.

635. The system of claim 632, wherein detecting includes processing steering wheel sensor data.

636. The system of claim 632, wherein adjusting includes modifying audio content.

637. The system of claim 632, wherein adjusting includes modifying climate settings.

638. The system of claim 632, wherein adjusting includes modifying seat position.

639. The system of claim 632, wherein detecting includes processing voice data.

640. The system of claim 632, wherein detecting includes processing facial expressions.

641. The system of claim 632, wherein adjusting includes modifying lighting conditions.

642. The system of claim 632, wherein adjusting includes modifying ventilation settings.

643. An AI convergence system of systems for vehicle interior optimization comprising: a sensor system configured to detect rider state; an artificial intelligence system configured to: process physiological parameters; identify stress indicators; and modify cabin environment to reduce detected stress.

644. The system of claim 643, wherein processing includes analyzing galvanic skin response.

645. The system of claim 643, wherein processing includes analyzing cortisol levels.

646. The system of claim 643, wherein identifying includes analyzing voice patterns.

647. The system of claim 643, wherein modifying includes adjusting audio content.

648. The system of claim 643, wherein modifying includes adjusting climate settings.

649. The system of claim 643, wherein modifying includes adjusting seat position.

650. The system of claim 643, wherein processing includes analyzing facial expressions.

651. The system of claim 643, wherein identifying includes analyzing emotional states.

652. The system of claim 643, wherein modifying includes adjusting lighting.

653. The system of claim 643, wherein modifying includes adjusting ventilation.

654. An AI convergence system of systems for vehicle interior management comprising: a hybrid neural network configured to: process rider physiological data; analyze cabin environmental conditions; predict optimal interior settings; and implement adaptive changes based on predictions.

655. The system of claim 654, wherein processing includes voice analysis.

656. The system of claim 654, wherein analyzing includes temperature monitoring.

657. The system of claim 654, wherein predicting includes comfort optimization.

658. The system of claim 654, wherein implementing includes seat adjustment.

659. The system of claim 654, wherein processing includes stress level analysis.

660. The system of claim 654, wherein analyzing includes humidity monitoring.SFT-107-A-PCT 661. The system of claim 654, wherein predicting includes ventilation optimization.

662. The system of claim 654, wherein implementing includes audio adjustment.

663. The system of claim 654, wherein processing includes emotional state analysis.

664. The system of claim 654, wherein analyzing includes lighting conditions.

665. A method for vehicle interior optimization comprising: monitoring rider physiological state; analyzing cabin environmental conditions; predicting optimal comfort settings; and implementing adaptive changes based on predictions.

666. The method of claim 665, wherein monitoring includes voice analysis.

667. The method of claim 665, wherein analyzing includes temperature monitoring.

668. The method of claim 665, wherein predicting includes comfort optimization.

669. The method of claim 665, wherein implementing includes seat adjustment.

670. The method of claim 665, wherein monitoring includes stress level analysis.

671. The method of claim 665, wherein analyzing includes humidity monitoring.

672. The method of claim 665, wherein predicting includes ventilation optimization.

673. The method of claim 665, wherein implementing includes audio adjustment.

674. The method of claim 665, wherein monitoring includes emotional state analysis.

675. The method of claim 665, wherein analyzing includes lighting conditions.

676. An AI convergence system of systems for vehicle interior adaptation comprising: a sensor system configured to monitor cabin conditions; an artificial intelligence system configured to: analyze rider comfort indicators; predict optimal environmental settings; and implement adaptive changes based on predictions.

677. The system of claim 676, wherein analyzing includes temperature monitoring.

678. The system of claim 676, wherein analyzing includes humidity monitoring.

679. The system of claim 676, wherein predicting includes ventilation optimization.

680. The system of claim 676, wherein implementing includes seat adjustment.

681. The system of claim 676, wherein analyzing includes stress level monitoring.

682. The system of claim 676, wherein analyzing includes emotional state monitoring.

683. The system of claim 676, wherein predicting includes lighting optimization.

684. The system of claim 676, wherein implementing includes audio adjustment.

685. The system of claim 676, wherein analyzing includes voice pattern monitoring.

686. The system of claim 676, wherein implementing includes climate control.

687. An AI convergence system of systems for vehicle interior optimization comprising: a digital twin system configured to: create virtual representations of cabin conditions; simulate environmental modifications;SFT-107-A-PCT predict rider responses to modifications; and implement optimal environmental changes.

