Software-defined vehicle

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 data and other data, quantum computing methods and systems, including a set of quantum computing services, generative-AI methods and systems, and biology-based systems and methods for communicating and / or handling data.

Description

SFT-106-A-PCT SOFTWARE-DEFINED VEHICLE CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S. provisional patent application 63 / 461,807, filed 25 April 2023. This application claims priority to U.S. provisional patent application 63 / 466,116, filed 12 May 2023. This application claims priority to U.S. provisional patent application 63 / 532,415, filed 13 August 2023. This application claims priority to U.S. provisional patent application 63 / 535,744, filed 31 August 2023. This application claims priority to U.S. provisional patent application 63 / 610,876, filed 15 December 2023. This application claims priority to U.S. provisional patent application 63 / 621,536, filed 16 January 2024. This application claims priority to U.S. provisional patent application 63 / 625,609, filed 26 January 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 an intelligent digital twin system that creates, manages, and provides digital twins for transportation systems using sensor data and other data, quantum computing methods and systems, including a set of quantum computing services, and biology-based systems and methods for communicating and / or handling data. BACKGROUND

[0003] A digital twin is a digital informational construct about a machine, physical device, system, process, person, etc. Once created, the digital twin can be used to represent the machine in a digital representation of a real-world system. The digital twin is created such that it is identical in form and behavior of the corresponding machine. Additionally, the digital twin may mirror the status of the machine within a greater system. For example, sensors may be placed on the machine to capture real-time (or near real-time) data from the physical object to relay it back to a remote digital twin.

[0004] Some digital twins may be used to simulate or otherwise mimic the operation of a machine or physical device within a virtual world. In doing so, the digital twins may display structural components of the machine, show steps in lifecycle and / or design, and be viewable via a user interface.

[0005] The proliferation of sensor, network, and communication technologies in transportation systems generates vast amounts of data. This data can be useful in predicting the need for maintenance and for classifying potential issues in the transportation systems. There are, however,SFT-106-A-PCT many unexplored uses for transportation system sensor data that can improve the operation and uptime of the transportation systems and provide transportation entities with agility in responding to conditions before the conditions can increase in severity.

[0006] Acquiring large data sets from thousands, or potentially millions of devices (containing large numbers of sensors) distributed across multiple locations has become more typical. For example, there is a proliferation of Radio Frequency Identification (RFID) tags to individual goods in retail stores. The challenge is that this vast number of data streams overwhelms both the ability to transmit the data and the ability to create effective automated centralized decisions. There exists a need in the art for biology-based communications and data handling.

[0007] Transportation enterprises that rely on subject matter experts may struggle to capture the knowledge of these subject matter experts when they move on to another enterprise or leave the workforce. There exists a need in the art to capture subject matter expertise and to use the captured subject matter expertise in guiding newer workers or mobile electronic transportation entities to perform transportation service-related tasks. SUMMARY

[0008] Among other things, provided herein are methods, systems, components, processes, modules, blocks, circuits, sub-systems, articles, and other elements (collectively referred to in some cases as the “platform” or the “system,” which terms should be understood to encompass any of the above except where context indicates otherwise) that individually or collectively enable advances in transportation systems.

[0009] In embodiments, a system for representing a set of operating states of a vehicle to a user of the vehicle includes a portion of the vehicle having a vehicle operating state; a digital twin system receiving vehicle parameter data from one or more inputs to determine the vehicle operating state; and an interface for the digital twin system to present the vehicle operating state to the user of the vehicle.

[0010] In embodiments, the vehicle operating state is a vehicle maintenance state. In embodiments, the vehicle operating state is a vehicle energy utilization state. In embodiments, the vehicle operating state is a vehicle navigation state. In embodiments, the vehicle operating state is a vehicle component state. In embodiments, the vehicle operating state is a vehicle driver state. In embodiments, inputs for the digital twin system include at least one of an on-board diagnostic system, a telemetry system, a vehicle-located sensor, or a system external to the vehicle.

[0011] In embodiments, the system includes an identity management system to manage a set of identities and roles of a user of the vehicle. In embodiments, the identity management system includes capabilities to view, modify and configure the digital twin system is based on an identitySFT-106-A-PCT from the set of identities of the user of the vehicle. In embodiments, the digital twin system is populated via an API from an edge intelligence system of the vehicle that provides 5G connectivity to a system external to the vehicle. In embodiments, the digital twin system is populated via an API from an edge intelligence system of the vehicle that provides internal 5G connectivity to a set of sensors and data sources of the vehicle. In embodiments, the digital twin system is populated via an API from an edge intelligence system of the vehicle that provides 5G connectivity to an onboard artificial intelligence system.

[0012] In embodiments, the digital twin system is automatically configured by an artificial intelligence system based on a training set of usage activity by a set of digital twin users. In embodiments, the digital twin system is automatically configured by an artificial intelligence system based on a training set of usage activity by a driver user. In embodiments, the digital twin system is automatically configured by an artificial intelligence system based on a training set of usage activity by a rider user.

[0013] In embodiments, the system includes a first neural network to detect a detected satisfaction state of a rider user occupying the vehicle through analysis of data gathered from sensors deployed in the vehicle for gathering physiological conditions of the rider user; and a second neural network to optimize, for achieving a favorable satisfaction state of the rider user, an operational parameter of the vehicle in response to the detected satisfaction state of the rider user.

[0014] In embodiments, the detected satisfaction state of the rider user is a detected emotional state of the rider user. In embodiments, the favorable satisfaction state of the rider user is a favorable emotional state of the rider user. In embodiments, the first neural network is a recurrent neural network and the second neural network is a radial basis function neural network. In embodiments, at least one of the neural networks is a hybrid neural network and includes a convolutional neural network. In embodiments, the second neural network optimizes the operational parameter based on a correlation between a vehicle operating state and a rider satisfaction state of the rider user. In embodiments, the second neural network optimizes the operational parameter in real time responsive to the detecting of the detected satisfaction state of the rider user by the first neural network. In embodiments, the first neural network comprises a plurality of connected nodes that form a directed cycle, the first neural network further facilitating bi-directional flow of data among the connected nodes. In embodiments, the operational parameter that is optimized affects at least one of: a route of the vehicle, in-vehicle audio contents, a speed of the vehicle, an acceleration of the vehicle, a deceleration of the vehicle, a proximity to objects along the route, and a proximity to other vehicles along the route.

[0015] In embodiments, a method for representing a set of states of a vehicle to a user of the vehicle includes obtaining parameters data of one or more components of the vehicle from one orSFT-106-A-PCT more inputs; updating a digital twin of the vehicle with the parameters data to generate one or more operating states of the vehicle; providing an interface to represent to the user of the vehicle the one or more operating states of the vehicle.

[0016] In embodiments, the one or more vehicle operating states include one or more of a vehicle maintenance state, a vehicle energy utilization state, a vehicle navigation state, a vehicle component state, or a vehicle driver state. In embodiments, inputs for the digital twin system include one or more of an on-board diagnostic system, a telemetry system, a vehicle-located sensor, or a system external to the vehicle.

[0017] In embodiments, the method includes managing a set of identities and roles of a user of the vehicle; and configuring the digital twin system based on an identity from the set of identities of the user of the vehicle.

[0018] In embodiments, the method includes populating the digital twin system via an API from an edge intelligence system of the vehicle configured with one or more of 5G connectivity to a system external to the vehicle, internal 5G connectivity to a set of sensors and data sources of the vehicle, or 5G connectivity to an onboard artificial intelligence system.

[0019] In embodiments, the method includes automatically configuring the digital twin system with an artificial intelligence system based on a training set of usage activity by a set of digital twin users.

[0020] In embodiments, the method includes detecting a detected satisfaction state of a rider user occupying the vehicle through analysis, using a first neural network, of data gathered from sensors deployed in the vehicle for gathering physiological conditions of the rider user; and optimizing to achieve a favorable satisfaction state of the rider user an operational parameter of the vehicle in response to the detected satisfaction state of the rider user using a second neural network.

[0021] In embodiments, the detected satisfaction state of the rider user is a detected emotional state of the rider user. In embodiments, the favorable satisfaction state of the rider user is a favorable emotional state of the rider user.

[0022] In embodiments, the first neural network is a recurrent neural network and the second neural network is a radial basis function neural network. In embodiments, at least one of the neural networks is a hybrid neural network and includes a convolutional neural network. In embodiments, optimizing the operational parameter with the second neural network is based on a correlation between a vehicle operating state and a rider user satisfaction state of the rider user. In embodiments, optimizing the operational parameter with the second neural network is responsive to the detecting of the detected satisfaction state of the rider user by the first neural network.

[0023] According to some embodiments of the present disclosure, methods and systems are provided herein for updating properties of digital twins of transportation entities and digital twinsSFT-106-A-PCT of transportation systems, such as, without limitation, based on the effect of collected vibration data on a set of digital twin dynamic models such that the digital twins provide a computer- generated representation of the transportation entity or system.

[0024] According to some embodiments of the present disclosure, a method for updating one or more properties of one or more transportation system digital twins is disclosed. The method includes receiving a request to update one or more properties of one or more transportation system digital twins; retrieving the one or more transportation system digital twins required to fulfill the request from a digital twin datastore; retrieving one or more dynamic models required to fulfill the request from a dynamic model datastore; selecting data sources from a set of available data sources for one or more inputs for the one or more dynamic models; retrieving data from the selected data sources; running the one or more dynamic models using the retrieved data as input data to determine one or more output values; and updating the one or more properties of the one or more transportation system digital twins based on the one or more output values of the one or more dynamic models.

[0025] In embodiments, the request is received from a client application that corresponds to a transportation system or one or more transportation entities within the transportation system.

[0026] In embodiments, the request is received from a client application that supports a network connected sensor system.

[0027] In embodiments, the request is received from a client application that supports a vibration sensor system.

[0028] In embodiments, the one or more transportation system digital twins include one or more digital twins of transportation entities.

[0029] In embodiments, the one or more dynamic models take data selected from the set of vibration, temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, audio, video, image, water level, quantum, flow rate, signal power, signal frequency, motion, displacement, velocity, acceleration, lighting level, financial, cost, stock market, news, social media, revenue, worker, maintenance, productivity, asset performance, worker performance, worker response time, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration data.

[0030] In embodiments, the selected data sources are selected from the group consisting of an analog vibration sensor, a digital vibration sensor, a fixed digital vibration sensor, a tri-axial vibration sensor, a single axis vibration sensor, an optical vibration sensor, a switch, a network connected device, and a machine vision system.SFT-106-A-PCT

[0031] In embodiments, retrieving the one or more dynamic models includes identifying the one or more dynamic models based on the one or more properties indicated in the request and a respective type of the one or more transportation system digital twins.

[0032] In embodiments, the one or more dynamic models are identified using a lookup table.

[0033] In embodiments, a digital twin dynamic model system retrieves the data from the selected data sources via a digital twin I / O system.

[0034] According to some embodiments of the present disclosure, a method for updating one or more bearing vibration fault level states of one or more transportation system digital twins is disclosed. The method includes receiving a request from a client application to update one or more bearing vibration fault level states of one or more transportation system digital twins; retrieving the one or more transportation system digital twins required to fulfill the request from a digital twin datastore; retrieving one or more dynamic models required to fulfill the request from a dynamic model datastore; selecting data sources from a set of available data sources for one or more inputs for the one or more dynamic models; retrieving data from the selected data sources; running the one or more dynamic models using the retrieved data as input data to calculate output values that represent the one or more bearing vibration fault level states; and updating the one or more bearing vibration fault level states of the one or more transportation system digital twins based on the output values of the one or more dynamic models.

[0035] In embodiments, the one or more bearing vibration fault level states are selected from the group consisting of normal, suboptimal, critical, and alarm.

[0036] In embodiments, the client application corresponds to a transportation system or one or more transportation entities within the transportation system.

[0037] In embodiments, the client application supports a network connected sensor system.

[0038] In embodiments, the client application supports a vibration sensor system.

[0039] In embodiments, the one or more transportation system digital twins include one or more digital twins of transportation entities.

[0040] In embodiments, the one or more dynamic models take data selected from the set of vibration, temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, audio, video, image, water level, quantum, flow rate, signal power, signal frequency, motion, displacement, velocity, acceleration, lighting level, financial, cost, stock market, news, social media, revenue, worker, maintenance, productivity, asset performance, worker performance, worker response time, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration data.

[0041] In embodiments, the selected data sources are selected from the group consisting of an analog vibration sensor, a digital vibration sensor, a fixed digital vibration sensor, a tri-axialSFT-106-A-PCT vibration sensor, a single axis vibration sensor, an optical vibration sensor, a switch, a network connected device, and a machine vision system.

[0042] In embodiments, retrieving the one or more dynamic models includes identifying the one or more dynamic models based on the request and a respective type of the one or more transportation system digital twins.

[0043] In embodiments, the one or more dynamic models are identified using a lookup table.

[0044] In embodiments, a digital twin dynamic model system retrieves the data from the selected data sources via a digital twin I / O system.

[0045] According to some embodiments of the present disclosure, a method for updating one or more vibration severity unit values of one or more transportation system digital twins is disclosed. The method includes receiving a request from a client application to update one or more vibration severity unit values of one or more transportation system digital twins; retrieving the one or more transportation system digital twins required to fulfill the request from a digital twin datastore; retrieving one or more dynamic models required to fulfill the request from a dynamic model datastore; selecting data sources from a set of available data sources for one or more inputs for the one or more dynamic models; retrieving data from the selected data sources; running the one or more dynamic models using the retrieved data as the one or more inputs to calculate one or more output values that represent the one or more vibration severity unit values; and updating the one or more vibration severity unit values of the one or more transportation system digital twins based on the one or more output values of the one or more dynamic models.

[0046] In embodiments, vibration severity units represent displacement.

[0047] In embodiments, vibration severity units represent velocity.

[0048] In embodiments, vibration severity units represent acceleration.

[0049] In embodiments, the client application corresponds to a transportation system or one or more transportation entities within the transportation system.

[0050] In embodiments, the client application supports a network connected sensor system.

[0051] In embodiments, the client application supports a vibration sensor system.

[0052] In embodiments, the one or more transportation system digital twins include one or more digital twins of transportation entities.

[0053] In embodiments, the one or more dynamic models take data selected from the set of vibration, temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, audio, video, image, water level, quantum, flow rate, signal power, signal frequency, motion, displacement, velocity, acceleration, lighting level, financial, cost, stock market, news, social media, revenue, worker, maintenance, productivity, asset performance,SFT-106-A-PCT worker performance, worker response time, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration data.

[0054] In embodiments, the selected data sources are selected from the group consisting of an analog vibration sensor, a digital vibration sensor, a fixed digital vibration sensor, a tri-axial vibration sensor, a single axis vibration sensor, an optical vibration sensor, a switch, a network connected device, and a machine vision system.

[0055] In embodiments, retrieving the one or more dynamic models includes identifying the one or more dynamic models based on the request and a respective type of the one or more transportation system digital twins.

[0056] In embodiments, the one or more dynamic models are identified using a lookup table.

[0057] In embodiments, a digital twin dynamic model system retrieves the data from the selected data sources via a digital twin I / O system.

[0058] According to some embodiments of the present disclosure, a method for updating one or more probability of failure values of one or more transportation system digital twins is disclosed. The method includes receiving a request from a client application to update one or more probability of failure values of one or more transportation system digital twins; retrieving the one or more transportation system digital twins to fulfill the request; retrieving one or more dynamic models to fulfill the request; selecting data sources from a set of available data sources for one or more inputs for the one or more dynamic models; retrieving data from the selected data sources; running the one or more dynamic models using the retrieved data as the one or more inputs to calculate one or more output values that represent the one or more probability of failure values; and updating the one or more probability of failure values of the one or more transportation system digital twins based on the one or more output values of the one or more dynamic models.

[0059] In embodiments, the client application corresponds to a transportation system or one or more transportation entities within the transportation system.

[0060] In embodiments, the client application supports a network connected sensor system.

[0061] In embodiments, the client application supports a vibration sensor system.

[0062] In embodiments, the one or more transportation system digital twins include one or more digital twins of transportation entities.

[0063] In embodiments, the one or more dynamic models take data selected from the set of vibration, temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, audio, video, image, water level, quantum, flow rate, signal power, signal frequency, motion, displacement, velocity, acceleration, lighting level, financial, cost, stock market, news, social media, revenue, worker, maintenance, productivity, asset performance,SFT-106-A-PCT worker performance, worker response time, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration data.

[0064] In embodiments, the selected data sources are selected from the group consisting of an analog vibration sensor, a digital vibration sensor, a fixed digital vibration sensor, a tri-axial vibration sensor, a single axis vibration sensor, an optical vibration sensor, a switch, a network connected device, and a machine vision system.

[0065] In embodiments, retrieving the one or more dynamic models includes identifying the one or more dynamic models based on the request and a respective type of the one or more transportation system digital twins.

[0066] In embodiments, the one or more dynamic models are identified using a lookup table.

[0067] In embodiments, a digital twin dynamic model system retrieves the data from the selected data sources via a digital twin I / O system.

[0068] According to some embodiments of the present disclosure, a method for updating one or more probability of downtime values of one or more transportation system digital twins is disclosed. The method includes receiving a request to update one or more probability of downtime values of one or more transportation system digital twins; retrieving the one or more transportation system digital twins to fulfill the request from a digital twin datastore; retrieving one or more dynamic models required to fulfill the request from a dynamic model datastore; selecting data sources from a set of available data sources for one or more inputs for the one or more dynamic models; retrieving data from the selected data sources; running the one or more dynamic models using the retrieved data as the one or more inputs to calculate one or more output values that represent the one or more probability of downtime values; and updating the one or more probability of downtime values for the one or more transportation system digital twins based on the one or more output values of the one or more dynamic models.

[0069] In embodiments, the request is received from a client application that corresponds to a transportation system or one or more transportation entities within the transportation system.

[0070] In embodiments, the request is received from a client application that supports a network connected sensor system.

[0071] In embodiments, the request is received from a client application that supports a vibration sensor system.

[0072] In embodiments, the one or more transportation system digital twins include one or more digital twins of transportation entities.

