Hybrid neural network for rider satisfaction

1. A system for transportation, comprising:

a vehicle having a vehicle state;

at least one sensor deployed in the vehicle for gathering physiological sensed data indicative of a hormone level of a rider occupying the vehicle, wherein the physiological sensed data includes galvanic skin response indicative of a cortisol level of the rider;

an artificial intelligence system in communication with the vehicle, the artificial intelligence system operative on at least one processor having access to a non-transitory storage medium that stores computer executable instructions to be executed by the at least one processor, the artificial intelligence system including a hybrid neural network for rider alertness, including:

a first neural network to detect a detected state of rider alertness through analysis of the physiological sensed data gathered from the at least one sensor; and

a second neural network to optimize an operational parameter of the vehicle in real time in response to the detected state of rider alertness, wherein the operational parameter of the vehicle that is optimized affects at least one of a speed of the vehicle, an acceleration of the vehicle, a deceleration of the vehicle, or a proximity to other vehicles to produce a change in the vehicle state, and wherein the change in the vehicle state is capable of inducing a first level of rider alertness at a first time and a second level of rider alertness at a second time distinct from the first time;

wherein the second neural network is configured to optimize the operational parameter of the vehicle in part by varying the operational parameter to vary the cortisol level of the rider occupying the vehicle based on a time of day.

2. The system for transportation of claim 1 wherein the first neural network is a recurrent neural network and the second neural network is a radial basis function neural network.

3. The system for transportation of claim 1 wherein at least one of the neural networks in the hybrid neural network is a convolutional neural network.

4. The system for transportation of claim 1 wherein the second neural network is to optimize the operational parameter based on a correlation between the vehicle state and the detected state of rider alertness.

5. The system for transportation of claim 1 wherein 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.

6. The system for transportation of claim 1 further comprising:

a third neural network to classify social media data sourced from a plurality of social media sources as indicative of an effect on the system for transportation;

a fourth neural network to predict at least one aspect of rider satisfaction affected by the effect on the system for transportation derived from the social media data classified as indicative of the effect on the system for transportation; and

a fifth neural network to optimize the at least one aspect of rider satisfaction for at least one rider occupying the vehicle in the system for transportation.

7. The system for transportation of claim 6 wherein at least one of the neural networks in the hybrid neural network is a convolutional neural network.

8. The system for transportation of claim 6 wherein the at least one aspect of rider satisfaction is: optimized by predicting an entertainment option for presenting to the rider.

9. The system for transportation of claim 6 wherein social media data specific to the rider is analyzed to determine at least one optimizing action likely to optimize the at least one aspect of rider satisfaction.

10. The system for transportation of claim 9 wherein the at least one optimizing action is one of adjusting a routing plan to include passing points of interest to the rider, deriving an altruistic benefit, or presenting entertainment options.

11. The system for transportation of claim 10 wherein the altruistic benefit is reduction of environmental impact.

12. The system for transportation of claim 6 wherein the social media data includes at least one of:

social media posts; social media feeds; like or dislike activity detected in the social media; indications of relationships; user behavior; discussion threads; chats; photographs; an indication of a specific individual at a location; or an indication of a celebrity at a location.

13. The system for transportation of claim 6 wherein the social media data includes at least one of: presence of a rare phenomena at a location; entertainment options; risk-related conditions; or modes of transportation used with an event.

14. The system for transportation of claim 6 wherein the effect on the system for transportation is one of reduced carbon footprint, or reduced pollution.

15. The system for transportation of claim 6 wherein the optimized at least one aspect of rider satisfaction is one of an operating state of the vehicle, an in-vehicle state, a rider state, a routing state, or a user experience state.

16. The system for transportation of claim 6 wherein a characterization of an outcome of the optimizing in the social media data is used as feedback to improve the optimizing.

17. The system for transportation of claim 16 wherein the feedback includes at least one of: likes or dislikes of the outcome; social media activity referencing the outcome; trending of the social media activity referencing the outcome; hash tags associated with the outcome; ratings of the outcome; or requests for the outcome.

18. The system for transportation of claim 1 wherein the at least one sensor deployed in the vehicle includes a set of wearable sensors worn by the rider occupying the vehicle.

19. The system for transportation of claim 1 , wherein the second neural network is further configured to promote rising cortisol levels in a morning as the time of day and dropping cortisol levels in an afternoon as the time of day.

20. The system for transportation of claim 1 , wherein the second neural network is further configured to vary the cortisol level with use of a first setting and a second setting, wherein the first setting is more aggressive to promote higher cortisol levels than does the second setting, and wherein the second neural network is further configured to use the first setting when the time of day indicates that rising cortisol levels are healthy and to use the second setting when the time of day indicates that dropping cortisol levels are healthy.

21. An artificial intelligence system for optimizing rider satisfaction, wherein the artificial intelligence system is in communication with a vehicle, the vehicle having at least one sensor deployed in the vehicle for gathering physiological sensed data indicative of a hormone level of a rider occupying the vehicle, wherein the physiological sensed data includes galvanic skin response indicative of a cortisol level of the rider, wherein the artificial intelligence system is operative on at least one processor having access to a non-transitory storage medium that stores computer executable instructions to be executed by the at least one processor, the artificial intelligence system comprising:

a hybrid neural network, including:

a recurrent neural network to indicate a change in a satisfaction state of the rider through recognition of patterns of the physiological sensed data captured by the at least one sensor; and

a radial basis function neural network to optimize, for achieving a favorable satisfaction state of the rider, an operational parameter of the vehicle in response to the indication of the change in the satisfaction state of the rider, wherein the radial basis function neural network is to optimize the operational parameter in real time responsive to the indication of the change in the satisfaction state of the rider by the recurrent neural network, and wherein the operational parameter of the vehicle that is optimized affects a speed of the vehicle or a proximity to other vehicles to produce a change in a vehicle state of the vehicle, and wherein the change in the vehicle state is capable of inducing a first level of rider alertness at a first time and a second level of rider alertness at a second time distinct from the first time;

wherein the radial basis function neural network is configured to optimize the operational parameter of the vehicle in part by varying the operational parameter to vary the cortisol level of the rider occupying the vehicle based on a time of day.

22. The artificial intelligence system of claim 21 wherein the operational parameter of the vehicle that is to be optimized is to be determined and adjusted to induce a healthy change in physiological rider state.