688. The system of claim 687, wherein creating includes temperature modeling.

689. The system of claim 687, wherein simulating includes humidity variations.

690. The system of claim 687, wherein predicting includes comfort analysis.

691. The system of claim 687, wherein implementing includes seat adjustment.

692. The system of claim 687, wherein creating includes lighting modeling.

693. The system of claim 687, wherein simulating includes ventilation changes.

694. The system of claim 687, wherein predicting includes stress response.

695. The system of claim 687, wherein implementing includes audio modification.

696. The system of claim 687, wherein creating includes acoustic modeling.

697. The system of claim 687, wherein simulating includes climate variations.

698. An AI convergence system of systems for vehicle interior management comprising: an operations layer configured to: monitor cabin environmental conditions; analyze rider comfort indicators; predict optimal settings; and implement adaptive changes.

699. The system of claim 698, wherein monitoring includes temperature analysis.

700. The system of claim 698, wherein analyzing includes humidity monitoring.

701. The system of claim 698, wherein predicting includes ventilation optimization.

702. The system of claim 698, wherein implementing includes seat adjustment.

703. The system of claim 698, wherein monitoring includes stress level analysis.

704. The system of claim 698, wherein analyzing includes emotional state monitoring.

705. The system of claim 698, wherein predicting includes lighting optimization.

706. The system of claim 698, wherein implementing includes audio adjustment.

707. The system of claim 698, wherein monitoring includes voice pattern analysis.

708. The system of claim 698, wherein implementing includes climate control.

709. An AI convergence system of systems for vehicle interior optimization comprising: a hybrid neural network configured to: process cabin environmental data; analyze rider physiological responses; predict optimal comfort settings; and implement adaptive changes.

710. The system of claim 709, wherein processing includes temperature analysis.

711. The system of claim 709, wherein analyzing includes humidity monitoring.

712. The system of claim 709, wherein predicting includes ventilation optimization.

713. The system of claim 709, wherein implementing includes seat adjustment.

714. The system of claim 709, wherein processing includes stress level analysis.

715. The system of claim 709, wherein analyzing includes emotional state monitoring.SFT-107-A-PCT 716. The system of claim 709, wherein predicting includes lighting optimization.

717. The system of claim 709, wherein implementing includes audio adjustment.

718. The system of claim 709, wherein processing includes voice pattern analysis.

719. The system of claim 709, wherein implementing includes climate control.

720. An AI convergence system of systems for vehicle interior adaptation comprising: an artificial intelligence system configured to: monitor cabin environmental conditions; analyze rider comfort indicators; generate environmental modifications; and implement adaptive changes based on analysis.

721. The system of claim 720, wherein monitoring includes temperature analysis.

722. The system of claim 720, wherein analyzing includes humidity monitoring.

723. The system of claim 720, wherein generating includes ventilation optimization.

724. The system of claim 720, wherein implementing includes seat adjustment.

725. The system of claim 720, wherein monitoring includes stress level analysis.

726. The system of claim 720, wherein analyzing includes emotional state monitoring.

727. The system of claim 720, wherein generating includes lighting optimization.

728. The system of claim 720, wherein implementing includes audio adjustment.

729. The system of claim 720, wherein monitoring includes voice pattern analysis.

730. The system of claim 720, wherein implementing includes climate control. AI convergence system of systems ecosystem to alter a vehicle’s performance or operating characteristics 731. An AI convergence system of systems for optimizing vehicle performance, comprising: a set of sensors configured to provide input to an expert system; an artificial intelligence system configured to: track vehicle operating states including energy utilization state, maintenance state, and component state; optimize powertrain parameters based on the tracked states; and adapt vehicle performance characteristics in real-time.

732. The system of claim 731, wherein the artificial intelligence system implements hybrid neural networks.

733. The system of claim 731, wherein the artificial intelligence system processes environmental data.

734. The system of claim 731, wherein the artificial intelligence system optimizes fuel usage.

735. The system of claim 731, wherein the artificial intelligence system optimizes electricity usage.

736. The system of claim 731, wherein the artificial intelligence system optimizes refueling timing.

737. The system of claim 731, wherein the artificial intelligence system optimizes recharging timing.SFT-107-A-PCT 738. The system of claim 731, wherein the artificial intelligence system processes traffic predictions.