[0073] In embodiments, the one or more dynamic models take data selected from the set of vibration, temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, audio, video, image, water level, quantum, flow rate, signal power, signalSFT-106-A-PCT frequency, motion, displacement, velocity, acceleration, lighting level, financial, cost, stock market, news, social media, revenue, worker, maintenance, productivity, asset performance, worker performance, worker response time, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration data.

[0074] In embodiments, the selected data sources are selected from the group consisting of an analog vibration sensor, a digital vibration sensor, a fixed digital vibration sensor, a tri-axial vibration sensor, a single axis vibration sensor, an optical vibration sensor, a switch, a network connected device, and a machine vision system.

[0075] In embodiments, retrieving the one or more dynamic models includes identifying the one or more dynamic models based on the request and a respective type of the one or more transportation system digital twins.

[0076] In embodiments, the one or more dynamic models are identified using a lookup table.

[0077] In embodiments, a digital twin dynamic model system retrieves the data from the selected data sources via a digital twin I / O system.

[0078] According to some embodiments of the present disclosure, a method for updating one or more probability of shutdown values of one or more transportation system digital twins having a set of transportation entities is disclosed. The method includes receiving a request from a client application to update one or more probability of shutdown values for the set of transportation entities within one or more transportation system digital twins; retrieving the one or more transportation system digital twins to fulfill the request from a digital twin datastore; retrieving one or more dynamic models to fulfill the request from a dynamic model datastore; selecting data sources from a set of available data sources for one or more inputs for the one or more dynamic models; retrieving data from the selected data sources; running the one or more dynamic models using the retrieved data as the one or more inputs to calculate one or more output values that represent the one or more probability of shutdown values; and updating the one or more probability of shutdown values for the set of transportation entities within the one or more transportation system digital twins based on the one or more output values of the one or more dynamic models.

[0079] In embodiments, the client application corresponds to a transportation system or one or more transportation entities within the transportation system.

[0080] In embodiments, the client application supports a network connected sensor system.

[0081] In embodiments, the client application supports a vibration sensor system.

[0082] In embodiments, the one or more transportation system digital twins include one or more digital twins of transportation entities.

[0083] In embodiments, the set of transportation entities includes a refueling center or a vehicle charging center.SFT-106-A-PCT

[0084] In embodiments, the one or more dynamic models take data selected from the set of vibration, temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, audio, video, image, water level, quantum, flow rate, signal power, signal frequency, motion, displacement, velocity, acceleration, lighting level, financial, cost, stock market, news, social media, revenue, worker, maintenance, productivity, asset performance, worker performance, worker response time, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration data.

[0085] In embodiments, the selected data sources are selected from the group consisting of an analog vibration sensor, a digital vibration sensor, a fixed digital vibration sensor, a tri-axial vibration sensor, a single axis vibration sensor, an optical vibration sensor, a switch, a network connected device, and a machine vision system.

[0086] In embodiments, retrieving the one or more dynamic models includes identifying the one or more dynamic models based on the request and a respective type of the one or more transportation system digital twins.

[0087] In embodiments, the one or more dynamic models are identified using a lookup table.

[0088] In embodiments, a digital twin dynamic model system retrieves the data from the selected data sources via a digital twin I / O system.

[0089] According to some embodiments of the present disclosure, a method for updating one or more cost of downtime values of one or more transportation system digital twins is disclosed. The method includes receiving a request to update one or more cost of downtime values of one or more transportation system digital twins; retrieving the one or more transportation system digital twins to fulfill the request from a digital twin datastore; retrieving one or more dynamic models to fulfill the request from a dynamic model datastore; selecting data sources from a set of available data sources for one or more inputs for the one or more dynamic models; retrieving data from the selected data sources; running the one or more dynamic models using the retrieved data as the one or more inputs to calculate one or more output values that represent the one or more cost of downtime values; and updating the one or more cost of downtime values for the one or more transportation system digital twins based on the one or more output values of the one or more dynamic models.

[0090] In embodiments, the cost of downtime value is selected from the set of cost of downtime per hour, cost of downtime per day, cost of downtime per week, cost of downtime per month, cost of downtime per quarter, and cost of downtime per year.

[0091] In embodiments, the request is received from a client application that corresponds to a transportation system or one or more transportation entities within the transportation system.SFT-106-A-PCT

[0092] In embodiments, the request is received from a client application that supports a network connected sensor system.

[0093] In embodiments, the request is received from a client application that supports a vibration sensor system.

[0094] In embodiments, the one or more transportation system digital twins include one or more digital twins of transportation entities.

[0095] In embodiments, the one or more dynamic models take data selected from the set of vibration, temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, audio, video, image, water level, quantum, flow rate, signal power, signal frequency, motion, displacement, velocity, acceleration, lighting level, financial, cost, stock market, news, social media, revenue, worker, maintenance, productivity, asset performance, worker performance, worker response time, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration data.

[0096] In embodiments, the selected data sources are selected from the group consisting of an analog vibration sensor, a digital vibration sensor, a fixed digital vibration sensor, a tri-axial vibration sensor, a single axis vibration sensor, an optical vibration sensor, a switch, a network connected device, and a machine vision system.

[0097] In embodiments, retrieving the one or more dynamic models includes identifying the one or more dynamic models based on the request and a respective type of the one or more transportation system digital twins.

[0098] In embodiments, the one or more dynamic models are identified using a lookup table.

[0099] In embodiments, a digital twin dynamic model system retrieves the data from the selected data sources via a digital twin I / O system.

[0100] According to some embodiments of the present disclosure, a method for updating one or more key performance indicator (KPI) values of one or more transportation system digital twins is disclosed. The method includes receiving a request to update one or more key performance indicator values of one or more transportation system digital twins; retrieving the one or more transportation system digital twins to fulfill the request from a digital twin datastore; retrieving one or more dynamic models to fulfill the request from a dynamic model datastore; selecting data sources from a set of available data sources for one or more inputs for the one or more dynamic models; retrieving data from the selected data sources; running the one or more dynamic models using the retrieved data as the one or more inputs to calculate one or more output values that represent the one or more key performance indicator values; and updating one or more key performance indicator values for the one or more transportation system digital twins based on the one or more output values of the one or more dynamic models.SFT-106-A-PCT

[0101] In embodiments, the key performance indicator is selected from the set of uptime, capacity utilization, on standard operating efficiency, overall operating efficiency, overall equipment effectiveness, machine downtime, unscheduled downtime, machine set up time, on-time delivery, training hours, employee turnover, reportable health & safety incidents, revenue per employee, profit per employee, schedule attainment, planned maintenance percentage, and availability.

[0102] In embodiments, the request is received from a client application that corresponds to a transportation system or one or more transportation entities within the transportation system.

[0103] In embodiments, the request is received from a client application that supports a network connected sensor system.

[0104] In embodiments, the request is received from a client application that supports a vibration sensor system.

[0105] In embodiments, the one or more transportation system digital twins include one or more digital twins of transportation entities.

[0106] In embodiments, the one or more dynamic models take data selected from the set of vibration, temperature, pressure, humidity, wind, rainfall, tide, storm surge, cloud cover, snowfall, visibility, radiation, audio, video, image, water level, quantum, flow rate, signal power, signal frequency, motion, displacement, velocity, acceleration, lighting level, financial, cost, stock market, news, social media, revenue, worker, maintenance, productivity, asset performance, worker performance, worker response time, analyte concentration, biological compound concentration, metal concentration, and organic compound concentration data.

[0107] In embodiments, the selected data sources are selected from the group consisting of an analog vibration sensor, a digital vibration sensor, a fixed digital vibration sensor, a tri-axial vibration sensor, a single axis vibration sensor, an optical vibration sensor, a switch, a network connected device, and a machine vision system.

[0108] In embodiments, retrieving the one or more dynamic models includes identifying the one or more dynamic models based on the request and a respective type of the one or more transportation system digital twins.

[0109] In embodiments, the one or more dynamic models are identified using a lookup table.

[0110] In embodiments, a digital twin dynamic model system retrieves the data from the selected data sources via a digital twin I / O system.

[0111] According to some embodiments of the present disclosure, a method is disclosed. The method includes: receiving imported data from one or more data sources, the imported data corresponding to a transportation system; generating a digital twin of a transportation system representing the transportation system based on the imported data; identifying one or more transportation entities within the transportation system; generating a set of discrete digital twinsSFT-106-A-PCT representing the one or more transportation entities within the transportation system; embedding the set of discrete digital twins within the digital twin of the transportation system; establishing a connection with a sensor system of the transportation system; receiving real-time sensor data from one or more sensors of the sensor system via the connection; and updating at least one of the transportation system digital twin and the set of discrete digital twins based on the real-time sensor data.

[0112] In embodiments, the connection with the sensor system is established via an application programming interface (API).

[0113] In embodiments, the transportation system digital twin and the set of discrete digital twins are visual digital twins that are configured to be rendered in a visual manner. In some embodiments, the method further includes outputting the visual digital twins to a client application that displays the visual digital twins via a virtual reality headset. In some embodiments, the method further includes outputting the visual digital twins to a client application that displays the visual digital twins via a display device of a user device. In some embodiments, the method further includes outputting the visual digital twins to a client application that displays the visual digital twins in a display interface with information related to the digital twins overlaid on the visual digital twins or displayed within the display interface. In some embodiments, the method further includes outputting the visual digital twins to a client application that displays the visual digital twins via an augmented reality-enabled device.

[0114] In some embodiments, the method further includes instantiating a graph database having a set of nodes connected by edges, wherein a first node of the set of nodes contains data defining the transportation system digital twin and one or more entity nodes respectively contain respective data defining a respective discrete digital twin of the set of discrete digital twins. In some embodiments, each edge represents a relationship between two respective digital twins. In some of these embodiments embedding a discrete digital twin includes connecting an entity node corresponding to a respective discrete digital twin to the first node with an edge representing a respective relationship between a respective transportation entity represented by the respective discrete digital twin and the transportation system. In some embodiments, each edge represents a spatial relationship between two respective digital twins. In some embodiments, each edge represents an operational relationship between two respective digital twins. In some embodiments, each edge stores metadata corresponding to the relationship between the two respective digital twins. In some embodiments, each entity node of the one or more entity nodes includes one or more properties of respective properties of the respective transportation entity represented by the entity node. In some embodiments, each entity node of the one or more entity nodes includes one or more behaviors of respective properties of the respective transportation entity represented bySFT-106-A-PCT the entity node. In some embodiments, the transportation system node includes one or more properties of the transportation system. In some embodiments, the transportation system node includes one or more behaviors of the transportation system.

[0115] In some embodiments, the method further includes executing a simulation based on the transportation system digital twin and the set of discrete digital twins. In some embodiments, the simulation simulates an operation of a machine that produces an output based on a set of inputs. In some embodiments, the simulation simulates vibrational patterns of a bearing in a machine of a transportation system.

[0116] In embodiments, the one or more transportation entities are selected from a set of machine components, infrastructure components, equipment components, workpiece components, tool components, vessel components, vehicle components, chassis components, drivetrain components, electrical components, fluid handling components, mechanical components, power components, manufacturing components, energy production components, material extraction components, workers, robots, assembly lines, and vehicles.

[0117] In embodiments, the transportation system includes one of a mobile factory, a mobile energy production facility, a mobile material extraction facility, a mining vehicle or device, a drilling / tunneling vehicle or device, a mobile food processing facility, a cargo vessel, a tanker vessel, and a mobile storage facility.

[0118] In embodiments, the imported data includes a three-dimensional scan of the transportation system.

[0119] In embodiments, the imported data includes a LIDAR scan of the transportation system.

[0120] In embodiments, generating the digital twin of the transportation system includes generating a set of surfaces of the transportation system.

[0121] In embodiments, generating the digital twin of the transportation system includes configuring a set of dimensions of the transportation system.

[0122] In embodiments, generating the set of discrete digital twins includes importing a predefined digital twin of a transportation entity from a manufacturer of the transportation entity, wherein the predefined digital twin includes properties and behaviors of the transportation entity.

[0123] In embodiments, generating the set of discrete digital twins includes classifying a transportation entity within the imported data of the transportation system and generating a discrete digital twin corresponding to the classified transportation entity.

[0124] According to aspects of the present disclosure, a system for monitoring interaction within a transportation system includes a digital twin datastore and one or more processors. The digital twin datastore includes data collected by a set of proximity sensors disposed within a transportation system. The data includes location data indicating respective locations of a plurality of elementsSFT-106-A-PCT within the transportation system. The one or more processors are configured to maintain, via the digital twin datastore, a transportation system digital twin for the transportation system, receive signals indicating actuation of at least one proximity sensor within the set of proximity sensors by a real-world element from the plurality of elements, collect, in response to actuation of the set of proximity sensors, updated location data for the real-world element using the set of proximity sensors, and update the transportation system digital twin within the digital twin datastore to include the updated location data.

[0125] In embodiments, each of the set of proximity sensors is configured to detect a device associated with a user.

[0126] In embodiments, the device is a wearable device.

[0127] In embodiments, the device is an RFID device.

[0128] In embodiments, each element of the plurality of elements is a mobile element.

[0129] In embodiments, each element of the plurality of elements is a respective worker.

[0130] In embodiments, the plurality of elements includes mobile equipment elements and workers, mobile-equipment-position data is determined using data transmitted by the respective mobile equipment element, and worker-position data is determined using data obtained by the system.

[0131] In embodiments, the worker-position data is determined using information transmitted from a device associated with respective workers.

[0132] In embodiments, the actuation of the set of proximity sensors occurs in response to interaction between the respective worker and the set of proximity sensors.

[0133] In embodiments, the actuation of the set of proximity sensors occurs in response to interaction between a worker and a respective at least one proximity-sensor digital twin corresponding to the set of proximity sensors.

[0134] In embodiments, the one or more processors collect updated location data for the plurality of elements using the set of proximity sensors in response to the actuation of the set of proximity sensors.

[0135] According to aspects of the present disclosure, a system for monitoring a transportation system having real-world elements disposed therein includes a digital twin datastore and one or more processors. The digital twin datastore includes a set of states stored therein. The set of states includes states for one or more of the real-world elements. Each state within the set of states is uniquely identifiable by a set of identifying criteria from a set of monitored attributes. The set of monitored attributes corresponds to signals received from a sensor array operatively coupled to the real-world elements. The one or more processors are configured to maintain, via the digital twin datastore, a transportation-system digital twin for the transportation system, receive, via the sensorSFT-106-A-PCT array, signals for one or more attributes within the set of monitored attributes, determine a present state for one or more of the real-world elements in response to determining that the signals for the one or more attributes satisfy a respective set of identifying criteria, and update, in response to determining the present state, the transportation system digital twin to include the present state of the one or more of the real-world elements. The present state corresponds to the respective state within the set of states.

[0136] In embodiments, a cognitive intelligence system stores the identifying criteria within the digital twin datastore.

[0137] In embodiments, a cognitive intelligence system, in response to receiving the identifying criteria, updates triggering conditions for the set of monitored attributes to include an updated triggering condition.

[0138] In embodiments, the updated triggering condition is reducing time intervals between receiving sensed attributes from the set of monitored attributes.

[0139] In embodiments, the sensed attributes are the one or more attributes that satisfy the respective set of identifying criteria.

[0140] In embodiments, the sensed attributes are all attributes corresponding to the respective real-world element.

[0141] In embodiments, a cognitive intelligence system determines whether instructions exist for responding to the state and the cognitive intelligence system, in response to determining no instructions exist, determines instructions for responding to the state using a digital twin simulation system.

[0142] In embodiments, the digital twin simulation system and the cognitive intelligence system repeatedly iterate simulated values and response actions until an associated cost function is minimized and the one or more processors are further configured to, in response to minimization of the associated cost function, store the response action that minimizes the associated cost function within the digital twin datastore.

[0143] In embodiments, a cognitive intelligence system is configured to affect the response actions associated with the state.

[0144] In embodiments, a cognitive intelligence system is configured to halt operation of one or more real-world elements that are identified by the response actions.

[0145] In embodiments, a cognitive intelligence system is configured to determine resources for the transportation system identified by the response actions and alter the resources in response thereto.

[0146] In embodiments, the resources include data transfer bandwidth and altering the resources includes establishing additional connections to thereby increase the data transfer bandwidth.SFT-106-A-PCT

[0147] According to aspects of the present disclosure, a system for monitoring navigational route data through a transportation system has real-world elements disposed therein includes a digital twin datastore and one or more processors. The digital twin datastore includes a transportation system digital twin corresponding to the transportation system and a worker digital twin corresponding to a respective worker of a set of workers within the transportation system. The one or more processors are configured to maintain, via the digital twin datastore, the transportation system digital twin to include contemporaneous positions for the set of workers within the transportation system, monitor movement of each worker in the set of workers via a sensor array, determine, in response to detecting movement of the respective worker, navigational route data for the respective worker, update the transportation system digital twin to include indicia of the navigational route data for the respective worker, and move the worker digital twin along a route of the navigational route data.

[0148] In embodiments, the one or more processors are further configured to update, in response to representing movement of the respective worker, determine navigational route data for remaining workers in the set of workers.

[0149] In embodiments, the navigational route data includes a route for collecting vibration measurements from one or more machines in the transportation system.

[0150] In embodiments, the navigational route data automatically transmitted to the system by one or more individual-associated devices.

[0151] In embodiments, the individual-associated device is a mobile device that has cellular data capabilities.

[0152] In embodiments, the individual-associated device is a wearable device associated with the worker.

[0153] In embodiments, the navigational route data is determined via environment-associated sensors.

[0154] In embodiments, the navigational route data is determined using historical routing data stored in the digital twin datastore.

[0155] In embodiments, the historical routing data was obtained using the respective worker.

[0156] In embodiments, the historical routing data was obtained using another worker.

[0157] In embodiments, the historical routing data is associated with a current task of the worker.

[0158] In embodiments, the digital twin datastore includes a transportation system digital twin.

[0159] In embodiments, the one or more processors are further configured to determine existence of a conflict between the navigational route data and the transportation system digital twin, alter, in response to determining accuracy of the transportation system digital twin via the sensor array, the navigational route data for the worker, and update, in response to determining inaccuracy ofSFT-106-A-PCT the transportation system digital twin via the sensor array, the transportation system digital twin to thereby resolve the conflict.

