Artificial intelligence system for processing voice of rider to improve emotional state and optimize operating parameter of vehicle

1. A system for transportation, comprising:

a vehicle occupied by a specific rider; and

an artificial intelligence system for processing a voice of the specific rider to classify an emotional state of the specific rider and optimizing at least one operating parameter of the vehicle to at least one of maintain or improve the emotional state of the specific rider, wherein the artificial intelligence system includes:

a neural network that includes one or more perceptrons that mimic human senses to determine the emotional state of the specific rider based on an extent to which at least one sense of the specific rider is stimulated;

a rider voice capture system deployed to capture a voice output of the specific rider;

a voice-analysis circuit trained using machine learning to classify the emotional state of the specific rider based on at least one emotional state indicative parameter including at least one of: the captured voice output of the specific rider or at least one other emotional state indicative parameter of the specific rider other than the captured voice output of the specific rider,

wherein the rider voice capture system includes an intelligent agent that engages in a dialog with the specific rider to obtain rider feedback for use by the voice-analysis circuit to improve the classifying of the emotional state of the specific rider by the voice-analysis circuit; and

wherein the one or more perceptrons mimic the human senses during the dialog engagement with the specific rider such that the at least one sense of the specific rider is stimulated to facilitate the determination of the emotional state of the specific rider based on the at least one emotional state indicative parameter; and

an expert system trained using machine learning that optimizes the at least one operating parameter of the vehicle to at least one of: maintain the emotional state of the specific rider as an emotional state classified as a favorable emotional state or change the emotional state of the specific rider to another emotional state classified as an improved emotional state.

2. The system for transportation of claim 1 wherein the voice-analysis circuit uses a first machine learning system and the expert system uses a second machine learning system.

3. The system for transportation of claim 1 wherein the expert system is trained to optimize the at least one operating parameter of the vehicle based on feedback of emotional state outcomes when adjusting the at least one operating parameter for the specific rider.

4. The system for transportation of claim 1 further comprising a rule-based rider state model that configures a set of iterations of a vehicle state while continuously monitoring the emotional state of the specific rider via an ongoing dialog with respect to model parameters of the rule-based rider state model.

5. The system for transportation of claim 1 wherein the at least one other emotional state indicative parameter of the specific rider is based at least in part on an output from a camera.

6. The system for transportation of claim 1 wherein the at least one other emotional state indicative parameter of the specific rider is based at least partially on traffic information.

7. The system for transportation of claim 1 wherein the at least one other emotional state indicative parameter of the specific rider is based at least partially on weather information.

8. The system for transportation of claim 1 wherein the at least one other emotional state indicative parameter of the specific rider is based at least partially on a vehicle state.

9. The system for transportation of claim 1 wherein the at least one other emotional state indicative parameter of the specific rider includes physiological data of the specific rider that is detected by an in-vehicle sensor in communication with the one or more perceptrons.

10. The system for transportation of claim 1 wherein the at least one other emotional state indicative parameter of the specific rider is based at least partially on a route of the vehicle.

11. The system for transportation of claim 1 wherein the at least one other emotional state indicative parameter of the specific rider is based at least partially on a proximity to objects or other vehicles along a route of the vehicle.

12. A system for transportation, comprising:

a vehicle occupied by a specific rider; and

an artificial intelligence system for processing a voice of the specific rider to classify an emotional state of the specific rider and optimizing at least one operating parameter of the vehicle to at least one of maintain or improve the emotional state of the specific rider, wherein the artificial intelligence system includes:

a first neural network that includes one or more perceptrons that mimic human senses that facilitates determining the emotional state of the specific rider based on an extent to which at least one of the human senses of the specific rider is stimulated;

a rider voice capture system deployed to capture voice output of the specific rider;

a voice-analysis circuit trained using machine learning to classify the emotional state of the specific rider based on at least one emotional state indicative parameter including a combination of: the captured voice output of the specific rider and at least one other emotional state indicative parameter of the specific rider other than the captured voice output of the specific rider,

wherein the rider voice capture system includes an intelligent agent that engages in a dialog with the specific rider to obtain rider feedback for use by the voice-analysis circuit to improve the classifying of the emotional state of the specific rider by the voice-analysis circuit; and

wherein the one or more perceptrons mimic the human senses during the dialog engagement with the specific rider such that at least one of the human senses of the specific rider is stimulated which facilitates in the determination of the emotional state of the specific rider based on the at least one emotional state indicative parameter; and

a second neural network, including an expert system trained using machine learning, that optimizes the at least one operating parameter of the vehicle to at least one of: maintain the emotional state of the specific rider as an emotional state classified as a favorable emotional state or change the emotional state of the specific rider to another emotional state classified as an improved emotional state.

13. The system for transportation of claim 12 wherein at least one of the neural networks is a convolutional neural network.

14. The system for transportation of claim 12 wherein the first neural network is trained through use of a training data set that associates emotional state classes with human voice patterns in combination with the at least one other emotional state indicative parameter of the specific rider.

15. The system for transportation of claim 12 wherein the first neural network is trained through the use of a training data set of voice recordings of the specific rider that are tagged with emotional state identifying data in combination with the at least one other emotional state indicative parameter of the specific rider.

16. The system for transportation of claim 12 further comprising a rule-based rider state model that configures a set of iterations of the at least one operating parameter of the vehicle while continuously monitoring the emotional state of the specific rider via an ongoing dialog with respect to model parameters of the rule-based rider state model.

17. The system for transportation of claim 12 wherein the at least one other emotional state indicative parameter of the specific rider is based at least partially on a vehicle state.

18. The system for transportation of claim 12 wherein the at least one other emotional state indicative parameter of the specific rider includes physiological data of the specific rider that is detected by an in-vehicle sensor in communication with the one or more perceptrons.

19. A computer-implemented method for transportation, comprising:

capturing a voice output of a specific rider in a vehicle occupied by the specific rider;

classifying an emotional state of the specific rider based on at least one emotional state indicative parameter including at least one of: the captured voice output of the specific rider or at least one other emotional state indicative parameter of the specific rider other than the captured voice output of the specific rider,

wherein the classifying the emotional state of the specific rider occurs, at least in part, by processing the voice output of the specific rider, and wherein the processing of the voice output includes engaging in a dialog between an intelligent agent and the specific rider to obtain rider feedback;

improving the classifying of the emotional state of the specific rider based on the rider feedback obtained via the dialog, wherein one or more perceptrons included in a first neural network of an artificial intelligence system mimic human senses during the dialog engagement with the specific rider such that at least one of the human senses of the specific rider is stimulated which facilitates classifying the emotional state of the specific rider based on the at least one emotional state indicative parameter;

optimizing at least one operating parameter of the vehicle to at least one of maintain or improve the emotional state of the specific rider; and

training, using machine learning, a second neural network to classify the emotional state of the specific rider based on the at least one emotional state indicative parameter including at least one of: the captured voice output of the specific rider or at least one other emotional state indicative parameter of the specific rider other than the captured voice output of the specific rider.

20. The method of claim 19 , further comprising utilizing a rule-based rider state model to configure a set of iterations of a vehicle state while continuously monitoring the emotional state of the specific rider via an ongoing dialog with respect to model parameters of the rule-based rider state model.