739. The system of claim 731, wherein the artificial intelligence system processes transportation predictions.

740. The system of claim 731, wherein the artificial intelligence system optimizes suspension profiles.

741. The system of claim 731, wherein the artificial intelligence system processes road profile data.

742. An AI convergence system of systems for transportation comprising: a hybrid neural network configured to: classify vehicle states including maintenance state, health state, and operating state; optimize powertrain operating parameters based on the classified states; and predict future vehicle states based on the optimization.

743. The system of claim 742, wherein the hybrid neural network processes sensor data.

744. The system of claim 742, wherein the hybrid neural network analyzes vehicle range.

745. The system of claim 742, wherein the hybrid neural network analyzes powertrain parameters.

746. The system of claim 742, wherein the hybrid neural network analyzes current gear state.

747. The system of claim 742, wherein the hybrid neural network analyzes speed parameters.

748. The system of claim 742, wherein the hybrid neural network analyzes acceleration parameters.

749. The system of claim 742, wherein the hybrid neural network analyzes suspension profiles.

750. The system of claim 742, wherein the hybrid neural network analyzes charge state.

751. The system of claim 742, wherein the hybrid neural network analyzes fuel state.

752. The system of claim 742, wherein the hybrid neural network implements feedback processing.

753. An AI convergence system of systems for vehicle performance optimization comprising: a data processing system configured to process data from vehicle sensors; an artificial intelligence system configured to: analyze vehicle operational states; optimize drivetrain parameters based on operational states; and implement adaptive changes based on real-time conditions.

754. The system of claim 753, wherein analyzing includes processing transmission data.

755. The system of claim 753, wherein analyzing includes processing gear system data.

756. The system of claim 753, wherein analyzing includes processing clutch system data.

757. The system of claim 753, wherein analyzing includes processing braking system data.

758. The system of claim 753, wherein analyzing includes processing fuel system data.

759. The system of claim 753, wherein analyzing includes processing lubrication system data.

760. The system of claim 753, wherein analyzing includes processing steering system data.

761. The system of claim 753, wherein analyzing includes processing suspension system data.SFT-107-A-PCT 762. The system of claim 753, wherein analyzing includes processing lighting system data.

763. The system of claim 753, wherein analyzing includes processing electrical system data.

764. An AI convergence system of systems for vehicle performance management comprising: a hybrid neural network configured to: process vehicle telemetry data; analyze component performance patterns; predict maintenance requirements; and optimize operational parameters based on predictions.

765. The system of claim 764, wherein processing includes analyzing sensor data.

766. The system of claim 764, wherein analyzing includes powertrain monitoring.

767. The system of claim 764, wherein predicting includes component wear analysis.

768. The system of claim 764, wherein optimizing includes transmission adjustment.

769. The system of claim 764, wherein processing includes suspension analysis.

770. The system of claim 764, wherein analyzing includes brake system monitoring.

771. The system of claim 764, wherein predicting includes fuel efficiency optimization.

772. The system of claim 764, wherein optimizing includes real-time adaptation.

773. The system of claim 764, wherein processing includes environmental analysis.

774. The system of claim 764, wherein analyzing includes performance feedback.

775. A method for vehicle performance optimization comprising: monitoring vehicle operational states; analyzing component performance patterns; predicting maintenance requirements; and implementing adaptive changes based on predictions.

776. The method of claim 775, wherein monitoring includes sensor data analysis.

777. The method of claim 775, wherein analyzing includes powertrain monitoring.

778. The method of claim 775, wherein predicting includes component wear analysis.

779. The method of claim 775, wherein implementing includes transmission adjustment.

780. The method of claim 775, wherein monitoring includes suspension analysis.

781. The method of claim 775, wherein analyzing includes brake system monitoring.

782. The method of claim 775, wherein predicting includes fuel efficiency optimization.

783. The method of claim 775, wherein implementing includes real-time adaptation.

784. The method of claim 775, wherein monitoring includes environmental analysis.

785. The method of claim 775, wherein analyzing includes performance feedback.

786. An AI convergence system of systems for vehicle performance optimization comprising: an operations layer configured to: implement control and optimization capabilities; monitor performance patterns and resource utilization; optimize operations based on real-time conditions; and coordinate with vehicle subsystems for operational efficiency.