[0160] In embodiments, the transportation system digital twin is updated using collected data transmitted from the worker.

[0161] In embodiments, the collected data includes proximity sensor data, image data, or combinations thereof.

[0162] According to aspects of the present disclosure, a system for monitoring navigational route data includes a digital twin datastore and one or more processors. The digital twin datastore stores a transportation system digital twin with real-world-element digital twins embedded therein. The transportation system digital twin provides a digital twin of a transportation system. Each real- world-element digital twin provides an other digital twin for corresponding real-world elements within the transportation system. The corresponding real-world-elements include a set of workers. The one or more processors are configured to monitor movement of each worker in the set of workers, determine navigational route data for at least one worker in the set of workers, and represent the movement of the at least one worker by movement of associated digital twins using the navigational route data.

[0163] In embodiments, the one or more processors are further configured to update, in response to representing movement of the at least one worker, determine navigational route data for remaining workers in the set of workers.

[0164] In embodiments, the navigational route data includes a route for collecting vibration measurements from one or more machines in the transportation system.

[0165] In embodiments, the navigational route data automatically transmitted to the system by one or more individual-associated devices.

[0166] In embodiments, the individual-associated device is a mobile device that has cellular data capabilities.

[0167] In embodiments, the individual-associated device is a wearable device associated with the worker.

[0168] In embodiments, the navigational route data is determined via environment-associated sensors.

[0169] In embodiments, the navigational route data is determined using historical routing data stored in the digital twin datastore.

[0170] In embodiments, the historical route data was obtained using the respective worker.

[0171] In embodiments, the historical route data was obtained using another worker.

[0172] In embodiments, the historical route data is associated with a current task of the worker.

[0173] In embodiments, the digital twin datastore includes a transportation system digital twin.SFT-106-A-PCT

[0174] In embodiments, the one or more processors are further configured to determine existence of a conflict between the navigational route data and the transportation system digital twin, alter, in response to determining accuracy of the transportation system digital twin via a sensor array, the navigational route data for the worker, and update, in response to determining inaccuracy of the transportation system digital twin via the sensor array, the transportation system digital twin to thereby resolve the conflict.

[0175] In embodiments, the transportation system digital twin is updated using collected data transmitted from the worker.

[0176] In embodiments, the collected data includes proximity sensor data, image data, or combinations thereof.

[0177] According to aspects of the present disclosure, a system for representing workpiece objects in a digital twin includes a digital twin datastore and one or more processors. The digital twin datastore stores a transportation-system digital twin with real-world-element digital twins embedded therein. The transportation system digital twin provides a digital twin of a transportation system. Each real-world-element digital twin providing an other digital twin for corresponding real-world elements within the transportation system. The corresponding real-world-elements including a workpiece and a worker. The one or more processors are configured to simulate, using a digital twin simulation system, a set of physical interactions to be performed on the workpiece by the worker. The simulation includes obtaining the set of physical interactions, determining an expected duration for performance of each physical interaction within the set of physical interactions based on historical data of the worker, and storing, within the digital twin datastore, workpiece digital twins corresponding to performance of the set of physical interactions on the workpiece.

[0178] In embodiments, the historical data is obtained from user-input data.

[0179] In embodiments, the historical data is obtained from a sensor array within the transportation system.

[0180] In embodiments, the historical data is obtained from a wearable device worn by the worker.

[0181] In embodiments, each datum of the historical data includes indicia of a first time and a second time, and the first time is a time of performance for the physical interaction.

[0182] In embodiments, the second time is a time for beginning an expected break time of the worker.

[0183] In embodiments, the historical data further includes indicia of a duration for the expected break time.

[0184] In embodiments, the second time is a time for ending an expected break time of the worker.SFT-106-A-PCT

[0185] In embodiments, the historical data further includes indicia of a duration for the expected break time.

[0186] In embodiments, the second time is a time for ending an unexpected break time of the worker.

[0187] In embodiments, the historical data further includes indicia of a duration for the unexpected break time.

[0188] In embodiments, each datum of the historical data includes indicia of consecutive interactions of the worker with a plurality of other workpieces prior to performing the set of physical interactions with the workpiece.

[0189] In embodiments, each datum of the historical data includes indicia of consecutive days the worker was present within the transportation system.

[0190] In embodiments, each datum of the historical data includes indicia of an age of the worker.

[0191] In embodiments, the historical data further includes indicia of a first duration for an expected break time of the worker and a second duration for an unexpected break time of the worker, each datum of the historical data includes indicia of a plurality of times, indicia of consecutive interactions of the worker with a plurality of other workpieces prior to performing the set of physical interactions with the workpiece and indicia of consecutive days the worker was present within the transportation system, or indicia of an age of the worker. The plurality of times includes a first time, a second time, a third time, and a fourth time. The first time is a time of performance for the physical interaction, the second time is a time for beginning the expected break time, the third time is a time for ending the expected break time, and the fourth time is a time for ending the unexpected break time.

[0192] In embodiments, the workpiece digital twins are a first workpiece digital twin corresponding to the workpiece prior to performance of the physical interaction and a second workpiece digital twin corresponding to the workpiece after performance of the set of physical interactions.

[0193] In embodiments, the workpiece digital twins are a plurality of workpiece digital twins, each of the plurality of workpiece digital twins corresponds to the workpiece after performance of a respective one of the set of physical interactions.

[0194] According to aspects of the present disclosure, a system for inducing an experience via a wearable device includes a digital twin datastore and one or more processors. The digital twin datastore stores a transportation-system digital twin with real-world-element digital twins embedded therein. The transportation system digital twin provides a digital twin of a transportation system. Each real-world-element digital twin providing an other digital twin for corresponding real-world elements within the transportation system. The corresponding real-world-elementsSFT-106-A-PCT including a wearable device worn by a wearer within the transportation system. The one or more processors are configured to embed a set of control instructions for a wearable device within the digital twins and induce, in response to an interaction between the wearable device and each respective one of the digital twins, an experience for the wearer of the wearable device.

[0195] In embodiments, the wearable device is configured to output video, audio, haptic feedback, or combinations thereof to induce the experience for the wearer.

[0196] In embodiments, the experience is a virtual reality experience.

[0197] In embodiments, the wearable device includes an image capture device and the interaction includes the wearable device capturing an image of the digital twin.

[0198] In embodiments, the wearable device includes a display device and the experience includes display of information related to the respective digital twin.

[0199] In embodiments, the information displayed includes financial data associated with the digital twin.

[0200] In embodiments, the information displayed includes a profit or loss associated with operation of the digital twin.

[0201] In embodiments, the information displayed includes information related to an occluded element that is at least partially occluded by a foreground element.

[0202] In embodiments, the information displayed includes an operating parameter for the occluded element.

[0203] In embodiments, the information displayed further includes a comparison to a design parameter corresponding to the operating parameter displayed.

[0204] In embodiments, the comparison includes altering display of the operating parameter to change a color, size, or display period for the operating parameter.

[0205] In embodiments, the information includes a virtual model of the occluded element overlaid on the occluded element and visible with the foreground element.

[0206] In embodiments, the information includes indicia for removable elements that are is configured to provide access to the occluded element. Each indicium is displayed proximate to the respective removable element.

[0207] In embodiments, the indicia are sequentially displayed such that a first indicium corresponding to a first removable element is displayed, and a second indicium corresponding to a second removable element is displayed in response to a worker removing the first removable element.

[0208] According to aspects of the present disclosure, a system for embedding device output in a transportation system digital twin includes a digital twin datastore and one or more processors. The digital twin datastore stores a transportation system digital twin having real-world-element digitalSFT-106-A-PCT twins embedded therein. The transportation system digital twin provides a digital twin of a transportation system. Each real-world-element digital twin providing an other digital twin for corresponding real-world elements within the transportation system. The real-world elements include a simultaneous location and mapping sensor. The one or more processors are configured to obtain location information from the simultaneous location and mapping sensor, determine that the simultaneous location and mapping sensor is disposed within the transportation system, collect mapping information, pathing information, or a combination thereof from the simultaneous location and mapping sensor, and update the transportation system digital twin using the mapping information, the pathing information, or the combination thereof. The collection is in response to determining the simultaneous location and mapping sensor is within the transportation system.

[0209] In embodiments, the one or more processors are further configured to detect objects within the mapping information and, for each detected object within the mapping information, determine whether the detected object corresponds to an existing real-world-element digital twin, add, in response to determining that the detected object does not correspond to an existing real-world- element digital twin, a detected-object digital twin to the real-world-element digital twins within the digital twin datastore using a digital twin management system, and update, in response to determining that the detected object corresponds to an existing real-world-element digital twin, the real-world-element digital twin to include new information detected by the simultaneous location and mapping sensor.

[0210] In embodiments, the simultaneous location and mapping sensor is configured to produce the mapping information using a sub-optimal mapping algorithm.

[0211] In embodiments, the sub-optimal mapping algorithm produces bounded-region representations for elements within the transportation system.

[0212] In embodiments, the one or more processors are further configured to obtain objects detected by the sub-optimal mapping algorithm, determine whether the detected object corresponds to an existing real-world-element digital twin, and update, in response to determining the detected object corresponds to the existing real-world-element digital twin, the mapping information to include dimensional information for the real-world-element digital twin.

[0213] In embodiments, the updated mapping information is provided to the simultaneous location and mapping sensor to thereby optimize navigation through the transportation system.

[0214] In embodiments, the one or more processors are further configured to request, in response to determining the detected object does not correspond to an existing real-world-element digital twin, updated data for the detected object from the simultaneous location and mapping sensor that is configured to produce a refined map of the detected object.SFT-106-A-PCT

[0215] In embodiments, the simultaneous location and mapping sensor provides the updated data using a second algorithm. The second algorithm is configured to increase resolution of the detected object.

[0216] In embodiments, the simultaneous location and mapping sensor, in response to receiving the request, captures the updated data for the real-world element corresponding to the detected object.

[0217] In embodiments, the simultaneous location and mapping sensor is within an autonomous vehicle navigating the transportation system.

[0218] In embodiments, navigation of the autonomous vehicle includes use of digital twins received from the digital twin datastore.

[0219] According to aspects of the present disclosure, a system for embedding device output in a transportation system digital twin includes a digital twin datastore and one or more processors. The digital twin datastore stores a transportation-system digital twin having real-world-element digital twins embedded therein. The transportation system digital twin provides a digital twin of a transportation system. Each real-world-element digital twin providing an other digital twin for corresponding real-world elements within the transportation system. The real-world elements including a light detection and ranging sensor. The one or more processors are configured to obtain output from the light detection and ranging sensor and embed the output of the light detection and ranging sensor into the transportation system digital twin to define external features of at least one of the real-world elements within the transportation system.

[0220] In embodiments, the one or more processors are further configured to analyze the output to determine a plurality of detected objects within the output of the light detection and ranging sensor. Each of the plurality of detected objects is a closed shape.

[0221] In embodiments, the one or more processors are further configured to compare the plurality of detected objects to the real-world-element digital twins within the digital twin datastore and, for each of the plurality of detected objects, update, in response to determining the detected object corresponds to one or more of the real-world-element digital twins, the respective real- world-element digital twin within the digital twin datastore, and add, in response to determining the detected object does not correspond to the real-world-element digital twins, a new real-world- element digital twin to the digital twin datastore.

[0222] In embodiments, the output from the light detection and ranging sensor is received in a first resolution and the one or more processors are further configured to compare the plurality of detected objects to the real-world-element digital twins within the digital twin datastore and, for each of the plurality of detected objects that does not correspond to a real-world-element digitalSFT-106-A-PCT twin, direct the light detection and ranging sensor to increase scan resolution to a second resolution and perform a scan of the detected object using the second resolution.

[0223] In embodiments, the scan is at least 5 times the resolution of the first resolution.

[0224] In embodiments, the scan is at least 10 times the resolution of the first resolution.

[0225] In embodiments, the output from the light detection and ranging sensor is received in a first resolution and the one or more processors are further configured to compare the plurality of detected objects to the real-world-element digital twins within the digital twin datastore and, for each of the plurality of detected objects, update, in response to determining the detected object corresponds to one or more of the real-world-element digital twins, the respective real-world- element digital twin within the digital twin datastore. In response to determining the detected object does not correspond to the real-world-element digital twins, the system is further configured to direct the light detection and ranging sensor to increase scan resolution to a second resolution, perform a scan of the detected object using the second resolution, and add a new real-world- element digital twin for the detected object to the digital twin datastore.

[0226] According to aspects of the present disclosure, a system for embedding device output in a transportation system digital twin includes a digital twin datastore and one or more processors. The digital twin datastore includes a transportation-system digital twin providing a digital twin of a transportation system. The transportation system includes real-world elements disposed therein. The real-world elements include a plurality of wearable devices. The transportation system digital twin includes a plurality of real-world-element digital twins embedded therein. Each real-world- element digital twin corresponds to a respective at least one of the real-world elements. The one or more processors are configured to, for each of the plurality of wearable devices, obtain output from the wearable device, and update, in response to detecting a triggering condition, the transportation system digital twin using the output from the wearable device.

[0227] In embodiments, the triggering condition is receipt of the output from the wearable device.

[0228] In embodiments, the triggering condition is a determination that the output from the wearable device is different from a previously stored output from the wearable device.

[0229] In embodiments, the triggering condition is a determination that received output from another wearable device within the plurality of wearable devices is different from a previously stored output from the other wearable device.

[0230] In embodiments, the triggering condition includes a mismatch between the output from the wearable device and contemporaneous output from another of the wearable devices.

[0231] In embodiments, the triggering condition includes a mismatch between the output from the wearable device and a simulated value for the wearable device.SFT-106-A-PCT

[0232] In embodiments, the triggering condition includes user interaction with a digital twin corresponding to the wearable device.

[0233] In embodiments, the one or more processors are further configured to detect objects within mapping information received from a simultaneous location and mapping sensor. For each detected object within the mapping information, the system is further configured to determine whether the detected object corresponds to an existing real-world-element digital twin, and, in response to determining that the detected object does not correspond to an existing real-world-element digital twin, a detected-object digital twin to the real-world-element digital twins within the digital twin datastore using a digital twin management system, and update, in response to determining that the detected object corresponds to an existing real-world-element digital twin, the real-world-element digital twin to include new information detected by the simultaneous location and mapping sensor.

[0234] In embodiments, a simultaneous location and mapping sensor is configured to produce mapping information using a sub-optimal mapping algorithm.

[0235] In embodiments, the sub-optimal mapping algorithm produces bounded-region representations for elements within the transportation system.

[0236] In embodiments, the one or more processors are further configured to obtain objects detected by the sub-optimal mapping algorithm, determine whether the detected object corresponds to an existing real-world-element digital twin, and update, in response to determining the detected object corresponds to the existing real-world-element digital twin, the mapping information to include dimensional information from the real-world-element digital twin.

[0237] In embodiments, the updated mapping information is provided to the simultaneous location and mapping sensor to thereby optimize navigation through the transportation system.

[0238] In embodiments, the one or more processors are further configured to request, in response to determining the detected object does not correspond to an existing real-world-element digital twin, updated data for the detected object from the simultaneous location and mapping sensor that is configured to produce a refined map of the detected object.

[0239] In embodiments, the simultaneous location and mapping sensor provides the updated data using a second algorithm. The second algorithm is configured to increase resolution of the detected object.

[0240] In embodiments, the simultaneous location and mapping sensor, in response to receiving the request, captures the updated data for the real-world element corresponding to the detected object.

[0241] In embodiments, the simultaneous location and mapping sensor is within an autonomous vehicle navigating the transportation system.SFT-106-A-PCT

[0242] In embodiments, navigation of the autonomous vehicle includes use of real-world-element digital twins received from the digital twin datastore.

[0243] According to aspects of the present disclosure, a system for representing attributes in a transportation system digital twin includes a digital twin datastore and one or more processors. The digital twin datastore stores a transportation-system digital twin including real-world-element digital twins embedded therein. The transportation system digital twin corresponds to a transportation system. Each real-world-element digital twin provides a digital twin of a respective real-world element that is disposed within the transportation system. The real-world-element digital twins include mobile-element digital twins. Each mobile-element digital twin provides a digital twin of a respective mobile element within the real-world elements. The one or more processors are configured to, for each mobile element, determine, in response to occurrence of a triggering condition, a position of the mobile element, and update, in response to determining the position of the mobile element, the mobile-element digital twin corresponding to the mobile element to reflect the position of the mobile element.

[0244] In embodiments, the mobile elements are workers within the transportation system.

[0245] In embodiments, the mobile elements are vehicles within the transportation system.

[0246] In embodiments, triggering condition is expiration of dynamically determined time interval.

[0247] In embodiments, the dynamically determined time interval is increased in response to determining a single mobile element within the transportation system.

[0248] In embodiments, the dynamically determined time interval is increased in response to determining occurrence of a predetermined period of reduced environmental activity.

[0249] In embodiments, the dynamically determined time interval is decreased in response to determining abnormal activity within the transportation system.

[0250] In embodiments, the dynamically determined time interval is a first time interval, and the dynamically determined time interval is decreased to a second time interval in response to determining movement of the mobile element.

[0251] In embodiments, the dynamically determined time interval is increased from the second time interval to the first time interval in response to determining nonmovement of the mobile element for at least a third time interval.

[0252] In embodiments, the triggering condition is expiration of a time interval. The time interval is calculated based on a probability that the mobile element has moved.

[0253] In embodiments, the triggering condition is proximity of the mobile element to another of the mobile elements.SFT-106-A-PCT

[0254] In embodiments, the triggering condition is based on density of movable elements within the transportation system.

[0255] In embodiments, the path information obtained from a navigation module of the mobile element.

[0256] In embodiments, the one or more processors are further configured to obtain the path information including detecting, using a plurality of sensors within the transportation system, movement of the mobile element, obtaining a destination for the mobile element, calculating, using the plurality of sensors within the transportation system, an optimized path for the mobile element, and instructing the mobile element to navigate the optimized path.

[0257] In embodiments, the optimized path includes using path information for other mobile elements within the real-world elements.

[0258] In embodiments, the optimized path minimizes interactions between mobile elements and humans within the transportation system.

[0259] In embodiments, the mobile elements include autonomous vehicles and non-autonomous vehicles, and the optimized path reduces interactions of the autonomous vehicles with the non- autonomous vehicles.

[0260] In embodiments, the traffic modeling includes use of a particle traffic model, a trigger- response mobile-element-following traffic model, a macroscopic traffic model, a microscopic traffic model, a submicroscopic traffic model, a mesoscopic traffic model, or a combination thereof.

[0261] According to aspects of the present disclosure, a system for representing design specification information includes a digital twin datastore and one or more processors. The digital twin datastore stores a transportation-system digital twin including real-world-element digital twins embedded therein. The transportation system digital twin corresponds to a transportation system. Each real-world-element digital twin provides a digital twin of a respective real-world element that is disposed within the transportation system. The one or more processors are configured to, for each of the real-world elements, determine a design specification for the real- world element, associate the design specification with the real-world-element digital twin, and display the design specification to a user in response to the user interacting with the real-world- element digital twin.

[0262] In embodiments, the user interacting with the real-world-element digital twin includes the user selecting the real-world-element digital twin.

[0263] In embodiments, the user interacting with the real-world-element digital twin includes the user directing an image capture device toward the real-world-element digital twin.

[0264] In embodiments, the image capture device is a wearable device.SFT-106-A-PCT

[0265] In embodiments, the real-world element digital twin is a transportation-system digital twin.

[0266] In embodiments, the design specification is stored in the digital twin datastore in response to input of the user.

[0267] In embodiments, the design specification is determined using a digital twin simulation system.

[0268] In embodiments, the one or more processors are further configured to, for each of the real- world elements, detect, using a sensor within the transportation system, one or more contemporaneous operating parameters, compare the one or more contemporaneous operating parameters to the design specification, and automatically display the design specification, the one or more contemporaneous operating parameters, or a combination thereof in response to a mismatch between the one or more contemporaneous operating parameters and the design specification. The one or more contemporaneous operating parameters correspond to the design specification of the real-world element.

[0269] In embodiments, display of the design specification includes indicia of contemporaneous operating parameters.

[0270] In embodiments, display of the design specification includes source indicia for the specification information.

[0271] In embodiments, the source indicia inform the user that the design specification was determined via use of a digital twin simulation system. A more complete understanding of the disclosure will be appreciated from the description and accompanying drawings and the claims, which follow.

[0272] According to aspects of the present disclosure, a method is provided for configuring role- based digital twins, comprising: receiving, by a processing system having one or more processors, an organizational definition of an enterprise, wherein the organizational definition defines a set of roles within the enterprise; generating, by the processing system, an organizational digital twin of the enterprise based on the organizational definition, wherein the organizational digital twin is a digital representation of an organizational structure of the enterprise; determining, by the processing system, a set of relationships between different roles within the set of roles based on the organizational! definition; determining, by the processing system, a set of settings for a role from the set of roles based on the determined set of relationships; !inking an identity of a respective individual to the role; determining, by the processing system, a configuration of a presentation layer of a role-based digital twin corresponding to the role based on the settings of the role that is linked to the identity, wherein the configuration of the presentation layer defines a set of states that is depicted in the role-based digital twin associated with the role; determining, by the processingSFT-106-A-PCT system, a set of data sources that provide data corresponding to the set of states, wherein each data source provides one or more respective types of data; and configuring one or more data structures that is received from the one or more data sources, wherein the one or more data structures are configured to provide data used to populate one or more of the set of states in the role-based digital twin.

[0273] In embodiments, an organizational definition may further identify a set of physical assets of the enterprise.

[0274] In embodiments, determining a set of relationships may include parsing the organizational definition to identify a reporting structure and one or more business units of the enterprise.

[0275] In embodiments, a set of relationships may be inferred from a reporting structure and a business unit.

[0276] In embodiments, a set of identities may be linked to a set of roles, wherein each identity corresponds to a respective role from the set of roles.

[0277] In embodiments, an organizational structure may include hierarchical components, which may be embodied in a graph data structure.

[0278] In embodiments, a set of settings for a set of roles may include role-based preference settings.

[0279] In embodiments, a role-based preference setting may be configured based on a set of role specific templates.

[0280] In embodiments, a set of templates may include at least one of a CEO template, a COO template, a CFO template, a counsel template, a board member template, a CTO template, a chief marketing officer template, an information technology manager template, a chief information officer template, a chief data oftl.cer template, an investor template, a customer template, a vendor template, a supplier template, an engineering manager template, a project manager template, an operations manager template, a sales manager template, a salesperson template, a service manager template, a maintenance operator template, and a business development template.

[0281] In embodiments, a set of settings for the set of roles may include role-based taxonomy settings.

[0282] In embodiments, a taxonomy setting may identify a taxonomy that is used to characterize data that is presented in a role-based digital twin, such that the data is presented in a taxonomy that is linked to the role corresponding to the role-based digital twin.

[0283] In embodiments, a set of taxonomies includes at least one of a CEO taxonomy, a COO taxonomy, a CFO taxonomy, a counsel taxonomy, a board member taxonomy, a CTO taxonomy, a chief marketing officer taxonomy, an information technology manager taxonomy, a chief information officer taxonomy, a chief data officer taxonomy, an investor taxonomy, a customerSFT-106-A-PCT taxonomy, a vendor taxonomy, a supplier taxonomy, an engineering manager taxonomy, a project manager taxonomy, an operations manager taxonomy, a sales manager taxonomy, a salesperson taxonomy, a service manager taxonomy, a maintenance operator taxonomy, and a business development taxonomy.

[0284] In embodiments, at least one role of the set of roles may be selected from among a CEO role, a COO role, a CFO role, a counsel role, a board member role, a CTO role, an information technology manager role, a chief information officer role, a chief data officer role, a human resources manager role, an investor role, an engineering manager role, an accountant role, an auditor role, a resource planning role, a public relations manager role, a project manager role, an operations manager role, a research and development role, an engineer role, including but not limited to mechanical engineer, electrical engineer, semiconductor engineer, chemical engineer, computer science engineer, data science engineer, network engineer, or some other type of engineer, and a business development role.

[0285] In embodiments, at least one role may be selected from among a factory manager role, a factory operations role, a factory worker role, a power plant manager role, a power plant operations role, a power plant worker role, an equipment service role, and an equipment maintenance operator role.

[0286] In embodiments, at least one role may be selected from among a chief marketing officer role, a product development role, a supply chain manager role, a product design role, a marketing analyst role, a product manager role, a competitive analyst role, a customer service representative role, a procurement operator, an inbound logistics operator, an outbound logistics operator, a customer role, a supplier role, a vendor role, a demand management role, a marketing manager role, a sales manager role, a service manager role, a demand forecasting role, a retail manager role, a warehouse manager role, a salesperson role, and a distribution center manager role.

[0287] According to aspects of the present disclosure, a method is provided for configuring a digital twin of a workforce, comprising: representing an enterprise organizational structure in a digital twin of an enterprise; parsing the structure to infer relationships among a set of roles within the organizational structure, the relationships and the roles defining a workforce of the enterprise; and configuring the presentation layer of a digital twin to represent the enterprise as a set of workforces having a set of attributes and relationships.

[0288] In embodiments, a digital twin may integrate with an enterprise resource planning system that operates on a data structure representing a set of roles in the enterprise, such that changes in the enterprise resource planning system are automatically reflected in the digital twin.

[0289] In embodiments, an organizational structure may include hierarchical components.

[0290] In embodiments, hierarchical components may be embodied in a graph data structure.SFT-106-A-PCT

[0291] In embodiments, a workforce may be a factory operations workforce, a plant operations workforce, a resource extraction operations workforce, or some other type of workforce.

[0292] In embodiments, at least one workforce role may be selected from among a CEO role, a COO role, a CFO role, a counsel role, a board member role, a CTO role, an information technology manager role, a chief information officer role, a chief data officer role, an investor role, an engineering manager role, a project manager role, an operations manager role, and a business development role.

[0293] In embodiments, a digital twin may represent a recommendation for training for the workforce, a recommendation for augmentation of the workforce, a recommendation for configuration of a set of operations involving the workforce, a recommendation for configuration of the workforce, or some other kind of recommendation.

[0294] In embodiments, a quantum computing system may provide a framework for providing a set of quantum computing services to one or more quantum computing clients within a transportation system. In some embodiments, the quantum computing system framework may be at least partially replicated in respective quantum computing clients. In embodiments, an individual client may include some or all of the capabilities of the quantum computing system, whereby the quantum computing system is adapted for the specific functions performed by the subsystems of the quantum computing client. Additionally, or alternatively, in some embodiments, the quantum computing system may be implemented as a set of microservices, such that different quantum computing clients may leverage the quantum computing system via one or more APIs exposed to the quantum computing clients within a transportation system. In these embodiments, the quantum computing system may be configured to perform various types of quantum computing services that may be adapted for different quantum computing clients within a transportation system. In either of these configurations, a quantum computing client may provide a request to the quantum computing system, whereby the request is to perform a specific task (e.g., an optimization). In response, the quantum computing system may execute the requested task and returns a response to the quantum computing client within the transportation system.

[0295] In embodiments, the transportation system may include a thalamus service and a set of input sensors streaming data from various sources across the system with centrally-managed data sources. The thalamus service may filter the into a control system such that the control system is never overwhelmed by the total volume of information. In embodiments, the thalamus service may provide an information suppression mechanism for information flows within the transportation system. This mechanism monitors all data streams and strips away irrelevant data streams by ensuring that the maximum data flows from all input sensors are always constrained.SFT-106-A-PCT

[0296] The thalamus service may be a gateway for all communication that responds to the prioritization of the control system. The control system may decide to change the prioritization of the data streamed from the thalamus service, for example, during a known fire in an isolated area, and the event may direct the thalamus service to continue to provide flame sensor information despite the fact that majority of this data is not unusual. The thalamus service may be an integral part of the overall system communication framework within the transportation system.

[0297] In embodiments, the thalamus service may include an intake management system. The intake management system may be configured to receive and process multiple large datasets by converting them into data streams that are sized and organized for subsequent use by a central control system 1operating within one or more transportation systems. For example, a robot may include vision and sensing systems that are used by its central control system to identify and move through an environment in real time. The intake management system may facilitate robot decision- making by parsing, filtering, classifying, or otherwise reducing the size and increasing the utility of multiple large datasets that would otherwise overwhelm the central control system. In embodiments, the intake management system may include an intake controller that works with an intelligence service to evaluate incoming data and take actions-based evaluation results. Evaluations and actions may include specific instruction sets received by the thalamus service, for example the use of a set of specific compression and prioritization tools stipulated within a “Networking” library module. In another example, thalamus service inputs may direct the use of specific filtering and suppression techniques. In a third example, thalamus service inputs may stipulate data filtering associated with an area of interest such as a certain type of financial transaction. The intake management system is also configured to recognize and manage datasets that are in a vectorized format such as PCMP, where they may be passed directly to central control, or alternatively deconstructed and processed separately. The intake management system may include a learning module that receives data from external sources that enables improvement and creation of application and data management library modules. In some cases, the intake management system may request external data to augment existing datasets.

[0298] In embodiments, the transportation system may include a dual process artificial neural network (DPANN) system. The DPANN system may include an artificial neural network (ANN) having behaviors and operational processes (such as decision-making) that are products of a training system and a retraining system. The training system may be configured to perform automatic, trained execution of ANN operations. The retraining system performs effortful, analytical, intentional retraining of the ANN, such as based on one or more relevant aspects of the ANN, such as memory, one or more input data sets (including time information with respect to elements in such data sets), one or more goals or objectives (including ones that may varySFT-106-A-PCT dynamically, such as periodically and / or based on contextual changes, such as ones relating to the usage context of the ANN), and / or others. In cases involving memory-based retraining, the memory may include original / historical training data and refined training data. The DPANN system may include a dual process learning function (DPLF) configured to manage and perform an ongoing data retention process. The DPLF (including, where applicable, memory management process) facilitate retraining and refining of behavior of the ANN. The DPLF provides a framework by which the ANN creates outputs such as predictions, classifications, recommendations, conclusions and / or other outputs based on a historic inputs, new inputs, and new outputs (including outputs configured for specific use cases, including ones determined by parameters of the context of utilization (which may include performance parameters such as latency parameters, accuracy parameters, consistency parameters, bandwidth utilization parameters, processing capacity utilization parameters, prioritization parameters, energy utilization parameters, and many others). Summary of Transportation systems with quantum and / or biological systems

[0299] In embodiments, a transportation system includes: a first data system configured to, receive a plurality of data values of a data stream, generate a predictive model for predicting future data values of the data stream based on the received plurality of data values, wherein generating the predictive model comprises determining a plurality of model parameters, and transmit the plurality of model parameters; and a second data system configured to, receive the plurality of model parameters transmitted by the first data system, parameterize a predictive model using the plurality of model parameters, predict a future data value of the data stream using the parameterized predictive model, and adjusting an operating state of a transportation system based on the future data value. In embodiments, adjusting the operating state of the transportation system includes, predicting, by the second data system, an effect of the operating state on the transportation system through an analysis of the social media-sourced data, and adjusting, by the second data system, at least one operating state of the transportation system responsive to the predicted effect thereon. In embodiments, adjusting the operating state of the transportation system includes, classifying, using a first neural network, social media data sourced from a plurality of social media sources as affecting the transportation system, predicting, using a second neural network, at least one operating objective of the transportation system based on the classified social media data, and adjusting, using a third neural network, the operating state of the transportation system to achieve the at least one operating objective of the transportation system. In embodiments, receiving the plurality of data values includes gathering social media-sourced data about a plurality of individuals, the data being sourced from a plurality of social media sources. In embodiments, the plurality of data values is received from one or more security cameras, and the data stream includes motion vectors extracted from video data captured by the security cameras.SFT-106-A-PCT

[0300] In embodiments, a method for prioritizing predictive model data streams, the method includes: receiving, by the first device, social media data sourced from a plurality of social media sources as affecting a transportation system; classifying, by the first device, the social media data based on a set of model parameters for each of a plurality of predictive models, wherein each predictive model is trained to predict future data values of the transportation system; selecting, by the first device and from the classified social media data, at least one predictive model data stream; parameterizing, by the first device, a predictive model using the set of model parameters included in the selected at least one predictive model stream; and predicting, by the first device, at least one future data value of the transportation system using the parameterized predictive model. In embodiments, selecting the at least one assigning, by the first device, priorities to each of a plurality of predictive model data streams included in the social media data, and selecting the at least one predictive model stream is based on the priorities assigned to each of the plurality of predictive model data streams. In embodiments, the selected at least one predictive model data stream is associated with a highest priority among the plurality of predictive model data streams. In embodiments, the selecting comprises suppressing at least one of the predictive model data streams that were not selected based on the priority assigned to each of the at least one non-selected predictive model data streams. Some embodiments further include adjusting an operating state of the transportation system based on the future data value of the transportation system.

[0301] In embodiments, a system for transportation includes: a hybrid neural network including, a first neural network configured to process a plurality of data values of a data stream to determine an emotional state of a rider of a vehicle, wherein the plurality of data values include sensor data collected from one or more sensors and associated with the rider of a vehicle, a second neural network configured to generate a predictive model for predicting a future emotional state of the rider of the vehicle based on the received plurality of data values, and a third neural network configured to adjust at least one operating parameter of the vehicle based on an output of the predictive model. In embodiments, generating the predictive model comprises determining a plurality of model parameters of the predictive model, and the third neural network adjusts the at least one operating parameter of the vehicle based on the plurality of model parameters determined by the predictive model. In embodiments, the predictive model includes a behavior analysis model, and the predicted future emotional state of the rider is based on a predicted behavior of the rider in response to the at least one operating parameter of the vehicle. In embodiments, the hybrid neural network is further configured to, receive additional data values of the data stream, and refine the predictive model based on the additional data values, wherein refining the predictive model adjusts one or more model parameters of the predictive model. In embodiments, the data stream includesSFT-106-A-PCT a video stream received from a camera associated with the vehicle, and the plurality of data values includes one or more vectors extracted from the video stream received from the camera.

[0302] In embodiments, a system for transportation includes: an expert system to select a configuration for a vehicle, wherein the configuration includes at least one parameter selected from the group consisting of a vehicle parameter, a user experience parameter, and combinations thereof, and the expert system includes, a first data system configured to, receive a plurality of data values of a data stream, wherein the data values comprise sensor data collected from one or more sensor devices, generate a predictive model for predicting the at least one parameter based on the received plurality of data values, wherein generating the predictive model includes determining a plurality of model parameters, and transmit the plurality of model parameters, and a second data system configured to, receive the plurality of model parameters, parameterize a predictive model based on the plurality of model parameters, and select the at least one parameter based on the parameterized predictive model. In embodiments, the predictive model includes a behavior analysis model, and the at least one parameter is based on a predicted behavior of a rider of the vehicle. In embodiments, the predictive model includes a classification model, and the at least one parameter includes a predicted future state of the vehicle based on classified data received from one or more sensor devices associated with the vehicle. In embodiments, the data stream includes a video stream received from a camera associated with the vehicle, and the plurality of data values includes one or more motion vectors extracted from the video stream received from the camera. In embodiments, the expert system includes, a first neural network configured operates to classify a state of the vehicle through analysis of information about the vehicle captured by an Internet-of-things device during operation of the vehicle, and a second neural network configured to optimize the at least one parameter of the vehicle based on the classified state of the vehicle, information about a state of a rider occupying the vehicle, and information that correlates vehicle operation with an effect on rider state.

[0303] In embodiments, a system for transportation includes: a quantum-enabled risk identification module configured to identify a risk associated with a vehicle, and a vehicle parameter selection module configured to adjust at least one vehicle parameter of the vehicle based on the risk to improve a margin of safety of the vehicle. In embodiments, the quantum-enabled risk identification module is configured to perform one or more of, identifying a risk associated with an operating state of the vehicle, assess an impact of the at least one vehicle parameter on the margin of safety of the vehicle, determine a current risk profile associated with the vehicle, determine a potential risk profile associated with the vehicle based on an adjustment of one or more operating parameters of the vehicle, or determine a probability of a risk associated with the vehicle and one or more predicted events. In embodiments, the quantum-enabled risk identification moduleSFT-106-A-PCT is configured to improve the margin of safety by determining a risk type of a risk associated with the vehicle based on a set of risk types. In embodiments, the quantum-enabled risk identification module is further configured to, predict one or more events associated with the vehicle, determine an impact of the predicted one or more events on the margin of safety associated with the vehicle, and determine an adjustment of the at least one vehicle parameter of the vehicle that improves the margin of safety of the vehicle based on the one or more predicted events. In embodiments, the quantum-enabled risk identification module is further configured to generate a classical prediction engine that identifies the risk associated with the vehicle, wherein the vehicle parameter selection module is based on an output of the classical prediction engine.