787. The system of claim 786, wherein implementing includes powertrain control.SFT-107-A-PCT 788. The system of claim 786, wherein monitoring includes transmission analysis.

789. The system of claim 786, wherein optimizing includes suspension adjustment.

790. The system of claim 786, wherein coordinating includes brake system control.

791. The system of claim 786, wherein implementing includes fuel system optimization.

792. The system of claim 786, wherein monitoring includes component wear analysis.

793. The system of claim 786, wherein optimizing includes efficiency improvement.

794. The system of claim 786, wherein coordinating includes real-time adaptation.

795. The system of claim 786, wherein implementing includes environmental analysis.

796. The system of claim 786, wherein monitoring includes performance feedback.

797. An AI convergence system of systems for vehicle performance management comprising: a digital twin system configured to: create virtual representations of vehicle components; simulate component operations in real-time; predict maintenance requirements; and optimize performance based on simulations.

798. The system of claim 797, wherein creating includes powertrain modeling.

799. The system of claim 797, wherein simulating includes transmission analysis.

800. The system of claim 797, wherein predicting includes component wear analysis.

801. The system of claim 797, wherein optimizing includes performance adjustment.

802. The system of claim 797, wherein creating includes suspension modeling.

803. The system of claim 797, wherein simulating includes brake system analysis.

804. The system of claim 797, wherein predicting includes efficiency optimization.

805. The system of claim 797, wherein optimizing includes real-time adaptation.

806. The system of claim 797, wherein creating includes environmental modeling.

807. The system of claim 797, wherein simulating includes performance feedback.

808. An AI convergence system of systems for vehicle performance optimization comprising: an artificial intelligence system configured to: process vehicle operational data; analyze performance patterns; predict maintenance requirements; and implement adaptive changes based on predictions.

809. The system of claim 808, wherein processing includes sensor analysis.

810. The system of claim 808, wherein analyzing includes powertrain monitoring.

811. The system of claim 808, wherein predicting includes component wear analysis.

812. The system of claim 808, wherein implementing includes transmission adjustment.

813. The system of claim 808, wherein processing includes suspension analysis.

814. The system of claim 808, wherein analyzing includes brake system monitoring.

815. The system of claim 808, wherein predicting includes efficiency optimization.

816. The system of claim 808, wherein implementing includes real-time adaptation.

817. The system of claim 808, wherein processing includes environmental analysis.SFT-107-A-PCT 818. The system of claim 808, wherein analyzing includes performance feedback.

819. An AI convergence system of systems for vehicle performance management comprising: a hybrid neural network configured to: analyze vehicle operational patterns; optimize component performance; predict maintenance requirements; and implement adaptive changes.

820. The system of claim 819, wherein analyzing includes sensor processing.

821. The system of claim 819, wherein optimizing includes powertrain control.

822. The system of claim 819, wherein predicting includes component analysis.

823. The system of claim 819, wherein implementing includes transmission adjustment.

824. The system of claim 819, wherein analyzing includes suspension monitoring.

825. The system of claim 819, wherein optimizing includes brake system control.

826. The system of claim 819, wherein predicting includes efficiency analysis.

827. The system of claim 819, wherein implementing includes real-time adaptation.

828. The system of claim 819, wherein analyzing includes environmental processing.

829. The system of claim 819, wherein optimizing includes performance feedback.

830. An AI convergence system of systems for vehicle performance optimization comprising: an artificial intelligence system configured to: monitor vehicle operational states; analyze component performance; predict maintenance needs; and implement adaptive changes.

831. The system of claim 830, wherein monitoring includes sensor analysis.

832. The system of claim 830, wherein analyzing includes powertrain monitoring.

833. The system of claim 830, wherein predicting includes wear analysis.

834. The system of claim 830, wherein implementing includes transmission control.

835. The system of claim 830, wherein monitoring includes suspension analysis.

836. The system of claim 830, wherein analyzing includes brake system monitoring.

837. The system of claim 830, wherein predicting includes efficiency optimization.

838. The system of claim 830, wherein implementing includes real-time adaptation.

839. The system of claim 830, wherein monitoring includes environmental analysis.

840. The system of claim 830, wherein analyzing includes performance feedback. Using digital twins in an AI convergence system of systems 841. An AI convergence system of systems for transportation analysis comprising: a digital twin system configured to: create digital replicas of transportation entities; process real-time sensor data to provide virtual representations; simulate possible future states of the transportation entities;SFT-107-A-PCT predict behavior and performance under various conditions; and optimize operations based on the simulations and predictions.