[0304] In embodiments, a method of vehicle routing includes: adjusting a quantum continual learning system based on an expression received from a user; determining, by the quantum continual learning system, a routing preference for a route of a vehicle; determining at least one vehicle-routing parameter used to route vehicles to reflect the routing preference; and adjusting, by a vehicle routing system, the route of the vehicle based on the at least one determined vehicle routing parameter. Some embodiments include: presenting, in a game-based interface, a vehicle route preference-affecting game activity; and receiving, through the game-based interface, a response of the user to the presented game activity, wherein the adjusting of the quantum continual learning system is based on the response of the user to the presented game activity. In embodiments, the quantum continual learning system is further configured to receive a continuous stream of realtime data, and the routing preference is determined by the quantum continual learning system based on the continuous stream of realtime data. In embodiments, the adjusting of the quantum continual learning system includes continuously training the quantum continual learning system based on realtime data, the realtime data including the expression received from the user. In embodiments, the vehicle is included in a set of vehicles, and adjusting the route of the vehicle includes adjusting a routing parameter of at least one other vehicle of the set of vehicles based on the at least one determined vehicle routing parameter.

[0305] In embodiments, a system includes: an artificial intelligence system including a quantum annealing module, the artificial intelligence system configured to, receive, from a plurality of rechargeable vehicles within a target geographic region, an operational status of each rechargeable vehicle; predict a near-term need for recharging each rechargeable vehicle based on the operational status of each rechargeable vehicle; and determine, by the quantum annealing module, at least one parameter of a recharging plan for a recharging infrastructure based on the predicted near-term need for recharging each rechargeable vehicle. In embodiments, determining the at least one parameter of the recharging plan further comprises, determining, by the quantum annealing module, a set of candidate state changes associated with each candidate parameter of a set ofSFT-106-A-PCT candidate parameters for the recharging plan for the recharging infrastructure, and applying, by the quantum annealing module, a quantum annealing selection to the set of candidate parameters to determine the at least one parameter of the recharging plan based on the set of candidate state changes determined by the quantum annealing module. In embodiments, determining the at least one parameter of the recharging plan further comprises, setting, by the quantum annealing module, an initial weight of a state of the recharging infrastructure associated with each candidate parameter of a set of candidate parameters for the recharging plan, and evolving, by the quantum annealing module, the initial weight of each state to an adjusted weight based on a time-dependent equation, wherein the at least one parameter is determined based on the adjusted weight of the state associated with each candidate parameter of the set of candidate parameters. In embodiments, the artificial intelligence system is further configured to receive capacity information associated with the recharging infrastructure, and the determining of the at least one parameter is based on the capacity information associated with the recharging infrastructure. In embodiments, the artificial intelligence system further comprises a hybrid neural network including, a first portion of the hybrid neural network configured to operate on a first portion of the operational status of each rechargeable vehicle, wherein the first portion is associated with a route plan of the rechargeable vehicle, and a second portion of the hybrid neural network configured to operate on a second portion of the operational status of each rechargeable vehicle, wherein the second portion is associated with a recharging range of each rechargeable vehicle.

[0306] In embodiments, a system for transportation includes: a cognitive system including a quantum annealing module, the cognitive system configured to, determine, by the quantum annealing module, at least one parameter of a reward to be made available to a rider of the vehicle in response to the rider undertaking a predetermined action while in the vehicle, and provide the reward to the rider in response to a performance of the predetermined action by the rider. In embodiments, the at least one parameter is based on at least one input received from the rider by a rider interface, and the cognitive system is configured to present to an offer of the reward the rider by the rider interface. In embodiments, the predetermined action includes a selection by the rider of a route of the vehicle, and a parameter of the reward to be made available to the rider is based on a routing preference of the rider. In embodiments, determining the at least one parameter of the reward includes, determining, by the quantum annealing module, an effect on a set of vehicles of each of a set of predetermined actions that could be undertaken by the rider, and determining, by the quantum annealing module, the parameter of the reward based on a quantum annealing selection among the set of predetermined actions. In embodiments, determining the at least one parameter of the reward includes determining, by the quantum annealing module, a set of candidate state changes associated with a routing of a set of vehicles based on the predetermined action ofSFT-106-A-PCT the rider, wherein the at least one parameter of the reward is based on the set of candidate state changes associated with the routing of the set of vehicles.

[0307] In embodiments, a system for transportation includes: a data capture module configured to capture a data set associated with an interaction between a rider within a vehicle and a user interface of the vehicle; and a dual purpose artificial neural network that is configured to, train based on the data set to perform actions on behalf of a rider within a vehicle, retrain based on a dual process learning function applied to the data set to adjust the actions performed on behalf of the rider, and update the data set in response to the retraining of the dual purpose artificial neural network. In embodiments, retraining the dual purpose artificial neural network further comprises, identifying a poor performance of the dual purpose artificial intelligence network on a classification task, updating the data set to include at least one additional data sample that is associated with the classification task, and retraining the dual purpose artificial neural network based on the data set including the at least one additional data sample that is associated with the classification task. In embodiments, retraining the dual purpose artificial neural network further comprises, updating the data set to include at least one additional data sample that is based on an additional action to be performed on behalf of the rider, and retraining the dual purpose artificial neural network based on the data set including the at least one additional data sample that is associated with the additional action. In embodiments, retraining the dual purpose artificial neural network further comprises, identifying a novel problem for which the dual purpose artificial neural network is not currently trained to perform actions on behalf of the rider, updating the data set to include at least one additional data sample that is associated with the novel problem, and retraining the dual purpose artificial neural network based on the data set including the at least one additional data sample that is associated with the novel problem. In embodiments, the data capture module includes a robotic process automation module that is configured to capture the data set associated with an action performed by the rider and associated with the vehicle, and the dual purpose artificial neural network is configured to train based on the data set to perform the action instead of the rider performing the action.

[0308] In embodiments, a system for transportation includes: an interface configured to configure a set of expert systems to provide one or more outputs associated with a set of parameters, wherein the parameters are selected from a group including at least one vehicle parameters, at least one fleet parameter, or at least one of user experience parameter; and a dual purpose artificial neural network that is configured to, train based on the data set to select the one or more parameters for the set of expert systems, retrain based on a dual process learning function applied to the data set to adjust the one or more parameters selected for the set of expert systems, and update the data set in response to the retraining of the dual purpose artificial neural network. In embodiments,SFT-106-A-PCT retraining the dual purpose artificial neural network further comprises, identifying a poor selection of the expert systems based on a first set of one or more parameters selected by the dual purpose artificial neural network, updating the data set to include at least one additional data sample that is associated with the one or more parameters, and retraining the dual purpose artificial neural network based on the data set including the at least one additional data sample that is associated with the one or more parameters. In embodiments, retraining the dual purpose artificial neural network further comprises, updating the data set to include at least one additional data sample that is based on an additional output of the set of expert systems in response to the one or more parameters, and retraining the dual purpose artificial neural network based on the data set including the at least one additional data sample that is associated with the additional output. In embodiments, retraining the dual purpose artificial neural network further comprises, identifying a novel problem for which the set of expert systems is not currently trained to provide one or more outputs, updating the data set to include at least one additional data sample that is associated with the novel problem, and retraining the dual purpose artificial neural network based on the data set including the at least one additional data sample that is associated with the novel problem. In embodiments, the dual- purpose learning function is further configured to receive at least one additional data sample associated with a new input value, consolidate the at least one additional data sample with at least one data sample of the data set, and update the data set based on the consolidating of the at least one additional data sample and the at least one data sample of the data set.

[0309] In some aspects, the techniques described herein relate to a software-defined vehicle for mitigating rider seat fatigue, the software-designed vehicle including: a plurality of seat sensors configured to detect and generate sensor data indicating physical parameters indicative of rider fatigue; a generative artificial intelligence (AI) engine configured to analyze the sensor data and generate personalized seat adjustment profiles to mitigate detected rider fatigue; and a vehicle control unit (VCU) communicatively coupled to the plurality of seat sensors and the generative AI engine, the VCU configured to implement the personalized seat adjustment profiles in the software-defined vehicle.

[0310] In some aspects, the techniques described herein relate to a software-defined vehicle, wherein the generative AI engine fuses the sensor data with rider preference data to create a comprehensive model of rider comfort and predict optimal seat adjustments.

[0311] In some aspects, the techniques described herein relate to a software-defined vehicle, wherein the generative AI engine employs machine learning algorithms to identify patterns of rider discomfort and dynamically suggest changes to seating ergonomics.SFT-106-A-PCT

[0312] In some aspects, the techniques described herein relate to a software-defined vehicle, wherein the generative AI engine generates real-time recommendations for micro-adjustments to seat positions to redistribute pressure and improve circulation for a rider.

[0313] In some aspects, the techniques described herein relate to a software-defined vehicle, further including an encryption module to secure a transmission of sensor data from the plurality of seat sensors to the VCU and the generative AI engine.

[0314] In some aspects, the techniques described herein relate to a software-defined vehicle, wherein the VCU includes a secure access control system that restricts modification of the generative AI engine to authorized personnel only.

[0315] In some aspects, the techniques described herein relate to a software-defined vehicle, wherein the generative AI engine is configured to detect and respond to cybersecurity threats by initiating protective protocols to safeguard rider data.

[0316] In some aspects, the techniques described herein relate to a software-defined vehicle, further including a digital twin of a seating system of the software-defined vehicle, which the generative AI engine uses to simulate and evaluate an effectiveness of fatigue mitigation strategies.

[0317] In some aspects, the techniques described herein relate to a software-defined vehicle, wherein the digital twin is configured to update in real-time with sensor data to reflect a current state of the seating system and rider fatigue levels.

[0318] In some aspects, the techniques described herein relate to a software-defined vehicle, wherein the digital twin is configured for virtual testing of potential new seat materials and designs for fatigue reduction before physical implementation.

[0319] In some aspects, the techniques described herein relate to a software-defined vehicle, further including a user interface that displays seat adjustment recommendations from the generative AI engine and allows a rider to provide feedback.

[0320] In some aspects, the techniques described herein relate to a software-defined vehicle, wherein the user interface includes a haptic feedback mechanism to alert the rider of a need for a change in seating position to mitigate fatigue.

[0321] In some aspects, the techniques described herein relate to a software-defined vehicle, wherein the user interface is integrated with a mobile application that tracks seating patterns and provides personalized fatigue mitigation advice based on output from the generative AI engine. Software defined vehicles with rider seat fatigue mitigation enhanced by generative AI.

[0322] In some aspects, the techniques described herein relate to a software-defined vehicle configured to mitigate brain atrophy, the software-defined vehicle including: a processor; a memory storing instructions that, when executed by the processor, cause the software-definedSFT-106-A-PCT vehicle to: monitor interactions of a driver with vehicle controls and navigation systems; analyze driving patterns to identify routine behaviors as identified routine behaviors; generate cognitive challenges based on the identified routine behaviors to engage cognitive functions of the driver; and adapt an operation of the software-defined vehicle to present the cognitive challenges to the driver during operation of the software-defined vehicle.

[0323] In some aspects, the techniques described herein relate to a software-defined vehicle, wherein the cognitive challenges include route deviation prompts that encourage the driver to navigate without step-by-step navigation assistance for familiar routes.

[0324] In some aspects, the techniques described herein relate to a software-defined vehicle, wherein the route deviation prompts are generated in response to real-time driving conditions to encourage the driver to adapt to changing conditions and engage in problem-solving activities.

[0325] In some aspects, the techniques described herein relate to a software-defined vehicle, wherein the cognitive challenges are generated by a generative AI configured to create tasks that stimulate memory, spatial awareness, and executive functioning based on information about the driver.

[0326] In some aspects, the techniques described herein relate to a software-defined vehicle, wherein the generative AI is further configured to adjust a complexity of the cognitive challenges based on a performance and an interaction with the cognitive challenges by the driver.

[0327] In some aspects, the techniques described herein relate to a software-defined vehicle, further including a gamification module that assigns points and rewards to the driver for successfully completing the cognitive challenges.

[0328] In some aspects, the techniques described herein relate to a software-defined vehicle, wherein the gamification module includes a leaderboard feature that compares a performance of the driver with historical performance data or peer performance data to foster a competitive environment for cognitive engagement.

[0329] In some aspects, the techniques described herein relate to a software-defined vehicle, wherein the rewards include at least one of virtual badges, unlocking new vehicle features, or personalized messages of encouragement.

[0330] In some aspects, the techniques described herein relate to a software-defined vehicle, further including a generative AI engine configured to work in conjunction with the gamification module to dynamically create the cognitive challenges with personalization based on preferences and past interactions of the driver with the gamification module.

[0331] In some aspects, the techniques described herein relate to a software-defined vehicle, wherein the generative AI engine uses driver feedback from the gamification module to refineSFT-106-A-PCT and optimize the cognitive challenges for enhanced engagement and effectiveness in mitigating brain atrophy.

[0332] In some aspects, the techniques described herein relate to a software-defined vehicle, further including a vehicle user interface configured to provide at least one of auditory, visual, or haptic feedback to the driver based on the cognitive challenges to utilize multiple sensory modalities to enhance cognitive stimulation.

[0333] In some aspects, the techniques described herein relate to a software-defined vehicle, further including an emergency intervention protocol that is activated in response to detecting a lack of driver response to the cognitive challenges, indicating potential acute cognitive impairment.

[0334] In some aspects, the techniques described herein relate to a software-defined vehicle, wherein the software-defined vehicle is further configured to encourage breaks for physical activity during long journeys.

[0335] In some aspects, the techniques described herein relate to a software-defined vehicle, further including a generative AI engine configured to suggest exercises tailored to physical capabilities and preferences of the driver.

[0336] In some aspects, the techniques described herein relate to a software-defined vehicle, further including a natural language processing module that allows the driver to interact with the cognitive challenges using voice commands to facilitate hands-free engagement and reduced driver distraction.

[0337] In some aspects, the techniques described herein relate to a software-defined vehicle, further including a generative AI engine, and wherein the software-defined vehicle is further configured to adjust environmental settings within the software-defined vehicle to create an optimal environment for cognitive function as determined by the generative AI engine.

[0338] In some aspects, the techniques described herein relate to a software-defined vehicle, wherein the generative AI engine is configured to determine the optimal environment based, at least in part, on a time of day and a driver state.

[0339] In some aspects, the techniques described herein relate to a software-defined vehicle, wherein the software-defined vehicle is configured to adjust at least one of lighting or temperature as the environmental settings to create the optimal environment. Software defined vehicles with brain atrophy mitigation features.

[0340] In some aspects, the techniques described herein relate to a vehicle maintenance system including: a data processing unit configured to generate an analysis of environmental data, user behavioral data, and vehicle diagnostic data; an emotional state detection module configured to determine patterns of emotional states of a user based on the analysis; a scheduling moduleSFT-106-A-PCT configured to: generate maintenance recommendations for a vehicle for which a user is associated with maintenance; deduce an optimal time within a maintenance window of the maintenance recommendations to recommend the maintenance recommendations to the user to result in a most favorable predicted emotional state of the user; and generate a maintenance reminder timing schedule for the user based on the patterns of emotional states of the user and on the optimal time; and a communication module configured to transmit the maintenance recommendations to the user according to the maintenance reminder timing schedule.

[0341] In some aspects, the techniques described herein relate to a vehicle maintenance system, wherein the environmental data includes weather patterns, daylight hours, and seasonal changes, and the emotional state detection module is further configured to infer mood states of the user associated with different seasons.

[0342] In some aspects, the techniques described herein relate to a vehicle maintenance system, wherein the user behavioral data includes at least one of historical maintenance records, social media activity, or vehicle usage patterns, and the emotional state detection module is further configured to detect periods when the user is more likely to engage in maintenance activities.

[0343] In some aspects, the techniques described herein relate to a vehicle maintenance system, wherein the scheduling module utilizes generative AI algorithms to predict the optimal time.

[0344] In some aspects, the techniques described herein relate to a vehicle maintenance system, wherein the communication module uses generative AI to create personalized maintenance notifications that the scheduling module predicts will resonate with a current mood of the user during a current season.

[0345] In some aspects, the techniques described herein relate to a vehicle maintenance system, further including a user feedback interface configured to receive user responses to maintenance notifications, wherein the scheduling module adjusts future maintenance recommendations based on the user responses.

[0346] In some aspects, the techniques described herein relate to a vehicle maintenance system, wherein the scheduling module is further configured to delay maintenance recommendations during seasons when the emotional state detection module determines a lower interest in maintenance activities for the user. Vehicle maintenance systems for improved emotional status of users.

[0347] In some aspects, the techniques described herein relate to a refueling planning system for vehicles, the refueling planning system including: an emotional state system configured to predict user emotional state changes in response to refueling decisions for a vehicle and a user; a fuel status system configured to identify a fuel status of the vehicle and to predict refueling requirements of the vehicle for a trip; and a refueling recommendation engine configured toSFT-106-A-PCT generate a refueling plan to achieve the refueling requirements with favorable emotional state changes, wherein the refueling recommendation engine is configured to consider combustion fuel refilling and electrical energy storage refueling in the refueling plan.

[0348] In some aspects, the techniques described herein relate to a refueling planning system, wherein the refueling recommendation engine is further configured to prioritize charging a battery of a hybrid vehicle over filling a gas tank based on user environmental preferences indicating a user desire for considering environmental factors.

[0349] In some aspects, the techniques described herein relate to a refueling planning system, wherein the refueling recommendation engine is further configured to adjust prioritization between charging and gas filling based on a comparison of an environmental impact of each option and user historical preference data.