842. The system of claim 841, wherein the digital twin system simulates physical elements and properties.

843. The system of claim 841, wherein the digital twin system simulates dynamics throughout lifecycles.

844. The system of claim 841, wherein the digital twin system provides hypothetical simulations.

845. The system of claim 841, wherein the digital twin system processes environmental data.

846. The system of claim 841, wherein the digital twin system processes marketplace data.

847. The system of claim 841, wherein the digital twin system adapts to real-time conditions.

848. The system of claim 841, wherein the digital twin system processes feedback data.

849. The system of claim 841, wherein the digital twin system simulates high stress conditions.

850. The system of claim 841, wherein the digital twin system simulates component wear.

851. The system of claim 841, wherein the digital twin system simulates planned improvements.

852. An AI convergence system of systems for transportation comprising: a digital twin management system configured to: interface with an environment; provide bi-directional transfer of data between coupled components; identify and store states related to transportation systems; and update properties based on client applications.

853. The system of claim 852, wherein the states include vehicle operating states.

854. The system of claim 852, wherein the states include user experience states.

855. The system of claim 852, wherein the states include maintenance states.

856. The system of claim 852, wherein the states include energy utilization states.

857. The system of claim 852, wherein the states include component states.

858. The system of claim 852, wherein the states include vehicle health states.

859. The system of claim 852, wherein the states include charging states.

860. The system of claim 852, wherein the states include satisfaction states.

861. The system of claim 852, wherein the states include subsystem states.

862. The system of claim 852, wherein the states include powertrain states.

863. An AI convergence system of systems for transportation analysis comprising: an enterprise digital twin system configured to: create digital representations at various levels of abstraction; simulate operations from different points of view; process real-time operational data; and optimize system parameters based on simulations.

864. The system of claim 863, wherein creating includes fleet operations modeling.

865. The system of claim 863, wherein simulating includes maintenance operations.

866. The system of claim 863, wherein processing includes performance data.

867. The system of claim 863, wherein optimizing includes resource allocation.SFT-107-A-PCT 868. The system of claim 863, wherein creating includes infrastructure modeling.

869. The system of claim 863, wherein simulating includes component operations.

870. The system of claim 863, wherein processing includes environmental data.

871. The system of claim 863, wherein optimizing includes efficiency parameters.

872. The system of claim 863, wherein creating includes system integration modeling.

873. The system of claim 863, wherein simulating includes future states.

874. An AI convergence system of systems for transportation comprising: a digital twin creation module configured to: create digital twins using imported data; process image scans of transportation systems; analyze 3D data from sensing devices; and generate 3D representations of environments.

875. The system of claim 874, wherein creating includes blueprint processing.

876. The system of claim 874, wherein processing includes LIDAR data analysis.

877. The system of claim 874, wherein analyzing includes SLAM sensor data.

878. The system of claim 874, wherein generating includes object classification.

879. The system of claim 874, wherein creating includes specification processing.

880. The system of claim 874, wherein processing includes IR scanner data.

881. The system of claim 874, wherein analyzing includes radar device data.

882. The system of claim 874, wherein generating includes pathway mapping.

883. The system of claim 874, wherein creating includes equipment modeling.

884. The system of claim 874, wherein processing includes EMF scanner data.

885. An AI convergence system of systems for transportation optimization comprising: a digital twin system configured to: alter traffic patterns through navigational data updates; achieve predetermined optimization criteria; coordinate mobile element interactions; and optimize process efficiency.

886. The system of claim 885, wherein altering includes route optimization.

887. The system of claim 885, wherein achieving includes safety criteria.

888. The system of claim 885, wherein coordinating includes path planning.

889. The system of claim 885, wherein optimizing includes resource utilization.

890. The system of claim 885, wherein altering includes real-time adaptation.

891. The system of claim 885, wherein achieving includes efficiency targets.

892. The system of claim 885, wherein coordinating includes collision avoidance.

893. The system of claim 885, wherein optimizing includes energy efficiency.

894. The system of claim 885, wherein altering includes congestion management.

895. The system of claim 885, wherein achieving includes performance goals.

896. An AI convergence system of systems for transportation analysis comprising: a digital twin dynamic model system configured to:SFT-107-A-PCT calculate device property values; update transportation worker digital twins; implement psychometric models; and predict reactions to stimuli.