[0350] In some aspects, the techniques described herein relate to a refueling planning system, wherein the refueling recommendation engine includes a generative AI module configured to simulate potential refueling and charging scenarios to generate the refueling plan.

[0351] In some aspects, the techniques described herein relate to a refueling planning system, wherein the generative AI module is further configured to generate personalized notifications and suggestions to the user to enhance an emotional benefit of the refueling plan.

[0352] In some aspects, the techniques described herein relate to a refueling planning system, further including a digital twin that simulates vehicle operation and predicts future refueling needs.

[0353] In some aspects, the techniques described herein relate to a refueling planning system, wherein the digital twin includes a model of user emotional responses to various refueling and charging scenarios, and wherein the refueling recommendation engine uses the model to optimize the refueling plan.

[0354] In some aspects, the techniques described herein relate to a refueling planning system, further including a user interface configured to visually represent an impact of refueling and charging options on a user emotional state through graphical elements.

[0355] In some aspects, the techniques described herein relate to a refueling planning system, wherein the user interface includes interactive elements that allow the user to provide real-time feedback on the user emotional state, and wherein the refueling planning system uses the real- time feedback to refine the refueling plan.

[0356] In some aspects, the techniques described herein relate to a refueling planning system, wherein the refueling planning system is further configured to analyze social media data to identify refueling locations associated with positive emotional feedback from users.SFT-106-A-PCT

[0357] In some aspects, the techniques described herein relate to a refueling planning system, wherein the refueling planning system prioritizes charging locations for a hybrid vehicle based on social media indicators that suggest an improved emotional state for users who prioritize environmental benefits over convenience.

[0358] In some aspects, the techniques described herein relate to a refueling planning system, wherein the refueling planning system adjusts the refueling plan to include locations that, according to social media data, offer amenities that contribute to a user emotional well-being.

[0359] In some aspects, the techniques described herein relate to a transportation system, including: a first data system configured to: receive a plurality of data values of a data stream, generate a predictive model for predicting future data values of the data stream based on the received plurality of data values, wherein generating the predictive model includes determining a plurality of model parameters, and transmit the plurality of model parameters; and a second data system configured to: receive the plurality of model parameters transmitted by the first data system, parameterize the predictive model using the plurality of model parameters, predict a future data value of the data stream using the parameterized predictive model, and adjust an operating state of the transportation system based on the future data value.

[0360] In some aspects, the techniques described herein relate to a transportation system, wherein adjusting the operating state of the transportation system includes: predicting, by the second data system, an effect of the operating state on the transportation system through an analysis of social media-sourced data, and adjusting, by the second data system, at least one operating state of the transportation system responsive to the predicted effect thereon.

[0361] In some aspects, the techniques described herein relate to a transportation system, wherein adjusting the operating state of the transportation system includes: classifying, using a first neural network, social media data sourced from a plurality of social media sources as affecting the transportation system, predicting, using a second neural network, at least one operating objective of the transportation system based on the classified social media data, and adjusting, using a third neural network, the operating state of the transportation system to achieve the at least one operating objective of the transportation system.

[0362] In some aspects, the techniques described herein relate to a transportation system, wherein receiving the plurality of data values includes gathering social media-sourced data about a plurality of individuals, the data being sourced from a plurality of social media sources.

[0363] In some aspects, the techniques described herein relate to a transportation system, wherein the plurality of data values is received from one or more security cameras, and the data stream includes motion vectors extracted from video data captured by the security cameras.SFT-106-A-PCT

[0364] In some aspects, the techniques described herein relate to a method for prioritizing predictive model data streams, the method including: receiving, by a first device, social media data sourced from a plurality of social media sources as affecting a transportation system; classifying, by the first device, the social media data based on a set of model parameters for each of a plurality of predictive models, wherein each predictive model is trained to predict future data values of the transportation system; selecting, by the first device and from the classified social media data, at least one predictive model data stream; parameterizing, by the first device, a predictive model using the set of model parameters included in the selected at least one predictive model stream; and predicting, by the first device, at least one future data value of the transportation system using the parameterized predictive model.

[0365] In some aspects, the techniques described herein relate to a method, wherein selecting the at least one assigning, by the first device, priorities to each of a plurality of predictive model data streams included in the social media data and selecting the at least one predictive model stream is based on the priorities assigned to each of the plurality of predictive model data streams.

[0366] In some aspects, the techniques described herein relate to a method, wherein the selected at least one predictive model data stream is associated with a highest priority among the plurality of predictive model data streams.

[0367] In some aspects, the techniques described herein relate to a method, wherein the selecting includes suppressing at least one of the predictive model data streams that were not selected based on the priority assigned to each of the predictive model data streams that were not selected.

[0368] In some aspects, the techniques described herein relate to a method, further including adjusting an operating state of the transportation system based on the future data value of the transportation system.

[0369] In some aspects, the techniques described herein relate to a system for transportation, including: a hybrid neural network including: a first neural network configured to process a plurality of data values of a data stream to determine an emotional state of a rider of a vehicle, wherein the plurality of data values include sensor data collected from one or more sensors and associated with the rider of a vehicle, a second neural network configured to generate a predictive model for predicting a future emotional state of the rider of the vehicle based on the plurality of data values, and a third neural network configured to adjust at least one operating parameter of the vehicle based on an output of the predictive model.

[0370] In some aspects, the techniques described herein relate to a system, wherein generating the predictive model includes determining a plurality of model parameters of the predictiveSFT-106-A-PCT model, and the third neural network adjusts the at least one operating parameter of the vehicle based on the plurality of model parameters determined by the predictive model.

[0371] In some aspects, the techniques described herein relate to a system, wherein the predictive model includes a behavior analysis model, and the predicted future emotional state of the rider is based on a predicted behavior of the rider in response to the at least one operating parameter of the vehicle.

[0372] In some aspects, the techniques described herein relate to a system, wherein the hybrid neural network is further configured to: receive additional data values of the data stream, and refine the predictive model based on the additional data values, wherein refining the predictive model adjusts one or more model parameters of the predictive model.

[0373] In some aspects, the techniques described herein relate to a system, wherein the data stream includes a video stream received from a camera associated with the vehicle, and the plurality of data values includes one or more vectors extracted from the video stream received from the camera.

[0374] In some aspects, the techniques described herein relate to a system for transportation, including: an expert system to select a configuration for a vehicle, wherein the configuration includes at least one parameter selected from the group consisting of a vehicle parameter, a user experience parameter, and combinations thereof, and the expert system includes, a first data system configured to: receive a plurality of data values of a data stream, wherein the data values include sensor data collected from one or more sensor devices, generate a predictive model for predicting the at least one parameter based on the received plurality of data values, wherein generating the predictive model includes determining a plurality of model parameters, and transmit the plurality of model parameters; and a second data system configured to: receive the plurality of model parameters, parameterize a predictive model based on the plurality of model parameters, and select the at least one parameter based on the parameterized predictive model.

[0375] In some aspects, the techniques described herein relate to a system, wherein the predictive model includes a behavior analysis model, and the at least one parameter is based on a predicted behavior of a rider of the vehicle.

[0376] In some aspects, the techniques described herein relate to a system, wherein the predictive model includes a classification model, and the at least one parameter includes a predicted future state of the vehicle based on classified data received from one or more sensor devices associated with the vehicle.

[0377] In some aspects, the techniques described herein relate to a system, wherein the data stream includes a video stream received from a camera associated with the vehicle, and theSFT-106-A-PCT plurality of data values includes one or more motion vectors extracted from the video stream received from the camera.

[0378] In some aspects, the techniques described herein relate to a system, wherein the expert system includes: a first neural network configured operates to classify a state of the vehicle through analysis of information about the vehicle captured by an Internet-of-things device during operation of the vehicle, and a second neural network configured to optimize the at least one parameter of the vehicle based on the classified state of the vehicle, information about a state of a rider occupying the vehicle, and information that correlates vehicle operation with an effect on rider state.

[0379] In some aspects, the techniques described herein relate to a system for transportation, including: a quantum-enabled risk identification module configured to identify a risk associated with a vehicle, and a vehicle parameter selection module configured to adjust at least one vehicle parameter of the vehicle based on the risk to improve a margin of safety of the vehicle.

[0380] In some aspects, the techniques described herein relate to a system, wherein the quantum-enabled risk identification module is configured to perform one or more of: identifying a risk associated with an operating state of the vehicle, assessing an impact of the at least one vehicle parameter on the margin of safety of the vehicle, determining a current risk profile associated with the vehicle, determining a potential risk profile associated with the vehicle based on an adjustment of one or more operating parameters of the vehicle, or determining a probability of a risk associated with the vehicle and one or more predicted events.

[0381] In some aspects, the techniques described herein relate to a system, wherein the quantum-enabled risk identification module is configured to improve the margin of safety by determining a risk type of a risk associated with the vehicle based on a set of risk types.

[0382] In some aspects, the techniques described herein relate to a system, wherein the quantum-enabled risk identification module is further configured to: predict one or more events associated with the vehicle, determine an impact of the predicted one or more events on the margin of safety associated with the vehicle, and determine an adjustment of the at least one vehicle parameter of the vehicle that improves the margin of safety of the vehicle based on the one or more predicted events.

[0383] In some aspects, the techniques described herein relate to a system, wherein the quantum-enabled risk identification module is further configured to generate a classical prediction engine that identifies the risk associated with the vehicle, wherein the vehicle parameter selection module is based on an output of the classical prediction engine.

[0384] In some aspects, the techniques described herein relate to a method of vehicle routing including: adjusting a quantum continual learning system based on an expression received from aSFT-106-A-PCT user; determining, by the quantum continual learning system, a routing preference for a route of a vehicle; determining at least one vehicle-routing parameter used to route vehicles to reflect the routing preference; and adjusting, by a vehicle routing system, the route of the vehicle based on the at least one determined vehicle routing parameter.

[0385] In some aspects, the techniques described herein relate to a method, further including: presenting, in a game-based interface, a vehicle route preference-affecting game activity; and receiving, through the game-based interface, a response of the user to the presented game activity, wherein the adjusting of the quantum continual learning system is based on the response of the user to the presented game activity.

[0386] In some aspects, the techniques described herein relate to a method, wherein the quantum continual learning system is further configured to receive a continuous stream of realtime data, and the routing preference is determined by the quantum continual learning system based on the continuous stream of realtime data.

[0387] In some aspects, the techniques described herein relate to a method, wherein the adjusting of the quantum continual learning system includes continuously training the quantum continual learning system based on realtime data, the realtime data including the expression received from the user.

[0388] In some aspects, the techniques described herein relate to a method, wherein the vehicle is included in a set of vehicles, and adjusting the route of the vehicle includes adjusting a routing parameter of at least one other vehicle of the set of vehicles based on the at least one determined vehicle routing parameter.

[0389] In some aspects, the techniques described herein relate to a system, including: an artificial intelligence system including a quantum annealing module, the artificial intelligence system configured to: receive, from a plurality of rechargeable vehicles within a target geographic region, an operational status of each rechargeable vehicle; predict a near-term need for recharging each rechargeable vehicle based on the operational status of each rechargeable vehicle; and determine, by the quantum annealing module, at least one parameter of a recharging plan for a recharging infrastructure based on the predicted near-term need for recharging each rechargeable vehicle.

[0390] In some aspects, the techniques described herein relate to a system, wherein determining the at least one parameter of the recharging plan further includes, determining, by the quantum annealing module, a set of candidate state changes associated with each candidate parameter of a set of candidate parameters for the recharging plan for the recharging infrastructure, and applying, by the quantum annealing module, a quantum annealing selection to the set ofSFT-106-A-PCT candidate parameters to determine the at least one parameter of the recharging plan based on the set of candidate state changes determined by the quantum annealing module.

[0391] In some aspects, the techniques described herein relate to a system, wherein determining the at least one parameter of the recharging plan further includes, setting, by the quantum annealing module, an initial weight of a state of the recharging infrastructure associated with each candidate parameter of a set of candidate parameters for the recharging plan, and evolving, by the quantum annealing module, the initial weight of each state to an adjusted weight based on a time-dependent equation, wherein the at least one parameter is determined based on the adjusted weight of the state associated with each candidate parameter of the set of candidate parameters.

[0392] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system is further configured to receive capacity information associated with the recharging infrastructure, and the determining of the at least one parameter is based on the capacity information associated with the recharging infrastructure.

[0393] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence system further includes a hybrid neural network including: a first portion of the hybrid neural network configured to operate on a first portion of the operational status of each rechargeable vehicle, wherein the first portion is associated with a route plan of the rechargeable vehicle, and a second portion of the hybrid neural network configured to operate on a second portion of the operational status of each rechargeable vehicle, wherein the second portion is associated with a recharging range of each rechargeable vehicle.

[0394] In some aspects, the techniques described herein relate to a system for transportation, including: a cognitive system including a quantum annealing module, the cognitive system configured to: determine, by the quantum annealing module, at least one parameter of a reward to be made available to a rider of a vehicle in response to the rider undertaking a predetermined action while in the vehicle, and provide the reward to the rider in response to a performance of the predetermined action by the rider.

[0395] In some aspects, the techniques described herein relate to a system, wherein the at least one parameter is based on at least one input received from the rider by a rider interface, and the cognitive system is configured to present to an offer of the reward the rider by the rider interface.

[0396] In some aspects, the techniques described herein relate to a system, wherein the predetermined action includes a selection by the rider of a route of the vehicle, and a parameter of the reward to be made available to the rider is based on a routing preference of the rider.

[0397] In some aspects, the techniques described herein relate to a system, wherein determining the at least one parameter of the reward includes: determining, by the quantum annealing module,SFT-106-A-PCT an effect on a set of vehicles of each of a set of predetermined actions that could be undertaken by the rider, and determining, by the quantum annealing module, the parameter of the reward based on a quantum annealing selection among the set of predetermined actions.

[0398] In some aspects, the techniques described herein relate to a system, wherein determining the at least one parameter of the reward includes determining, by the quantum annealing module, a set of candidate state changes associated with a routing of a set of vehicles based on the predetermined action of the rider, wherein the at least one parameter of the reward is based on the set of candidate state changes associated with the routing of the set of vehicles.

[0399] In some aspects, the techniques described herein relate to a system for transportation, including: a data capture module configured to capture a data set associated with an interaction between a rider within a vehicle and a user interface of the vehicle; and a dual purpose artificial neural network that is configured to: train based on the data set to perform actions on behalf of a rider within a vehicle, retrain based on a dual process learning function applied to the data set to adjust the actions performed on behalf of the rider, and update the data set in response to the retraining of the dual purpose artificial neural network.

[0400] In some aspects, the techniques described herein relate to a system, wherein retraining the dual purpose artificial neural network further includes: identifying a poor performance of the dual purpose artificial neural network on a classification task, updating the data set to include at least one additional data sample that is associated with the classification task, and retraining the dual purpose artificial neural network based on the data set including the at least one additional data sample that is associated with the classification task.

[0401] In some aspects, the techniques described herein relate to a system, wherein retraining the dual purpose artificial neural network further includes: updating the data set to include at least one additional data sample that is based on an additional action to be performed on behalf of the rider, and retraining the dual purpose artificial neural network based on the data set including the at least one additional data sample that is associated with the additional action.

[0402] In some aspects, the techniques described herein relate to a system, wherein retraining the dual purpose artificial neural network further includes: identifying a novel problem for which the dual purpose artificial neural network is not currently trained to perform actions on behalf of the rider, updating the data set to include at least one additional data sample that is associated with the novel problem, and retraining the dual purpose artificial neural network based on the data set including the at least one additional data sample that is associated with the novel problem.

[0403] In some aspects, the techniques described herein relate to a system, wherein the data capture module includes a robotic process automation module that is configured to capture theSFT-106-A-PCT data set associated with an action performed by the rider and associated with the vehicle, and the dual purpose artificial neural network is configured to train based on the data set to perform the action instead of the rider performing the action.

[0404] In some aspects, the techniques described herein relate to a system for transportation, including: an interface configured to configure a set of expert systems to provide one or more outputs associated with a set of parameters, wherein the parameters are selected from a group including at least one vehicle parameters, at least one fleet parameter, or at least one of user experience parameter; and a dual purpose artificial neural network that is configured to: train based on a data set to select the one or more parameters for the set of expert systems, retrain based on a dual process learning function applied to the data set to adjust the one or more parameters selected for the set of expert systems, and update the data set in response to the retraining of the dual purpose artificial neural network.

[0405] In some aspects, the techniques described herein relate to a system, wherein retraining the dual purpose artificial neural network further includes: identifying a poor selection of the expert systems based on a first set of one or more parameters selected by the dual purpose artificial neural network, updating the data set to include at least one additional data sample that is associated with the one or more parameters, and retraining the dual purpose artificial neural network based on the data set including the at least one additional data sample that is associated with the one or more parameters.

[0406] In some aspects, the techniques described herein relate to a system, wherein retraining the dual purpose artificial neural network further includes: updating the data set to include at least one additional data sample that is based on an additional output of the set of expert systems in response to the one or more parameters, and retraining the dual purpose artificial neural network based on the data set including the at least one additional data sample that is associated with the additional output.

[0407] In some aspects, the techniques described herein relate to a system, wherein retraining the dual purpose artificial neural network further includes: identifying a novel problem for which the set of expert systems is not currently trained to provide one or more outputs, updating the data set to include at least one additional data sample that is associated with the novel problem, and retraining the dual purpose artificial neural network based on the data set including the at least one additional data sample that is associated with the novel problem.

[0408] In some aspects, the techniques described herein relate to a system, wherein the dual purpose learning function is further configured to: receive at least one additional data sample associated with a new input value, consolidate the at least one additional data sample with at leastSFT-106-A-PCT one data sample of the data set, and update the data set based on the consolidating of the at least one additional data sample and the at least one data sample of the data set.

[0409] 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

[0410] 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.

[0411] 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.

[0412] 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.

[0413] 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.

[0414] 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.

[0415] Fig. 5 is a diagrammatic view that illustrates a set of vehicle user interfaces relating to various embodiments of the present disclosure.

[0416] Fig. 6 is a diagrammatic view that illustrates a set of interfaces among transportation system components relating to various embodiments of the present disclosure.

[0417] 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.

[0418] 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.SFT-106-A-PCT

[0419] Fig.9 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0420] Fig.10 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0421] Fig. 11 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0422] Fig.12 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0423] Fig. 13 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0424] Fig.14 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0425] Fig. 15 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0426] Fig.16 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0427] Fig. 17 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0428] Fig.18 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0429] Fig. 19 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0430] Fig. 20 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0431] Fig. 21 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0432] Fig.22 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0433] Fig. 23 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0434] Fig. 24 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0435] Fig.25 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.SFT-106-A-PCT

[0436] Fig. 26 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0437] Fig. 26A is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0438] Fig.27 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0439] Fig. 28 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0440] Fig.29 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0441] Fig.30 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0442] Fig.31 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0443] Fig.32 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0444] Fig. 33 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0445] Fig.34 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0446] Fig. 35 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0447] Fig.36 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0448] Fig.37 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0449] Fig. 38 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0450] Fig. 39 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0451] Fig. 40 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0452] Fig.41 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.SFT-106-A-PCT

[0453] Fig. 42 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0454] Fig. 43 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0455] Fig.44 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0456] Fig.45 is a diagrammatic view that illustrates systems and methods described throughout this disclosure relating to various embodiments of the present disclosure.

[0457] Fig.46 is a diagrammatic view that illustrates systems and methods described throughout this disclosure relating to various embodiments of the present disclosure.

[0458] Fig.47 is a diagrammatic view that illustrates systems and methods described throughout this disclosure relating to various embodiments of the present disclosure.

[0459] Fig.48 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0460] Fig. 49 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0461] Fig. 50 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0462] Fig.51 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0463] Fig.52 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0464] Fig.53 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0465] Fig. 54 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0466] Fig. 55 is a diagrammatic view that illustrates a method described throughout this disclosure relating to various embodiments of the present disclosure.

[0467] Fig.56 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0468] Fig.57 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.

[0469] Fig.58 is a diagrammatic view that illustrates systems described throughout this disclosure relating to various embodiments of the present disclosure.SFT-106-A-PCT

[0470] 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.

[0471] 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.

[0472] 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.

[0473] 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.

[0474] 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.

[0475] 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.

[0476] 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.

[0477] 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.

[0478] 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.

[0479] 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.

[0480] 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.

[0481] 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.

[0482] 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.SFT-106-A-PCT

[0483] 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.

[0484] 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.

[0485] 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.

[0486] Fig. 75 is a schematic illustrating examples of architecture of a digital twin system according to embodiments of the present disclosure.

[0487] Fig. 76 is a schematic illustrating exemplary components of a digital twin management system according to embodiments of the present disclosure.

[0488] 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.

[0489] 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.

[0490] 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.

[0491] 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.

[0492] 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.

[0493] 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.

[0494] 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.SFT-106-A-PCT

[0495] 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.

[0496] 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.

[0497] 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.

[0498] 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.

[0499] Fig. 88 is a schematic illustrating an example embodiment of a method of the present disclosure.

[0500] 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.

[0501] 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.

[0502] 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.

[0503] FIG. 92 is a schematic view of an exemplary embodiment of the quantum computing service according to some embodiments of the present disclosure.

[0504] FIG. 93 illustrates quantum computing service request handling according to some embodiments of the present disclosure.

[0505] FIG.94 is a diagrammatic view that illustrates embodiments of the biology-based system in accordance with the present disclosure.

[0506] FIG.95 is a diagrammatic view of the thalamus service and how it coordinates within the modules in accordance with the present disclosure.

[0507] FIG.96 is a diagrammatic view of the dual process artificial neural network system.

[0508] 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.

[0509] FIG.97 is a diagrammatic view of artificial intelligence capabilities, convergence technology stack capabilities and software-defined vehicle modules of a transportation system.SFT-106-A-PCT

[0510] FIG. 98 is a diagrammatic view of software defined vehicle modules of a transportation system.

[0511] FIG. 99 depicts a block diagram of exemplary features, capabilities, and interfaces of a generative artificial intelligence platform of a transportation system.

[0512] FIG. 100 is a diagrammatic view of data and visualization methods and systems of a transportation system.

[0513] FIG. 101 is a diagrammatic view of data and visualization methods and systems of a transportation system. DETAILED DESCRIPTION

[0514] 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.

[0515] 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 and 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.

[0516] 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.

[0517] 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 oneSFT-106-A-PCT 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.

[0518] 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.

[0519] 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 more 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 parametersSFT-106-A-PCT 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.

[0520] 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, 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.

[0521] 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 componentsSFT-106-A-PCT 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.

[0522] 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.

[0523] 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.

[0524] 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-106-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.

[0525] 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.

[0526] 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 876. Algorithms 849 may include energy calculation algorithms 877, such asSFT-106-A-PCT 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, 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.

[0527] 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.

[0528] 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.

[0529] 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.

[0530] 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.

[0531] 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 maySFT-106-A-PCT 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.

[0532] 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.

[0533] 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.

[0534] 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.

[0535] 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, valueSFT-106-A-PCT 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).

[0536] 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 may 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).

[0537] 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.SFT-106-A-PCT

[0538] 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 points 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 measuresSFT-106-A-PCT 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.

[0539] 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 as 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 humanSFT-106-A-PCT 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); any 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.

[0540] 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.

[0541] Referring to Fig.9, in embodiments provided herein are transportation systems 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. In embodiments, the genetic algorithm 975 takes inputs relating to at least one vehicle performance parameter 982 and at least one rider state 937.

[0542] 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 geneticSFT-106-A-PCT 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.

[0543] 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.

[0544] 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.

[0545] 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.

[0546] 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 selectedSFT-106-A-PCT from the group consisting of: a rider state parameter, a rider comfort parameter, a rider emotional state parameter, a rider satisfaction parameter, a rider goals parameter, a classification of the trip, and combinations thereof.

[0547] 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.

[0548] 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.

[0549] 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 indicateSFT-106-A-PCT 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.

[0550] 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. In embodiments, the genetic algorithm 975 is to optimize the set of vehicle parameters for vehicle performance. In embodiments, the genetic algorithm 975 is to optimize the set of vehicle parameters for the state of the rider and is to 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.

[0551] 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.

[0552] Referring to Fig. 10, in embodiments provided herein are transportation systems 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 99060 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 takingSFT-106-A-PCT 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).

[0553] 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 portion 1086 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.

[0554] 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 portion 1086 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.

[0555] 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 methodsSFT-106-A-PCT disclosed herein. At 19902, 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 the inputs by the second network causes 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.

[0556] 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, 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.

[0557] 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.

[0558] 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 theSFT-106-A-PCT 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.

[0559] 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.

[0560] 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.SFT-106-A-PCT

[0561] An aspect provided herein includes a system for transportation 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.

[0562] 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.

[0563] 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.

[0564] 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.

[0565] An aspect provided herein includes a transportation system 1211 for negotiation-based vehicle routing comprising: a route adjustment negotiation system 1289 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 1293 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 theSFT-106-A-PCT 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 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.

[0566] 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.

[0567] 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 least 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 theSFT-106-A-PCT 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.

[0568] An aspect provided herein includes a system for transportation 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 a vehicle 1410 in the designated set of vehicles 1498.

[0569] 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.

[0570] 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.

[0571] 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-based interface 1499 includes strategy games. In embodiments, the game activity 14101 offered in theSFT-106-A-PCT 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.

[0572] 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.

[0573] 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.

[0574] 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-routingSFT-106-A-PCT 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 adjusted 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.

[0575] 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.

[0576] 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 1496 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.

[0577] 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 footprintSFT-106-A-PCT 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 to the at least one determined vehicle routing parameter 14103 achieves a reduced traffic congestion objective.

[0578] 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.

[0579] 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.SFT-106-A-PCT

[0580] An aspect provided herein includes a system for transportation 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.

[0581] An aspect provided herein includes a transportation system 1611 for reward-based coordinated vehicle routing comprising: a reward-based interface 16104 to offer a reward 16102 and through which a user 1690 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 16104; 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 system 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.

[0582] 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 16104 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.SFT-106-A-PCT

[0583] 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.

[0584] 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 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 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.

[0585] 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 routingSFT-106-A-PCT parameter to govern routing of the set of vehicles to achieve an avoidance of driver-operated vehicles objective.

[0586] 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.

[0587] 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.

[0588] In embodiments, the reward-based interface 16104 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 1690 responds to the reward 16102 offered in the reward-based interface 16104 by accepting the reward 16102 offered in the interface, rejecting the reward 16102 offered in the reward-based interface 16104, or ignoring the reward 16102 offered in the reward-based interface 16104. In embodiments, the user 1690 indicates the routing preference by either accepting or rejecting the reward 16102 offered in the reward-based interface 16104. 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.

[0589] 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 theSFT-106-A-PCT 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.

[0590] 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.

[0591] 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.

[0592] 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 described 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 andSFT-106-A-PCT 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.

[0593] 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.

[0594] 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.

[0595] 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.

[0596] 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 dataSFT-106-A-PCT 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.

[0597] 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.

[0598] 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.

[0599] 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 routingSFT-106-A-PCT vehicles associated with users whose social media-sourced data 18114 do not indicate the 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.

[0600] 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.

[0601] Referring to Fig. 22, in embodiments provided herein are transportation systems 2211 having a data processing system 2211 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).

[0602] An aspect provided herein includes a system for transportation, comprising: a data processing system 2211 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.SFT-106-A-PCT

[0603] 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.

[0604] 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.

[0605] 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.

[0606] 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 dataSFT-106-A-PCT 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.

[0607] 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 social media data analyzed to predict a localized effect on a transportation system includes entertainment options.

[0608] 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.

[0609] 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.

[0610] 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.SFT-106-A-PCT

[0611] 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.

[0612] 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.

[0613] 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, the 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.

[0614] 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.

[0615] 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 predictingSFT-106-A-PCT 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.

[0616] 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.

[0617] 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 transportation system includes commerce-related conditions. In embodiments, the social media data analyzed to predict an effect on a transportation system includes goal-related conditions.

[0618] 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.

[0619] 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 systemSFT-106-A-PCT 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.

[0620] 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.

[0621] 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.

[0622] 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 sources 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.SFT-106-A-PCT

[0623] An aspect provided herein includes a system for transportation 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.

[0624] 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.

[0625] 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.

[0626] 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 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.

[0627] 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. InSFT-106-A-PCT 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.

[0628] In embodiments, the optimized state 2544 of the at least one vehicle 2510 is an operating state of the vehicle 2545. 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.

[0629] 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.

[0630] 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.

[0631] 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.

[0632] 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-106-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.

[0633] 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.

[0634] 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.

[0635] 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 routing state. In embodiments, the optimized state of the vehicle includes user experience state. In embodiments, a characterization of an outcome of the optimizing in the social media data is used as feedback to improve the optimizing. In embodiments, the feedback includes likes or dislikes of the outcome. In embodiments, the feedback includes social medial activity referencing the outcome. In embodiments, the feedback includes trending of social media activity referencing the outcome.

[0636] 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.

[0637] Referring to Fig. 27, in embodiments provided herein are transportation systems 2711 having a data processing system 2762 for taking social data 27114 from a plurality 2769 of socialSFT-106-A-PCT data sources 27107 and using a hybrid neural network 2747 to optimize satisfaction 27121 of at least one rider 27120 in a vehicle 2710 based on processing the social data sources with the hybrid neural network 2747. Social data sources 2769 may be used, for example, to predict what entertainment options are most likely to be effective for a rider 27120 by one neural network category, while another neural network category may be used to optimize a routing plan (such as based on social data that indicates likely traffic, points of interest, or the like). Social data 27114 may also be used for outcome tracking and feedback to optimize the system, both as to entertainment options and as to transportation planning, routing, or the...

Claims

SFT-106-A-PCT CLAIMS What is claimed is:

1. A software-defined vehicle for mitigating rider seat fatigue, the software-defined vehicle comprising: a plurality of seat sensors configured to detect and generate sensor data indicating physical parameters indicative of rider fatigue; a generative artificial intelligence (AI) engine configured to analyze the sensor data and generate personalized seat adjustment profiles to mitigate detected rider fatigue; and a vehicle control unit (VCU) communicatively coupled to the plurality of seat sensors and the generative AI engine, the VCU configured to implement the personalized seat adjustment profiles in the software-defined vehicle.

2. The software-defined vehicle of claim 1, wherein the generative AI engine fuses the sensor data with rider preference data to create a comprehensive model of rider comfort and predict optimal seat adjustments.

3. The software-defined vehicle of claim 1, wherein the generative AI engine employs machine learning algorithms to identify patterns of rider discomfort and dynamically suggest changes to seating ergonomics.

4. The software-defined vehicle of claim 1, wherein the generative AI engine generates real-time recommendations for micro-adjustments to seat positions to redistribute pressure and improve circulation for a rider.

5. The software-defined vehicle of claim 1, further comprising an encryption module to secure a transmission of sensor data from the plurality of seat sensors to the VCU and the generative AI engine.

6. The software-defined vehicle of claim 1, wherein the VCU includes a secure access control system that restricts modification of the generative AI engine to authorized personnel only.

7. The software-defined vehicle of claim 1, wherein the generative AI engine is configured to detect and respond to cybersecurity threats by initiating protective protocols to safeguard rider data.

8. The software-defined vehicle of claim 1, further comprising a digital twin of a seating system of the software-defined vehicle, which the generative AI engine uses to simulate and evaluate an effectiveness of fatigue mitigation strategies.SFT-106-A-PCT 9. The software-defined vehicle of claim 8, wherein the digital twin is configured to update in real-time with sensor data to reflect a current state of the seating system and rider fatigue levels.

10. The software-defined vehicle of claim 8, wherein the digital twin is configured for virtual testing of potential new seat materials and designs for fatigue reduction before physical implementation.

11. The software-defined vehicle of claim 1, further comprising a user interface that displays seat adjustment recommendations from the generative AI engine and allows a rider to provide feedback.

12. The software-defined vehicle of claim 11, wherein the user interface includes a haptic feedback mechanism to alert the rider of a need for a change in seating position to mitigate fatigue.

13. The software-defined vehicle of claim 11, wherein the user interface is integrated with a mobile application that tracks seating patterns and provides personalized fatigue mitigation advice based on output from the generative AI engine.

14. A software-defined vehicle configured to mitigate brain atrophy, the software- defined vehicle comprising: a processor; a memory storing instructions that, when executed by the processor, cause the software-defined vehicle to: monitor interactions of a driver with vehicle controls and navigation systems; analyze driving patterns to identify routine behaviors as identified routine behaviors; generate cognitive challenges based on the identified routine behaviors to engage cognitive functions of the driver; and adapt an operation of the software-defined vehicle to present the cognitive challenges to the driver during operation of the software-defined vehicle.

15. The software-defined vehicle of claim 14, wherein the cognitive challenges include route deviation prompts that encourage the driver to navigate without step-by-step navigation assistance for familiar routes.

16. The software-defined vehicle of claim 15, wherein the route deviation prompts are generated in response to real-time driving conditions to encourage the driver to adapt to changing conditions and engage in problem-solving activities.SFT-106-A-PCT 17. The software-defined vehicle of claim 14, wherein the cognitive challenges are generated by a generative AI configured to create tasks that stimulate memory, spatial awareness, and executive functioning based on information about the driver.

18. The software-defined vehicle of claim 17, wherein the generative AI is further configured to adjust a complexity of the cognitive challenges based on a performance and an interaction with the cognitive challenges by the driver.

19. The software-defined vehicle of claim 14, further comprising a gamification module that assigns points and rewards to the driver for successfully completing the cognitive challenges.

20. The software-defined vehicle of claim 19, wherein the gamification module includes a leaderboard feature that compares a performance of the driver with historical performance data or peer performance data to foster a competitive environment for cognitive engagement.

21. The software-defined vehicle of claim 19, wherein the rewards include at least one of virtual badges, unlocking new vehicle features, or personalized messages of encouragement.

22. The software-defined vehicle of claim 19, further including a generative AI engine configured to work in conjunction with the gamification module to dynamically create the cognitive challenges with personalization based on preferences and past interactions of the driver with the gamification module.

23. The software-defined vehicle of claim 22, wherein the generative AI engine uses driver feedback from the gamification module to refine and optimize the cognitive challenges for enhanced engagement and effectiveness in mitigating brain atrophy.

24. The software-defined vehicle of claim 14, further including a vehicle user interface configured to provide at least one of auditory, visual, or haptic feedback to the driver based on the cognitive challenges to utilize multiple sensory modalities to enhance cognitive stimulation.

25. The software-defined vehicle of claim 14, further comprising an emergency intervention protocol that is activated in response to detecting a lack of driver response to the cognitive challenges, indicating potential acute cognitive impairment.

26. The software-defined vehicle of claim 14, wherein the software-defined vehicle is further configured to encourage breaks for physical activity during long journeys.

27. The software-defined vehicle of claim 26, further including a generative AI engine configured to suggest exercises tailored to physical capabilities and preferences of the driver.

28. The software-defined vehicle of claim 14, further including a natural language processing module that allows the driver to interact with the cognitive challenges using voice commands to facilitate hands-free engagement and reduced driver distraction.

29. The software-defined vehicle of claim 14, further including a generative AI engine, and wherein the software-defined vehicle is further configured to adjust environmental settingsSFT-106-A-PCT within the software-defined vehicle to create an optimal environment for cognitive function as determined by the generative AI engine.

30. The software-defined vehicle of claim 29, wherein the generative AI engine is configured to determine the optimal environment based, at least in part, on a time of day and a driver state.

31. The software-defined vehicle of claim 29, wherein the software-defined vehicle is configured to adjust at least one of lighting or temperature as the environmental settings to create the optimal environment.

32. A vehicle maintenance system comprising: a data processing unit configured to generate an analysis of environmental data, user behavioral data, and vehicle diagnostic data; an emotional state detection module configured to determine patterns of emotional states of a user based on the analysis; a scheduling module configured to: generate maintenance recommendations for a vehicle for which a user is associated with maintenance; deduce an optimal time within a maintenance window of the maintenance recommendations to recommend the maintenance recommendations to the user to result in a most favorable predicted emotional state of the user; and generate a maintenance reminder timing schedule for the user based on the patterns of emotional states of the user and on the optimal time; and a communication module configured to transmit the maintenance recommendations to the user according to the maintenance reminder timing schedule.