897. The system of claim 896, wherein calculating includes status monitoring.

898. The system of claim 896, wherein updating includes location tracking.

899. The system of claim 896, wherein implementing includes workflow models.

900. The system of claim 896, wherein predicting includes behavioral responses.

901. The system of claim 896, wherein calculating includes temperature analysis.

902. The system of claim 896, wherein updating includes trajectory tracking.

903. The system of claim 896, wherein implementing includes FMEA models.

904. The system of claim 896, wherein predicting includes performance measures.

905. The system of claim 896, wherein calculating includes stress measures.

906. The system of claim 896, wherein updating includes task monitoring.

907. An AI convergence system of systems for transportation modeling comprising: a digital twin system configured to: adhere to physical laws and principles; implement dynamic models; conform to real-world conditions; and adapt based on behavioral differences.

908. The system of claim 907, wherein adhering includes thermodynamic laws.

909. The system of claim 907, wherein implementing includes motion laws.

910. The system of claim 907, wherein conforming includes fluid dynamics.

911. The system of claim 907, wherein adapting includes model correction.

912. The system of claim 907, wherein adhering includes buoyancy laws.

913. The system of claim 907, wherein implementing includes heat transfer laws.

914. The system of claim 907, wherein conforming includes radiation laws.

915. The system of claim 907, wherein adapting includes assumption validation.

916. The system of claim 907, wherein adhering includes quantum dynamics.

917. The system of claim 907, wherein implementing includes aging principles.

918. An AI convergence system of systems for transportation analysis comprising: a digital twin interface system configured to: enable user inspection of digital twins; facilitate interaction with transportation entities; monitor processes being performed; and control digital twin properties.

919. The system of claim 918, wherein enabling includes measurement monitoring.

920. The system of claim 918, wherein facilitating includes movement tracking.

921. The system of claim 918, wherein monitoring includes interaction analysis.

922. The system of claim 918, wherein controlling includes loading operations.SFT-107-A-PCT 923. The system of claim 918, wherein enabling includes maintenance tracking.

924. The system of claim 918, wherein facilitating includes cleaning operations.

925. The system of claim 918, wherein monitoring includes fueling processes.

926. The system of claim 918, wherein controlling includes resupply operations.

927. The system of claim 918, wherein enabling includes painting operations.

928. The system of claim 918, wherein facilitating includes process control.

929. An AI convergence system of systems for transportation optimization comprising: a digital twin generation system configured to: receive digital twin requests; determine required data types; structure collected data; and generate requested digital twins.

930. The system of claim 929, wherein receiving includes type specification.

931. The system of claim 929, wherein determining includes data classification.

932. The system of claim 929, wherein structuring includes historical data.

933. The system of claim 929, wherein generating includes real-time data.

934. The system of claim 929, wherein receiving includes role specification.

935. The system of claim 929, wherein determining includes CRM data.

936. The system of claim 929, wherein structuring includes market data.

937. The system of claim 929, wherein generating includes configuration data.

938. The system of claim 929, wherein receiving includes purpose specification.

939. The system of claim 929, wherein determining includes operational data.

940. An AI convergence system of systems for transportation analysis comprising: an enterprise layer configured to: implement executive digital twins for vehicle fleet operations; create vehicle digital twins for design and simulation; provide enterprise access for fleet transactions; and manage software-defined vehicle fleets.

941. The system of claim 940, wherein implementing includes contextual simulation.

942. The system of claim 940, wherein creating includes performance forecasting.

943. The system of claim 940, wherein providing includes transaction processing.

944. The system of claim 940, wherein managing includes fleet optimization.

945. The system of claim 940, wherein implementing includes operational modeling.

946. The system of claim 940, wherein creating includes component simulation.

947. The system of claim 940, wherein providing includes access control.

948. The system of claim 940, wherein managing includes resource allocation.

949. The system of claim 940, wherein implementing includes efficiency analysis.

950. The system of claim 940, wherein creating includes maintenance planning.