33. The vehicle maintenance system of claim 32, wherein the environmental data includes weather patterns, daylight hours, and seasonal changes, and the emotional state detection module is further configured to infer mood states of the user associated with different seasons.

34. The vehicle maintenance system of claim 32, wherein the user behavioral data includes at least one of historical maintenance records, social media activity, or vehicle usage patterns, and the emotional state detection module is further configured to detect periods when the user is more likely to engage in maintenance activities.

35. The vehicle maintenance system of claim 32, wherein the scheduling module utilizes generative AI algorithms to predict the optimal time.SFT-106-A-PCT 36. The vehicle maintenance system of claim 32, wherein the communication module uses generative AI to create personalized maintenance notifications that the scheduling module predicts will resonate with a current mood of the user during a current season.

37. The vehicle maintenance system of claim 32, further comprising a user feedback interface configured to receive user responses to maintenance notifications, wherein the scheduling module adjusts future maintenance recommendations based on the user responses.

38. The vehicle maintenance system of claim 32, wherein the scheduling module is further configured to delay maintenance recommendations during seasons when the emotional state detection module determines a lower interest in maintenance activities for the user.

39. A refueling planning system for vehicles, the refueling planning system comprising: an emotional state system configured to predict user emotional state changes in response to refueling decisions for a vehicle and a user; a fuel status system configured to identify a fuel status of the vehicle and to predict refueling requirements of the vehicle for a trip; and a refueling recommendation engine configured to generate a refueling plan to achieve the refueling requirements with favorable emotional state changes, wherein the refueling recommendation engine is configured to consider combustion fuel refilling and electrical energy storage refueling in the refueling plan.

40. The refueling planning system of claim 39, wherein the refueling recommendation engine is further configured to prioritize charging a battery of a hybrid vehicle over filling a gas tank based on user environmental preferences indicating a user desire for considering environmental factors.

41. The refueling planning system of claim 40, wherein the refueling recommendation engine is further configured to adjust prioritization between charging and gas filling based on a comparison of an environmental impact of each option and user historical preference data.

42. The refueling planning system of claim 39, wherein the refueling recommendation engine includes a generative AI module configured to simulate potential refueling and charging scenarios to generate the refueling plan.

43. The refueling planning system of claim 42, wherein the generative AI module is further configured to generate personalized notifications and suggestions to the user to enhance an emotional benefit of the refueling plan.

44. The refueling planning system of claim 39, further comprising a digital twin that simulates vehicle operation and predicts future refueling needs.SFT-106-A-PCT 45. The refueling planning system of claim 44, wherein the digital twin includes a model of user emotional responses to various refueling and charging scenarios, and wherein the refueling recommendation engine uses the model to optimize the refueling plan.

46. The refueling planning system of claim 39, further including a user interface configured to visually represent an impact of refueling and charging options on a user emotional state through graphical elements.

47. The refueling planning system of claim 46, wherein the user interface includes interactive elements that allow the user to provide real-time feedback on the user emotional state, and wherein the refueling planning system uses the real-time feedback to refine the refueling plan.

48. The refueling planning system of claim 39, wherein the refueling planning system is further configured to analyze social media data to identify refueling locations associated with positive emotional feedback from users.

49. The refueling planning system of claim 48, wherein the refueling planning system prioritizes charging locations for a hybrid vehicle based on social media indicators that suggest an improved emotional state for users who prioritize environmental benefits over convenience.

50. The refueling planning system of claim 49, wherein the refueling planning system adjusts the refueling plan to include locations that, according to social media data, offer amenities that contribute to a user emotional well-being.

51. A transportation system, comprising: a first data system configured to: receive a plurality of data values of a data stream, generate a predictive model for predicting future data values of the data stream based on the received plurality of data values, wherein generating the predictive model comprises determining a plurality of model parameters, and transmit the plurality of model parameters; and a second data system configured to: receive the plurality of model parameters transmitted by the first data system, parameterize the predictive model using the plurality of model parameters, predict a future data value of the data stream using the parameterized predictive model, and adjust an operating state of the transportation system based on the future data value.SFT-106-A-PCT 52. The transportation system of claim 51, wherein adjusting the operating state of the transportation system includes: predicting, by the second data system, an effect of the operating state on the transportation system through an analysis of social media-sourced data, and adjusting, by the second data system, at least one operating state of the transportation system responsive to the predicted effect thereon.

53. The transportation system of claim 51, wherein adjusting the operating state of the transportation system includes: classifying, using a first neural network, social media data sourced from a plurality of social media sources as affecting the transportation system, predicting, using a second neural network, at least one operating objective of the transportation system based on the classified social media data, and adjusting, using a third neural network, the operating state of the transportation system to achieve the at least one operating objective of the transportation system.

54. The transportation system of claim 51, wherein receiving the plurality of data values includes gathering social media-sourced data about a plurality of individuals, the data being sourced from a plurality of social media sources.

55. The transportation system of claim 51, wherein the plurality of data values is received from one or more security cameras, and the data stream includes motion vectors extracted from video data captured by the security cameras.

56. A method for prioritizing predictive model data streams, the method comprising: receiving, by a first device, social media data sourced from a plurality of social media sources as affecting a transportation system; classifying, by the first device, the social media data based on a set of model parameters for each of a plurality of predictive models, wherein each predictive model is trained to predict future data values of the transportation system; selecting, by the first device and from the classified social media data, at least one predictive model data stream; parameterizing, by the first device, a predictive model using the set of model parameters included in the selected at least one predictive model stream; and predicting, by the first device, at least one future data value of the transportation system using the parameterized predictive model.

57. The method of claim 56, wherein selecting the at least one assigning, by the first device, priorities to each of a plurality of predictive model data streams included in the socialSFT-106-A-PCT media data, and selecting the at least one predictive model stream is based on the priorities assigned to each of the plurality of predictive model data streams.

58. The method of claim 57, wherein the selected at least one predictive model data stream is associated with a highest priority among the plurality of predictive model data streams.

59. The method of claim 57, wherein the selecting comprises suppressing at least one of the predictive model data streams that were not selected based on the priority assigned to each of the predictive model data streams that were not selected.

60. The method of claim 56, further comprising adjusting an operating state of the transportation system based on the future data value of the transportation system.

61. A system for transportation, comprising: a hybrid neural network including: a first neural network configured to process a plurality of data values of a data stream to determine an emotional state of a rider of a vehicle, wherein the plurality of data values include sensor data collected from one or more sensors and associated with the rider of a vehicle, a second neural network configured to generate a predictive model for predicting a future emotional state of the rider of the vehicle based on the plurality of data values, and a third neural network configured to adjust at least one operating parameter of the vehicle based on an output of the predictive model.

62. The system of claim 61, wherein generating the predictive model comprises determining a plurality of model parameters of the predictive model, and the third neural network adjusts the at least one operating parameter of the vehicle based on the plurality of model parameters determined by the predictive model.

63. The system of claim 61, wherein the predictive model includes a behavior analysis model, and the predicted future emotional state of the rider is based on a predicted behavior of the rider in response to the at least one operating parameter of the vehicle.

64. The system of claim 61, wherein the hybrid neural network is further configured to: receive additional data values of the data stream, and refine the predictive model based on the additional data values, wherein refining the predictive model adjusts one or more model parameters of the predictive model.

65. The system of claim 61, wherein the data stream includes a video stream received from a camera associated with the vehicle, and the plurality of data values includes one or more vectors extracted from the video stream received from the camera.SFT-106-A-PCT 66. A system for transportation, comprising: an expert system to select a configuration for a vehicle, wherein the configuration includes at least one parameter selected from the group consisting of a vehicle parameter, a user experience parameter, and combinations thereof, and the expert system includes, a first data system configured to: receive a plurality of data values of a data stream, wherein the data values comprise sensor data collected from one or more sensor devices, generate a predictive model for predicting the at least one parameter based on the received plurality of data values, wherein generating the predictive model includes determining a plurality of model parameters, and transmit the plurality of model parameters; and a second data system configured to: receive the plurality of model parameters, parameterize a predictive model based on the plurality of model parameters, and select the at least one parameter based on the parameterized predictive model.

67. The system of claim 66, wherein the predictive model includes a behavior analysis model, and the at least one parameter is based on a predicted behavior of a rider of the vehicle.

68. The system of claim 66, wherein the predictive model includes a classification model, and the at least one parameter includes a predicted future state of the vehicle based on classified data received from one or more sensor devices associated with the vehicle.

69. The system of claim 66, wherein the data stream includes a video stream received from a camera associated with the vehicle, and the plurality of data values includes one or more motion vectors extracted from the video stream received from the camera.

70. The system of claim 66, wherein the expert system includes: a first neural network configured operates to classify a state of the vehicle through analysis of information about the vehicle captured by an Internet-of-things device during operation of the vehicle, and a second neural network configured to optimize the at least one parameter of the vehicle based on the classified state of the vehicle, information about a state of a rider occupying the vehicle, and information that correlates vehicle operation with an effect on rider state.

71. A system for transportation, comprising: a quantum-enabled risk identification module configured to identify a risk associated with a vehicle, and a vehicle parameter selection module configured to adjust at least one vehicle parameter of the vehicle based on the risk to improve a margin of safety of the vehicle.SFT-106-A-PCT 72. The system of claim 71, wherein the quantum-enabled risk identification module is configured to perform one or more of: identifying a risk associated with an operating state of the vehicle, assessing an impact of the at least one vehicle parameter on the margin of safety of the vehicle, determining a current risk profile associated with the vehicle, determining a potential risk profile associated with the vehicle based on an adjustment of one or more operating parameters of the vehicle, or determining a probability of a risk associated with the vehicle and one or more predicted events.

73. The system of claim 71, wherein the quantum-enabled risk identification module is configured to improve the margin of safety by determining a risk type of a risk associated with the vehicle based on a set of risk types.

74. The system of claim 71, wherein the quantum-enabled risk identification module is further configured to: predict one or more events associated with the vehicle, determine an impact of the predicted one or more events on the margin of safety associated with the vehicle, and determine an adjustment of the at least one vehicle parameter of the vehicle that improves the margin of safety of the vehicle based on the one or more predicted events.

75. The system of claim 71, wherein the quantum-enabled risk identification module is further configured to generate a classical prediction engine that identifies the risk associated with the vehicle, wherein the vehicle parameter selection module is based on an output of the classical prediction engine.

76. A method of vehicle routing comprising: adjusting a quantum continual learning system based on an expression received from a user; determining, by the quantum continual learning system, a routing preference for a route of a vehicle; determining at least one vehicle-routing parameter used to route vehicles to reflect the routing preference; and adjusting, by a vehicle routing system, the route of the vehicle based on the at least one determined vehicle routing parameter.

77. The method of claim 76, further comprising:SFT-106-A-PCT presenting, in a game-based interface, a vehicle route preference-affecting game activity; and receiving, through the game-based interface, a response of the user to the presented game activity, wherein the adjusting of the quantum continual learning system is based on the response of the user to the presented game activity.

78. The method of claim 76, wherein the quantum continual learning system is further configured to receive a continuous stream of realtime data, and the routing preference is determined by the quantum continual learning system based on the continuous stream of realtime data.

79. The method of claim 76, wherein the adjusting of the quantum continual learning system includes continuously training the quantum continual learning system based on realtime data, the realtime data including the expression received from the user.

80. The method of claim 76, wherein the vehicle is included in a set of vehicles, and adjusting the route of the vehicle includes adjusting a routing parameter of at least one other vehicle of the set of vehicles based on the at least one determined vehicle routing parameter.

81. A system, comprising: an artificial intelligence system including a quantum annealing module, the artificial intelligence system configured to: receive, from a plurality of rechargeable vehicles within a target geographic region, an operational status of each rechargeable vehicle; predict a near-term need for recharging each rechargeable vehicle based on the operational status of each rechargeable vehicle; and determine, by the quantum annealing module, at least one parameter of a recharging plan for a recharging infrastructure based on the predicted near-term need for recharging each rechargeable vehicle.

82. The system of claim 81, wherein determining the at least one parameter of the recharging plan further comprises, determining, by the quantum annealing module, a set of candidate state changes associated with each candidate parameter of a set of candidate parameters for the recharging plan for the recharging infrastructure, and applying, by the quantum annealing module, a quantum annealing selection to the set of candidate parameters to determine the at least one parameter of the recharging plan based on the set of candidate state changes determined by the quantum annealing module.

83. The system of claim 81, wherein determining the at least one parameter of the recharging plan further comprises,SFT-106-A-PCT setting, by the quantum annealing module, an initial weight of a state of the recharging infrastructure associated with each candidate parameter of a set of candidate parameters for the recharging plan, and evolving, by the quantum annealing module, the initial weight of each state to an adjusted weight based on a time-dependent equation, wherein the at least one parameter is determined based on the adjusted weight of the state associated with each candidate parameter of the set of candidate parameters.

84. The system of claim 81, wherein the artificial intelligence system is further configured to receive capacity information associated with the recharging infrastructure, and the determining of the at least one parameter is based on the capacity information associated with the recharging infrastructure.

85. The system of claim 81, wherein the artificial intelligence system further comprises a hybrid neural network including: a first portion of the hybrid neural network configured to operate on a first portion of the operational status of each rechargeable vehicle, wherein the first portion is associated with a route plan of the rechargeable vehicle, and a second portion of the hybrid neural network configured to operate on a second portion of the operational status of each rechargeable vehicle, wherein the second portion is associated with a recharging range of each rechargeable vehicle.

86. A system for transportation, comprising: a cognitive system including a quantum annealing module, the cognitive system configured to: determine, by the quantum annealing module, at least one parameter of a reward to be made available to a rider of a vehicle in response to the rider undertaking a predetermined action while in the vehicle, and provide the reward to the rider in response to a performance of the predetermined action by the rider.

87. The system of claim 86, wherein the at least one parameter is based on at least one input received from the rider by a rider interface, and the cognitive system is configured to present to an offer of the reward the rider by the rider interface.

88. The system of claim 86, wherein the predetermined action includes a selection by the rider of a route of the vehicle, and a parameter of the reward to be made available to the rider is based on a routing preference of the rider.

89. The system of claim 86, wherein determining the at least one parameter of the reward includes:SFT-106-A-PCT determining, by the quantum annealing module, an effect on a set of vehicles of each of a set of predetermined actions that could be undertaken by the rider, and determining, by the quantum annealing module, the parameter of the reward based on a quantum annealing selection among the set of predetermined actions.

90. The system of claim 86, wherein determining the at least one parameter of the reward includes determining, by the quantum annealing module, a set of candidate state changes associated with a routing of a set of vehicles based on the predetermined action of the rider, wherein the at least one parameter of the reward is based on the set of candidate state changes associated with the routing of the set of vehicles.

91. A system for transportation, comprising: a data capture module configured to capture a data set associated with an interaction between a rider within a vehicle and a user interface of the vehicle; and a dual purpose artificial neural network that is configured to: train based on the data set to perform actions on behalf of a rider within a vehicle, retrain based on a dual process learning function applied to the data set to adjust the actions performed on behalf of the rider, and update the data set in response to the retraining of the dual purpose artificial neural network.

92. The system of claim 91, wherein retraining the dual purpose artificial neural network further comprises: identifying a poor performance of the dual purpose artificial neural network on a classification task, updating the data set to include at least one additional data sample that is associated with the classification task, and retraining the dual purpose artificial neural network based on the data set including the at least one additional data sample that is associated with the classification task.

93. The system of claim 91, wherein retraining the dual purpose artificial neural network further comprises: updating the data set to include at least one additional data sample that is based on an additional action to be performed on behalf of the rider, and retraining the dual purpose artificial neural network based on the data set including the at least one additional data sample that is associated with the additional action.

94. The system of claim 91, wherein retraining the dual purpose artificial neural network further comprises:SFT-106-A-PCT identifying a novel problem for which the dual purpose artificial neural network is not currently trained to perform actions on behalf of the rider, updating the data set to include at least one additional data sample that is associated with the novel problem, and retraining the dual purpose artificial neural network based on the data set including the at least one additional data sample that is associated with the novel problem.

95. The system of claim 91, wherein the data capture module includes a robotic process automation module that is configured to capture the data set associated with an action performed by the rider and associated with the vehicle, and the dual purpose artificial neural network is configured to train based on the data set to perform the action instead of the rider performing the action.

96. A system for transportation, comprising: an interface configured to configure a set of expert systems to provide one or more outputs associated with a set of parameters, wherein the parameters are selected from a group including at least one vehicle parameters, at least one fleet parameter, or at least one of user experience parameter; and a dual purpose artificial neural network that is configured to: train based on a data set to select the one or more parameters for the set of expert systems, retrain based on a dual process learning function applied to the data set to adjust the one or more parameters selected for the set of expert systems, and update the data set in response to the retraining of the dual purpose artificial neural network.

97. The system of claim 96, wherein retraining the dual purpose artificial neural network further comprises: identifying a poor selection of the expert systems based on a first set of one or more parameters selected by the dual purpose artificial neural network, updating the data set to include at least one additional data sample that is associated with the one or more parameters, and retraining the dual purpose artificial neural network based on the data set including the at least one additional data sample that is associated with the one or more parameters.

98. The system of claim 96, wherein retraining the dual purpose artificial neural network further comprises:SFT-106-A-PCT updating the data set to include at least one additional data sample that is based on an additional output of the set of expert systems in response to the one or more parameters, and retraining the dual purpose artificial neural network based on the data set including the at least one additional data sample that is associated with the additional output.

99. The system of claim 96, wherein retraining the dual purpose artificial neural network further comprises: identifying a novel problem for which the set of expert systems is not currently trained to provide one or more outputs, updating the data set to include at least one additional data sample that is associated with the novel problem, and retraining the dual purpose artificial neural network based on the data set including the at least one additional data sample that is associated with the novel problem.

100. The system of claim 96, wherein the dual purpose learning function is further configured to: receive at least one additional data sample associated with a new input value, consolidate the at least one additional data sample with at least one data sample of the data set, and update the data set based on the consolidating of the at least one additional data sample and the at least one data sample of the data set.

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